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Created September 24, 2020 19:01
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decomposition.ipynb
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"source": [
"## A Detailed Decomposition of the Market Composition"
]
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"source": [
"import os\n",
"os.environ['TIINGO_API_KEY'] = \"put your own key here\"\n",
"finnhub_token = 'put your own key here'"
]
},
{
"cell_type": "code",
"execution_count": 159,
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"slide_type": "fragment"
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"outputs": [],
"source": [
"import pandas as pd\n",
"import requests\n",
"from pandas_datareader import data as web\n",
"import pandas as pd\n",
"from dateutil.relativedelta import relativedelta\n",
"from datetime import datetime\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
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"source": [
"**First we are going to gather the necessary data to perform our analysis.**\n",
"\n",
"**We need to get a list of all of the companies in the S&P 500, along with their size. I am going to use a dataset based on the holdings of the popular ETF, SPY, and use the weights as a proxy for their size. The ETF weights companies based on their market capitalization, so it should serve its purpose**"
]
},
{
"cell_type": "code",
"execution_count": 160,
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"source": [
"constituents = requests.get('https://finnhub.io/api/v1/etf/holdings?symbol=SPY&token={}'.format(finnhub_token))"
]
},
{
"cell_type": "code",
"execution_count": 161,
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"source": [
"constituents_df = pd.DataFrame.from_records(constituents.json()['holdings'])"
]
},
{
"cell_type": "code",
"execution_count": 162,
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>percent</th>\n",
" <th>share</th>\n",
" <th>symbol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.918711</td>\n",
" <td>43766308</td>\n",
" <td>AAPL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5.815551</td>\n",
" <td>81420240</td>\n",
" <td>MSFT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.730508</td>\n",
" <td>4501968</td>\n",
" <td>AMZN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.180427</td>\n",
" <td>25837586</td>\n",
" <td>FB</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.710792</td>\n",
" <td>3224102</td>\n",
" <td>GOOGL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1.670869</td>\n",
" <td>3142476</td>\n",
" <td>GOOG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.479941</td>\n",
" <td>28309444</td>\n",
" <td>JNJ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1.397719</td>\n",
" <td>20891280</td>\n",
" <td>BRK.B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1.228678</td>\n",
" <td>18128360</td>\n",
" <td>V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1.162121</td>\n",
" <td>26593612</td>\n",
" <td>PG</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" percent share symbol\n",
"0 5.918711 43766308 AAPL\n",
"1 5.815551 81420240 MSFT\n",
"2 4.730508 4501968 AMZN\n",
"3 2.180427 25837586 FB\n",
"4 1.710792 3224102 GOOGL\n",
"5 1.670869 3142476 GOOG\n",
"6 1.479941 28309444 JNJ\n",
"7 1.397719 20891280 BRK.B\n",
"8 1.228678 18128360 V\n",
"9 1.162121 26593612 PG"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df.head(10)"
]
},
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"source": [
"**Now that we have a nice list of the companies along with weights, we need to get price data so we can calculate trailing returns for each company, before we move to the next step.**\n",
"1. First we are going to drop the last row, which has None as the ticker, which means it is probably cash in the ETF\n",
"2. We are going to use adjusted close prices instead of unadjusted, to account for any dividends or splits in the companies, to put every company on the same page."
]
},
{
"cell_type": "code",
"execution_count": 163,
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"outputs": [],
"source": [
"constituents_df = constituents_df.iloc[:505]\n",
"constituents_df.loc[:, 'symbol'] = constituents_df['symbol'].apply(lambda x: x.replace(\".\", \"-\"))"
]
},
{
"cell_type": "code",
"execution_count": 164,
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},
"outputs": [],
"source": [
"tickers = list(set(constituents_df['symbol'].to_list()))"
]
},
{
"cell_type": "code",
"execution_count": 165,
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},
"outputs": [],
"source": [
"adjusted_close = pd.DataFrame(columns=tickers)\n",
"null_tickers = []\n",
"\n",
"for ticker in tickers:\n",
" try:\n",
" data_panel = web.DataReader([ticker], \"tiingo\").loc[ticker]['adjClose'].to_frame()\n",
" data_panel.columns = [ticker]\n",
" data_panel.index = pd.to_datetime(data_panel.index)\n",
" if data_panel.index.max().tz_localize(None) < datetime.today() - relativedelta(days=5):\n",
" print(\"{} most recent date is {}\".format(ticker, str(data_panel.index.max())))\n",
" else:\n",
" adjusted_close[ticker] = data_panel[ticker]\n",
" except:\n",
" null_tickers.append(ticker)\n",
" print(\"{} not found\".format(ticker))"
]
},
{
"cell_type": "code",
"execution_count": 166,
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"outputs": [],
"source": [
"filtered_adjusted_close = adjusted_close.loc['2019-12-31':]"
]
},
{
"cell_type": "code",
"execution_count": 167,
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"outputs": [],
"source": [
"constituents_df.set_index('symbol', inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
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},
"source": [
"**I am interested in breaking up the returns this year into 4 groups or intervals.**\n",
"1. The return year-to-date (from the start of the year to today)\n",
"2. The return peak-to-trough (from the peak of the market to the trough of the market this year)\n",
"3. The return trough-to-date (from the market bottom to today)\n",
"4. The return start-to-peak (from the start of the year to the peak, right before the crash)"
]
},
{
"cell_type": "code",
"execution_count": 168,
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},
"outputs": [],
"source": [
"peak = '2020-02-19'\n",
"trough = '2020-03-23'"
]
},
{
"cell_type": "code",
"execution_count": 169,
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},
"outputs": [],
"source": [
"for ticker in filtered_adjusted_close.columns:\n",
" constituents_df.loc[ticker, 'ytd_return'] = (filtered_adjusted_close[ticker].iloc[-1] - filtered_adjusted_close[ticker].iloc[0])/filtered_adjusted_close[ticker].iloc[0]\n",
" constituents_df.loc[ticker, 'peak_to_trough'] = (filtered_adjusted_close[ticker].loc[trough] - filtered_adjusted_close[ticker].loc[peak])/filtered_adjusted_close[ticker].loc[peak]\n",
" constituents_df.loc[ticker, 'trough_to_date'] = (filtered_adjusted_close[ticker].iloc[-1] - filtered_adjusted_close[ticker].loc[trough])/filtered_adjusted_close[ticker].loc[trough]\n",
" constituents_df.loc[ticker, 'start_to_peak'] = (filtered_adjusted_close[ticker].loc[peak] - filtered_adjusted_close[ticker].iloc[0])/filtered_adjusted_close[ticker].iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 170,
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"outputs": [
{
"data": {
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" }\n",
"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>percent</th>\n",
" <th>share</th>\n",
" <th>ytd_return</th>\n",
" <th>peak_to_trough</th>\n",
" <th>trough_to_date</th>\n",
" <th>start_to_peak</th>\n",
" </tr>\n",
" <tr>\n",
" <th>symbol</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AAPL</th>\n",
" <td>5.918711</td>\n",
" <td>43766308</td>\n",
" <td>0.469236</td>\n",
" <td>-0.306687</td>\n",
" <td>0.918285</td>\n",
" <td>0.104712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSFT</th>\n",
" <td>5.815551</td>\n",
" <td>81420240</td>\n",
" <td>0.282050</td>\n",
" <td>-0.273921</td>\n",
" <td>0.482793</td>\n",
" <td>0.190805</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AMZN</th>\n",
" <td>4.730508</td>\n",
" <td>4501968</td>\n",
" <td>0.623441</td>\n",
" <td>-0.123209</td>\n",
" <td>0.576525</td>\n",
" <td>0.174463</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FB</th>\n",
" <td>2.180427</td>\n",
" <td>25837586</td>\n",
" <td>0.213252</td>\n",
" <td>-0.319049</td>\n",
" <td>0.681431</td>\n",
" <td>0.059635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GOOGL</th>\n",
" <td>1.710792</td>\n",
" <td>3224102</td>\n",
" <td>0.052263</td>\n",
" <td>-0.308708</td>\n",
" <td>0.337017</td>\n",
" <td>0.138481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GOOG</th>\n",
" <td>1.670869</td>\n",
" <td>3142476</td>\n",
" <td>0.058481</td>\n",
" <td>-0.307901</td>\n",
" <td>0.339375</td>\n",
" <td>0.141860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>JNJ</th>\n",
" <td>1.479941</td>\n",
" <td>28309444</td>\n",
" <td>0.010278</td>\n",
" <td>-0.248935</td>\n",
" <td>0.317402</td>\n",
" <td>0.021046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BRK-B</th>\n",
" <td>1.397719</td>\n",
" <td>20891280</td>\n",
" <td>-0.074570</td>\n",
" <td>-0.292750</td>\n",
" <td>0.292851</td>\n",
" <td>0.012097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>V</th>\n",
" <td>1.228678</td>\n",
" <td>18128360</td>\n",
" <td>0.044607</td>\n",
" <td>-0.363649</td>\n",
" <td>0.443923</td>\n",
" <td>0.136874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PG</th>\n",
" <td>1.162121</td>\n",
" <td>26593612</td>\n",
" <td>0.112063</td>\n",
" <td>-0.221142</td>\n",
" <td>0.413231</td>\n",
" <td>0.010317</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" percent share ytd_return peak_to_trough trough_to_date \\\n",
"symbol \n",
"AAPL 5.918711 43766308 0.469236 -0.306687 0.918285 \n",
"MSFT 5.815551 81420240 0.282050 -0.273921 0.482793 \n",
"AMZN 4.730508 4501968 0.623441 -0.123209 0.576525 \n",
"FB 2.180427 25837586 0.213252 -0.319049 0.681431 \n",
"GOOGL 1.710792 3224102 0.052263 -0.308708 0.337017 \n",
"GOOG 1.670869 3142476 0.058481 -0.307901 0.339375 \n",
"JNJ 1.479941 28309444 0.010278 -0.248935 0.317402 \n",
"BRK-B 1.397719 20891280 -0.074570 -0.292750 0.292851 \n",
"V 1.228678 18128360 0.044607 -0.363649 0.443923 \n",
"PG 1.162121 26593612 0.112063 -0.221142 0.413231 \n",
"\n",
" start_to_peak \n",
"symbol \n",
"AAPL 0.104712 \n",
"MSFT 0.190805 \n",
"AMZN 0.174463 \n",
"FB 0.059635 \n",
"GOOGL 0.138481 \n",
"GOOG 0.141860 \n",
"JNJ 0.021046 \n",
"BRK-B 0.012097 \n",
"V 0.136874 \n",
"PG 0.010317 "
]
},
"execution_count": 170,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
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},
"source": [
"**Next, we are going to split up the S&P 500 by market cap into deciles, which should give us around 50 companies per decile. We will be using these deciles to group the returns of the companies, to see if there is any pattern or information that may be interesting.**"
]
},
{
"cell_type": "code",
"execution_count": 171,
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},
"outputs": [],
"source": [
"constituents_df.loc[:, 'decile'] = pd.qcut(constituents_df['percent'].to_list(), 10, labels=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
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},
"source": [
"**Decile 9 is the 50 largest companies, while decile 0 represents the 50 smallest companies in the index**"
]
},
{
"cell_type": "code",
"execution_count": 172,
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{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>percent</th>\n",
" <th>share</th>\n",
" <th>ytd_return</th>\n",
" <th>peak_to_trough</th>\n",
" <th>trough_to_date</th>\n",
" <th>start_to_peak</th>\n",
" <th>decile</th>\n",
" </tr>\n",
" <tr>\n",
" <th>symbol</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AAPL</th>\n",
" <td>5.918711</td>\n",
" <td>43766308</td>\n",
" <td>0.469236</td>\n",
" <td>-0.306687</td>\n",
" <td>0.918285</td>\n",
" <td>0.104712</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSFT</th>\n",
" <td>5.815551</td>\n",
" <td>81420240</td>\n",
" <td>0.282050</td>\n",
" <td>-0.273921</td>\n",
" <td>0.482793</td>\n",
" <td>0.190805</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AMZN</th>\n",
" <td>4.730508</td>\n",
" <td>4501968</td>\n",
" <td>0.623441</td>\n",
" <td>-0.123209</td>\n",
" <td>0.576525</td>\n",
" <td>0.174463</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FB</th>\n",
" <td>2.180427</td>\n",
" <td>25837586</td>\n",
" <td>0.213252</td>\n",
" <td>-0.319049</td>\n",
" <td>0.681431</td>\n",
" <td>0.059635</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GOOGL</th>\n",
" <td>1.710792</td>\n",
" <td>3224102</td>\n",
" <td>0.052263</td>\n",
" <td>-0.308708</td>\n",
" <td>0.337017</td>\n",
" <td>0.138481</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GPS</th>\n",
" <td>0.010548</td>\n",
" <td>2304094</td>\n",
" <td>-0.051324</td>\n",
" <td>-0.634459</td>\n",
" <td>1.635350</td>\n",
" <td>-0.015211</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>UAA</th>\n",
" <td>0.007050</td>\n",
" <td>1942878</td>\n",
" <td>-0.470833</td>\n",
" <td>-0.513579</td>\n",
" <td>0.418114</td>\n",
" <td>-0.232870</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>UA</th>\n",
" <td>0.006668</td>\n",
" <td>2071528</td>\n",
" <td>-0.478624</td>\n",
" <td>-0.522727</td>\n",
" <td>0.400560</td>\n",
" <td>-0.220021</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NWS</th>\n",
" <td>0.005914</td>\n",
" <td>1320019</td>\n",
" <td>-0.008832</td>\n",
" <td>-0.454246</td>\n",
" <td>0.750980</td>\n",
" <td>0.037216</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COTY</th>\n",
" <td>0.004843</td>\n",
" <td>3215328</td>\n",
" <td>-0.737601</td>\n",
" <td>-0.551784</td>\n",
" <td>-0.433010</td>\n",
" <td>0.032522</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>505 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" percent share ytd_return peak_to_trough trough_to_date \\\n",
"symbol \n",
"AAPL 5.918711 43766308 0.469236 -0.306687 0.918285 \n",
"MSFT 5.815551 81420240 0.282050 -0.273921 0.482793 \n",
"AMZN 4.730508 4501968 0.623441 -0.123209 0.576525 \n",
"FB 2.180427 25837586 0.213252 -0.319049 0.681431 \n",
"GOOGL 1.710792 3224102 0.052263 -0.308708 0.337017 \n",
"... ... ... ... ... ... \n",
"GPS 0.010548 2304094 -0.051324 -0.634459 1.635350 \n",
"UAA 0.007050 1942878 -0.470833 -0.513579 0.418114 \n",
"UA 0.006668 2071528 -0.478624 -0.522727 0.400560 \n",
"NWS 0.005914 1320019 -0.008832 -0.454246 0.750980 \n",
"COTY 0.004843 3215328 -0.737601 -0.551784 -0.433010 \n",
"\n",
" start_to_peak decile \n",
"symbol \n",
"AAPL 0.104712 9 \n",
"MSFT 0.190805 9 \n",
"AMZN 0.174463 9 \n",
"FB 0.059635 9 \n",
"GOOGL 0.138481 9 \n",
"... ... ... \n",
"GPS -0.015211 0 \n",
"UAA -0.232870 0 \n",
"UA -0.220021 0 \n",
"NWS 0.037216 0 \n",
"COTY 0.032522 0 \n",
"\n",
"[505 rows x 7 columns]"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df"
]
},
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"source": [
"**Now that we have all of the data we need to start digging in, we are going to group the data by decile, and look at some descriptive statistics for each return interval. This will give us the average, min, max, standard deviation, etc. so it will be helpful to get a peak into the variation within each group.**"
]
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" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
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" <th>25%</th>\n",
" <th>50%</th>\n",
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" <th>decile</th>\n",
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" <th>0</th>\n",
" <td>51.0</td>\n",
" <td>-0.361757</td>\n",
" <td>0.275103</td>\n",
" <td>-0.740284</td>\n",
" <td>-0.533002</td>\n",
" <td>-0.415173</td>\n",
" <td>-0.290259</td>\n",
" <td>0.682426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50.0</td>\n",
" <td>-0.193704</td>\n",
" <td>0.233456</td>\n",
" <td>-0.718744</td>\n",
" <td>-0.323039</td>\n",
" <td>-0.210089</td>\n",
" <td>-0.114146</td>\n",
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" <th>2</th>\n",
" <td>51.0</td>\n",
" <td>-0.115670</td>\n",
" <td>0.236562</td>\n",
" <td>-0.649174</td>\n",
" <td>-0.261785</td>\n",
" <td>-0.121119</td>\n",
" <td>0.001729</td>\n",
" <td>0.524767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>50.0</td>\n",
" <td>-0.102911</td>\n",
" <td>0.232186</td>\n",
" <td>-0.731030</td>\n",
" <td>-0.284916</td>\n",
" <td>-0.083619</td>\n",
" <td>0.068838</td>\n",
" <td>0.416864</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>51.0</td>\n",
" <td>-0.012489</td>\n",
" <td>0.256862</td>\n",
" <td>-0.562281</td>\n",
" <td>-0.178875</td>\n",
" <td>-0.017957</td>\n",
" <td>0.109715</td>\n",
" <td>0.789365</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>48.0</td>\n",
" <td>-0.086948</td>\n",
" <td>0.202346</td>\n",
" <td>-0.575181</td>\n",
" <td>-0.200709</td>\n",
" <td>-0.068079</td>\n",
" <td>0.046012</td>\n",
" <td>0.361301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>50.0</td>\n",
" <td>-0.023803</td>\n",
" <td>0.260095</td>\n",
" <td>-0.556683</td>\n",
" <td>-0.210106</td>\n",
" <td>-0.003814</td>\n",
" <td>0.129845</td>\n",
" <td>0.491309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>51.0</td>\n",
" <td>0.030680</td>\n",
" <td>0.238403</td>\n",
" <td>-0.479776</td>\n",
" <td>-0.131881</td>\n",
" <td>0.010095</td>\n",
" <td>0.137846</td>\n",
" <td>0.764972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>50.0</td>\n",
" <td>0.018359</td>\n",
" <td>0.269111</td>\n",
" <td>-0.560666</td>\n",
" <td>-0.177307</td>\n",
" <td>0.001764</td>\n",
" <td>0.202872</td>\n",
" <td>0.631022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>51.0</td>\n",
" <td>0.097196</td>\n",
" <td>0.275127</td>\n",
" <td>-0.480095</td>\n",
" <td>-0.063144</td>\n",
" <td>0.077347</td>\n",
" <td>0.205125</td>\n",
" <td>1.063830</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% 75% \\\n",
"decile \n",
"0 51.0 -0.361757 0.275103 -0.740284 -0.533002 -0.415173 -0.290259 \n",
"1 50.0 -0.193704 0.233456 -0.718744 -0.323039 -0.210089 -0.114146 \n",
"2 51.0 -0.115670 0.236562 -0.649174 -0.261785 -0.121119 0.001729 \n",
"3 50.0 -0.102911 0.232186 -0.731030 -0.284916 -0.083619 0.068838 \n",
"4 51.0 -0.012489 0.256862 -0.562281 -0.178875 -0.017957 0.109715 \n",
"5 48.0 -0.086948 0.202346 -0.575181 -0.200709 -0.068079 0.046012 \n",
"6 50.0 -0.023803 0.260095 -0.556683 -0.210106 -0.003814 0.129845 \n",
"7 51.0 0.030680 0.238403 -0.479776 -0.131881 0.010095 0.137846 \n",
"8 50.0 0.018359 0.269111 -0.560666 -0.177307 0.001764 0.202872 \n",
"9 51.0 0.097196 0.275127 -0.480095 -0.063144 0.077347 0.205125 \n",
"\n",
" max \n",
"decile \n",
"0 0.682426 \n",
"1 0.587410 \n",
"2 0.524767 \n",
"3 0.416864 \n",
"4 0.789365 \n",
"5 0.361301 \n",
"6 0.491309 \n",
"7 0.764972 \n",
"8 0.631022 \n",
"9 1.063830 "
]
},
"execution_count": 173,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df.groupby('decile')['ytd_return'].describe()"
]
},
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"source": [
"Interesting, so far this year there has been an almost linear relationship between size and the magnitude of return for each batch of companies. The 51 largest companies (decile 9) have return roughly 8.25% on average, while the bottom 51 (decile 0) have returned -37.2% roughly. The second largest group, for comparison, return 3.24% on average and the second smallest group returned -16.73% on average.\n",
"\n",
"One thing to note, is that the standard deviation was actually higher for the 51 largest companies, relative to the 51 smallest, which means not every company in the top 51 benefited as much as the average implies. The worst of the top 51 is down 37.6% YTD, while the best is up 81.26%!"
