analysis and prediction of nse indices (1)

1

Analysis and Prediction of National Stock

Exchange Indices

A PROJECT REPORT SUBMITTED TO

DEPARTMENT OF STATISTICS, SHIVAJI UNIVERSITY,

KOLHAPUR

FOR THE DEGREE OF

MASTER OF SCIENCE

IN STATISTICS

BY

SAGARE AMRUT SUNIL DEPARTMENT OF STATISTICS,

SHIVAJI UNIVRESITY, KOLHAPUR-416004

2015-2016

2

CIRTIFICATE

This is to certify that the project report entitled “Analysis

and prediction of Nation Stock Exchange indices ”, being

submitted by Mr.Sagare Amrut Sunil, as a partial fulfillment for the

award of degree of M.Sc. in Statistics of Shivaji university,

Kolhapur, is a record of bonafide work carried out by her under

my supervision and guidance.

To the best of my knowledge the matter presented

in the project in the project has not been submitted earlier.

Place: Kolhapur

Date:

Mr. S. D.Pawar.

Project Guide

Dr. D. N. Kashid.

Head of department,

Department of

statistics,

Shivaji university,

Kolhapur.

3

Acknowledgement

We wish to thank Department of Statistics (Shivaji University,

Kolhapur) for giving us an opportunity to do a project.

This report has been prepared under the guidance of

Mr.S.D.Pawar. We would like to express our profound gratitude towards

him for her guidance we constructive throughout this project.

Also we would like to thank Dr. H. V. Kulkarni for their support,

suggestions and guidance for this project.

Finally we would like to thank HOD, teachers, non-teaching staff

and friends for their valuable co-operation in this project.

4

INDEX

Sr.no Contents Page no

1 Introduction and description of the

problem 6

2 Data collection 7

3 Objectives 8

4 Terminology 9

5 Methodology 12

6 Exploratory data analysis 13

7 Forecasting 20

8 Major finding 45

9 Limitations 46

10 References 47

5

1. Introduction and description of the problem:

The National Stock Exchange of India Limited (NSE) is the

leading stock exchange of India, located in Mumbai. NSE has a market

capitalization of more than US$1.65 trillion, making it the world’s 12th-

largest stock exchange as of 23 January 2015. NSE offers trading,

clearing and settlement services in equity, equity derivatives, debt and

currency derivatives segments. It is the first exchange in India to

introduce electronic trading facility thus connecting together the investor

base of the entire country. Trading on the equities segment takes place

on all days of the week (except Saturdays and Sundays and holidays

declared by the Exchange in advance).Time of trading is 9.00 am to 3.30

pm.

If we invest in bank we will get fix percentage of return on fix

deposit or on saving account which is dependent on bank, but in share

market we will get more return if we know the idea about share market

and where to invest the money. Sometimes price of share will go up or

down which is dependent on lots of factor. Overall there is risk to invest

money in share market as compare to bank. There are approximately

5000 companies are listed in NSE. Then main problem is that which

company we take to invest our money. Thanks to NSE there are lots of

indices which shows index of particular sector (like bank, IT, Energy,

Pharmacy and more) using this indices we make our strategy to invest.

Each sector contain number of companies in which we invest In this

project we will study on some sector indices of NSE to predict its future

value and to find confidence interval to minimize the risk to invest

money in share market using statistics. Using this we can make our

strategy to invest the money in which company of particular sector

whose index value shows positive response to make some money by

investment to become reach.

In this project we use daily closing price of Nifty50, Nifty next 50,

Bank, IT, FMCG, Pharma, Metal, Energy indices which can cover lots

of company in NSE. Using this value we try to fit Time series model to

predict future value to give some idea to invest.

https://en.wikipedia.org/wiki/Stock_exchange

https://en.wikipedia.org/wiki/Mumbai

https://en.wikipedia.org/wiki/Market_capitalization

https://en.wikipedia.org/wiki/Market_capitalization

https://en.wikipedia.org/wiki/List_of_stock_exchanges#Major_stock_exchanges

https://en.wikipedia.org/wiki/List_of_stock_exchanges#Major_stock_exchanges

6

2. Data collection: Data is collected from the official website of

National Stock Exchange of India Limited (NSE)

http://www.nseindia.com/products/content/equities/indices/historical_in

dex_data.htm

I collected data from the above website of following eight indices of

National Stock Exchange (NSE)

Nifty50

Next50

Bank

Energy

Metal

IT

FMCG

Pharma

This is in the form of Excel file of .csv format, which contain following

7 variables

Date

Open

High

Low

Close

Shares Traded

Turnover (Rs. Cr)

This is categorical data, in which open, high, low, close shows value of

index while share traded shows total number of share traded and

turnover shows turnover in ₨ on that perticular day

http://www.nseindia.com/products/content/equities/indices/historical_index_data.htm

http://www.nseindia.com/products/content/equities/indices/historical_index_data.htm

7

3. Objectives:

Following are our main objective of project,

To analysis the data of eight indices to find out some interesting

pattern from that to give idea about the market behavior.

