% importing excel data

% data1   36x10 double
data1 = xlsread('C:\Users\PGDM Test\Desktop\gowtham\Month_Comp_data.xlsx');


% imports the second sheet i.e. TCS
% data2   36x1 double
data2 = xlsread('C:\Users\PGDM Test\Desktop\gowtham\Month_Comp_data.xlsx', 'TCS');



% selecting particular range
% selects the cells from B2 to B14
% data3    13x1 double
data3 = xlsread('C:\Users\PGDM Test\Desktop\gowtham\Month_Comp_data.xlsx', 'TCS', 'B2:B14');


% plots the all rows of first column of data1
a1 = plot(data1(:,1));


% 'figure' command in between two plot command diplays plots in two 
% different pages else only plot 2 will be displayed in absence of 
% figure command
figure;


% plots the all rows of second column
a2 = plot(data1(:,2));

% returns the number of rows in a column. Here the length of column 1 is
% returned to len i.e. 36
len = length(data1(:,1));

% get the current price of the stocks
pt = data1(2:len,:);

% get the previous day prices of the stocks
pt_1 = data1(1:len-1,:);

% calucualte the continuous componding return i.e. 
%log( current price/previous price)
% ret_cc 35x10 double
ret_cc = log(pt./pt_1);

% calculate the simple return 
% simple return = current price / previous price
% ret_sim 35x10 double
ret_sim = (pt./pt_1);


% retuns of each company
asian_paints = ret_cc(:,1);
reddy = ret_cc(:,2);
icici = ret_cc(:,3);
itc = ret_cc(:,4);

% density plot of returns of each company
ksdensity(asian_paints);
figure
ksdensity(reddy);
figure
ksdensity(icici);
figure
ksdensity(itc);

% statistical analysis of asian paints

% mean of returns of asian paints
% asian_mean   0.007602870458122
asian_mean = mean(asian_paints);

% median of returns of asian paints
% asian_median -7.808330298679778e-04
asian_median = median(asian_paints);

% find the kurtosis
% asian_kurt  2.793589748452653
asian_kurt = kurtosis(asian_paints);

% skewness
% asian_skew  -0.366401516942248  negatively skew
asian_skew = skewness(asian_paints);


% Augmented Dicky Fuller (ADF) test to find weather the distribution is
% stationary

[h pval stat] = adftest(asian_paints);

% h = 1 & pavl = 0.01 and stat = -5.287963764334884
% pval < 0.05 so reject null hypothesis i.e. series is unit root (not
% stationary) 
% therefore asian_paints return series is stationary

% Jarque Bera test used to check weather the given series is in normal
% distribution or not
% u1: series is normal (null hypothesis)
% u2: series is not normal (alternative hypothesis)
% If h=1 and Pval <0.05 then reject the null
[h1 pval1 stat1] = jbtest(asian_paints);

% h1 = 0 , pval 1 = 0.5 and stat1 = 0.845257989353985
% h1 is zero therefore it is in normal distribution