]
},
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{
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" <td>-0.848613</td>\n",
" <td>-0.632865</td>\n",
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" <th>1</th>\n",
" <td>50.0</td>\n",
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" <td>-0.505031</td>\n",
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" <td>-0.505906</td>\n",
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" <td>-0.400695</td>\n",
" <td>0.154246</td>\n",
" <td>-0.757659</td>\n",
" <td>-0.471202</td>\n",
" <td>-0.400703</td>\n",
" <td>-0.316084</td>\n",
" <td>0.092786</td>\n",
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" <td>0.138485</td>\n",
" <td>-0.633895</td>\n",
" <td>-0.440915</td>\n",
" <td>-0.352053</td>\n",
" <td>-0.258753</td>\n",
" <td>-0.002328</td>\n",
" </tr>\n",
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" <th>5</th>\n",
" <td>48.0</td>\n",
" <td>-0.411574</td>\n",
" <td>0.118316</td>\n",
" <td>-0.711908</td>\n",
" <td>-0.499475</td>\n",
" <td>-0.418547</td>\n",
" <td>-0.318803</td>\n",
" <td>-0.169801</td>\n",
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" <th>6</th>\n",
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" <td>-0.374610</td>\n",
" <td>0.127997</td>\n",
" <td>-0.605005</td>\n",
" <td>-0.457082</td>\n",
" <td>-0.381765</td>\n",
" <td>-0.315632</td>\n",
" <td>0.052133</td>\n",
" </tr>\n",
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" <th>7</th>\n",
" <td>51.0</td>\n",
" <td>-0.335487</td>\n",
" <td>0.107150</td>\n",
" <td>-0.585724</td>\n",
" <td>-0.403734</td>\n",
" <td>-0.331818</td>\n",
" <td>-0.286962</td>\n",
" <td>-0.095206</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>50.0</td>\n",
" <td>-0.334608</td>\n",
" <td>0.144594</td>\n",
" <td>-0.687792</td>\n",
" <td>-0.423223</td>\n",
" <td>-0.351906</td>\n",
" <td>-0.280180</td>\n",
" <td>0.136773</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>51.0</td>\n",
" <td>-0.291236</td>\n",
" <td>0.099796</td>\n",
" <td>-0.510385</td>\n",
" <td>-0.359144</td>\n",
" <td>-0.304407</td>\n",
" <td>-0.250737</td>\n",
" <td>-0.024502</td>\n",
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],
"text/plain": [
" count mean std min 25% 50% 75% \\\n",
"decile \n",
"0 51.0 -0.544885 0.124625 -0.848613 -0.632865 -0.533997 -0.458527 \n",
"1 50.0 -0.426122 0.152007 -0.743048 -0.505031 -0.432222 -0.334520 \n",
"2 51.0 -0.418262 0.138979 -0.762157 -0.505906 -0.421385 -0.331606 \n",
"3 50.0 -0.400695 0.154246 -0.757659 -0.471202 -0.400703 -0.316084 \n",
"4 51.0 -0.346275 0.138485 -0.633895 -0.440915 -0.352053 -0.258753 \n",
"5 48.0 -0.411574 0.118316 -0.711908 -0.499475 -0.418547 -0.318803 \n",
"6 50.0 -0.374610 0.127997 -0.605005 -0.457082 -0.381765 -0.315632 \n",
"7 51.0 -0.335487 0.107150 -0.585724 -0.403734 -0.331818 -0.286962 \n",
"8 50.0 -0.334608 0.144594 -0.687792 -0.423223 -0.351906 -0.280180 \n",
"9 51.0 -0.291236 0.099796 -0.510385 -0.359144 -0.304407 -0.250737 \n",
"\n",
" max \n",
"decile \n",
"0 -0.235627 \n",
"1 -0.093590 \n",
"2 -0.132745 \n",
"3 0.092786 \n",
"4 -0.002328 \n",
"5 -0.169801 \n",
"6 0.052133 \n",
"7 -0.095206 \n",
"8 0.136773 \n",
"9 -0.024502 "
]
},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df.groupby('decile')['peak_to_trough'].describe()"
]
},
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"source": [
"Now, from the peak on 2-19-2020 to the trough on 3-23-2020, the hardest hit companies were definitely in the lower deciles, again, almost in perfect linear fashion. The 51 largest companies on average fell 29%, while the 51 smallest companies fell by 54% on average. This may explain the disconnect in performance YTD across the deciles, but lets keep going."
]
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" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>decile</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>51.0</td>\n",
" <td>0.486848</td>\n",
" <td>0.467512</td>\n",
" <td>-0.433010</td>\n",
" <td>0.162753</td>\n",
" <td>0.416067</td>\n",
" <td>0.638598</td>\n",
" <td>2.282443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50.0</td>\n",
" <td>0.436507</td>\n",
" <td>0.418166</td>\n",
" <td>-0.087039</td>\n",
" <td>0.184367</td>\n",
" <td>0.302439</td>\n",
" <td>0.563996</td>\n",
" <td>1.785619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>51.0</td>\n",
" <td>0.520162</td>\n",
" <td>0.313662</td>\n",
" <td>0.035517</td>\n",
" <td>0.272221</td>\n",
" <td>0.483365</td>\n",
" <td>0.688861</td>\n",
" <td>1.429400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>50.0</td>\n",
" <td>0.494735</td>\n",
" <td>0.345187</td>\n",
" <td>-0.033762</td>\n",
" <td>0.241289</td>\n",
" <td>0.476579</td>\n",
" <td>0.649846</td>\n",
" <td>1.602583</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>51.0</td>\n",
" <td>0.489281</td>\n",
" <td>0.354146</td>\n",
" <td>0.004595</td>\n",
" <td>0.256936</td>\n",
" <td>0.431013</td>\n",
" <td>0.612724</td>\n",
" <td>1.740260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>50.0</td>\n",
" <td>0.558957</td>\n",
" <td>0.328029</td>\n",
" <td>-0.149710</td>\n",
" <td>0.362342</td>\n",
" <td>0.511366</td>\n",
" <td>0.678417</td>\n",
" <td>1.531908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>50.0</td>\n",
" <td>0.502220</td>\n",
" <td>0.268148</td>\n",
" <td>-0.177481</td>\n",
" <td>0.288539</td>\n",
" <td>0.501251</td>\n",
" <td>0.700160</td>\n",
" <td>1.120552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>51.0</td>\n",
" <td>0.464633</td>\n",
" <td>0.249492</td>\n",
" <td>0.011182</td>\n",
" <td>0.293393</td>\n",
" <td>0.439894</td>\n",
" <td>0.627419</td>\n",
" <td>1.177345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>50.0</td>\n",
" <td>0.450687</td>\n",
" <td>0.262648</td>\n",
" <td>-0.114010</td>\n",
" <td>0.295203</td>\n",
" <td>0.419168</td>\n",
" <td>0.607576</td>\n",
" <td>1.362199</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>51.0</td>\n",
" <td>0.461017</td>\n",
" <td>0.264327</td>\n",
" <td>-0.003111</td>\n",
" <td>0.292151</td>\n",
" <td>0.393949</td>\n",
" <td>0.609720</td>\n",
" <td>1.281780</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% 75% \\\n",
"decile \n",
"0 51.0 0.486848 0.467512 -0.433010 0.162753 0.416067 0.638598 \n",
"1 50.0 0.436507 0.418166 -0.087039 0.184367 0.302439 0.563996 \n",
"2 51.0 0.520162 0.313662 0.035517 0.272221 0.483365 0.688861 \n",
"3 50.0 0.494735 0.345187 -0.033762 0.241289 0.476579 0.649846 \n",
"4 51.0 0.489281 0.354146 0.004595 0.256936 0.431013 0.612724 \n",
"5 50.0 0.558957 0.328029 -0.149710 0.362342 0.511366 0.678417 \n",
"6 50.0 0.502220 0.268148 -0.177481 0.288539 0.501251 0.700160 \n",
"7 51.0 0.464633 0.249492 0.011182 0.293393 0.439894 0.627419 \n",
"8 50.0 0.450687 0.262648 -0.114010 0.295203 0.419168 0.607576 \n",
"9 51.0 0.461017 0.264327 -0.003111 0.292151 0.393949 0.609720 \n",
"\n",
" max \n",
"decile \n",
"0 2.282443 \n",
"1 1.785619 \n",
"2 1.429400 \n",
"3 1.602583 \n",
"4 1.740260 \n",
"5 1.531908 \n",
"6 1.120552 \n",
"7 1.177345 \n",
"8 1.362199 \n",
"9 1.281780 "
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df.groupby('decile')['trough_to_date'].describe()"
]
},
{
"cell_type": "markdown",
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"source": [
"Looking at the deciles from the market bottom on 3-23 to today, it is much more even. The standard deviation is about 50% lower for the largest companies relative to the smallest, which means there was a very wide dispersion amongst the smallest 51 companies. The best performer in the smallest companies returned 262%, while the worst fell another 22% from the bottom.."
]
},
{
"cell_type": "code",
"execution_count": 176,
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{
"data": {
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" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>decile</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>51.0</td>\n",
" <td>-0.045662</td>\n",
" <td>0.110187</td>\n",
" <td>-0.233767</td>\n",
" <td>-0.120562</td>\n",
" <td>-0.029722</td>\n",
" <td>0.025701</td>\n",
" <td>0.357616</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>50.0</td>\n",
" <td>0.014358</td>\n",
" <td>0.100328</td>\n",
" <td>-0.198018</td>\n",
" <td>-0.055254</td>\n",
" <td>0.006523</td>\n",
" <td>0.079673</td>\n",
" <td>0.223165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>51.0</td>\n",
" <td>0.019718</td>\n",
" <td>0.100213</td>\n",
" <td>-0.181313</td>\n",
" <td>-0.050403</td>\n",
" <td>0.017610</td>\n",
" <td>0.073119</td>\n",
" <td>0.378682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>50.0</td>\n",
" <td>0.025327</td>\n",
" <td>0.091033</td>\n",
" <td>-0.152274</td>\n",
" <td>-0.055989</td>\n",
" <td>0.020843</td>\n",
" <td>0.104207</td>\n",
" <td>0.225563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>51.0</td>\n",
" <td>0.032224</td>\n",
" <td>0.096794</td>\n",
" <td>-0.163155</td>\n",
" <td>-0.043005</td>\n",
" <td>0.049676</td>\n",
" <td>0.099346</td>\n",
" <td>0.264938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>48.0</td>\n",
" <td>0.023937</td>\n",
" <td>0.087010</td>\n",
" <td>-0.150016</td>\n",
" <td>-0.026984</td>\n",
" <td>0.011011</td>\n",
" <td>0.077885</td>\n",
" <td>0.214647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>50.0</td>\n",
" <td>0.041920</td>\n",
" <td>0.100891</td>\n",
" <td>-0.198915</td>\n",
" <td>-0.035336</td>\n",
" <td>0.055233</td>\n",
" <td>0.114250</td>\n",
" <td>0.286351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>51.0</td>\n",
" <td>0.062912</td>\n",
" <td>0.088798</td>\n",
" <td>-0.166667</td>\n",
" <td>0.015278</td>\n",
" <td>0.065670</td>\n",
" <td>0.107925</td>\n",
" <td>0.333455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>50.0</td>\n",
" <td>0.059398</td>\n",
" <td>0.084855</td>\n",
" <td>-0.115417</td>\n",
" <td>0.022958</td>\n",
" <td>0.065255</td>\n",
" <td>0.107521</td>\n",
" <td>0.284344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>51.0</td>\n",
" <td>0.057052</td>\n",
" <td>0.087140</td>\n",
" <td>-0.122736</td>\n",
" <td>0.000888</td>\n",
" <td>0.044998</td>\n",
" <td>0.121244</td>\n",
" <td>0.337442</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% 75% \\\n",
"decile \n",
"0 51.0 -0.045662 0.110187 -0.233767 -0.120562 -0.029722 0.025701 \n",
"1 50.0 0.014358 0.100328 -0.198018 -0.055254 0.006523 0.079673 \n",
"2 51.0 0.019718 0.100213 -0.181313 -0.050403 0.017610 0.073119 \n",
"3 50.0 0.025327 0.091033 -0.152274 -0.055989 0.020843 0.104207 \n",
"4 51.0 0.032224 0.096794 -0.163155 -0.043005 0.049676 0.099346 \n",
"5 48.0 0.023937 0.087010 -0.150016 -0.026984 0.011011 0.077885 \n",
"6 50.0 0.041920 0.100891 -0.198915 -0.035336 0.055233 0.114250 \n",
"7 51.0 0.062912 0.088798 -0.166667 0.015278 0.065670 0.107925 \n",
"8 50.0 0.059398 0.084855 -0.115417 0.022958 0.065255 0.107521 \n",
"9 51.0 0.057052 0.087140 -0.122736 0.000888 0.044998 0.121244 \n",
"\n",
" max \n",
"decile \n",
"0 0.357616 \n",
"1 0.223165 \n",
"2 0.378682 \n",
"3 0.225563 \n",
"4 0.264938 \n",
"5 0.214647 \n",
"6 0.286351 \n",
"7 0.333455 \n",
"8 0.284344 \n",
"9 0.337442 "
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df.groupby('decile')['start_to_peak'].describe()"
]
},
{
"cell_type": "markdown",
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"source": [
"Finally, looking at the deciles from the start of the year to the peak on 2-19, we can see that the largest companies were doing the best going into the market drop and the smaller companies were off to a fairly rocky 2020. "
]
},
{
"cell_type": "markdown",
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},
"source": [
"To recap, the bigger companies:\n",
"\n",
"1. Were off to the best start before the COVID market crash.\n",
"2. Declined the least, on average, compared to smaller companies.\n",
"3. Recovered around the same on average, from the trough of the market to today.\n",
"4. Have the best returns on average, from the start of the year to date."
]
},
{
"cell_type": "markdown",
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"source": [
"Next, we are going to look at the actual return contribution of each company and decile. To calculate this, we are going to multiply each return series by the companies corresponding weight in the index (as approximated by the weight in the SPY ETF). We will then be able to see what the effect has been on the total index return in each time period."
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {
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}
},
"outputs": [],
"source": [
"constituents_df.loc[:, 'weight'] = constituents_df['percent'].apply(lambda x: x/constituents_df['percent'].sum())"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {
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"outputs": [
{
"data": {
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" <th></th>\n",
" <th>percent</th>\n",
" <th>share</th>\n",
" <th>ytd_return</th>\n",
" <th>peak_to_trough</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AAPL</th>\n",
" <td>5.918711</td>\n",
" <td>43766308</td>\n",
" <td>0.469236</td>\n",
" <td>-0.306687</td>\n",
" <td>0.918285</td>\n",
" <td>0.104712</td>\n",
" <td>9</td>\n",
" <td>0.059189</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSFT</th>\n",
" <td>5.815551</td>\n",
" <td>81420240</td>\n",
" <td>0.282050</td>\n",
" <td>-0.273921</td>\n",
" <td>0.482793</td>\n",
" <td>0.190805</td>\n",
" <td>9</td>\n",
" <td>0.058157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AMZN</th>\n",
" <td>4.730508</td>\n",
" <td>4501968</td>\n",
" <td>0.623441</td>\n",
" <td>-0.123209</td>\n",
" <td>0.576525</td>\n",
" <td>0.174463</td>\n",
" <td>9</td>\n",
" <td>0.047307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FB</th>\n",
" <td>2.180427</td>\n",
" <td>25837586</td>\n",
" <td>0.213252</td>\n",
" <td>-0.319049</td>\n",
" <td>0.681431</td>\n",
" <td>0.059635</td>\n",
" <td>9</td>\n",
" <td>0.021805</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GOOGL</th>\n",
" <td>1.710792</td>\n",
" <td>3224102</td>\n",
" <td>0.052263</td>\n",
" <td>-0.308708</td>\n",
" <td>0.337017</td>\n",
" <td>0.138481</td>\n",
" <td>9</td>\n",
" <td>0.017108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GPS</th>\n",
" <td>0.010548</td>\n",
" <td>2304094</td>\n",
" <td>-0.051324</td>\n",
" <td>-0.634459</td>\n",
" <td>1.635350</td>\n",
" <td>-0.015211</td>\n",
" <td>0</td>\n",
" <td>0.000105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>UAA</th>\n",
" <td>0.007050</td>\n",
" <td>1942878</td>\n",
" <td>-0.470833</td>\n",
" <td>-0.513579</td>\n",
" <td>0.418114</td>\n",
" <td>-0.232870</td>\n",
" <td>0</td>\n",
" <td>0.000071</td>\n",
" </tr>\n",
" <tr>\n",
" <th>UA</th>\n",
" <td>0.006668</td>\n",
" <td>2071528</td>\n",
" <td>-0.478624</td>\n",
" <td>-0.522727</td>\n",
" <td>0.400560</td>\n",
" <td>-0.220021</td>\n",
" <td>0</td>\n",
" <td>0.000067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NWS</th>\n",
" <td>0.005914</td>\n",
" <td>1320019</td>\n",
" <td>-0.008832</td>\n",
" <td>-0.454246</td>\n",
" <td>0.750980</td>\n",
" <td>0.037216</td>\n",
" <td>0</td>\n",
" <td>0.000059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COTY</th>\n",
" <td>0.004843</td>\n",
" <td>3215328</td>\n",
" <td>-0.737601</td>\n",
" <td>-0.551784</td>\n",
" <td>-0.433010</td>\n",
" <td>0.032522</td>\n",
" <td>0</td>\n",
" <td>0.000048</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>505 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" percent share ytd_return peak_to_trough trough_to_date \\\n",
"symbol \n",
"AAPL 5.918711 43766308 0.469236 -0.306687 0.918285 \n",
"MSFT 5.815551 81420240 0.282050 -0.273921 0.482793 \n",
"AMZN 4.730508 4501968 0.623441 -0.123209 0.576525 \n",
"FB 2.180427 25837586 0.213252 -0.319049 0.681431 \n",
"GOOGL 1.710792 3224102 0.052263 -0.308708 0.337017 \n",
"... ... ... ... ... ... \n",
"GPS 0.010548 2304094 -0.051324 -0.634459 1.635350 \n",
"UAA 0.007050 1942878 -0.470833 -0.513579 0.418114 \n",
"UA 0.006668 2071528 -0.478624 -0.522727 0.400560 \n",
"NWS 0.005914 1320019 -0.008832 -0.454246 0.750980 \n",
"COTY 0.004843 3215328 -0.737601 -0.551784 -0.433010 \n",
"\n",
" start_to_peak decile weight \n",
"symbol \n",
"AAPL 0.104712 9 0.059189 \n",
"MSFT 0.190805 9 0.058157 \n",
"AMZN 0.174463 9 0.047307 \n",
"FB 0.059635 9 0.021805 \n",
"GOOGL 0.138481 9 0.017108 \n",
"... ... ... ... \n",
"GPS -0.015211 0 0.000105 \n",
"UAA -0.232870 0 0.000071 \n",
"UA -0.220021 0 0.000067 \n",
"NWS 0.037216 0 0.000059 \n",
"COTY 0.032522 0 0.000048 \n",
"\n",
"[505 rows x 8 columns]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"constituents_df.loc[:, 'return_contribution_ytd'] = constituents_df['weight'] * constituents_df['ytd_return']\n",
"constituents_df.loc[:, 'return_contribution_ptt'] = constituents_df['weight'] * constituents_df['peak_to_trough']\n",
"constituents_df.loc[:, 'return_contribution_ttd'] = constituents_df['weight'] * constituents_df['trough_to_date']\n",
"constituents_df.loc[:, 'return_contribution_stp'] = constituents_df['weight'] * constituents_df['start_to_peak']"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e24e5f550>"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"constituents_df.groupby('decile')['return_contribution_ytd'].sum().plot(kind='barh', figsize=[12,6], legend=True) # alpha for transparency"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.08961325361431384"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df['return_contribution_ytd'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"decile\n",
"0 -0.033660\n",
"1 -0.033130\n",
"2 -0.026311\n",
"3 -0.029485\n",
"4 -0.005181\n",
"5 -0.042286\n",
"6 -0.016872\n",
"7 0.026067\n",
"8 0.020668\n",
"9 1.140191\n",
"Name: return_contribution_ytd, dtype: float64"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df.groupby('decile')['return_contribution_ytd'].sum() / constituents_df['return_contribution_ytd'].sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"The results are very interesting, you can see in the chart that the only meaningful return when accounting for the weights has come from the top 51 companies. When you total the return for decile 9, and put it over the total of the return for the whole index, it accounts for 104.6% of the index return to-date! Meaning the top 51 companies are pulling up the index performance drastically.."
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e24d1f588>"
]
},
"execution_count": 183,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"constituents_df.groupby('decile')['return_contribution_ptt'].sum().plot(kind='barh', figsize=[12,6], legend=True) # alpha for transparency"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"decile\n",
"0 0.014632\n",
"1 0.020735\n",
"2 0.027868\n",
"3 0.033228\n",
"4 0.038980\n",
"5 0.055400\n",
"6 0.066126\n",
"7 0.097248\n",
"8 0.157604\n",
"9 0.488179\n",
"Name: return_contribution_ptt, dtype: float64"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df.groupby('decile')['return_contribution_ptt'].sum() / constituents_df['return_contribution_ptt'].sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"On the flipside, while the top 51 companies declined the least during the market drop, their weights in the index had them contribute the most to the negative performance. This is purely a function of their weights in the index and not much else."
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e24c7d240>"
]
},
"execution_count": 185,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"constituents_df.groupby('decile')['return_contribution_ttd'].sum().plot(kind='barh', figsize=[12,6], legend=True) # alpha for transparency"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"decile\n",
"0 0.008497\n",
"1 0.013830\n",
"2 0.022387\n",
"3 0.026453\n",
"4 0.035411\n",
"5 0.049885\n",
"6 0.056228\n",
"7 0.085021\n",
"8 0.136124\n",
"9 0.566164\n",
"Name: return_contribution_ttd, dtype: float64"
]
},
"execution_count": 186,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df.groupby('decile')['return_contribution_ttd'].sum() / constituents_df['return_contribution_ttd'].sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"From the bottom of the market in March, no surprises here, the top 51 companies contributed the most because of their weight and positive performance."
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e24f847b8>"
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"constituents_df.groupby('decile')['return_contribution_stp'].sum().plot(kind='barh', figsize=[12,6], legend=True) # alpha for transparency"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"decile\n",
"0 -0.005149\n",
"1 0.003750\n",
"2 0.006610\n",
"3 0.010232\n",
"4 0.017272\n",
"5 0.014333\n",
"6 0.035481\n",
"7 0.086102\n",
"8 0.122381\n",
"9 0.708988\n",
"Name: return_contribution_stp, dtype: float64"
]
},
"execution_count": 188,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"constituents_df.groupby('decile')['return_contribution_stp'].sum() / constituents_df['return_contribution_stp'].sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"Finally, from the start of the year to the day before the market started on it's downward spiral, 70% of the market's positive performance came from the top 51 companies."
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1728x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"boxplot = constituents_df.boxplot(column=['ytd_return'], by=['decile'], figsize=[24,12])"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1728x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"boxplot = constituents_df.boxplot(column=['peak_to_trough'], by=['decile'], figsize=[24,12])"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1728x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"boxplot = constituents_df.boxplot(column=['trough_to_date'], by=['decile'], figsize=[24,12])"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1728x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"boxplot = constituents_df.boxplot(column=['start_to_peak'], by=['decile'], figsize=[24,12])"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"What about more broadly? Lets dive into the Russell 3000 constituents to see if we can find similar distributions of returns."
