Estimate the fair value of your stonks using this DCF model. Based on: https://www.buffettsbooks.com/how-to-invest-in-stocks/advanced-course/lesson-35/. Also, added a formula to understand how much growth is priced-in (Page 295, The Intelligent Investor).

value_estimator.py

from lxml import html
import requests
import json
import argparse
from collections import OrderedDict

import warnings
warnings.filterwarnings('ignore')

def parse(ticker):
    url = "https://stockanalysis.com/stocks/{}/financials/cash-flow-statement".format(ticker)
    response = requests.get(url, verify=False)
    parser = html.fromstring(response.content)
    fcfs = parser.xpath('//table[contains(@id,"financial-table")]//tr[td/span/text()[contains(., "Free Cash Flow")]]')[0].xpath('.//td/span/text()')[1:]
    last_fcf = float(fcfs[0].replace(',', ''))
    
    url = "https://finance.yahoo.com/quote/{}/analysis?p={}".format(ticker, ticker)
    response = requests.get(url, headers={'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:20.0) Gecko/20100101 Firefox/20.0'})
    parser = html.fromstring(response.content)
    ge = parser.xpath('//table//tbody//tr')

    for row in ge:
        label = row.xpath("td/span/text()")[0]
        if 'Next 5 Years' in label:
            try:
                ge = float(row.xpath("td/text()")[0].replace('%', ''))
            except:
                ge = []
            break

    url = "https://stockanalysis.com/stocks/{}/".format(ticker)
    response = requests.get(url, verify=False)
    parser = html.fromstring(response.content)
    shares = parser.xpath('//div[@class="order-1 flex flex-row gap-4"]//table//tbody//tr[td/text()[contains(., "Shares Out")]]')

    shares = shares[0].xpath('td/text()')[1]
    factor = 1000 if 'B' in shares else 1 
    shares = float(shares.replace('B', '').replace('M', '')) * factor

    url = "https://stockanalysis.com/stocks/{}/financials/".format(ticker)
    response = requests.get(url, verify=False)
    parser = html.fromstring(response.content)
    eps = parser.xpath('//table[contains(@id,"financial-table")]//tr[td/span/text()[contains(., "EPS (Diluted)")]]')[0].xpath('.//td/span/text()')[1:]
    eps = float(eps[0].replace(",", ""))
    market_price = float(parser.xpath('//div[@class="price-ext"]/text()')[0].replace('$', '').replace(',', ''))
    return {'fcf': last_fcf, 'ge': ge, 'yr': 5, 'dr': 10, 'pr': 2.5, 'shares': shares, 'eps': eps, 'mp': market_price}

def dcf(data):
    forecast = [data['fcf']]

    if data['ge'] == []:
    	raise ValueError("No growth rate available from Yahoo Finance")

    for i in range(1, data['yr']):
        forecast.append(round(forecast[-1] + (data['ge'] / 100) * forecast[-1], 2))

    forecast.append(round(forecast[-1] * (1 + (data['pr'] / 100)) / (data['dr'] / 100 - data['pr'] / 100), 2)) #terminal value
    discount_factors = [1 / (1 + (data['dr'] / 100))**(i + 1) for i in range(len(forecast) - 1)]

    pvs = [round(f * d, 2) for f, d in zip(forecast[:-1], discount_factors)]
    pvs.append(round(discount_factors[-1] * forecast[-1], 2)) # discounted terminal value
    
    print("Forecasted cash flows: {}".format(", ".join(map(str, forecast))))
    print("PV of cash flows: {}".format(", ".join(map(str, pvs))))

    dcf = sum(pvs)
    print("Fair value: {}\n".format(dcf / data['shares']))

def reverse_dcf(data):
    pass

def graham(data):
    if data['eps'] > 0:
        expected_value = data['eps'] * (8.5 + 2 * (data['ge']))
        ge_priced_in = (data['mp'] / data['eps'] - 8.5) / 2

        print("Expected value based on growth rate: {}".format(expected_value))
        print("Growth rate priced in for next 7-10 years: {}\n".format(ge_priced_in))
    else:
        print("Not applicable since EPS is negative.")

