Shreyes2010

## Set the working directory using setwd() ##
# Reading the relevant file.

infy <- read.csv("01-10-2010-TO-01-10-2011INFYEQN.csv")

# Plotting the past one years closing price of INFY

plot(as.Date(infy$Date, "%d-%b-%y"), infy$Close.Price, xlab= "Dates", ylab= "Adjusted closing price", type='l', col='red', main="Adjusted closing price of INFOSYS for past 1 year")

Shreyes2010

Similarly for APT the mathematical form is: 
\begin{eqnarray*}
			R_i &=& a_j + \beta_{j1} F_1 + \beta_{j2} F_2 + \dots + \beta_{jn} F_n + \epsilon_j \\
 			a_j &:& \mbox {constant for asset `j'}\\
			F_k &:& \mbox{ are systemic factors}\\
			\beta_{jk} &:& \mbox{ is the sensitivity of $j^{th}$ asset to factor `k'}\\
			\epsilon_j &:& \mbox{risky asset's idiosyncratic random shock with mean zero}
\end{eqnarray*}	

Shreyes2010

###############################
## Access the relevant files ##
###############################

returns <- read.csv("Returns_CNX_500.csv")
returns1 <- returns
nifty <- read.csv("Nifty_returns.csv")
mibor <- read.csv("MIBOR.csv", na.strings="#N/A")
exchange <- read.csv("Exchange_rates.csv", na.strings="#N/A")

Shreyes2010

###############################
## Access the relevant files ##
###############################
returns <- read.csv("Returns_CNX_500.csv")
returns1 <- returns
nifty <- read.csv("Nifty_returns.csv")
mibor <- read.csv("MIBOR.csv", na.strings="#N/A")
exchange <- read.csv("Exchange_rates.csv", na.strings="#N/A")

###################################################################

Shreyes2010

Problem 14:

The following iterative sequence is defined for the set of positive integers:

n  n/2 (n is even)
n  3n + 1 (n is odd)

Using the rule above and starting with 13, we generate the following sequence:

13  40  20  10  5  16  8  4  2  1

Shreyes2010

shreyes <- function(temp)  ## Cute function that returns the number of iterations that were preformed.
  {   c <- 0
      while(temp > 1)
        { if(temp%%2==0) temp <- temp/2 else temp <- 3*temp + 1 
          c <- c+1
         }
  return(c)    
  }

largest <- 0 

Shreyes2010

single.call <- function(limit) { # Another cute function that returns the vector that contains the number of iterations for each number.

        memo <- rep(-1, limit)   
        memo[1] <- 0

        for(i in c(2:limit)) {  
                l <- 0
                n <- i
                while(n >= i) {  # Check only so long as "n > i" and not "1" this is basically the optimization we wanted. 
                        l <- l + 1

Shreyes2010

Y = C + I + G + (X - M)

Y: Output produced in the economy
C: Total consumption demand
I: Total investment
G: Total govt. spending
X: Total value of exports
M: Total value of imports

We assume a closed economy so the identity boils down to 

Shreyes2010

# IS curve equation
# y = output; c = marginal propensity to consume; alpha = 1/(1-c) A = autonomous component;
# b = Senstivity to interest rates; i = interest rates
# I = I.0 - b*i (investment equation)
# y = alpha*A - alpha*b*i

IS.curve <- function(c, A, b, i)
{ 
  y = (1/(1-c))*A - (1/(1-c))*b*i
  return(y)

Shreyes2010

# Aggregate demand curve 

ad.curve <- function(c, A, b, ms, h, k ,y) # We are trying to arrive at a relation between 
# Prices and output.
{
  alpha <- 1/(1-c)
  omega <- (k/h) - (1/alpha*b)
  P <- (ms/h)/(y*omega + (A/b)) # This is just basic algebra, you substitute "i" in terms of 
# "P" and "Y" from the IS and LM equations and find a relation between prices and output.
  return(P)