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"russell_3000 = pd.read_csv('russell_3000.csv')"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"russell_3000.loc[:, 'mcap_decile'] = pd.qcut(russell_3000['market_cap'].to_list(), 10, labels=False)"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"russell_3000.loc[:, 'pe_decile'] = pd.qcut(russell_3000['pe_ratio'].to_list(), 10, labels=False, duplicates='drop')"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"russell_3000.loc[russell_3000['pe_decile'] == 0, 'pe_decile'] = 'no_earnings'\n",
"russell_3000['pe_decile'] = russell_3000['pe_decile'].astype(str)"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>mcap_decile</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>270.0</td>\n",
" <td>-0.392259</td>\n",
" <td>0.281965</td>\n",
" <td>-0.92</td>\n",
" <td>-0.5375</td>\n",
" <td>-0.440</td>\n",
" <td>-0.3100</td>\n",
" <td>1.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>270.0</td>\n",
" <td>-0.228704</td>\n",
" <td>0.390330</td>\n",
" <td>-0.90</td>\n",
" <td>-0.5000</td>\n",
" <td>-0.285</td>\n",
" <td>-0.0925</td>\n",
" <td>2.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>270.0</td>\n",
" <td>-0.094370</td>\n",
" <td>1.240263</td>\n",
" <td>-0.87</td>\n",
" <td>-0.4675</td>\n",
" <td>-0.305</td>\n",
" <td>-0.0700</td>\n",
" <td>14.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>269.0</td>\n",
" <td>-0.030929</td>\n",
" <td>1.313641</td>\n",
" <td>-0.80</td>\n",
" <td>-0.4300</td>\n",
" <td>-0.210</td>\n",
" <td>0.0800</td>\n",
" <td>19.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>270.0</td>\n",
" <td>-0.098370</td>\n",
" <td>0.512600</td>\n",
" <td>-0.71</td>\n",
" <td>-0.4100</td>\n",
" <td>-0.220</td>\n",
" <td>0.0075</td>\n",
" <td>3.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>270.0</td>\n",
" <td>-0.077889</td>\n",
" <td>0.567776</td>\n",
" <td>-0.76</td>\n",
" <td>-0.3500</td>\n",
" <td>-0.180</td>\n",
" <td>0.0275</td>\n",
" <td>6.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>269.0</td>\n",
" <td>0.046506</td>\n",
" <td>0.883623</td>\n",
" <td>-0.74</td>\n",
" <td>-0.3100</td>\n",
" <td>-0.130</td>\n",
" <td>0.1400</td>\n",
" <td>9.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>270.0</td>\n",
" <td>0.096778</td>\n",
" <td>1.569675</td>\n",
" <td>-0.68</td>\n",
" <td>-0.2500</td>\n",
" <td>-0.100</td>\n",
" <td>0.1000</td>\n",
" <td>24.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>270.0</td>\n",
" <td>0.072000</td>\n",
" <td>0.569468</td>\n",
" <td>-0.75</td>\n",
" <td>-0.2300</td>\n",
" <td>-0.045</td>\n",
" <td>0.1700</td>\n",
" <td>4.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>270.0</td>\n",
" <td>0.129704</td>\n",
" <td>0.601798</td>\n",
" <td>-0.59</td>\n",
" <td>-0.1100</td>\n",
" <td>0.030</td>\n",
" <td>0.2100</td>\n",
" <td>6.36</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% 75% max\n",
"mcap_decile \n",
"0 270.0 -0.392259 0.281965 -0.92 -0.5375 -0.440 -0.3100 1.15\n",
"1 270.0 -0.228704 0.390330 -0.90 -0.5000 -0.285 -0.0925 2.43\n",
"2 270.0 -0.094370 1.240263 -0.87 -0.4675 -0.305 -0.0700 14.23\n",
"3 269.0 -0.030929 1.313641 -0.80 -0.4300 -0.210 0.0800 19.86\n",
"4 270.0 -0.098370 0.512600 -0.71 -0.4100 -0.220 0.0075 3.09\n",
"5 270.0 -0.077889 0.567776 -0.76 -0.3500 -0.180 0.0275 6.39\n",
"6 269.0 0.046506 0.883623 -0.74 -0.3100 -0.130 0.1400 9.13\n",
"7 270.0 0.096778 1.569675 -0.68 -0.2500 -0.100 0.1000 24.55\n",
"8 270.0 0.072000 0.569468 -0.75 -0.2300 -0.045 0.1700 4.38\n",
"9 270.0 0.129704 0.601798 -0.59 -0.1100 0.030 0.2100 6.36"
]
},
"execution_count": 197,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mcap_decile_stats = russell_3000.groupby('mcap_decile')['ytd_return'].describe()\n",
"mcap_decile_stats"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e246bd470>"
]
},
"execution_count": 198,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"russell_3000.groupby('mcap_decile')['ytd_return'].mean().sort_index().plot(kind='barh', figsize=[12,6], legend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"Nothing too shocking here. Year-to-date, the largest 270 companies by market cap have outperformed the smallest 270 companies by over 50%. What is somewhat surprising to me is, if you look at the standard deviation for each decile, it is much lower (less than half) for the bottom decile than for the top decile. This means that in the bottom decile, the companies have all performed relatively poorly, and in the top decile, you have a much wider range of returns. That makes sense intuitively, since we uncovered a similar amount of dispersion between the deciles of the S&P 500 earlier in this notebook. In this example, the best performer year-to-date (the max column shows this) returned 636% while the worst performer returned -59% for the top decile. Conversely, the bottom decile's best performer returned 115%, while the worst was -92%. So it appears that the distribution may be skewed to the right for the megacap companies in the US while the smallest companies looks more evenly distributed around the mean, which makes sense given the data we have examined so far."
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>pe_decile</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>269.0</td>\n",
" <td>-0.369591</td>\n",
" <td>0.221672</td>\n",
" <td>-0.78</td>\n",
" <td>-0.4900</td>\n",
" <td>-0.430</td>\n",
" <td>-0.3100</td>\n",
" <td>0.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>270.0</td>\n",
" <td>-0.250889</td>\n",
" <td>0.243552</td>\n",
" <td>-0.74</td>\n",
" <td>-0.4200</td>\n",
" <td>-0.280</td>\n",
" <td>-0.1600</td>\n",
" <td>0.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>270.0</td>\n",
" <td>-0.177444</td>\n",
" <td>0.270350</td>\n",
" <td>-0.76</td>\n",
" <td>-0.3300</td>\n",
" <td>-0.210</td>\n",
" <td>-0.0425</td>\n",
" <td>1.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>269.0</td>\n",
" <td>-0.111599</td>\n",
" <td>0.236732</td>\n",
" <td>-0.67</td>\n",
" <td>-0.2700</td>\n",
" <td>-0.110</td>\n",
" <td>0.0200</td>\n",
" <td>0.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>270.0</td>\n",
" <td>-0.051370</td>\n",
" <td>0.275376</td>\n",
" <td>-0.88</td>\n",
" <td>-0.2000</td>\n",
" <td>-0.060</td>\n",
" <td>0.0875</td>\n",
" <td>1.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>270.0</td>\n",
" <td>0.016852</td>\n",
" <td>0.389380</td>\n",
" <td>-0.67</td>\n",
" <td>-0.2175</td>\n",
" <td>-0.030</td>\n",
" <td>0.1875</td>\n",
" <td>1.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>270.0</td>\n",
" <td>0.170185</td>\n",
" <td>1.134109</td>\n",
" <td>-0.71</td>\n",
" <td>-0.2875</td>\n",
" <td>-0.050</td>\n",
" <td>0.2800</td>\n",
" <td>14.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>no_earnings</th>\n",
" <td>810.0</td>\n",
" <td>0.064827</td>\n",
" <td>1.439242</td>\n",
" <td>-0.92</td>\n",
" <td>-0.4700</td>\n",
" <td>-0.205</td>\n",
" <td>0.1700</td>\n",
" <td>24.55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% 75% max\n",
"pe_decile \n",
"1 269.0 -0.369591 0.221672 -0.78 -0.4900 -0.430 -0.3100 0.70\n",
"2 270.0 -0.250889 0.243552 -0.74 -0.4200 -0.280 -0.1600 0.73\n",
"3 270.0 -0.177444 0.270350 -0.76 -0.3300 -0.210 -0.0425 1.13\n",
"4 269.0 -0.111599 0.236732 -0.67 -0.2700 -0.110 0.0200 0.63\n",
"5 270.0 -0.051370 0.275376 -0.88 -0.2000 -0.060 0.0875 1.21\n",
"6 270.0 0.016852 0.389380 -0.67 -0.2175 -0.030 0.1875 1.75\n",
"7 270.0 0.170185 1.134109 -0.71 -0.2875 -0.050 0.2800 14.23\n",
"no_earnings 810.0 0.064827 1.439242 -0.92 -0.4700 -0.205 0.1700 24.55"
]
},
"execution_count": 199,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pe_decile_stats = russell_3000.groupby('pe_decile')['ytd_return'].describe()\n",
"pe_decile_stats"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e24630b38>"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"russell_3000.groupby('pe_decile')['ytd_return'].mean().sort_index().plot(kind='barh', figsize=[12,6], legend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"I decided to take this example one step further, by breaking down the same data into deciles by Price-to-earnings ratio to see if we find anything interesting. One thing to note - this was not as clean of an exercise as when we broke out deciles by market cap. Due to the fact that the P/E ratio can only be calculated if a company has earnings (shocker), it appears as though 810 companies for the 2700 or so examined do not have earnings. So, rather than exclude them altogether, I decided to put them into their own group for comparison purposes. The results are not surprising, given the fact that by now, most investors are aware of the chronic underperformance of value vs. growth stocks. The cheapest 269 companies underperformed the most expensive 270 companies year-to-date by almost 54%! Whats more, companies with negative or zero earnings have outperformed the cheapest stocks by over 42%. The distribution of returns for the expensive and no-earnings groups is much wider than that of the cheapest, with standard deviations of returns within the group roughly 5-6X greater than the low P/E companies."
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sector</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Communication Services</th>\n",
" <td>98.0</td>\n",
" <td>-0.081531</td>\n",
" <td>0.433112</td>\n",
" <td>-0.81</td>\n",
" <td>-0.3500</td>\n",
" <td>-0.180</td>\n",
" <td>0.1350</td>\n",
" <td>1.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Consumer Discretionary</th>\n",
" <td>304.0</td>\n",
" <td>0.120592</td>\n",
" <td>1.101363</td>\n",
" <td>-0.86</td>\n",
" <td>-0.3100</td>\n",
" <td>-0.090</td>\n",
" <td>0.2100</td>\n",
" <td>9.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Consumer Staples</th>\n",
" <td>100.0</td>\n",
" <td>-0.011800</td>\n",
" <td>0.467783</td>\n",
" <td>-0.74</td>\n",
" <td>-0.2425</td>\n",
" <td>-0.075</td>\n",
" <td>0.0925</td>\n",
" <td>3.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Energy</th>\n",
" <td>104.0</td>\n",
" <td>-0.485962</td>\n",
" <td>0.288365</td>\n",
" <td>-0.90</td>\n",
" <td>-0.6725</td>\n",
" <td>-0.555</td>\n",
" <td>-0.3975</td>\n",
" <td>0.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Financials</th>\n",
" <td>478.0</td>\n",
" <td>-0.314163</td>\n",
" <td>0.253560</td>\n",
" <td>-0.74</td>\n",
" <td>-0.4600</td>\n",
" <td>-0.390</td>\n",
" <td>-0.2400</td>\n",
" <td>1.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Health Care</th>\n",
" <td>491.0</td>\n",
" <td>0.234481</td>\n",
" <td>1.695760</td>\n",
" <td>-0.88</td>\n",
" <td>-0.2700</td>\n",
" <td>-0.060</td>\n",
" <td>0.3200</td>\n",
" <td>24.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Industrials</th>\n",
" <td>365.0</td>\n",
" <td>-0.104356</td>\n",
" <td>0.441116</td>\n",
" <td>-0.92</td>\n",
" <td>-0.3400</td>\n",
" <td>-0.150</td>\n",
" <td>0.0300</td>\n",
" <td>3.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Information Technology</th>\n",
" <td>354.0</td>\n",
" <td>0.101610</td>\n",
" <td>0.608896</td>\n",
" <td>-0.78</td>\n",
" <td>-0.1900</td>\n",
" <td>-0.015</td>\n",
" <td>0.2375</td>\n",
" <td>6.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Materials</th>\n",
" <td>119.0</td>\n",
" <td>-0.162437</td>\n",
" <td>0.219772</td>\n",
" <td>-0.63</td>\n",
" <td>-0.3100</td>\n",
" <td>-0.150</td>\n",
" <td>-0.0600</td>\n",
" <td>0.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Real Estate</th>\n",
" <td>215.0</td>\n",
" <td>-0.295442</td>\n",
" <td>0.320547</td>\n",
" <td>-0.88</td>\n",
" <td>-0.5100</td>\n",
" <td>-0.320</td>\n",
" <td>-0.1300</td>\n",
" <td>2.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Utilities</th>\n",
" <td>70.0</td>\n",
" <td>-0.184143</td>\n",
" <td>0.227140</td>\n",
" <td>-0.44</td>\n",
" <td>-0.3000</td>\n",
" <td>-0.210</td>\n",
" <td>-0.1225</td>\n",
" <td>1.32</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% \\\n",
"sector \n",
"Communication Services 98.0 -0.081531 0.433112 -0.81 -0.3500 -0.180 \n",
"Consumer Discretionary 304.0 0.120592 1.101363 -0.86 -0.3100 -0.090 \n",
"Consumer Staples 100.0 -0.011800 0.467783 -0.74 -0.2425 -0.075 \n",
"Energy 104.0 -0.485962 0.288365 -0.90 -0.6725 -0.555 \n",
"Financials 478.0 -0.314163 0.253560 -0.74 -0.4600 -0.390 \n",
"Health Care 491.0 0.234481 1.695760 -0.88 -0.2700 -0.060 \n",
"Industrials 365.0 -0.104356 0.441116 -0.92 -0.3400 -0.150 \n",
"Information Technology 354.0 0.101610 0.608896 -0.78 -0.1900 -0.015 \n",
"Materials 119.0 -0.162437 0.219772 -0.63 -0.3100 -0.150 \n",
"Real Estate 215.0 -0.295442 0.320547 -0.88 -0.5100 -0.320 \n",
"Utilities 70.0 -0.184143 0.227140 -0.44 -0.3000 -0.210 \n",
"\n",
" 75% max \n",
"sector \n",
"Communication Services 0.1350 1.54 \n",
"Consumer Discretionary 0.2100 9.19 \n",
"Consumer Staples 0.0925 3.09 \n",
"Energy -0.3975 0.70 \n",
"Financials -0.2400 1.15 \n",
"Health Care 0.3200 24.55 \n",
"Industrials 0.0300 3.66 \n",
"Information Technology 0.2375 6.36 \n",
"Materials -0.0600 0.63 \n",
"Real Estate -0.1300 2.30 \n",
"Utilities -0.1225 1.32 "
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sector_stats = russell_3000.groupby('sector')['ytd_return'].describe()\n",
"sector_stats"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e2461ba58>"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"russell_3000.groupby('sector')['ytd_return'].mean().sort_values().plot(kind='barh', figsize=[12,6], legend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"What about sectors? Nothing shocking here, but wanted to show it anyway. Health care has been the best performer while Energy has been the clear loser. The health care performance looks like it may be due to a few outliers to the upside, which may have brought up the performance a bit. For this reason, it is sometimes helpful to use the median performance to examine further, which is done below."
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e24599828>"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"russell_3000.groupby('sector')['ytd_return'].describe()['50%'].sort_values().plot(kind='barh', figsize=[12,6], legend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"Interestingly enough, it appears that the rankings held up for the most part, but it did knock off healthcare from the leader to 2nd behind IT. It is somewhat troubling that the median for each sector has been negative across the board to varying degrees. As shown in all of the data above, the indexes (market-cap weighted) have been a horrible representation of the overall performance of the stock market year-to-date, as performance has been inflated upwards by a handful of outliers in each grouping we have examined. Referring back to the market cap decile example a few cells earlier, we can also see that the only decile with a positive median year-to-date performance has been the top decile by market cap. "
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>industry</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.0</td>\n",
" <td>-0.075000</td>\n",
" <td>0.190919</td>\n",
" <td>-0.21</td>\n",
" <td>-0.1425</td>\n",
" <td>-0.075</td>\n",
" <td>-0.0075</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Aerospace &amp; Defense</th>\n",
" <td>29.0</td>\n",
" <td>-0.252414</td>\n",
" <td>0.265096</td>\n",
" <td>-0.76</td>\n",
" <td>-0.4400</td>\n",
" <td>-0.190</td>\n",
" <td>-0.0700</td>\n",
" <td>0.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Automobiles &amp; Auto Parts</th>\n",
" <td>31.0</td>\n",
" <td>0.135484</td>\n",
" <td>1.376108</td>\n",
" <td>-0.60</td>\n",
" <td>-0.3400</td>\n",
" <td>-0.210</td>\n",
" <td>-0.1100</td>\n",
" <td>6.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Banking Services</th>\n",
" <td>317.0</td>\n",
" <td>-0.395710</td>\n",
" <td>0.173159</td>\n",
" <td>-0.74</td>\n",
" <td>-0.4800</td>\n",
" <td>-0.430</td>\n",
" <td>-0.3500</td>\n",
" <td>1.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beverages</th>\n",
" <td>13.0</td>\n",
" <td>0.316923</td>\n",
" <td>0.939365</td>\n",
" <td>-0.39</td>\n",
" <td>-0.1300</td>\n",
" <td>-0.020</td>\n",
" <td>0.2200</td>\n",
" <td>3.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Biotechnology &amp; Medical Research</th>\n",
" <td>230.0</td>\n",
" <td>0.339652</td>\n",
" <td>2.210579</td>\n",
" <td>-0.75</td>\n",
" <td>-0.2900</td>\n",
" <td>-0.045</td>\n",
" <td>0.3675</td>\n",
" <td>24.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chemicals</th>\n",
" <td>55.0</td>\n",
" <td>-0.102727</td>\n",
" <td>0.422525</td>\n",
" <td>-0.48</td>\n",
" <td>-0.3000</td>\n",
" <td>-0.150</td>\n",
" <td>-0.0350</td>\n",
" <td>2.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coal</th>\n",
" <td>4.0</td>\n",
" <td>-0.410000</td>\n",
" <td>0.434434</td>\n",
" <td>-0.67</td>\n",
" <td>-0.6250</td>\n",
" <td>-0.605</td>\n",
" <td>-0.3900</td>\n",
" <td>0.24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Communications &amp; Networking</th>\n",
" <td>40.0</td>\n",
" <td>-0.097750</td>\n",
" <td>0.350827</td>\n",
" <td>-0.61</td>\n",
" <td>-0.3050</td>\n",
" <td>-0.175</td>\n",
" <td>0.0125</td>\n",
" <td>1.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Computers, Phones &amp; Household Electronics</th>\n",
" <td>22.0</td>\n",
" <td>-0.088182</td>\n",
" <td>0.312481</td>\n",
" <td>-0.49</td>\n",
" <td>-0.3275</td>\n",
" <td>-0.090</td>\n",
" <td>0.0275</td>\n",
" <td>0.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Construction &amp; Engineering</th>\n",
" <td>23.0</td>\n",
" <td>-0.061739</td>\n",
" <td>0.254570</td>\n",
" <td>-0.55</td>\n",
" <td>-0.2250</td>\n",
" <td>-0.050</td>\n",
" <td>0.0750</td>\n",
" <td>0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Construction Materials</th>\n",
" <td>9.0</td>\n",
" <td>-0.173333</td>\n",
" <td>0.122678</td>\n",
" <td>-0.38</td>\n",
" <td>-0.2100</td>\n",
" <td>-0.120</td>\n",
" <td>-0.0900</td>\n",
" <td>-0.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Containers &amp; Packaging</th>\n",
" <td>19.0</td>\n",
" <td>-0.096316</td>\n",