if __name__ == "__main__":
    argparser = argparse.ArgumentParser()
    argparser.add_argument('ticker', help='Ticker to analyse. Example: GOOG')
    argparser.add_argument('--discount_rate', help='Discount rate in %. Default: 10', default=10)
    argparser.add_argument('--growth_estimate', help='Estimated yoy growth rate. Default: Fetched from Yahoo Finance')
    argparser.add_argument('--terminal_rate', help='Terminal growth rate. Default: 2.5')
    argparser.add_argument('--period', help='Time period in years. Default: 5')
    args = argparser.parse_args()
    
    ticker = args.ticker

    print("Fetching data for %s...\n" % (ticker))
    data = parse(ticker)
    print("=" * 80)
    print("DCF model (basic)")
    print("=" * 80 + "\n")

    if args.period is not None:
        data['yr'] = int(args.period)
    if args.growth_estimate is not None:
        data['ge'] = float(args.growth_estimate)
    if args.discount_rate is not None:
        data['dr'] = float(args.discount_rate)
    if args.terminal_rate is not None:
        data['pr'] = float(args.terminal_rate)

    print("Market price: {}".format(data['mp']))
    print("EPS: {}".format(data['eps']))
    print("Growth estimate: {}".format(data['ge']))
    print("Term: {} years".format(data['yr']))
    print("Discount Rate: {}%".format(data['dr']))
    print("Perpetual Rate: {}%\n".format(data['pr']))

    dcf(data)

    print("=" * 80)
    print("Graham style valuation basic (Page 295, The Intelligent Investor)")
    print("=" * 80 + "\n")

    graham(data)

good work, thank you!

good work, thank you!

Ofcourse! Let me know if you have any troubles / suggestions.

Thank you for your work.

I have never read up on value investment strategies. Boomer stocks like GE, F, GM are resulting in fair values that are several times higher than their respective market price, while hype/rallying stocks of last year such as PLTR, TDOC are resulting in negative fair value.

Not sure of the utility of this type of valuation.

Thank you for your work.

I have never read up on value investment strategies. Boomer stocks like GE, F, GM are resulting in fair values that are several times higher than their respective market price, while hype/rallying stocks of last year such as PLTR, TDOC are resulting in negative fair value.

Not sure of the utility of this type of valuation.

Hi there! Thank you for trying the tool out. Couple of things to note to get the most out of this:

• Make sure that the company you're trying to value actually makes money. Hence, it's not a very good way to understand the value of growth stocks which probably won't generate a profit for another 3-5 years. There's ways to do this, but it's out of the scope of this tool (as of now).
• This only takes into account the cash flow of a company. Hence, F & GM look good on paper. These companies, however, have a good amount of debt which this method never takes into account. As a general rule, make sure that the Total assets > 1.5 times the total debt before completely relying on the generated result.

To run, python3 value_estimator.py <ticker_name>

Added some parameters for your customization. Optional usage now looks like:
python3 -B value_estimator.py <ticker_name> --period 10 --growth_estimate 12 --discount_rate 10 --terminal_rate 3

Note: All the above parameters are optional and the code will work the same way if you choose not to use this.

I tried to fetch the data for TSLA. IndexError: list index out of range erre is happening. Could you please suggest me how to solve it? thank you, Sir.

To make it work, change line 65

- market_price = float(parser.xpath('//div[@class="price-ext"]/text()')[0].replace('$', '').replace(',', ''))
+ market_price = float(parser.xpath('//div[@class="p"]/text()')[0].replace('$', '').replace(',', ''))

(e.g. replace "price-ext" with "p")

I am getting a list index out of range error. Can you provide any help with this?

Updates to the xpath lines:

fcfs = parser.xpath('//table[contains(@data-test,"financials")]//tr[td/span/text()[contains(., "Free Cash Flow")]]')[0].xpath('.//td/text()')[1:]

shares = parser.xpath('//table[@data-test="overview-info"]//tbody//tr[td/text()[contains(., "Shares Out")]]')
shares = shares[0].xpath('td/text()')[2]

eps = parser.xpath('//table[contains(@data-test,"financials")]//tr[td/span/text()[contains(., "EPS (Diluted)")]]')[0].xpath('.//td/text()')[1:]