" <td>0.159903</td>\n",
" <td>-0.35</td>\n",
" <td>-0.2200</td>\n",
" <td>-0.090</td>\n",
" <td>-0.0100</td>\n",
" <td>0.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Diversified Industrial Goods Wholesale</th>\n",
" <td>2.0</td>\n",
" <td>-0.085000</td>\n",
" <td>0.077782</td>\n",
" <td>-0.14</td>\n",
" <td>-0.1125</td>\n",
" <td>-0.085</td>\n",
" <td>-0.0575</td>\n",
" <td>-0.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Diversified Retail</th>\n",
" <td>19.0</td>\n",
" <td>0.181579</td>\n",
" <td>0.742573</td>\n",
" <td>-0.70</td>\n",
" <td>-0.1500</td>\n",
" <td>0.000</td>\n",
" <td>0.4250</td>\n",
" <td>2.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Electrical Utilities &amp; IPPs</th>\n",
" <td>36.0</td>\n",
" <td>-0.189722</td>\n",
" <td>0.144350</td>\n",
" <td>-0.43</td>\n",
" <td>-0.2850</td>\n",
" <td>-0.210</td>\n",
" <td>-0.1175</td>\n",
" <td>0.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Electronic Equipment &amp; Parts</th>\n",
" <td>20.0</td>\n",
" <td>-0.061500</td>\n",
" <td>0.276848</td>\n",
" <td>-0.41</td>\n",
" <td>-0.2525</td>\n",
" <td>-0.085</td>\n",
" <td>0.1025</td>\n",
" <td>0.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Food &amp; Drug Retailing</th>\n",
" <td>19.0</td>\n",
" <td>-0.074737</td>\n",
" <td>0.395676</td>\n",
" <td>-0.69</td>\n",
" <td>-0.3750</td>\n",
" <td>0.020</td>\n",
" <td>0.1450</td>\n",
" <td>0.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Food &amp; Tobacco</th>\n",
" <td>54.0</td>\n",
" <td>-0.069444</td>\n",
" <td>0.293864</td>\n",
" <td>-0.70</td>\n",
" <td>-0.2500</td>\n",
" <td>-0.105</td>\n",
" <td>0.0550</td>\n",
" <td>0.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Freight &amp; Logistics Services</th>\n",
" <td>39.0</td>\n",
" <td>0.133590</td>\n",
" <td>0.430987</td>\n",
" <td>-0.59</td>\n",
" <td>-0.0950</td>\n",
" <td>0.100</td>\n",
" <td>0.2450</td>\n",
" <td>1.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Healthcare Equipment &amp; Supplies</th>\n",
" <td>130.0</td>\n",
" <td>0.295077</td>\n",
" <td>1.400220</td>\n",
" <td>-0.63</td>\n",
" <td>-0.2300</td>\n",
" <td>0.005</td>\n",
" <td>0.4650</td>\n",
" <td>14.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Healthcare Providers &amp; Services</th>\n",
" <td>49.0</td>\n",
" <td>-0.041020</td>\n",
" <td>0.329509</td>\n",
" <td>-0.66</td>\n",
" <td>-0.2100</td>\n",
" <td>-0.090</td>\n",
" <td>0.0800</td>\n",
" <td>1.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Holding Companies</th>\n",
" <td>3.0</td>\n",
" <td>-0.083333</td>\n",
" <td>0.410528</td>\n",
" <td>-0.43</td>\n",
" <td>-0.3100</td>\n",
" <td>-0.190</td>\n",
" <td>0.0900</td>\n",
" <td>0.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Homebuilding &amp; Construction Supplies</th>\n",
" <td>43.0</td>\n",
" <td>0.065116</td>\n",
" <td>0.258359</td>\n",
" <td>-0.37</td>\n",
" <td>-0.1500</td>\n",
" <td>0.060</td>\n",
" <td>0.2250</td>\n",
" <td>0.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hotels &amp; Entertainment Services</th>\n",
" <td>75.0</td>\n",
" <td>-0.202400</td>\n",
" <td>0.369641</td>\n",
" <td>-0.76</td>\n",
" <td>-0.4050</td>\n",
" <td>-0.250</td>\n",
" <td>-0.0500</td>\n",
" <td>1.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Household Goods</th>\n",
" <td>16.0</td>\n",
" <td>0.070625</td>\n",
" <td>0.551779</td>\n",
" <td>-0.64</td>\n",
" <td>-0.1650</td>\n",
" <td>-0.025</td>\n",
" <td>0.0700</td>\n",
" <td>1.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Industrial Conglomerates</th>\n",
" <td>7.0</td>\n",
" <td>-0.197143</td>\n",
" <td>0.179696</td>\n",
" <td>-0.45</td>\n",
" <td>-0.3300</td>\n",
" <td>-0.130</td>\n",
" <td>-0.1000</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Insurance</th>\n",
" <td>84.0</td>\n",
" <td>-0.190119</td>\n",
" <td>0.320273</td>\n",
" <td>-0.61</td>\n",
" <td>-0.3625</td>\n",
" <td>-0.275</td>\n",
" <td>-0.1400</td>\n",
" <td>0.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Investment Banking &amp; Investment Services</th>\n",
" <td>67.0</td>\n",
" <td>-0.163582</td>\n",
" <td>0.239404</td>\n",
" <td>-0.66</td>\n",
" <td>-0.2750</td>\n",
" <td>-0.190</td>\n",
" <td>0.0250</td>\n",
" <td>0.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Leisure Products</th>\n",
" <td>24.0</td>\n",
" <td>0.603333</td>\n",
" <td>1.766767</td>\n",
" <td>-0.66</td>\n",
" <td>-0.0300</td>\n",
" <td>0.085</td>\n",
" <td>0.3600</td>\n",
" <td>8.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Machinery, Equipment &amp; Components</th>\n",
" <td>131.0</td>\n",
" <td>-0.134198</td>\n",
" <td>0.261337</td>\n",
" <td>-0.63</td>\n",
" <td>-0.2750</td>\n",
" <td>-0.150</td>\n",
" <td>-0.0100</td>\n",
" <td>1.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Media &amp; Publishing</th>\n",
" <td>55.0</td>\n",
" <td>-0.187091</td>\n",
" <td>0.459495</td>\n",
" <td>-0.79</td>\n",
" <td>-0.4100</td>\n",
" <td>-0.290</td>\n",
" <td>-0.0700</td>\n",
" <td>2.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Metals &amp; Mining</th>\n",
" <td>31.0</td>\n",
" <td>-0.218065</td>\n",
" <td>0.266000</td>\n",
" <td>-0.63</td>\n",
" <td>-0.4300</td>\n",
" <td>-0.210</td>\n",
" <td>-0.1050</td>\n",
" <td>0.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Multiline Utilities</th>\n",
" <td>11.0</td>\n",
" <td>-0.223636</td>\n",
" <td>0.128396</td>\n",
" <td>-0.39</td>\n",
" <td>-0.3100</td>\n",
" <td>-0.270</td>\n",
" <td>-0.1350</td>\n",
" <td>-0.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Natural Gas Utilities</th>\n",
" <td>11.0</td>\n",
" <td>-0.285455</td>\n",
" <td>0.110758</td>\n",
" <td>-0.44</td>\n",
" <td>-0.3950</td>\n",
" <td>-0.290</td>\n",
" <td>-0.1950</td>\n",
" <td>-0.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Office Equipment</th>\n",
" <td>2.0</td>\n",
" <td>0.310000</td>\n",
" <td>0.084853</td>\n",
" <td>0.25</td>\n",
" <td>0.2800</td>\n",
" <td>0.310</td>\n",
" <td>0.3400</td>\n",
" <td>0.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oil &amp; Gas</th>\n",
" <td>51.0</td>\n",
" <td>-0.498824</td>\n",
" <td>0.310533</td>\n",
" <td>-0.90</td>\n",
" <td>-0.6850</td>\n",
" <td>-0.580</td>\n",
" <td>-0.4600</td>\n",
" <td>0.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oil &amp; Gas Related Equipment and Services</th>\n",
" <td>44.0</td>\n",
" <td>-0.528182</td>\n",
" <td>0.185576</td>\n",
" <td>-0.87</td>\n",
" <td>-0.6650</td>\n",
" <td>-0.520</td>\n",
" <td>-0.4050</td>\n",
" <td>-0.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Paper &amp; Forest Products</th>\n",
" <td>8.0</td>\n",
" <td>-0.167500</td>\n",
" <td>0.382613</td>\n",
" <td>-0.56</td>\n",
" <td>-0.3875</td>\n",
" <td>-0.305</td>\n",
" <td>0.0025</td>\n",
" <td>0.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Passenger Transportation Services</th>\n",
" <td>14.0</td>\n",
" <td>-0.502857</td>\n",
" <td>0.190078</td>\n",
" <td>-0.92</td>\n",
" <td>-0.5950</td>\n",
" <td>-0.520</td>\n",
" <td>-0.4075</td>\n",
" <td>-0.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Personal &amp; Household Products &amp; Services</th>\n",
" <td>31.0</td>\n",
" <td>-0.137419</td>\n",
" <td>0.309763</td>\n",
" <td>-0.74</td>\n",
" <td>-0.3600</td>\n",
" <td>-0.110</td>\n",
" <td>0.0350</td>\n",
" <td>0.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pharmaceuticals</th>\n",
" <td>69.0</td>\n",
" <td>0.001449</td>\n",
" <td>0.505901</td>\n",
" <td>-0.88</td>\n",
" <td>-0.2900</td>\n",
" <td>-0.080</td>\n",
" <td>0.2000</td>\n",
" <td>2.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Professional &amp; Commercial Services</th>\n",
" <td>111.0</td>\n",
" <td>-0.166036</td>\n",
" <td>0.323718</td>\n",
" <td>-0.81</td>\n",
" <td>-0.3850</td>\n",
" <td>-0.190</td>\n",
" <td>-0.0050</td>\n",
" <td>1.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Real Estate Operations</th>\n",
" <td>21.0</td>\n",
" <td>0.003333</td>\n",
" <td>0.760318</td>\n",
" <td>-0.69</td>\n",
" <td>-0.4000</td>\n",
" <td>-0.270</td>\n",
" <td>-0.0400</td>\n",
" <td>2.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Renewable Energy</th>\n",
" <td>15.0</td>\n",
" <td>1.040000</td>\n",
" <td>1.312506</td>\n",
" <td>-0.26</td>\n",
" <td>0.0200</td>\n",
" <td>0.700</td>\n",
" <td>1.5100</td>\n",
" <td>3.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Residential &amp; Commercial REIT</th>\n",
" <td>194.0</td>\n",
" <td>-0.318402</td>\n",
" <td>0.249565</td>\n",
" <td>-0.88</td>\n",
" <td>-0.5200</td>\n",
" <td>-0.340</td>\n",
" <td>-0.1400</td>\n",
" <td>0.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Semiconductors &amp; Semiconductor Equipment</th>\n",
" <td>66.0</td>\n",
" <td>0.008182</td>\n",
" <td>0.357766</td>\n",
" <td>-0.45</td>\n",
" <td>-0.1900</td>\n",
" <td>-0.060</td>\n",
" <td>0.1100</td>\n",
" <td>1.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Software &amp; IT Services</th>\n",
" <td>213.0</td>\n",
" <td>0.328216</td>\n",
" <td>1.112396</td>\n",
" <td>-0.78</td>\n",
" <td>-0.1300</td>\n",
" <td>0.110</td>\n",
" <td>0.4300</td>\n",
" <td>9.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Specialty Retailers</th>\n",
" <td>76.0</td>\n",
" <td>0.054605</td>\n",
" <td>0.499120</td>\n",
" <td>-0.86</td>\n",
" <td>-0.2150</td>\n",
" <td>-0.045</td>\n",
" <td>0.2650</td>\n",
" <td>1.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Telecommunications Services</th>\n",
" <td>25.0</td>\n",
" <td>0.012000</td>\n",
" <td>0.410173</td>\n",
" <td>-0.60</td>\n",
" <td>-0.1800</td>\n",
" <td>-0.100</td>\n",
" <td>0.0200</td>\n",
" <td>1.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Textiles &amp; Apparel</th>\n",
" <td>25.0</td>\n",
" <td>-0.202000</td>\n",
" <td>0.331273</td>\n",
" <td>-0.57</td>\n",
" <td>-0.4700</td>\n",
" <td>-0.310</td>\n",
" <td>0.0200</td>\n",
" <td>0.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Transport Infrastructure</th>\n",
" <td>1.0</td>\n",
" <td>-0.590000</td>\n",
" <td>NaN</td>\n",
" <td>-0.59</td>\n",
" <td>-0.5900</td>\n",
" <td>-0.590</td>\n",
" <td>-0.5900</td>\n",
" <td>-0.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Uranium</th>\n",
" <td>2.0</td>\n",
" <td>-0.060000</td>\n",
" <td>0.155563</td>\n",
" <td>-0.17</td>\n",
" <td>-0.1150</td>\n",
" <td>-0.060</td>\n",
" <td>-0.0050</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Water Utilities</th>\n",
" <td>10.0</td>\n",
" <td>-0.159000</td>\n",
" <td>0.126091</td>\n",
" <td>-0.35</td>\n",
" <td>-0.1950</td>\n",
" <td>-0.175</td>\n",
" <td>-0.1325</td>\n",
" <td>0.12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min \\\n",
"industry \n",
"0 2.0 -0.075000 0.190919 -0.21 \n",
"Aerospace & Defense 29.0 -0.252414 0.265096 -0.76 \n",
"Automobiles & Auto Parts 31.0 0.135484 1.376108 -0.60 \n",
"Banking Services 317.0 -0.395710 0.173159 -0.74 \n",
"Beverages 13.0 0.316923 0.939365 -0.39 \n",
"Biotechnology & Medical Research 230.0 0.339652 2.210579 -0.75 \n",
"Chemicals 55.0 -0.102727 0.422525 -0.48 \n",
"Coal 4.0 -0.410000 0.434434 -0.67 \n",
"Communications & Networking 40.0 -0.097750 0.350827 -0.61 \n",
"Computers, Phones & Household Electronics 22.0 -0.088182 0.312481 -0.49 \n",
"Construction & Engineering 23.0 -0.061739 0.254570 -0.55 \n",
"Construction Materials 9.0 -0.173333 0.122678 -0.38 \n",
"Containers & Packaging 19.0 -0.096316 0.159903 -0.35 \n",
"Diversified Industrial Goods Wholesale 2.0 -0.085000 0.077782 -0.14 \n",
"Diversified Retail 19.0 0.181579 0.742573 -0.70 \n",
"Electrical Utilities & IPPs 36.0 -0.189722 0.144350 -0.43 \n",
"Electronic Equipment & Parts 20.0 -0.061500 0.276848 -0.41 \n",
"Food & Drug Retailing 19.0 -0.074737 0.395676 -0.69 \n",
"Food & Tobacco 54.0 -0.069444 0.293864 -0.70 \n",
"Freight & Logistics Services 39.0 0.133590 0.430987 -0.59 \n",
"Healthcare Equipment & Supplies 130.0 0.295077 1.400220 -0.63 \n",
"Healthcare Providers & Services 49.0 -0.041020 0.329509 -0.66 \n",
"Holding Companies 3.0 -0.083333 0.410528 -0.43 \n",
"Homebuilding & Construction Supplies 43.0 0.065116 0.258359 -0.37 \n",
"Hotels & Entertainment Services 75.0 -0.202400 0.369641 -0.76 \n",
"Household Goods 16.0 0.070625 0.551779 -0.64 \n",
"Industrial Conglomerates 7.0 -0.197143 0.179696 -0.45 \n",
"Insurance 84.0 -0.190119 0.320273 -0.61 \n",
"Investment Banking & Investment Services 67.0 -0.163582 0.239404 -0.66 \n",
"Leisure Products 24.0 0.603333 1.766767 -0.66 \n",
"Machinery, Equipment & Components 131.0 -0.134198 0.261337 -0.63 \n",
"Media & Publishing 55.0 -0.187091 0.459495 -0.79 \n",
"Metals & Mining 31.0 -0.218065 0.266000 -0.63 \n",
"Multiline Utilities 11.0 -0.223636 0.128396 -0.39 \n",
"Natural Gas Utilities 11.0 -0.285455 0.110758 -0.44 \n",
"Office Equipment 2.0 0.310000 0.084853 0.25 \n",
"Oil & Gas 51.0 -0.498824 0.310533 -0.90 \n",
"Oil & Gas Related Equipment and Services 44.0 -0.528182 0.185576 -0.87 \n",
"Paper & Forest Products 8.0 -0.167500 0.382613 -0.56 \n",
"Passenger Transportation Services 14.0 -0.502857 0.190078 -0.92 \n",
"Personal & Household Products & Services 31.0 -0.137419 0.309763 -0.74 \n",
"Pharmaceuticals 69.0 0.001449 0.505901 -0.88 \n",
"Professional & Commercial Services 111.0 -0.166036 0.323718 -0.81 \n",
"Real Estate Operations 21.0 0.003333 0.760318 -0.69 \n",
"Renewable Energy 15.0 1.040000 1.312506 -0.26 \n",
"Residential & Commercial REIT 194.0 -0.318402 0.249565 -0.88 \n",
"Semiconductors & Semiconductor Equipment 66.0 0.008182 0.357766 -0.45 \n",
"Software & IT Services 213.0 0.328216 1.112396 -0.78 \n",
"Specialty Retailers 76.0 0.054605 0.499120 -0.86 \n",
"Telecommunications Services 25.0 0.012000 0.410173 -0.60 \n",
"Textiles & Apparel 25.0 -0.202000 0.331273 -0.57 \n",
"Transport Infrastructure 1.0 -0.590000 NaN -0.59 \n",
"Uranium 2.0 -0.060000 0.155563 -0.17 \n",
"Water Utilities 10.0 -0.159000 0.126091 -0.35 \n",
"\n",
" 25% 50% 75% max \n",
"industry \n",
"0 -0.1425 -0.075 -0.0075 0.06 \n",
"Aerospace & Defense -0.4400 -0.190 -0.0700 0.18 \n",
"Automobiles & Auto Parts -0.3400 -0.210 -0.1100 6.48 \n",
"Banking Services -0.4800 -0.430 -0.3500 1.07 \n",
"Beverages -0.1300 -0.020 0.2200 3.09 \n",
"Biotechnology & Medical Research -0.2900 -0.045 0.3675 24.55 \n",
"Chemicals -0.3000 -0.150 -0.0350 2.68 \n",
"Coal -0.6250 -0.605 -0.3900 0.24 \n",
"Communications & Networking -0.3050 -0.175 0.0125 1.22 \n",
"Computers, Phones & Household Electronics -0.3275 -0.090 0.0275 0.78 \n",
"Construction & Engineering -0.2250 -0.050 0.0750 0.50 \n",
"Construction Materials -0.2100 -0.120 -0.0900 -0.02 \n",
"Containers & Packaging -0.2200 -0.090 -0.0100 0.23 \n",
"Diversified Industrial Goods Wholesale -0.1125 -0.085 -0.0575 -0.03 \n",
"Diversified Retail -0.1500 0.000 0.4250 2.18 \n",
"Electrical Utilities & IPPs -0.2850 -0.210 -0.1175 0.22 \n",
"Electronic Equipment & Parts -0.2525 -0.085 0.1025 0.61 \n",
"Food & Drug Retailing -0.3750 0.020 0.1450 0.98 \n",
"Food & Tobacco -0.2500 -0.105 0.0550 0.99 \n",
"Freight & Logistics Services -0.0950 0.100 0.2450 1.79 \n",
"Healthcare Equipment & Supplies -0.2300 0.005 0.4650 14.23 \n",
"Healthcare Providers & Services -0.2100 -0.090 0.0800 1.50 \n",
"Holding Companies -0.3100 -0.190 0.0900 0.37 \n",
"Homebuilding & Construction Supplies -0.1500 0.060 0.2250 0.66 \n",
"Hotels & Entertainment Services -0.4050 -0.250 -0.0500 1.70 \n",
"Household Goods -0.1650 -0.025 0.0700 1.88 \n",
"Industrial Conglomerates -0.3300 -0.130 -0.1000 0.06 \n",
"Insurance -0.3625 -0.275 -0.1400 0.97 \n",
"Investment Banking & Investment Services -0.2750 -0.190 0.0250 0.40 \n",
"Leisure Products -0.0300 0.085 0.3600 8.36 \n",
"Machinery, Equipment & Components -0.2750 -0.150 -0.0100 1.06 \n",
"Media & Publishing -0.4100 -0.290 -0.0700 2.43 \n",
"Metals & Mining -0.4300 -0.210 -0.1050 0.39 \n",
"Multiline Utilities -0.3100 -0.270 -0.1350 -0.02 \n",
"Natural Gas Utilities -0.3950 -0.290 -0.1950 -0.13 \n",
"Office Equipment 0.2800 0.310 0.3400 0.37 \n",
"Oil & Gas -0.6850 -0.580 -0.4600 0.54 \n",
"Oil & Gas Related Equipment and Services -0.6650 -0.520 -0.4050 -0.09 \n",
"Paper & Forest Products -0.3875 -0.305 0.0025 0.63 \n",
"Passenger Transportation Services -0.5950 -0.520 -0.4075 -0.14 \n",
"Personal & Household Products & Services -0.3600 -0.110 0.0350 0.38 \n",
"Pharmaceuticals -0.2900 -0.080 0.2000 2.12 \n",
"Professional & Commercial Services -0.3850 -0.190 -0.0050 1.17 \n",
"Real Estate Operations -0.4000 -0.270 -0.0400 2.30 \n",
"Renewable Energy 0.0200 0.700 1.5100 3.66 \n",
"Residential & Commercial REIT -0.5200 -0.340 -0.1400 0.56 \n",
"Semiconductors & Semiconductor Equipment -0.1900 -0.060 0.1100 1.93 \n",
"Software & IT Services -0.1300 0.110 0.4300 9.19 \n",
"Specialty Retailers -0.2150 -0.045 0.2650 1.75 \n",
"Telecommunications Services -0.1800 -0.100 0.0200 1.54 \n",
"Textiles & Apparel -0.4700 -0.310 0.0200 0.61 \n",
"Transport Infrastructure -0.5900 -0.590 -0.5900 -0.59 \n",
"Uranium -0.1150 -0.060 -0.0050 0.05 \n",
"Water Utilities -0.1950 -0.175 -0.1325 0.12 "
]
},
"execution_count": 204,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"industry_stats = russell_3000.groupby('industry')['ytd_return'].describe()\n",
"industry_stats"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e2451e4e0>"
]
},
"execution_count": 205,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"russell_3000.groupby('industry')['ytd_return'].mean().sort_values().plot(kind='barh', figsize=[12,12], legend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"Industries? I put this here for reference, but it is obviously too much data to be worth examining in this notebook. I will save this for a later date and look at industry performance leading up to the election and follow up with how performance changed after the election (or not)."
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"Lets take one final look at another way to look at the market, but expand our analysis globally and include fixed income. For convenience, I created a CSV file that I use to break apart performance globally to see what has and has not worked with ETF's that represent a wide variety of styles, regions, and security types."
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"etfs_by_factor = pd.read_csv('etf_types.csv')"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"factor_tickers = etfs_by_factor['ticker'].to_list()"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"factor_adjusted_close = pd.DataFrame(columns=factor_tickers)\n",
"null_tickers = []\n",
"\n",
"for ticker in factor_tickers:\n",
" try:\n",
" data_panel = web.DataReader([ticker], \"tiingo\").loc[ticker]['adjClose'].to_frame()\n",
" data_panel.columns = [ticker]\n",
" data_panel.index = pd.to_datetime(data_panel.index)\n",
" if data_panel.index.max().tz_localize(None) < datetime.today() - relativedelta(days=5):\n",
" print(\"{} most recent date is {}\".format(ticker, str(data_panel.index.max())))\n",
" else:\n",
" factor_adjusted_close[ticker] = data_panel[ticker]\n",
" except:\n",
" null_tickers.append(ticker)\n",
" print(\"{} not found\".format(ticker))"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"filtered_factor_adjusted_close = factor_adjusted_close.loc['2019-12-31':]"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"etfs_by_factor.set_index('ticker', inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"for ticker in filtered_factor_adjusted_close.columns:\n",
" etfs_by_factor.loc[ticker, 'ytd_return'] = (filtered_factor_adjusted_close[ticker].iloc[-1] - filtered_factor_adjusted_close[ticker].iloc[0])/filtered_factor_adjusted_close[ticker].iloc[0]\n",
" etfs_by_factor.loc[ticker, 'peak_to_trough'] = (filtered_factor_adjusted_close[ticker].loc[trough] - filtered_factor_adjusted_close[ticker].loc[peak])/filtered_factor_adjusted_close[ticker].loc[peak]\n",
" etfs_by_factor.loc[ticker, 'trough_to_date'] = (filtered_factor_adjusted_close[ticker].iloc[-1] - filtered_factor_adjusted_close[ticker].loc[trough])/filtered_factor_adjusted_close[ticker].loc[trough]\n",
" etfs_by_factor.loc[ticker, 'start_to_peak'] = (filtered_factor_adjusted_close[ticker].loc[peak] - filtered_factor_adjusted_close[ticker].iloc[0])/filtered_factor_adjusted_close[ticker].iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 212,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>asset_class</th>\n",
" <th>type</th>\n",
" <th>factor</th>\n",
" <th>ytd_return</th>\n",
" <th>peak_to_trough</th>\n",
" <th>trough_to_date</th>\n",
" <th>start_to_peak</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ticker</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>IPO</th>\n",
" <td>equity</td>\n",
" <td>qualitative</td>\n",
" <td>ipo</td>\n",
" <td>0.631696</td>\n",
" <td>-0.332509</td>\n",
" <td>1.164173</td>\n",
" <td>0.129540</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TLT</th>\n",
" <td>fixed_income</td>\n",
" <td>us_treasury_duration</td>\n",
" <td>20_plus_year_treasury</td>\n",
" <td>0.227017</td>\n",
" <td>0.142357</td>\n",
" <td>-0.001726</td>\n",
" <td>0.075967</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CWB</th>\n",
" <td>fixed_income</td>\n",
" <td>us_corporate_credit</td>\n",
" <td>convertibles</td>\n",
" <td>0.216678</td>\n",
" <td>-0.320608</td>\n",
" <td>0.643655</td>\n",
" <td>0.089543</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IWF</th>\n",
" <td>equity</td>\n",
" <td>style</td>\n",
" <td>growth</td>\n",
" <td>0.184621</td>\n",
" <td>-0.315461</td>\n",
" <td>0.583214</td>\n",
" <td>0.093054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TLH</th>\n",
" <td>fixed_income</td>\n",
" <td>us_treasury_duration</td>\n",
" <td>10_20_year_treasury</td>\n",
" <td>0.175230</td>\n",
" <td>0.112517</td>\n",
" <td>0.002433</td>\n",
" <td>0.053807</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IVW</th>\n",
" <td>equity</td>\n",
" <td>size_vs_value</td>\n",
" <td>large_cap_growth</td>\n",
" <td>0.152948</td>\n",
" <td>-0.313515</td>\n",
" <td>0.544389</td>\n",
" <td>0.087482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PLW</th>\n",
" <td>fixed_income</td>\n",
" <td>us_treasury_duration</td>\n",
" <td>laddered_treasury</td>\n",
" <td>0.151535</td>\n",
" <td>0.095514</td>\n",
" <td>0.005569</td>\n",
" <td>0.045316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MTUM</th>\n",
" <td>equity</td>\n",
" <td>style</td>\n",
" <td>momentum</td>\n",
" <td>0.128250</td>\n",
" <td>-0.340823</td>\n",
" <td>0.567762</td>\n",
" <td>0.091749</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IEF</th>\n",
" <td>fixed_income</td>\n",
" <td>us_treasury_duration</td>\n",
" <td>7_10_year_treasury</td>\n",
" <td>0.115282</td>\n",
" <td>0.063763</td>\n",
" <td>0.017122</td>\n",
" <td>0.030783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GOVT</th>\n",
" <td>fixed_income</td>\n",
" <td>us_government_agencies</td>\n",
" <td>us_treasury</td>\n",
" <td>0.088712</td>\n",
" <td>0.057348</td>\n",
" <td>0.007121</td>\n",
" <td>0.022382</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" asset_class type factor \\\n",
"ticker \n",
"IPO equity qualitative ipo \n",
"TLT fixed_income us_treasury_duration 20_plus_year_treasury \n",
"CWB fixed_income us_corporate_credit convertibles \n",
"IWF equity style growth \n",
"TLH fixed_income us_treasury_duration 10_20_year_treasury \n",
"IVW equity size_vs_value large_cap_growth \n",
"PLW fixed_income us_treasury_duration laddered_treasury \n",
"MTUM equity style momentum \n",
"IEF fixed_income us_treasury_duration 7_10_year_treasury \n",
"GOVT fixed_income us_government_agencies us_treasury \n",
"\n",
" ytd_return peak_to_trough trough_to_date start_to_peak \n",
"ticker \n",
"IPO 0.631696 -0.332509 1.164173 0.129540 \n",
"TLT 0.227017 0.142357 -0.001726 0.075967 \n",
"CWB 0.216678 -0.320608 0.643655 0.089543 \n",
"IWF 0.184621 -0.315461 0.583214 0.093054 \n",
"TLH 0.175230 0.112517 0.002433 0.053807 \n",
"IVW 0.152948 -0.313515 0.544389 0.087482 \n",
"PLW 0.151535 0.095514 0.005569 0.045316 \n",
"MTUM 0.128250 -0.340823 0.567762 0.091749 \n",
"IEF 0.115282 0.063763 0.017122 0.030783 \n",
"GOVT 0.088712 0.057348 0.007121 0.022382 "
]
},
"execution_count": 212,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"etfs_by_factor.sort_values(by='ytd_return', ascending=False).head(10)"
]
},
{
"cell_type": "markdown",
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"source": [
"The top ten performers year-to-date across these asset classes we examined are shown above. Roughly half are fixed income and the other half are equity. IPOs (as represented by the ETF with ticker IPO) has been the clear winner so far. I will dive deeper into that in a seperate piece. The second best performance has been from long-duration treasuries, which shouldn't be that surprising given the zero interest rate policy that the Fed put back in place in March of this year."
]
},
{
"cell_type": "code",
"execution_count": 213,
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{
"data": {
"text/html": [
"<div>\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>asset_class</th>\n",
" <th>type</th>\n",
" <th>factor</th>\n",
" <th>ytd_return</th>\n",
" <th>peak_to_trough</th>\n",
" <th>trough_to_date</th>\n",
" <th>start_to_peak</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ticker</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>VYM</th>\n",
" <td>equity</td>\n",
" <td>style</td>\n",
" <td>high_dividend_yield</td>\n",
" <td>-0.137354</td>\n",
" <td>-0.346374</td>\n",
" <td>0.316694</td>\n",
" <td>0.002348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PKW</th>\n",
" <td>equity</td>\n",
" <td>qualitative</td>\n",
" <td>buybacks</td>\n",
" <td>-0.140367</td>\n",
" <td>-0.396632</td>\n",
" <td>0.420605</td>\n",
" <td>0.002899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IVE</th>\n",
" <td>equity</td>\n",
" <td>size_vs_value</td>\n",
" <td>large_cap_value</td>\n",
" <td>-0.141397</td>\n",
" <td>-0.368233</td>\n",
" <td>0.346217</td>\n",
" <td>0.009532</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IWD</th>\n",
" <td>equity</td>\n",
" <td>style</td>\n",
" <td>value</td>\n",
" <td>-0.141565</td>\n",
" <td>-0.383719</td>\n",
" <td>0.375592</td>\n",
" <td>0.012603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KNOW</th>\n",
" <td>equity</td>\n",
" <td>qualitative</td>\n",
" <td>insider_sentiment</td>\n",
" <td>-0.151528</td>\n",
" <td>-0.391635</td>\n",
" <td>0.394322</td>\n",
" <td>0.000254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CSD</th>\n",
" <td>equity</td>\n",
" <td>qualitative</td>\n",
" <td>spin_offs</td>\n",
" <td>-0.172612</td>\n",
" <td>-0.519009</td>\n",
" <td>0.742342</td>\n",
" <td>-0.012723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IJR</th>\n",
" <td>equity</td>\n",
" <td>style</td>\n",
" <td>small_cap</td>\n",
" <td>-0.184944</td>\n",
" <td>-0.412208</td>\n",
" <td>0.391620</td>\n",
" <td>-0.003578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EFV</th>\n",
" <td>equity</td>\n",
" <td>global_style</td>\n",
" <td>international_value</td>\n",
" <td>-0.191864</td>\n",
" <td>-0.363132</td>\n",
" <td>0.302040</td>\n",
" <td>-0.025436</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IJJ</th>\n",
" <td>equity</td>\n",
" <td>size_vs_value</td>\n",
" <td>mid_cap_value</td>\n",
" <td>-0.224159</td>\n",
" <td>-0.452110</td>\n",
" <td>0.420628</td>\n",
" <td>-0.003221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IWN</th>\n",
" <td>equity</td>\n",
" <td>size_vs_value</td>\n",
" <td>small_cap_value</td>\n",
" <td>-0.245257</td>\n",
" <td>-0.430645</td>\n",
" <td>0.351788</td>\n",
" <td>-0.019365</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" asset_class type factor ytd_return \\\n",
"ticker \n",
"VYM equity style high_dividend_yield -0.137354 \n",
"PKW equity qualitative buybacks -0.140367 \n",
"IVE equity size_vs_value large_cap_value -0.141397 \n",
"IWD equity style value -0.141565 \n",
"KNOW equity qualitative insider_sentiment -0.151528 \n",
"CSD equity qualitative spin_offs -0.172612 \n",
"IJR equity style small_cap -0.184944 \n",
"EFV equity global_style international_value -0.191864 \n",
"IJJ equity size_vs_value mid_cap_value -0.224159 \n",
"IWN equity size_vs_value small_cap_value -0.245257 \n",
"\n",
" peak_to_trough trough_to_date start_to_peak \n",
"ticker \n",
"VYM -0.346374 0.316694 0.002348 \n",
"PKW -0.396632 0.420605 0.002899 \n",
"IVE -0.368233 0.346217 0.009532 \n",
"IWD -0.383719 0.375592 0.012603 \n",
"KNOW -0.391635 0.394322 0.000254 \n",
"CSD -0.519009 0.742342 -0.012723 \n",
"IJR -0.412208 0.391620 -0.003578 \n",
"EFV -0.363132 0.302040 -0.025436 \n",
"IJJ -0.452110 0.420628 -0.003221 \n",
"IWN -0.430645 0.351788 -0.019365 "
]
},
"execution_count": 213,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"etfs_by_factor.sort_values(by='ytd_return', ascending=False).tail(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"The results here look fairly consistent with our previous analysis, where we found that the market has penalized Smaller Market Cap companies, and that it has also penalized low P/E (cheaper) companies. No surpise that if you were both a small company and had a cheap valuation, you were left in the dust performance wise. The worst performance year-to-date was in Small Cap Value, Mid Cap Value, and International Value which makes it look like the market penalized cheap companies more than small cap companies so far."
]
},
{
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},
"source": [
"Now let's look at equity on it's own."
]
},
{
"cell_type": "code",
"execution_count": 214,
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},
"outputs": [],
"source": [
"equity_factors = etfs_by_factor[etfs_by_factor['asset_class'] == 'equity']"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {
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},
"outputs": [],
"source": [
"equity_factors.set_index('factor', inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 216,
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{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e243ae5c0>"
]
},
"execution_count": 216,
"metadata": {},
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{
"data": {
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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"equity_factors['ytd_return'].sort_values().plot(kind='barh', figsize=[12,12], legend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"The same pattern emerges...the smaller and cheaper companies were dominated by speculative or large cap companies year to date. The top 5 are either momentum based, growth, or IPOs. It almost leads me to conclude that most of the smart money is still on the sidelines, and investors-to-date have been treating the market as a substitute for not being able to gamble on sports or go to the casino (just speculating here).."
]
},
{
"cell_type": "code",
"execution_count": 217,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e24511eb8>"
]
},
"execution_count": 217,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"equity_factors['peak_to_trough'].sort_values().plot(kind='barh', figsize=[12,12], legend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"From the market's peak in February to the trough in March of this year, small cap and value both were hit the hardest and sold off the most during the rapid market decline, while the same companies that have outperformed year-to-date were also penalized far less in the drawdown."
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e2427a080>"
]
},
"execution_count": 218,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"equity_factors['trough_to_date'].sort_values().plot(kind='barh', figsize=[12,12], legend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"From the market bottom in March to today, you see the same bunch that held up the best in the correction, have outperformed in the recovery (with the exception of the spin-offs, which were both hit the hardest and performed the best out of the trough of the market)."
]
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e24f44f28>"
]
},
"execution_count": 219,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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W1a7AE8BHgD8ADgB2bpvjU8Anq2of4B3A59ey3keAh6tq96raA/h+0/63VdUL7AG8LskebWMerqrdgfOBf1rL3J8CPtX0va/fub2BD1bVK5NMB44H9muu58+S7AUsAGY0/XuBKUkmNW3XV9Us4DdVNa2qjmn7jj7TfEe/aq7/OZKc0IS9vhUrVqzlEiRJkqTx1c1hKMDfJ1kC/DuwI/DC5txPq+qm5nhf4Lqq+p+qegK4pG2OQ4DzkywGvg08P8mUQdY7BPjMmg9V9cvm8J1JFgG3ArsCu7SNuajt5/5ruZb92+r6ar9zN1fVvc3xgcClVfVYVa0EvkUr8CwEpid5PvA4cCOtUDSDVlAayL1Vtbg5Xgj0DNSpquZUVW9V9U6dOnUtlyBJkiSNr27+PUPHAFOB6VX1RJLlwOTm3GPDnGMT4A+qatVICkiyE3AasE9V/TLJ3LYaAGqQ43Ux5LU0138vcBzwA2AJrd2ylwN3DjLs8bbjpwBvk5MkSdIGpZt3hl4A/LwJAgcDvzdIv1to3b62TfMSgvbbwf4NOGnNhyTT1rLeVcAH2vpuAzyfVlh5OMkLgTf3G3NU288b1zL3TW11Hb2WfguAtyXZIsmWwOE8s/OzgFYwu745fh9wa1WtCWFPNLfOSZIkSRuFbg5D84DeJEuBPwbuGqhTVf038PfAzcANwHLg4eb0yc0cS5LcQStADOYsYJvmxQW3AQdX1W20bo+7i9btbTf0G7NNcxvfB4FT1zL3KcCHmr4vb6uv/7UsAuY21/JD4PNVdWtzegHwIuDGqnqQ1jNV7bfIzQGWtL1AQZIkSdqg5Zn/8K/BJJlSVSubnaFLgS9U1aVjvOZyoLeqfjGMvlvQesFBJTkaeFdVHTaW9Y1Eb29v9fX1dboMdVjPrPmdLkGSNkrLZ8/sdAnShJBkYfOCsiF18zND6+KMJIfQep7n34DLOlxPf9NpvcghtN7s9icdrkcalP9nLUmSJgrD0DBU1WnD7ZvkeFq3tbW7oao+MFD/tazZM8Dcfwsc2a/5kqo6G9hzXeaXJEmSup23yWnceJucJEmSxtq63CbXzS9QkCRJktTFDEOSJEmSupJhSJIkSVJXMgxJkiRJ6kqGIUmSJEldyTAkSZIkqSsZhiRJkiR1JcOQJEmSpK5kGJIkSZLUlTbrdAGSukvPrPmdLkHSCCyfPbPTJUjSqHNnSJIkSVJXMgxJkiRJ6kqGoVGWZOUEqKEnybJh9Hl32+feJOc1x8clOb85fl+SP25r32Esa5ckSZLGi88Mda8e4N3AVwGqqg/o69+pqi5o+3gcsAy4f+zLkyRJksaWO0NjJC3/mGRZkqVJjmraP5Pk0Ob40iRfaI7/JMnZg8w1O8kH2j6fkeS0wdboN7YnyYIki5o/r2lOzQZmJFmc5NQkByW5fIDxa9Y6AugF5jVjZia5rK3fHya5dOTfmCRJkjS+DENj5+3ANGBP4BDgH5O8CFgAzGj67Ajs0hzPAK4fZK6LgXe2fX5n0zbYGu1+DvxhVe0NHAWc17TPAhZU1bSq+uRQF1NV36C1c3RMVU0DvgvsnGRq0+V44Av9xyU5IUlfkr4VK1YMtYwkSZI0bgxDY+dA4KKqeqqqHgSuA/ahCUNJdgHuAB5sAsz+wA8GmqiqbgV+N8kOSfYEfllV/7WWNdpNAi5MshS4hGfC13qpqgK+AhybZOum/u8N0G9OVfVWVe/UqVP7n5YkSZI6xmeGxllV/XcTHt5EaydoW1o7PSur6tG1DL0EOAL4f2jtCg3XqcCDtHaPNgFWjaTuQXwR+E4z5yVV9eQozi1JkiSNKXeGxs4C4Kgkmza3kr0WuLk5dxNwCq0wtAA4rfm5NhcDR9MKRJcMY401XgA8UFWrgfcAmzbtjwJbreM1PWtMVd1P62UKH6YVjCRJkqQNhmFo7FwKLAFuA74P/L9V9bPm3AJgs6r6v8AiWrtDaw1DVXU7rSDy31X1wDDWWOOzwHuT3AbsDDzWtC8BnkpyW5JTh3lNc4ELmhcobN60zQP+q6ruHOYckiRJ0oSQ1qMf0sg0v4/o1qr6P0P17e3trb6+57y9W12mZ9b8TpcgaQSWz57Z6RIkaViSLKyq3uH09ZkhjViShbR2mv6y07Vow+E/qCRJ0kRhGJpAkmwHXD3AqTdU1UPjXc9Qqmp6p2uQJEmSRsowNIE0gWdap+uQJEmSuoEvUJAkSZLUlQxDkiRJkrqSYUiSJElSVzIMSZIkSepKhiFJkiRJXckwJEmSJKkrGYYkSZIkdSXDkCRJkqSuZBiSJEmS1JU263QBkrpLz6z5nS5BmvCWz57Z6RIkqSu4MyRJkiSpKxmGJEmSJHUlw9B6SrKy+dmTZFmn6xkrSU5JskXb55WdrEeSJElaX4ahLpGW9fn7PgXYYshekiRJ0gaiq8JQki2TzE9yW5JlSY5KsjzJPyRZnKQvyd5Jrkzy4yTva8ZNSXJ1kkVJliY5bARrb5rk3GbdJUlOato/muSWpn1OkjTt1yb5VFPXsiT7rmXuqUmuSnJ7ks8n+WmS7ZvdqruTfBlYBrwkybuaa1iW5Jxm/JFJ/ndz/MEkP2mOfz/JDUlOBnYArklyTdu6Zzff5U1JXriu34kkSZLUSV0VhoA3AfdX1Z5VtRtwRdP+n1U1DVgAzAWOAP4AOLM5vwo4vKr2Bg4GPrEmtKyDE4AeYFpV7QHMa9rPr6p9mno2B97aNmaLpq73A19Yy9wfA75fVbsC3wBe2nbuFcBnm3NPAOcArwemAfskeVtz3TOa/jOAh5Ls2BxfX1XnAfcDB1fVwU2/LYGbqmpP4HrgzwYqLMkJTcjsW7FixVouQZIkSRpf3RaGlgJ/mOScJDOq6uGm/dtt539YVY9W1Qrg8SRbAwH+PskS4N+BHYF13Qk5BPjnqnoSoKr+p2k/OMkPkyylFVJ2bRtzUdP3euD5TS0DORD4WtP3CuCXbed+WlU3Ncf7ANdW1YqmjnnAa6vqZ8CUJFsBLwG+CryWVhhaMMiavwUub44X0gp6z1FVc6qqt6p6p06dOshUkiRJ0vjrqjBUVfcAe9MKPWcl+Whz6vHm5+q24zWfNwOOAaYC05udmgeByetbT5LJwGeBI6pqd+DCfvNW/0sYwTKPDbPfD4Djgbt5Zqdof+CGQfo/UVVr6nkKf2eVJEmSNjBdFYaS7AD8uqr+BfhHWsFoOF4A/LyqnkhyMPB7I1j+KuDPk2zW1LItzwSfXySZQuv2vHZHNX0PBB5u28nq7wbgnU3f/wVsM0i/m4HXNc8TbQq8C7iuObcAOI3WLW+30rod8PG2NR8FthrmtUqSJEkTXrf91/zdgX9MsprW8zMn0nrGZijzgO80t7L1AXeNYO3PA68EliR5Ariwqs5PciGtlxv8DLil35hVSW4FJgF/spa5zwQuSvIe4MZmrkeBKe2dquqBJLOAa2jd+je/qv61Ob2A1i1y11fVU0n+q991zgGuSHJ/23NDkiRJ0gYrz9zppIkkybXAaVXVN4y+vwM8VVVPJtkf+FxzO9+E0tvbW319Q16ONnI9s+Z3ugRpwls+e2anS5CkDVaShVXVO5y+3bYztLF6KfD15vcI/ZZB3uwmTQT+I0+SJE0UhqFRluSNtF5f3e7eqjp8XeapqoMGmPt44IP9mm+oqg8Ae63L/JIkSVK3MwyNsqq6ErhyjOb+IvDFsZhbkiRJ6jZd9TY5SZIkSVrDMCRJkiSpKxmGJEmSJHUlw5AkSZKkrmQYkiRJktSVDEOSJEmSupJhSJIkSVJXMgxJkiRJ6kqGIUmSJEldabNOFyCpu/TMmt/pEtSFls+e2ekSJEkTkDtDkiRJkrqSYUiSJElSVzIM6VmS9CRZ1hz3JjmvOT4oyWs6W50kSZI0enxmSIOqqj6gr/l4ELAS+EHHCpIkSZJGkTtDG5Ekf5vkniT/keSiJKcluTZJb3N++yTLm+OeJAuSLGr+PGfXp9kNujxJD/A+4NQki5PMSHJvkklNv+e3f5YkSZI2BO4MbSSSTAeOBqbR+ntdBCxcy5CfA39YVauSvAK4COgdqGNVLU9yAbCyqs5t1rsWmAlc1qz7rap6YpQuR5IkSRpz7gxtPGYAl1bVr6vqEeDbQ/SfBFyYZClwCbDLOq73eeD45vh44IsDdUpyQpK+JH0rVqxYxyUkSZKksWMY2vg9yTN/z5Pb2k8FHgT2pLUj9Lx1mbSqbgB6khwEbFpVywbpN6eqequqd+rUqetauyRJkjRmDEMbj+uBtyXZPMlWwB817cuB6c3xEW39XwA8UFWrgfcAmw4x/6PAVv3avgx8lUF2hSRJkqSJzDC0kaiqRcDFwG3A94BbmlPnAicmuRXYvm3IZ4H3JrkN2Bl4bIglvgMcvuYFCk3bPGAbWs8bSZIkSRsUX6CwEamqs4GzAZKc0bTdBezR1u3DTfuP+rWf3rQvB3Zrjq8Frm2O7+nXH+BA4BtV9avRvA5JkiRpPBiGNCJJPg28GXhLp2uRJEmSRiJV1eka1CV6e3urr69v6I6SJEnSCCVZWFUD/sqY/nxmSJIkSVJXMgxJkiRJ6kqGIUmSJEldyTAkSZIkqSsZhiRJkiR1JcOQJEmSpK5kGJIkSZLUlQxDkiRJkrqSYUiSJElSVzIMSZIkSepKhiFJkiRJXWmzThcgqbv0zJrf6RLUBZbPntnpEiRJGwB3hiRJkiR1JcOQJEmSpK5kGFpHSQ5NMmuQcyvHu57xkuS4JDu0fV6eZPtO1iRJkiStD8PQOqqqb1fV7E7XMRJJNl2P4ccBOwzVSZIkSdpQGIbaJOlJcleSuUnuSTIvySFJbkjyoyT7Njsk5zf9d0pyY5KlSc4axvynN31vSzK7afuzJLc0bd9MskXTPjfJBUn6mlreupZ5t0jy9SR3JLk0yQ+T9DbnVib5RJLbgP2TvCHJrU0dX0jyO0n2SfKtpv9hSX6T5HlJJif5SZIjgF5gXpLFSTZvlj4pyaJmrp3X57uXJEmSxpth6LleDnwC2Ln5827gQOA04G/69f0U8Lmq2h14YG2TJnkzcBiwX1XtCXy8OfWtqtqnabsT+NO2YT3AvsBM4IIkkweZ/v3AL6tqF+AjwPS2c1sCP2zm7wPmAkc1NW8GnAjcCkxr+s8AlgH7APs1Y7/RjD2mqqZV1W+avr+oqr2BzzXfz0DXfUIT6PpWrFixtq9IkiRJGleGoee6t6qWVtVq4Hbg6qoqYCmtcNLuAOCi5vgrQ8x7CPDFqvo1QFX9T9O+W5IFSZYCxwC7to35elWtrqofAT+hFc4GciDwtWbeZcCStnNPAd9sjl/VXN89zecvAa+tqieBHyd5Na3w9b+B19IKRgvWck3fan4u5LnfDU09c6qqt6p6p06dupapJEmSpPFlGHqux9uOV7d9Xs3Av5ep1nO9ucBfNDs1ZwLtuz/95x7JWquq6qlh9LseeDPwBPDvtALWgaw9DK35bp7C31klSZKkDYxhaP3cABzdHB8zRN+rgOPbngnatmnfCnggyaQB5jgyySZJXgb8PnD3Wup4ZzPvLsDug/S7G+hJ8vLm83uA65rjBcApwI1VtQLYjtZO0rLm/KNNrZIkSdJGwTC0fj4IfKC5xW3HtXWsqiuAbwN9SRbzzDM2HwF+SCvQ3NVv2H8CNwPfA95XVasGmf6zwNQkdwBn0bq97+EBalgFHA9c0tS8GrigOf1D4IW0doigdavd0uYWQWjtYF3Q7wUKkiRJ0gYrz/xbVxNJkrnA5c3LC4bquykwqapWNbtI/w68qqp+O8ZlrpPe3t7q6+vrdBnqsJ5Z8ztdgrrA8tkzO12CJKlDkiysqt7h9PU5j43DFsA1za12Ad4/0YKQtIb/SJUkSROFYWiUJdmd575Z7vGq2m9d5qmq4waY+43AOf2a762qw2n9HiBJkiRJw2QYGmVVtZRnfmfPaM99JXDlWMwtSZIkdRtfoCBJkiSpKxmGJEmSJHUlw5AkSZKkrmQYkiRJktSVDEOSJEmSupJhSJIkSVJXMgxJkiRJ6kqGIUmSJEldyTAkSZIkqStt1ukCJHWXnlnzO12CNiDLZ8/sdAmSpI2YO0OSJEmSupJhSJIkSVJXMgyNsiQ9SZatx/iVo1zPzkmWFYyjAAAgAElEQVQWJ7k1yctGYb5rk/SORm2SJElSJxmGNn5vA75RVXtV1Y87XYwkSZI0URiGxsamSS5McnuSf0uyeZKXJbkiycIkC5LsDJBkpyQ3Jlma5Kw1EyTZJMlnk9yV5Kok301yRHNuepLrmrmuTPKigYpI8hbgFODEJNf037VKclqSM5rja5Ock+TmJPckmdG0b57ka0nuTHIpsHnTvmmSuUmWNbWfOjZfpSRJkjQ2DENj4xXAZ6pqV+BXwDuAOcBJVTUdOA34bNP3U8Dnqmp34IG2Od4O9AC7AO8B9gdIMgn4NHBEM9cXgLMHKqKqvgtcAHyyqg4eRt2bVdW+tALUx5q2E4FfV9Wrm7bpTfs0YMeq2q2p/YsDTZjkhCR9SfpWrFgxjBIkSZKk8eGrtcfGvVW1uDleSCvUvAa4JMmaPr/T/DyAVlgC+ApwTnN8IHBJVa0Gfpbkmqb9VcBuwFXNXJvy7BC1Pr7Vr2aA1wLnAVTVkiRLmvafAL+f5NPAfODfBpqwqubQCoL09vbWKNUpSZIkrTfD0Nh4vO34KeCFwK+qatog/dclJAS4var2H0FdT/Ls3cDJ/c6vqfsphvjfRlX9MsmewBuB9wHvBP5kBDVJkiRJHeFtcuPjEeDeJEcCpGXP5twNwNHN8TFtY24A3tE8O/RC4KCm/W5gapKnb5tLsusw63gQ+N0k2yX5HeCtwxhzPfDuZq3dgD2a4+2BTarqm8CHgb2HWYMkSZI0IRiGxs8xwJ8muQ24HTisaf8g8IEkS4Ed2/p/E7gPuAP4F2AR8HBV/RY4AjinmWsxrVvwhlRVTwB/B9wMXAXcNYxhnwOmJLmzGbuwad8RuDbJ4qa+vx5ODZIkSdJEkSof45iokkypqpVJtqMVYA6oqp91uq6R6u3trb6+vk6XoQ7rmTW/0yVoA7J89sxOlyBJ2sAkWVhVw/q9mD4zNLFdnmRr4HnA/7chByFpDf9xK0mSJgrD0ARWVQcNt2+Sz9B6M127T1XVgK+8liRJkrqdYWgjUVUf6HQNkiRJ0obEFyhIkiRJ6kqGIUmSJEldyTAkSZIkqSsZhiRJkiR1JcOQJEmSpK5kGJIkSZLUlQxDkiRJkrqSYUiSJElSVzIMSZIkSepKm3W6AEndpWfW/E6XoBFYPntmp0uQJGnUuTMkSZIkqSsZhiRJkiR1pTELQ0l+MIw+pyTZYqxqaFunJ8m72z73JjlvDNZZnmT70Z53LCWZluQtna5DkiRJGm9jFoaq6jXD6HYKsE5hKMmmIyinB3g6DFVVX1WdPIJ5NkbTAMOQJEmSus5Y7gytbH4elOTaJN9IcleSeWk5GdgBuCbJNU3f/5XkxiSLklySZErTvjzJOUkWAUc2852T5OYk9ySZ0fTrSbKgGb8oyZpANhuYkWRxklObmi5vxmyb5LIkS5LclGSPpv2MJF9o1vpJU++aa7ssycIktyc5YZjfR09z/XObmuclOSTJDUl+lGTfYdTzpeb6fprk7Uk+nmRpkiuSTGr6TU9yXVPflUle1LQ/5ztL8jzg74Cjmu/mqGad09rqXtbUPqz6JUmSpA3FeD0ztBetXaBdgN8HDqiq84D7gYOr6uDm9rIPA4dU1d5AH/Chtjkeqqq9q+przefNqmrfZt6PNW0/B/6wGX8UsOZWuFnAgqqaVlWf7FfbmcCtVbUH8DfAl9vO7Qy8EdgX+NiawAH8SVVNB3qBk5NsN8zv4eXAJ5p5d6a1W3UgcFqz9lD1vAx4PXAo8C/ANVW1O/AbYGZT36eBI5r6vgCc3Tb+Wd9ZVf0W+ChwcfPdXDwK9T9LkhOS9CXpW7FixRDTS5IkSeNnvF6tfXNV3QeQZDGt29b+o1+fP6AVlm5IAvA84Ma28/3/of6t5ufCZj6AScD5SaYBTwGvHEZtBwLvAKiq7yfZLsnzm3Pzq+px4PEkPwdeCNxHKwAd3vR5CfAK4KFhrHVvVS0FSHI7cHVVVZKlbdewtnq+V1VPNP03Ba5o2teMfxWwG3BV8x1uCjzQtv5A39m6GE79z1JVc4A5AL29vTWCNSVJkqQxMV5h6PG246cGWTfAVVX1rkHmeGyQOdvnOxV4ENiT1q7XqhFV+9w1nl4nyUHAIcD+VfXrJNcCk0cw3+q2z6sZ3t/F4wBVtTrJE1W1JlysGR/g9qraf4j1B/s7AHiSZ+8Ytl/b+tYvSZIkTRidfrX2o8BWzfFNwAFJXg6QZMskw9nZafcC4IGqWg28h9bOSP91+lsAHNOseRDwi6p6ZIg1ftkEoZ1p7WiNpnWtp93dwNQk+zfjJyXZdYgx/b+b5cDezfi9gZ2GXbkkSZK0Ael0GJoDXJHkmqpaARwHXJRkCa1b5HZex/k+C7w3yW3N2DW7SUuAp5LcluTUfmPOAKY3a84G3jvEGlfQ2iG6s+l/0zrWOJR1redpzTNARwDnNN/BYmCot/pdA+yy5gUKwDeBbZvb4P4CuGfdL0GSJEma+PLMnVbS2Ort7a2+vr5Ol6EO65k1v9MlaASWz57Z6RIkSRqWJAurqnc4fX3OQ9K48h/VkiRpojAMjbLmNdtXD3DqDVU1nDfOSZIkSRoHhqFR1gSeaZ2uQ5IkSdLadfoFCpIkSZLUEYYhSZIkSV3JMCRJkiSpKxmGJEmSJHUlw5AkSZKkrmQYkiRJktSVDEOSJEmSupJhSJIkSVJXMgxJkiRJ6kqbdboASd2lZ9b8TpegISyfPbPTJUiSNC7cGZIkSZLUlQxDkiRJkrqSYWgMJfm7JIeMcOwpSbYY7ZpGUMd3k2zd/Hl/p+uRJEmSRothaIwk2bSqPlpV/z7CKU4BOh6GquotVfUrYGvAMCRJkqSNhmFoBJL0JLkrybwkdyb5RpItkixPck6SRcCRSeYmOSLJm5Jc0jb+oCSXN8efS9KX5PYkZzZtJwM7ANckuaZp+19JbkyyKMklSaaspb7pSa5LsjDJlUle1NZ+W/PnH5Msa9qPS3J+2/jLkxzUHC9Psj0wG3hZksXN2C8neVvbmHlJDhut71iSJEkaa4ahkXsV8NmqejXwCM/smjxUVXtX1dfa+v47sF+SLZvPRwFrzv9tVfUCewCvS7JHVZ0H3A8cXFUHN2Hkw8AhVbU30Ad8aKCikkwCPg0cUVXTgS8AZzenvwicVFV7juB6ZwE/rqppVfVXwP8BjmvWfAHwGuA5rwlLckIT9vpWrFgxgmUlSZKksWEYGrn/qqobmuN/AQ5sji/u37GqngSuAP4oyWbATOBfm9PvbHaSbgV2BXYZYK0/aNpvSLIYeC/we4PU9SpgN+Cqpu+HgRcn2RrYuqqub/p9ZdhXOoCqug54RZKpwLuAbzbX2b/fnKrqrareqVOnrs+SkiRJ0qjy9wyNXA3y+bFB+n8N+Avgf4C+qno0yU7AacA+VfXLJHOByQOMDXBVVb1rGHUFuL2q9n9WYysMDeZJnh2MB6phIF8GjgWOBo4f5hhJkiRpQnBnaORemmRN4Hg38B9D9L8O2Bv4M565Re75tMLTw0leCLy5rf+jwFbN8U3AAUleDpBkyySvHGSdu4Gpa2pLMinJrs1LEH6VZM0O1jFtY5YD05JskuQlwL4DzNtezxpzab3ogaq6Y7ALlyRJkiYiw9DI3Q18IMmdwDbA59bWuaqeAi6nFXgub9puo3V73F3AV4Eb2obMAa5Ick1VraD1fM5FSZYANwI7D7LOb4EjgHOS3AYspvU8D7R2bz7T3D6XtmE3APcCdwDnAYsGmPchWrfpLUvyj03bg8CdtJ5FkiRJkjYoqep/t5eGkqQHuLyqdutwKSM2GtfQ/B6kpcDeVfXwUP17e3urr69vpMtpI9Ez6znv2dAEs3z2zE6XIEnSiCVZ2LygbEg+M6QRaX6Z7P8BPjmcICSt4T+0JUnSRGEYGoGqWk7rjW0dleRSYKd+zadX1ZVDjV3fa2h+mexgb7STJEmSJjzD0Aasqg7vdA2SJEnShsoXKEiSJEnqSoYhSZIkSV3JMCRJkiSpKxmGJEmSJHUlw5AkSZKkrmQYkiRJktSVDEOSJEmSupJhSJIkSVJXMgxJkiRJ6kqbdboASd2lZ9b8TpegfpbPntnpEiRJ6gh3hiRJkiR1JcOQJEmSpK5kGNqAJFk5RvN+N8nWQ/S5NknvAO3TkrxlLOqSJEmSxpJhSFTVW6rqVyMcPg0wDEmSJGmDYxgaRUmOTXJzksVJ/jnJpkmOT3JP035hkvObvnOTHNE2dmXzc0qSq5MsSrI0yWHDXPszSQ5tji9N8oXm+E+SnD1YfU378iTbN8cfSXJ3kv9IclGS09qWObIZf0+SGUmeB/wdcFQz51Hr/SVKkiRJ48QwNEqSvBo4CjigqqYBTwHHAmcCBwAHArsMY6pVwOFVtTdwMPCJJBnGuAXAjOZ4x7a1ZgDXD1LfMf2uYR/gHcCewJuB/rfFbVZV+wKnAB+rqt8CHwUurqppVXVx/6KSnJCkL0nfihUrhnEZkiRJ0vjw1dqj5w3AdOCWJrtsDrwGuLaqVgAkuRh45RDzBPj7JK8FVtMKNi8EfjbEuAXAKUl2Ae4AtknyImB/4GTgvQPU9/N+cxwA/GtVrQJWJflOv/Pfan4uBHqGqAeAqpoDzAHo7e2t4YyRJEmSxoNhaPQE+FJV/fXTDcnbgLcP0v9Jmp25JJsAz2vajwGmAtOr6okky4HJQy1eVf/dvAThTcD1wLbAO4GVVfVos7v0rPpG4PHm51P4vx1JkiRt4LxNbvRcDRyR5HcBkmwL3Aq8Lsl2SSYBR7b1X05rpwbgUGBSc/wC4OdNEDoY+L11qOEmWrewXU9rp+i05ueA9SXpP/cNwB8lmZxkCvDWYaz5KLDVOtQoSZIkTQiGoVFSVXcAHwb+LckS4CrgRcAZwI20gsadbUMupBWUbqN1K9tjTfs8oDfJUuCPgbvWoYwFtJ7r+b/AIlq7QwuGqK/9Gm4Bvg0sAb4HLAUeHmLNa4BdfIGCJEmSNjSp8jGO8ZLkOKC3qv6i07UMJsmUqlqZZAtaO0wnVNWi0Zi7t7e3+vr6RmMqbcB6Zs3vdAnqZ/nsmZ0uQZKkUZNkYVU95/djDsTnPtTfnOYlDJNpPWM0KkFIWsN/eEuSpInCMDSOqmouMHd95kiyO/CVfs2PV9V+6zPvGlX17tGYR5IkSZroDEMbmKpaCkzrdB2SJEnShs4XKEiSJEnqSoYhSZIkSV3JMCRJkiSpKxmGJEmSJHUlw5AkSZKkrmQYkiRJktSVDEOSJEmSupJhSJIkSVJXMgxJkiRJ6kqbdboASd2lZ9b8TpewwVg+e2anS5AkaaPmzpAkSZKkrmQYkiRJktSVDEOSJEmSupJhaBwkOTTJrEHOrRzveiRJkiT5AoVxUVXfBr7d6TpGS5JNq+qpTtchSZIkrQ93htZTkp4kdyWZm+SeJPOSHJLkhiQ/SrJvkuOSnN/03ynJjUmWJjlrGPOf3vS9Lcnspu3PktzStH0zyRZN+9wkFyTpa2p561rm3TTJuUmWJVmS5KSm/Q1Jbm3W/EKS32nalyc5J8ki4MgkL0tyRZKFSRYk2XmQdU5o6ulbsWLFOn+/kiRJ0lgxDI2OlwOfAHZu/rwbOBA4Dfibfn0/BXyuqnYHHljbpEneDBwG7FdVewIfb059q6r2adruBP60bVgPsC8wE7ggyeRBpj+h6TutqvYA5jV95wJHNfVtBpzYNuahqtq7qr4GzAFOqqrpzXV+dqBFqmpOVfVWVe/UqVPXdrmSJEnSuDIMjY57q2ppVa0GbgeurqoCltIKHO0OAC5qjr8yxLyHAF+sql8DVNX/NO27NbsxS4FjgF3bxny9qlZX1Y+An9AKZ4PN/c9V9WTb3K9qruWeps+XgNe2jbkYIMkU4DXAJUkWA/8MvGiIa5EkSZImFJ8ZGh2Ptx2vbvu8moG/41rP9eYCb6uq25IcBxy0lrnXd612jzU/NwF+VVXTRnFuSZIkaVy5MzT+bgCObo6PGaLvVcDxbc8Ebdu0bwU8kGTSAHMcmWSTJC8Dfh+4ey1z/3mSzdrmvhvoSfLyps97gOv6D6yqR4B7kxzZjE2SPYe4FkmSJGlCMQyNvw8CH2hucdtxbR2r6gpab6Hra25HO6059RHgh7SC1V39hv0ncDPwPeB9VbVqkOk/3/RdkuQ24N1N3+Np3f62lNbO1gWDjD8G+NNm7O20nm2SJEmSNhhpPdqijUGSucDlVfWNTtcykN7e3urr6+t0GeqwnlnzO13CBmP57JmdLkGSpA1OkoVV1Tucvj4zJGlc+Q98SZI0URiGJoAku/PcN8s9XlX7rcs8VXXcAHO/ETinX/O9VXX4OhUpSZIkbWQMQxNAVS0FxuTNbFV1JXDlWMwtSZIkbch8gYIkSZKkrrTWMNS8ovk141WMJEmSJI2XtYahqloNfGacapEkSZKkcTOc2+SuTvKOJBnzaiRJkiRpnAwnDP05cAnw2ySPJHk0ySNjXJckSZIkjakh3yZXVVuNRyGSJEmSNJ6G9WrtJIcCr20+XltVl49dSZIkSZI09oa8TS7JbOCDwB3Nnw8m+YexLkySJEmSxtJwdobeAkxr3ixHki8BtwJ/PZaFSZIkSdJYGtZtcsDWwP80xy8Yo1okdYGeWfM7XULHLZ89s9MlSJIkhheG/gG4Nck1QGg9O+SukCRJkqQN2nDeJndRkmuBfZqm06vqZ2NalSRJkiSNseG8QOHqqnqgqr7d/PlZkqvHo7iNVZKVzc+eJMs6XY8kSZLUjQbdGUoyGdgC2D7JNrRukQN4PrDjONSmCSrJplX1VKfrkCRJktbH2naG/hxYCOzc/Fzz51+B88e+tIkhyZZJ5ie5LcmyJEclWZ7kH5IsTtKXZO8kVyb5cZL3NeOmJLk6yaIkS5McNoK1N01ybrPukiQnNe0fTXJL0z4nSZr2a5N8qqlrWZJ91zL3lCRfbGpbkuQdTfu7mrZlSc5p678yySeS3Absn2R6kuuSLGyu/UXren2SJElSJw26M1RVnwI+leSkqvr0ONY00bwJuL+qZgIkeQFwDvCfVTUtySeBucABwGRgGXABsAo4vKoeSbI9cFOSb1dVrcPa/z979x9mZ1nf+/79kWApoCCautVdGKkiBYGYDFQEbCL4o8bWckRRqYp6GbFWlG52m91NBXuohlo3LaWo0Y20ykGEKkXTghShRgRhAiEBBTxbxt+VoJaKHFJIvuePdQ8sxplkZjIza5L1fl0X13rW/dw/vs8Kf+Rz3c/zZBkwQOfV5g8n2au1n1tVf9bq+STwCuDz7dyura4XAucDzx1n7j8F7quqg9o8T0ry9HZti4CfAl9M8rtVdRmwG/C1qvpvSXYG/hV4ZVVtSHI88OfAW0YvkmRZuw723nvvSVy6JEmSNLO2+swQsDnJniNf2l+af38Ga5pr1gMvTnJWkqOq6r7WfnnX+a9V1c+qagOwsf1eAd6fZB3wL3RuLXzqJNc+BvhoVT0MUFUjrzdfkuRrSdYDLwIO7BpzUev7ZeCJ3X92Y8z9tyNfquqndF6ScW1VbWhrXkjn7YEAm4B/aMfPoROyrkqyFjgN+K9jLVJVK6tqsKoG58+fP4lLlyRJkmbWRF6t/baqesxfmpO8DThv5sqaO6rqriQL6fzjs2d2vTxiY/vc3HU88n0ecAIwH1hUVQ8lGaazc7RN2rNc5wGDVfXdJGeMmnf0ztNkdqK25MGu54QC3F5Vh0/T3JIkSdKsm8jO0E4jz6RA5zkW4PEzV9Lc0m4de6CqPgV8EFg4waF7APe0ILQE2GcKy18FvD3JvFbLXjwafO5Nsjtw3Kgxx7e+R9K5De4+xnYV8M6RL+0lGTcCv5nkKe3P+XV0bocb7U5gfpLD29idkxw4Rj9JkiRpzppIGLoCuDjJ0UmOpnMb1hUzW9acchBwY7sd7HTgzAmOuxAYbLeyvRG4Ywprfxz4DrCuvbjg9VX178DH6DybdCVw06gxDya5hc5zS2/dwtxnAk9qL0q4FVhSVT8ElgPXALcCa6rqH0cPrKr/pBPCzmpj1wIvmML1SZIkST2TrT3Pn+RxdN4sd3Rrugr4uK9WnnvaP457alUN9bqWsQwODtbQ0JwsTbNoYPmqXpfQc8Mrlva6BEmSdlhJ1lTV4ET6bvWZoaraDHy4/SdJ28QgIEmS5oqthqEkzwY+ABxA14P6VbXvDNbVV5K8lM4rrbvdXVXHTmaeqlo8xtxvBt49qvm6qnrn6L6SJElSP5nI2+Q+QedZmbOBJcCbmdizRpqgqrqSzvM/MzH3J+j8GUqSJEnqMpFQ88tVdTWd54u+XVVnAN7nIkmSJGm7NpGdoY3tJQrfTPIHwPeB3We2LEmSJEmaWePuDCX5ZDu8DNgVOBlYBLwBeNPMlyZJkiRJM2dLO0OL2j84egKdf9fmAeC/zUpVkiRJkjTDthSGPgJcDewLrAECVNenb5OTJEmStN0a9za5qjqnqn4dOL+q9q2qZ3Z/zmKNkiRJkjTttvo2uap6x2wUIkmSJEmzyX8vSJIkSVJfMgxJkiRJ6ksT+XeGJGnaDCxf1esSps3wCv/9aUmStmfuDEmSJEnqS4YhSZIkSX1puwxDSb46gT7vSbLrLNQykOT1Xd8Hk5wzA+sMJ3nKdM87ifUXJ3lB1/cLkhzXq3okSZKkbbVdhqGqesHWe/EeYFJhKMlOUyhnAHgkDFXVUFWdPIV5ZlySbXlGbDEwkd9dkiRJ2i5sl2Eoyf3tc3GSa5NcmuSOJBem42Tg6cA1Sa5pfV+S5PokNye5JMnurX04yVlJbgZe3eY7K8mNSe5KclTrN5BkdRt/c9cuyQrgqCRrk5zSavpCG7NXksuSrEtyQ5KDW/sZSc5va32r1TtybZclWZPk9iTLJvGb/GmSO5N8JclFSU5t7dcm+askQ8C723V8qdV0dZK9k+yU5O722+2ZZFOSF7bxX07ybOAk4JR2nUe1ZV+Y5KvtGtwlkiRJ0nZluwxDozyPzi7QAcC+wBFVdQ7wA2BJVS1pt5edBhxTVQuBIeAPu+b4cVUtrKpPt+/zquqwNu/pre0e4MVt/PHAyK1wy4HVVbWgqs4eVdv7gFuq6mDgT4C/7zq3P/BS4DDg9CQ7t/a3VNUiYBA4OcmTt/YDJDkUeBVwCPBbbWy3x1fVYFV9CPgb4O9aTRcC51TVJuDO9hseCdxMJ+D9EvCrVfVN4CPA2e06V7d5n9b6v4JOKByrtmVJhpIMbdiwYWuXIkmSJM2aHSEM3VhV36uqzcBaOretjfZ8On/Rvy7JWuBNwD5d5y8e1f+z7XNN13w7Ax9Lsh64pM23NUcCnwSoqi8BT07yxHZuVVVtrKp76QStp7b2k5PcCtwA/Crw7AmscwTwj1X1YFX9DPj8qPPd13c48P+040+2GgFWAy9s/32gtR8K3LSFdS+rqs1V9fWu+h+jqla2IDY4f/78CVyKJEmSNDt2hH9naGPX8SbGvqYAV1XV68aZ4+fjzNk93ynAj+jsvjwOeHBK1f7iGo+sk2QxcAxweFU9kORaYJdtXAd+8frG8mXgHXRuL3wv8N/pPCe0egtjuq8hUy1OkiRJ6oUdYWdoPD8DntCObwCOSPIsgCS7JdlvkvPtAfyw7UC9ARh52UL3OqOtBk5oay4G7q2q/9jKGj9tQWh/OjtaE3Ed8NtJdmnPQr1iC32/Cry2HZ/Ao2HnRjovSNhcVQ/S2WV7O52QBFu+TkmSJGm7syOHoZXAFUmuqaoNwInARUnWAdfTeWZnMs4D3tRuYdufR3db1gGbktya5JRRY84AFrU1V9C5PW9LrqCzQ/SN1v+GiRRWVTcBl7da/hlYD9w3Tvd3AW9uNb0BeHebYyPw3a41V9MJP+vb988Dx456gYIkSZK03UpV9boGTYMku1fV/e3fVvoysKyqbu51Xd0GBwdraGio12WoxwaWr+p1CdNmeMXSXpcgSZJGSbKmqka/UGxMO8IzQ+pYmeQAOs8Y/d1cC0LSCAOEJEmaKwxD25H2mu2rxzh1dFW9fox2SZIkSeMwDG1HqurHwIJe1yFJkiTtCHbkFyhIkiRJ0rgMQ5IkSZL6kmFIkiRJUl8yDEmSJEnqS4YhSZIkSX3JMCRJkiSpLxmGJEmSJPUlw5AkSZKkvmQYkiRJktSX5vW6AEn9ZWD5ql6XMC2GVyztdQmSJGkbuTMkSZIkqS8ZhvQLktzf6xokSZKkmWYYkiRJktSXDEN9IMmKJO/s+n5GktOSXJ3k5iTrk7xyjHGLk3yh6/u5SU5sx4uS/GuSNUmuTPK0WbkYSZIkaZoYhvrDxcBrur6/Bvg74NiqWggsAT6UJBOZLMnOwN8Ax1XVIuB84M+nt2RJkiRpZvk2uT5QVbck+ZUkTwfmAz8F/g04O8kLgc3AM4CntvateQ7wXOCqlp92An44Vscky4BlAHvvvfc2XokkSZI0fQxD/eMS4Djgv9DZKTqBTjBaVFUPJRkGdhk15mEeu3s4cj7A7VV1+NYWraqVwEqAwcHB2pYLkCRJkqaTt8n1j4uB19IJRJcAewD3tCC0BNhnjDHfBg5I8ktJ9gSObu13AvOTHA6d2+aSHDjjVyBJkiRNI3eG+kRV3Z7kCcD3q+qHSS4EPp9kPTAE3DHGmO8m+QxwG3A3cEtr/88kxwHnJNmDzv9HfwXcPkuXI0mSJG0zw1AfqaqDuo7vBca8za2qdu86/iPgj8bosxZ44QyUKUmSJM0Kb5OTJEmS1JfcGZI0q4ZXLO11CZIkSYA7Q5IkSZL6lGFIkiRJUl8yDEmSJEnqS4YhSZIkSX3JMCRJkiSpLxmGJEmSJPUlw5AkSZKkvmQYkiRJktSXDEOSJEmS+pJhSJIkSVJfMgxJkiRJ6kvzel2ApP4ysHxVr0sY0/CKpb0uQZIkzTJ3hiRJkiT1JcOQJEmSpL5kGBolyf29rqHXkpyR5NRe1yFJkiTNJPiRuvIAACAASURBVMPQNkiHv6EkSZK0HfIv8uNIsnuSq5PcnGR9kle29oEkdyb5e+A24FeTvDXJXUluTPKxJOe2vvOT/EOSm9p/R2xlvU+0tdYleVVr/3CSoSS3J3lfV//hJH/R+t+Y5FnjzLtHkm+PhLYkuyX5bpKdk7yt1XVrq3PXMcZfm2SwHT8lyXA73inJB9v4dUnePsWfWpIkSeoJw9D4HgSOraqFwBLgQ0nSzj0bOK+qDgQeAv4UeD5wBLB/1xx/DZxdVYcCrwI+voX1/hS4r6oOqqqDgS+19v9ZVYPAwcBvJjm4a8x9VXUQcC7wV2NNWlX3AWuB32xNrwCurKqHgM9W1aFVdQjwDeCtW/5JHuOtbf1DgUOBtyV55uhOSZa1MDe0YcOGSUwvSZIkzSxfrT2+AO9P8kJgM/AM4Knt3Ler6oZ2fBjwr1X1E4AklwD7tXPHAAc8mqF4YpLdq2qs55KOAV478qWqftoOX5NkGZ0/q6cBBwDr2rmLuj7P3sK1XAwcD1zT1jivtT83yZnAnsDuwJVbmGO0lwAHJzmufd+DTki8u7tTVa0EVgIMDg7WJOaXJEmSZpRhaHwnAPOBRVX1ULs9bJd27ucTnONxwPOr6sGpFNB2Wk4FDq2qnya5oKsGgBrneLTL6QS7vYBFPLrrdAHwu1V1a5ITgcVjjH2YR3cQu9cO8K6qmkyAkiRJkuYMb5Mb3x7APS0ILQH2GaffTXRuX3tSknl0bocb8UXgXSNfkizYwnpXAe/s6vsk4Il0gtd9SZ4K/NaoMcd3fV4/3sRtJ+omOrftfaGqNrVTTwB+mGRnOuFvLMN0AhTAcV3tVwLvaGNJsl+S3bZwfZIkSdKc4s7Q+C4EPp9kPTAE3DFWp6r6fpL3AzcCP2n97munTwb+Nsk6Or/1l4GTxlnvzNb3NmAT8L6q+mySW9qc3wWuGzXmSW3ujcDrtnI9FwOX8Njdnz8FvgZsaJ9PGGPcXwKfabfqrepq/zgwANzcnqXaAPzuVmqQJEmS5oxU+RjHthp5DqjtDH0OOL+qPjfDaw4Dg1V170yuM50GBwdraGio12WoxwaWr9p6px4YXrG01yVIkqRpkGRNewHZVrkzND3OSHIMnWdqvghc1uN6pDnL0CFJkuYKw9A0qKpTJ9o3yZuBd49qvq6q3jlW/y2sOTDG3P8TePWo5kuq6s8nM7ckSZLUD7xNTrPG2+QkSZI00yZzm5xvk5MkSZLUlwxDkiRJkvqSYUiSJElSXzIMSZIkSepLhiFJkiRJfckwJEmSJKkvGYYkSZIk9SXDkCRJkqS+ZBiSJEmS1Jfm9boASf1lYPmqSfUfXrF0hiqRJEn9zp0hSZIkSX3JMCRJkiSpLxmG5qAkA0lum4Z5Lkhy3DTMc22SwW2dR5IkSZpLDEOSJEmS+pJhaO6al+TCJN9IcmmSXZMMJ3kKQJLBtmPzuCTfTDK/tT8uyf878h04JslQkruSvKL1GUiyOsnN7b8XjCya5I+TrE9ya5IV3QW1uS9IcmaSndrxba3/KbP0u0iSJEnTwrfJzV3PAd5aVdclOR/4/bE6VdXmJJ8CTgD+CjgGuLWqNiQBGAAOA34NuCbJs4B7gBdX1YNJng1cBAwm+S3glcBvVNUDSfbqWmoecCFwW1X9eZJFwDOq6rkASfYcq74ky4BlAHvvvfc2/BySJEnS9HJnaO76blVd144/BRy5hb7nA29sx28BPtF17jNVtbmqvgl8C9gf2Bn4WJL1wCXAAa3vMcAnquoBgKr6Sdc8H6UFofb9W8C+Sf4mycuA/xirsKpaWVWDVTU4f/78sbpIkiRJPWEYmrtqjO8P8+if2S6PnKj6LvCjJC+iswv0z1uZ5xTgR8AhwCDw+AnU81VgSZJd2po/beOvBU4CPj6BOSRJkqQ5wzA0d+2d5PB2/HrgK8AwsKi1vWpU/4/T2UG6pKo2dbW/uj3r82vAvsCdwB7AD6tqM/AGYKfW9yrgzUl2BRh1m9z/Bv4J+EySee3ZpcdV1T8ApwELt/WCJUmSpNlkGJq77gTemeQbwJOADwPvA/46yRCwaVT/y4HdeewtcgDfAW6ks1t0UlU9CJwHvCnJrXRum/s5QFVd0eYZSrIWOLV7oqr6X8AtwCeBZwDXtn6fAv7HdFy0JEmSNFt8gcIcVFXDdELKaKuB/cYZdgidFyfc0TXPiePM/03g4K6mP+46twJYMar/4q7j07tOuRskSZKk7ZZhaAeQZDnwDjpvlJPmtOEVS3tdgiRJEuBtcjuEqlpRVftU1Vd6XYskSZK0vTAMSZIkSepLhiFJkiRJfckwJEmSJKkvGYYkSZIk9SXDkCRJkqS+ZBiSJEmS1JcMQ5IkSZL6kmFIkiRJUl8yDEmSJEnqS4YhSZIkSX1pXq8LkNRfBpav2mqf4RVLZ6ESSZLU79wZkiRJktSXDEOSJEmS+pJhSJIkSVJf2iHCUJKBJLeN0f5nSY7Zytgzkpw6xXUfGTvBtf4pyZ7TWcMYc12Q5LgpjBuztlF9hpM8ZYz2aatfkiRJmi079AsUquq9c2mtqnr5bNQyFXO5NkmSJGkm7BA7Q81OST6W5PYkX0zyy927JElenuSOJGuSnJPkC11jD0hybZJvJTl5S4sk+Z9J7kryFeA5Xe0XJDkuycuSXNLVvnhkre6dlS3M82tJrmh1rk6yf9f85yT5aqtz5LqS5Nwkdyb5F+BXtlD7i5Jc1vX9xUk+N0Ztv5fkxiRrk3w0yU4T/R3G6LcsyVCSoQ0bNmzpp5UkSZJm1Y4Uhp4N/G1VHQj8O/CqkRNJdgE+CvxWVS0C5o8auz/wUuAw4PQkO4+1QJJFwGuBBcDLgUPH6PYvwG8k2a19Px749CTmWQm8q9V5KnBe17mnAUcCrwBWtLZj6YSRA4A3Ai8Yq/bmGmD/JCPX/2bg/FG1/Xqr+YiqWgBsAk6YRP2PUVUrq2qwqgbnzx/9s0uSJEm9syOFoburam07XgMMdJ3bH/hWVd3dvl80auyqqtpYVfcC9wBPHWeNo4DPVdUDVfUfwOWjO1TVw8AVwG8nmQcsBf5xIvMk2Z1OmLkkyVo6Ae5pXeMuq6rNVfX1rhpfCFxUVZuq6gfAl8apnaoq4JPA77Xngw4H/nlUt6OBRcBNrYajgX0n+ztIkiRJc92O9MzQxq7jTcAvb8PYbf1dPg38AfATYKiqfjbBcY8D/r3tyIylu85MsbZPAJ8HHgQuaeGtW4C/q6r/McX5JUmSpO3CjrQztCV3AvsmGWjfj5/iPF8Gfrc9j/QE4LfH6fevwELgbYy6RW5L87RdlruTvBoeeR7okAnUdHySnZI8DViypc5t9+gHwGl0gtFoVwPHJfmVVsNeSfaZSP2SJEnS9mRH2hkaV1X9f0l+H7giyc+Bm6Y4z81JLgZupXM73ZjzVNWm9tKEE4E3TXKeE4APJzkN2JlOmLp1C2V9DngR8HXgO8D1E7iUC4H5VfWNMWr7elv7i0keBzwEvBP49gTrlyRJkrYL6TxGsuNLsntV3Z8kwN8C36yqs3tdVy8kORe4par+92yuOzg4WENDQ7O5pCRJkvpMkjVVNTiRvv1ymxzA29oLAW4H9qDzcoK+k2QNcDDwqV7XIkmSJPVSX9wmB9B2gSa0E5TkyXSenRnt6Kr68bQWNkPavx/0zFHNf9xe2S1JkiT1vb4JQ5PRAs94b3TbLlTVsb2uQZIkSZrL+uk2OUmSJEl6hGFIkiRJUl8yDEmSJEnqS4YhSZIkSX3JMCRJkiSpLxmGJEmSJPUlw5AkSZKkvmQYkiRJktSXDEOSJEmS+pJhSNKsGli+qtclSJIkAYYhSZIkSX3KMCRJkiSpL/VVGEry1SmOOynJG8doH0hy27ZXNj2SLEjy8q7vv5Nk+QyvuTjJC2ZyDUmSJGkmzOt1AbOpqqb0l/aq+sh0rJ9kXlU9PB1zjWMBMAj8E0BVXQ5cPoPrASwG7gemFDQlSZKkXum3naH72+fiJNcmuTTJHUkuTJJ2bkWSrydZl+QvW9sZSU5tx4uS3JrkVuCdXXPvlOSDSW5qY9/etdbqJJcDXx+nrt2SrGrz3pbk+K61/jXJmiRXJnlaa782yVlJbkxyV5Kjkjwe+DPg+CRrkxyf5MQk57YxFyT5cJIbknyr1XV+km8kuaCrlpckuT7JzUkuSbJ7ax9O8r7Wvj7J/kkGgJOAU9qaR03bH5YkSZI0w/oqDI3yPOA9wAHAvsARSZ4MHAscWFUHA2eOMe4TwLuq6pBR7W8F7quqQ4FDgbcleWY7txB4d1XtN04tLwN+UFWHVNVzgSuS7Az8DXBcVS0Czgf+vGvMvKo6rF3D6VX1n8B7gYurakFVXTzGOk8CDgdOobNjdDZwIHBQu8XuKcBpwDFVtRAYAv6wa/y9rf3DwKlVNQx8BDi7rbl69IJJliUZSjK0YcOGcS5fkiRJmn39HIZurKrvVdVmYC0wANwHPAj87yT/F/BA94AkewJ7VtWXW9Mnu06/BHhjkrXA14AnA8/uWuvuLdSyHnhx2+05qqruA54DPBe4qs15GvBfu8Z8tn2uabVPxOerqtp6P6qq9e36b29zPJ9OOLyurfkmYJ9tWbOqVlbVYFUNzp8/f4JlSpIkSTOvr54ZGmVj1/EmOjstDyc5DDgaOA74A+BFE5wvdHaMrnxMY7IY+PmWBlbVXUkWAi8HzkxyNfA54PaqOnwr9W9i4n+OI2M289jr39zm2ARcVVWvm8Y1JUmSpDmpn3eGfkF7PmaPqvonOreSPeZWuKr6d+DfkxzZmk7oOn0l8I52extJ9kuy2wTXfTrwQFV9Cvggndvq7gTmJzm89dk5yYFbmepnwBMmsuY4bqBzu+Cz2pq7JRnv1r7pWlOSJEnqCcPQYz0B+EKSdcBXeOzzMiPeDPxtu40sXe0fp/OChJvb67Y/ysR3Tw4Cbmxzng6c2Z4BOg44q72sYS2wtbfhXQMcMPIChQmu/Yiq2gCcCFzUfoPrgf23MuzzwLG+QEGSJEnbm3QeIZFm3uDgYA0NDfW6DPXYwPJVDK9Y2usyJEnSDirJmqoanEhfd4YkzSqDkCRJmit8CH4WtVd3Xz3GqaOr6sezXY8kSZLUzwxDs6gFngW9rkOSJEmSt8lJkiRJ6lOGIUmSJEl9yTAkSZIkqS8ZhiRJkiT1JcOQJEmSpL5kGJIkSZLUlwxDkiRJkvqSYUiSJElSXzIMSZIkSepL83pdgKTt28DyVZPqP7xi6QxVIkmSNDnuDEmSJEnqS4YhSZIkSX3JMDTNkvxOkuXjnLt/tuuZiiSLk3yh13VIkiRJM8lnhqZZVV0OXN7rOiRJkiRtmTtDk5BkIMkdSS5IcleSC5Mck+S6JN9McliSE5Oc2/o/M8n1SdYnOXMC8/9x63trkhWt7W1Jbmpt/5Bk19Z+QZKPJBlqtbxiC/PekOTAru/XJhls9V6f5JYkX03ynDHGnpHk1K7vtyUZaMe/l+TGJGuTfDTJThP/NSVJkqTeMgxN3rOADwH7t/9eDxwJnAr8yai+fw18uKoOAn64pUmT/BbwSuA3quoQ4C/aqc9W1aGt7RvAW7uGDQCHAUuBjyTZZZzpLwZe09Z5GvC0qhoC7gCOqqrnAe8F3r/lS39Mvb8OHA8cUVULgE3ACWP0W9YC29CGDRsmOr0kSZI04wxDk3d3Va2vqs3A7cDVVVXAejrhpNsRwEXt+JNbmfcY4BNV9QBAVf2ktT83yeok6+mEjQO7xnymqjZX1TeBb9EJZ2P5DHBcO34NcGk73gO4JMltwNmj5t6ao4FFwE1J1rbv+47uVFUrq2qwqgbnz58/ieklSZKkmeUzQ5O3set4c9f3zYz9e9Y2rncB8LtVdWuSE4HFW5h7zLWq6vtJfpzkYDq7OSe1U/83cE1VHdtufbt2jOEP89jQPLL7FODvqup/TPRCJEmSpLnEnaGZdR3w2nb8C7eQjXIV8OauZ4L2au1PAH6YZOcx5nh1kscl+TU6uzJ3bmH+i4E/AvaoqnWtbQ/g++34xHHGDQMLW00LgWe29quB45L8yki9SfbZyjVKkiRJc4ZhaGa9G3hnu8XtGVvqWFVX0HkL3VC77WzkpQV/CnyNTrC6Y9Sw7wA3Av8MnFRVD25hiUvpBLPPdLX9BfCBJLcw/i7hPwB7Jbkd+APgrlbv14HTgC8mWUcnzD1tS9coSZIkzSXpPO6i7U2SC4AvVNWlW+s7VwwODtbQ0FCvy9A0G1i+alL9h1csnaFKJEmSIMmaqhqcSF+fGZK0TQw3kiRpe2UYmmVJDuIX3yy3sap+YzLzVNWJY8z9UuCsUc13V9WxkypSkiRJ6gOGoVlWVeuBBTM095XAlTMxtyRJkrSj8QUKkiRJkvqSYUiSJElSXzIMSZIkSepLhiFJkiRJfckwJEmSJKkvGYYkSZIk9SXDkCRJkqS+ZBiSJEmS1JcMQ5IkSZL60rxeFyBp+zSwfNWUxg2vWDrNlUiSJE2NO0OSJEmS+pJhSJIkSVJfMgzt4JLc3z4HktzW63okSZKkucIwJEmSJKkvGYbmkCS7JVmV5NYktyU5Pslwkg8kWZtkKMnCJFcm+T9JTmrjdk9ydZKbk6xP8soprL1Tkr9s665L8q7W/t4kN7X2lUnS2q9N8tetrtuSHDa9v4YkSZI0swxDc8vLgB9U1SFV9Vzgitb+napaAKwGLgCOA54PvK+dfxA4tqoWAkuAD42ElklYBgwAC6rqYODC1n5uVR3a6vll4BVdY3Ztdf0+cP5YkyZZ1kLc0IYNGyZZkiRJkjRzDENzy3rgxUnOSnJUVd3X2i/vOv+1qvpZVW0ANibZEwjw/iTrgH8BngE8dZJrHwN8tKoeBqiqn7T2JUm+lmQ98CLgwK4xF7W+Xwae2Gp5jKpaWVWDVTU4f/78SZYkSZIkzRz/naE5pKruSrIQeDlwZpKr26mN7XNz1/HI93nACcB8YFFVPZRkGNhlW+tJsgtwHjBYVd9NcsaoeWv0JWzrmpIkSdJscWdoDknydOCBqvoU8EFg4QSH7gHc04LQEmCfKSx/FfD2JPNaLXvxaPC5N8nudG7P63Z863skcF/XTpYkSZI057kzNLccBHwwyWbgIeAdwKUTGHch8Pl2K9sQcMcU1v44sB+wLslDwMeq6twkHwNuA/4NuGnUmAeT3ALsDLxlCmtKkiRJPZMq72zS5CW5Fji1qoYmOmZwcLCGhibcXXPcwPJVUxo3vGLpNFciSZL0qCRrqmpwIn3dGZI0JYYaSZK0vTMM9ZkkLwXOGtV8d1UdO5l5qmrxtBUlSZIk9YBhqM9U1ZXAlb2uQ5IkSeo13yYnSZIkqS8ZhiRJkiT1JcOQJEmSpL5kGJIkSZLUlwxDkiRJkvqSYUiSJElSXzIMSZIkSepLhiFJkiRJfckwJEmSJKkvzet1AZLmjoHlq2Z8jeEVS2d8DUmSpIlwZ0iSJElSXzIMzYAkX51An/ck2XUWahlI8vqu74NJzpmBdYaTPGW655UkSZJmimFoBlTVCybQ7T3ApMJQkp2mUM4A8EgYqqqhqjp5CvNIkiRJOxTD0AxIcn/7XJzk2iSXJrkjyYXpOBl4OnBNkmta35ckuT7JzUkuSbJ7ax9OclaSm4FXt/nOSnJjkruSHNX6DSRZ3cbfnGQkkK0AjkqyNskpraYvtDF7JbksybokNyQ5uLWfkeT8tta3Wr0j13ZZkjVJbk+ybJZ+UkmSJGnaGYZm3vPo7AIdAOwLHFFV5wA/AJZU1ZJ2e9lpwDFVtRAYAv6wa44fV9XCqvp0+z6vqg5r857e2u4BXtzGHw+M3Aq3HFhdVQuq6uxRtb0PuKWqDgb+BPj7rnP7Ay8FDgNOT7Jza39LVS0CBoGTkzx5ir+LJEmS1FO+TW7m3VhV3wNIspbObWtfGdXn+XTC0nVJAB4PXN91/uJR/T/bPte0+QB2Bs5NsgDYBOw3gdqOBF4FUFVfSvLkJE9s51ZV1UZgY5J7gKcC36MTgI5tfX4VeDbw4/EWaLtHywD23nvvCZQkSZIkzQ7D0Mzb2HW8ibF/8wBXVdXrxpnj5+PM2T3fKcCPgEPo7Pg9OKVqf3GNR9ZJshg4Bji8qh5Ici2wy5YmqaqVwEqAwcHB2saaJEmSpGnjbXK98zPgCe34BuCIJM8CSLJbkons7HTbA/hhVW0G3gCMvGyhe53RVgMntDUXA/dW1X9sZY2ftiC0P50dLUmSJGm7ZBjqnZXAFUmuqaoNwInARUnW0blFbv9Jznce8KYkt7axI7tJ64BNSW5NcsqoMWcAi9qaK4A3bWWNK+jsEH2j9b9hkjVKkiRJc0aqvHNJs2NwcLCGhoZ6XYa2YGD5qhlfY3jF0hlfQ5Ik9a8ka6pqcCJ93RmSJEmS1Jd8gYKkR7hrI0mS+ok7Q5IkSZL6kmFIkiRJUl8yDEmSJEnqS4YhSZIkSX3JMCRJkiSpLxmGJEmSJPUlw5AkSZKkvmQYkiRJktSXDEOSJEmS+pJhSJIkSVJfMgxJkiRJ6kvzel2ApIkZWL6q1yVMi+EVS3tdgiRJEuDOkCRJkqQ+ZRiSJEmS1JcMQzuQJCcleeM0z3lRknVJTkmyf5K1SW5J8mvTuY4kSZI023xmaAdSVR+ZzvmS/Bfg0Kp6Vvu+HLi0qs6cznUkSZKkXnBnaI5LsluSVUluTXJbkuOTDCf5iyTrk9yYZCSsnJHk1HZ8bZKz2vm7khy1hTV2SfKJNt8tSZa0U18EntF2g04H3gO8I8k1Y9U107+FJEmSNJ3cGZr7Xgb8oKqWAiTZAzgLuK+qDmq3xf0V8Ioxxs6rqsOSvBw4HThmnDXeCVSbb3/gi0n2A34H+EJVLWhrB7i/qv4yyavGqOsXJFkGLAPYe++9p3L9kiRJ0oxwZ2juWw+8uO3yHFVV97X2i7o+Dx9n7Gfb5xpgYAtrHAl8CqCq7gC+Dew3xboeo6pWVtVgVQ3Onz9/K1NKkiRJs8cwNMdV1V3AQjrh48wk7x051d1tnOEb2+cmpnkXcAt1SZIkSdsFw9Acl+TpwANV9Sngg3QCCMDxXZ/Xb+Myq4ET2nr7AXsDd06xLkmSJGm74DNDc99BwAeTbAYeAt4BXAo8Kck6Ors/r9vGNc4DPpxkPfAwcGJVbew8IjSpuiRJkqTtRqrGu8NKc1WSYWCwqu7tdS2TMTg4WENDQ70uY7s1sHxVr0uYFsMrlva6BEmStANLsqaqBifS150haTthiJAkSZpehqHtUFUNTGVckpfSeS13t7ur6thtLkqSJEnazhiG+khVXQlc2es6JEmSpLnAt8lJkiRJ6kuGIUmSJEl9yTAkSZIkqS8ZhiRJkiT1JcOQJEmSpL5kGJIkSZLUlwxDkiRJkvqSYUiSJElSXzIMSZIkSepL83pdgLSjGli+qtclzEnDK5b2ugRJkiTAnSFJkiRJfcowJEmSJKkvGYYkSZIk9aWehaEkX51An/ck2XUWahlI8vqu74NJzpmBdYaTPGW6593Kmve3z4Ekt83m2pIkSdJc1rMwVFUvmEC39wCTCkNJdppCOQPAI2Goqoaq6uQpzCNJkiRpO9HLnaGRHYvFSa5NcmmSO5JcmI6TgacD1yS5pvV9SZLrk9yc5JIku7f24SRnJbkZeHWb76wkNya5K8lRrd9AktVt/M1JRgLZCuCoJGuTnNJq+kIbs1eSy5KsS3JDkoNb+xlJzm9rfavVO3JtlyVZk+T2JMsm+HvslmRVkluT3Jbk+K5r+0CrbSjJwiRXJvk/SU5qfXZPcnW7pvVJXjmFP4+dkvxlW3tdkne19vcmuam1r0yS1n5tkr9udd2W5LBx5l3W6h7asGHDZMuSJEmSZsxceWboeXR2gQ4A9gWOqKpzgB8AS6pqSbu97DTgmKpaCAwBf9g1x4+ramFVfbp9n1dVh7V5T29t9wAvbuOPB0ZuhVsOrK6qBVV19qja3gfcUlUHA38C/H3Xuf2BlwKHAacn2bm1v6WqFgGDwMlJnjyB3+BlwA+q6pCqei5wRde571TVAmA1cAFwHPD8VhvAg8Cx7bqWAB8aCS2TsIzODtmCdq0XtvZzq+rQVtMvA6/oGrNrq+v3gfPHmrSqVlbVYFUNzp8/f5IlSZIkSTNnrvw7QzdW1fcAkqyl85fyr4zq83w6Yem69vf8xwPXd52/eFT/z7bPNW0+gJ2Bc5MsADYB+02gtiOBVwFU1ZeSPDnJE9u5VVW1EdiY5B7gqcD36ASgY1ufXwWeDfx4K+uspxNizgK+UFWru85d3tVn96r6GfCzJBuT7An8HHh/khcCm4FntFr+bQLXN+IY4CNV9XC71p+09iVJ/ojO7Yp7AbcDn2/nLmp9v5zkiUn2rKp/n8SakiRJUs/MlTC0set4E2PXFeCqqnrdOHP8fJw5u+c7BfgRcAidXbEHp1TtL67xyDpJFtMJFodX1QNJrgV22dpEVXVXkoXAy4Ezk1xdVX82ap3No9bcTOfaTgDmA4uq6qEkwxNZc2uS7AKcBwxW1XeTnDFq3hp9Gdu6piRJkjRb5sptcuP5GfCEdnwDcESSZ8Ejz9hMZGen2x7AD6tqM/AGYORlC93rjLaaTtigBZ17q+o/trLGT1sQ2p/OjtZWJXk68EBVfQr4ILBwIuO61rynBaElwD6TGDviKuDtSea1evbi0eBzb3s+67hRY0aeazoSuK+q7pvCupIkSVJPzJWdofGsBK5I8oP23NCJwEVJfqmdPw24axLznQf8Q5I30nkmZ2Q3aR2wKcmtdJ7JuaVrzBnA+UnWAQ8Ab9rKGlcAJyX5BnAnnRA3EQcBH0yyGXgIeMcEx0Hn+Z7PJ1lP51mqOyYxdsTH6dw2uC7JQ8DHqurcJB8DbqNzy91No8Y8mOQWOrcfvmUKa0qSJEk9kyrvbNLktdv/Tq2qoYmOGRwcrKGhCXeXJEmSJi3JmqoanEjfuX6bnCRJkiTNozWE4QAADtNJREFUiLl+m9wOp71m++oxTh1dVVt749x0rP9S4KxRzXdX1bFj9R9PVS2etqIkSZKkHjAMzbIWeBb0cP0rgSt7tb4kSZI0V3ibnCRJkqS+ZBiSJEmS1JcMQ5IkSZL6kmFIkiRJUl8yDEmSJEnqS4YhSZIkSX3JMCRJkiSpLxmGJEmSJPUlw5AkSZKkvjSv1wVI27uB5at6XcJ2ZXjF0l6XIEmSBLgzJEmSJKlPGYYkSZIk9SXD0AxKcn/7HEhyW6/rmYgkJyY5t9d1SJIkSTPNMCRJkiSpLxmGmiS7JVmV5NYktyU5Pslwkg8kWZtkKMnCJFcm+T9JTmrjdk9ydZKbk6xP8soprL1Tkr9s665L8q7W/t4kN7X2lUnS2q9N8tetrtuSHDbOvI9r17BnV9s3kzw1yW8n+VqSW5L8S5KnjjH+giTHdX2/v+v4v7fa1iV532SvWZIkSeo1w9CjXgb8oKoOqarnAle09u9U1QJgNXABcBzwfGAkADwIHFtVC4ElwIdGQsskLAMGgAVVdTBwYWs/t6oObfX8MvCKrjG7trp+Hzh/rEmrajPwj8CxAEl+A/h2Vf0I+Arw/Kp6HvBp4I8mWmySlwDPBg4DFgCLkrxwnL7LWpAc2rBhw0SXkCRJkmacYehR64EXJzkryVFVdV9rv7zr/Neq6mdVtQHY2HZcArw/yTrgX4BnAL+wy7IVxwAfraqHAarqJ619Sdu9WQ+8CDiwa8xFre+XgSd27/6McjFwfDt+bfsO8F+BK9vc/33U3FvzkvbfLcDNwP50wtEvqKqVVTVYVYPz58+fxBKSJEnSzPLfGWqq6q4kC4GXA2cmubqd2tg+N3cdj3yfB5wAzAcWVdVDSYaBXba1niS7AOcBg1X13SRnjJq3Rl/COFNdDzwryXzgd4EzW/vfAP+rqi5Pshg4Y4yxD9MCc5LHAY8fKQ/4QFV9dDLXJEmSJM0l7gw1SZ4OPFBVnwI+CCyc4NA9gHtaEFoC7DOF5a8C3p5kXqtlLx4NPvcm2Z3O7Xndjm99jwTu69rJeoyqKuBzwP8CvlFVP+6q+/vt+E3j1DUMLGrHvwPs3I6vBN7S6iLJM5L8ygSuU5IkSZoz3Bl61EHAB5NsBh4C3gFcOoFxFwKfb7ebDQF3TGHtjwP7AeuSPAR8rKrOTfIx4Dbg34CbRo15MMktdALKW7Yy/8Vt/IldbWcAlyT5KfAl4JljjPsY8I9JbqXzDNXPAarqi0l+Hbi+PR51P/B7wD0TulpJkiRpDkhn40DbkyTXAqdW1VCva5mMwcHBGhrarkqekIHlq3pdwnZleMXSXpcgSZJ2YEnWVNXgRPq6MyRtI/9yL0mStH0yDM2iJC8FzhrVfHdVHTuZeapq8Rhzvxl496jm66rqnZMqUpIkSeoThqFZVFVX0nn5wEzM/QngEzMxtyRJkrQj8m1ykiRJkvqSYUiSJElSXzIMSZIkSepLhiFJkiRJfckwJEmSJKkvGYYkSZIk9SXDkCRJkqS+ZBiSJEmS1JcMQ5IkSZL60rxeFyDNtIHlq3pdgroMr1ja6xIkSZIAd4YkSZIk9SnDkCRJkqS+tMOFoSRfnUCf9yTZdRZqGUjy+q7vg0nOmYF1hpM8ZRrnu3+65pIkSZLmqh0uDFXVCybQ7T3ApMJQkp2mUM4A8EgYqqqhqjp5CvNIkiRJmmY7XBga2dVIsjjJtUkuTXJHkgvTcTLwdOCaJNe0vi9Jcn2Sm5NckmT31j6c5KwkNwOvbvOdleTGJHclOar1G0iyuo2/OclIIFsBHJVkbZJTWk1faGP2SnJZknVJbkhycGs/I8n5ba1vtXpHru2yJGuS3J5k2QR/jxVJ3tn1/YwkpybZPcnVrd71SV45xthH6m3fz01yYjtelORfWz1XJnnaRP+MJEmSpLlghwtDozyPzi7QAcC+wBFVdQ7wA2BJVS1pt5edBhxTVQuBIeAPu+b4cVUtrKpPt+/zquqwNu/pre0e4MVt/PHAyK1wy4HVVbWgqs4eVdv7gFuq6mDgT4C/7zq3P/BS4DDg9CQ7t/a3VNUiYBA4OcmTJ/AbXAy8puv7a1rbg8CxreYlwIeSZALz0er5G+C4Vs/5wJ+P03dZkqEkQxs2bJjI9JIkSdKs2NFfrX1jVX0PIMlaOretfWVUn+fTCUvXtSzweOD6rvMXj+r/2fa5ps0HsDNwbpIFwCZgvwnUdiTwKoCq+lKSJyd5Yju3qqo2AhuT3AM8FfgenQB0bOvzq8CzgR9vaZGquiXJryR5OjAf+GlVfbcFmvcneSGwGXhGW+ffJlD7c4DnAle132wn4IfjrL8SWAkwODhYE5hbkiRJmhU7ehja2HW8ibGvN8BVVfW6ceb4+Thzds93CvAj4BA6u20PTqnaX1zjkXWSLAaOAQ6vqgeSXAvsMsH5LgGOA/4Lj4a7E+iEo0VV9VCS4THme5jH7h6OnA9we1UdPsH1JUmSpDlnR79Nbjw/A57Qjm8AjkjyLIAkuyWZyM5Otz2AH1bVZuANdHZKRq8z2mo6gYQWdO6tqv/Yyho/bUFofzo7WhN1MfBaOoHokq757mlBaAmwzxjjvg0ckOSXkuwJHN3a7wTmJzm81b9zkgMnUY8kSZLUc/0ahlYCVyS5pqo2ACcCFyVZR+cWuf0nOd95wJuS3NrGjuwmrQM2Jbk1ySmjxpwBLGprrgDetJU1rqCzQ/SN1v+GiRZXVbfTCWXfr6qR29kuBAaTrAfeCNwxxrjvAp8Bbmuft7T2/6QTrM5q17wWmMhb/CRJkqQ5I1U+xqHZMTg4WENDQ7O+7sDyVbO+psY3vGJpr0uQJEk7sCRrqmpwIn139GeGJP/yLUmSpDEZhnYQ7TXbV49x6uiq2uIb5yRJkqR+ZBjaQbTAs6DXdUiSJEnbi359gYIkSZKkPmcYkiRJktSXDEOSJEmS+pKv1tasSbKBzj/kuqN4CnBvr4vYDvm7TZ2/3dT4u02dv93U+LtNnb/d1PnbPWqfqpo/kY6GIWmKkvz/7d1bjB5jHMfx7y+WHgjdSlCWbCV14VCHaLlwPtQhcUg00YhoiCtXLopKXZSI0EiIuBBxUy5UNBGSiqYtQsS5KEXtaoVWaRxCWkEPfxfzlMl6V9fM7sy8O79PMnnnfeZ53/yfX/Z9J8/MvLPvjfQe9vYP51acsyvGuRXn7IpxbsU5u+KcXTG+TM7MzMzMzFrJkyEzMzMzM2slT4bMinu87gK6lHMrztkV49yKc3bFOLfinF1xzq4A/2bIzMzMzMxayWeGzMzMzMyslTwZMhuGpKmSVkkaSI+9w/Sbn/oMSJqf2iZLWiHpc0nrJd1fbfX1kHSppA2SBiUt7LB9gqRn0va3JfXntt2Z2jdIuqTKuutWNDdJF0t6X9LH6fGCqmuvW5m/ubT9GEnbJS2oquYmKPlZnSnpzfTd9rGkiVXWXrcSn9f9JS1NmX0m6c6qa6/bCLI7R9JaSbskzR2y7V/72rYompukU3Kf1XWSrq228i4REV68eOmwAEuAhWl9IfBAhz5TgY3psTet9wKTgfNTnwOA14HL6h7TGOe1H/AlcGwa80fA8UP63AI8ltbnAc+k9eNT/wnA9PQ++9U9pi7I7VTgyLR+IrCl7vF0S3a57cuBZ4EFdY+nG3IDeoB1wMnp+aFt+ayOQnbXAcvS+mTgK6C/7jE1LLt+YCbwJDA3195xX1v3mLogt+OAGWn9SGArMKXuMTVt8Zkhs+FdBSxN60uBqzv0uQRYFRE/RcTPwCrg0oj4LSJeAYiIP4G1QF8FNddpNjAYERvTmJeRZZiXz3Q5cKEkpfZlEfFHRGwCBtP7tUHh3CLig4j4NrWvByZJmlBJ1c1Q5m8OSVcDm8iya5Myuc0B1kXERwAR8WNE7K6o7iYok10AB0rqASYBfwK/VlN2I+wzu4j4KiLWAXuGvLbjvraKohugcG4R8UVEDKT1b4FtwIj+EWmbeDJkNrzDI2JrWv8OOLxDn6OAb3LPN6e2v0maAlwBrBmLIhtkn1nk+0TELuAXsiPLI3nteFUmt7xrgLUR8ccY1dlEhbOTdBBwB3B3BXU2TZm/ueOAkLQyXZZzewX1NkmZ7JYDO8iOzn8NPBgRP411wQ1S5nve+4h/FBq7pNlkZ5a+HKW6xo2eugswq5Ok1cARHTYtyj+JiJD0v2+9mI4APg08EhEbi1Vp9t8knQA8QHbU3kZmMfBQRGxPJ4psZHqAs4BZwG/AGknvR8R4P9gzGmYDu8kuV+oFXpe02vsGG2uSpgFPAfMjYuhZt9bzZMhaLSIuGm6bpO8lTYuIremLZFuHbluA83LP+4BXc88fBwYi4uFRKLfptgBH5573pbZOfTanieIhwI8jfO14VSY3JPUBzwE3RETbjviVye4MYK6kJcAUYI+k3yPi0bEvu3ZlctsMvBYRPwBIehE4jfF/5nuvMtldB7wUETuBbZLeAE4n+/1LG5T5nt/XvnY8K7V/lHQwsAJYFBFvjXJt44IvkzMb3gvA3jvWzAee79BnJTBHUq+yu83NSW1IupdsJ3hrBbU2wbvADEnTJR1A9sPhF4b0yWc6F3g5IiK1z0t3YZoOzADeqajuuhXOLV2CuYLsRh9vVFZxcxTOLiLOjoj+iOgHHgbua8lECMp9VlcCJym7Y2YPcC7waUV1N0GZ7L4GLgCQdCBwJvB5JVU3w0iyG86w+9oWKJxb6v8c8GRELB/DGrtb3Xdw8OKlqQvZNd5rgAFgNTA1tZ8OPJHrdxPZD/4HgRtTWx/Zj2U/Az5My811j6mCzC4HviC7JnlRarsHuDKtTyS7c9cg2WTn2NxrF6XXbWCc33lvtHID7iL7DcKHueWwusfTDdkNeY/FtOhucmVzA64nu+nEJ8CSusfSLdkBB6X29WQTyNvqHksDs5tFdvZxB9nZtPW51/5rX9uWpWhu6bO6c8g+4pS6x9O0RSksMzMzMzOzVvFlcmZmZmZm1kqeDJmZmZmZWSt5MmRmZmZmZq3kyZCZmZmZmbWSJ0NmZmZmZtZKngyZmZmZmVkreTJkZmZmZmat5MmQmZmZmZm10l/hHV2G0Y3h+QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"equity_factors['start_to_peak'].sort_values().plot(kind='barh', figsize=[12,12], legend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"The year started out very similarly, with the most expensive and largest companies as clear favorites leading up to the Februrary drawdown."
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"Finally, we take a look at fixed income by factors."
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"fixed_income_factors = etfs_by_factor[etfs_by_factor['asset_class'] == 'fixed_income']"
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": [
"fixed_income_factors.set_index('factor', inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e24ab0f98>"
]
},
"execution_count": 222,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fixed_income_factors['ytd_return'].sort_values().plot(kind='barh', figsize=[12,12], legend=True)"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e24adb908>"
]
},
"execution_count": 223,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fixed_income_factors['peak_to_trough'].sort_values().plot(kind='barh', figsize=[12,12], legend=True)"
]
},
{
"cell_type": "code",
"execution_count": 224,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e249ee7f0>"
]
},
"execution_count": 224,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fixed_income_factors['trough_to_date'].sort_values().plot(kind='barh', figsize=[12,12], legend=True)"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4e24241cf8>"
]
},
"execution_count": 225,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fixed_income_factors['start_to_peak'].sort_values().plot(kind='barh', figsize=[12,12], legend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"I am not going to say too much here, but the convertible bond performance has been interesting. YTD it was the second best performer behind long duration treasuries, the worst performer during the drawdown in February, and was the best performer coming out of the bottom in the market, and was the leader from the start of the year up until the peak of the market in February. That one is worth exploring down the road and digging in a bit further to see what may or may not be driving that one."
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"In conclusion, it is hard to look at the performance in the equity markets and think the market internals are healthy at this point. The positive performance shown in the market-cap weighted indices year-to-date has been extremely top-heavy, with mega cap companies getting even larger and small cap companies getting smaller on a relative basis, and expensive companies have been getting more expensive, while cheap companies have been getting cheaper.\n",
"\n",
"I can't help but think that this may represent a once in a generation entry point for investors that need to deploy cash that is on the sidelines. The market is not trading based on fundamentals, which is nothing new, but at some point this will normalize. I am not sure what the catalyst will be, but I have to imagine the US will start to enforce Antitrust laws again at some point and force the top 10 companies by size to break up into smaller companies. Competition is one of the hallmarks of capitalism, and it appears to be on hiatus at the moment. Investors appear to have lost faith in the concept, and are shoveling money into expensive companies just because they are large and dominate their sectors or industries.\n",
"\n",
"The bright side is that for patient investors, their are a lot of bargains present in the market today, and even within the S&P 500 you can find relative value by avoiding the top 100 by market cap. There is a record amount of cash sitting on the sidelines and at some point, investors will be dissatisfied with earning close to 0% on their cash holdings, and I want to be on the right side of that trade."
]
},
{
"cell_type": "markdown",
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"source": [
"Data sources used in this notebook: Finnhub (finnhub.io) and Tiingo (tiingo.com) APIs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"source": []
}
],
"metadata": {
"extensions": {
"jupyter_dashboards": {
"activeView": "report_default",
"version": 1,
"views": {
"grid_default": {
"name": "grid",
"type": "grid"
},
"report_default": {
"name": "report",
"type": "report"
}
}
}
},
"finalized": {
"timestamp": 1596477489466,
"trusted": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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