To minimize the risk in share market by predicting the future index

value using time series analysis.

To find the confidence interval for forcasted value

8

4. Terminology:

1) Indices: An Index is used to give information about the price

movements of products in the financial, commodities or any other

markets. Financial indexes are constructed to measure price movements

of stocks, bonds, T-bills and other forms of investments. Stock market

indexes are meant to capture the overall behavior of equity markets. A

stock market index is created by selecting a group of stocks that are

representative of the whole market or a specified sector or segment of

the market. An Index is calculated with reference to a base period and a

base index value.

The different indices in stock market out of these we will study on 8

indices whose information is given below

Nifty 50 :

The Nifty 50 is a well diversified 50 stock index accounting for 13

sectors of the economy. It is used for a variety of purposes such as

benchmarking fund portfolios, index based derivatives and index funds.

Nifty Next 50

The Nifty Next 50 Index represents 50 companies from Nifty 100

after excluding the Nifty 50 companies.

FMCG

FMCGs (Fast Moving Consumer Goods) are those goods and

products, which are non-durable, mass consumption products and

available off the shelf. The Nifty FMCG Index comprises of maximum

of 15 companies who manufacture such products which are listed on the

National Stock Exchange (NSE).

9

Nifty Bank

Nifty Bank Index is an index comprised of the most liquid and

large capitalized Indian Banking stocks. It provides investors and market

intermediaries with a benchmark that captures the capital market

performance of Indian Banks. Index has 12 stocks from the banking

sector which trade on the National Stock Exchange.

Nifty IT

Companies in this index are those that have more than 50% of their

turnover from IT related activities like IT Infrastructure , IT Education

and Software Training , Telecommunication Services and Networking

Infrastructure, Software Development, Hardware Manufacturer’s,

Vending, Support and Maintenance.

Nifty Energy

Energy sector is universally recognized as one of the most

significant inputs for economic growth. Nifty Energy Index will include

companies belonging to Petroleum, Gas and Power sub sectors.

Nifty Metal

The Nifty Metal Index is designed to reflect the behavior and

performance of the Metals sector including mining. The Nifty Metal

Index comprises of maximum of 15 stocks that are listed on the National

Stock Exchange.

10

2) Share Trading

In our data we have daily share trade for each index which shows

total share traded by investor in companies which include in that index

that is number of buy and sell of shares by investors.

3) Closing Price

Closing value is the value of that index which shows daily closing

price or value of that index when market was close at that day

4) Turnover (Rs. Cr)

This is the total turnover in Rs in that index for one day by investors.

11

5. Methodology:

Collected data of indices are time dependent and I want to predict future

value of that indices. I used time series analysis to forcasting,

For this I used the following softwares:

R software

MINITAB

R package ‘forecast’ has been used to fit appropriate time series model

and to forecast the value. It contains the functions auto.arima( ) and

forecast( ) to fit time series model and forecasting.

R code:

w=read.csv(file.choose(),header=T) # To select Excel file in csv format

attach(w)

library(forecast)

fit=auto.arima(Close)

summary(fit)

f=forecast(fit,h=5)

plot(f)

f

12

6. Exploratory Data Analysis:

1) Closing Price

I ) Average of Indices –

The following table and graph represent the average of index of

particular index for each year

Average

Year Nifty 50 Bank Energy FMCG IT Next 50 Metal Pharma

2005 2268.91 3862.00 3898.77 3404.87 3066.22 4699.62 1100.14 2033.11

2006 3357.09 4735.47 5257.85 5273.30 4337.71 6143.26 1643.96 2555.71

2007 4571.29 6887.50 7462.06 5214.25 5054.30 8632.13 3140.22 2765.14

2008 4339.11 6564.79 8104.08 5540.57 3633.18 7378.32 3240.33 2928.15

2009 4113.96 6771.39 7985.37 6057.47 3738.89 7258.93 2902.21 2767.17

2010 5461.12 10359.09 9340.96 8219.75 6203.26 11494.92 4428.92 4241.57

2011 5335.91 10297.86 8395.28 9700.00 6375.92 10528.22 3663.80 4634.39

2012 5343.77 10509.24 7638.48 12632.03 6124.36 10363.78 2838.02 5277.25

2013 5915.90 11414.96 7872.61 16520.62 7488.17 11820.39 2247.90 6768.61

2014 7360.30 14522.65 8952.19 18387.19 10197.92 15447.97 2801.41 9022.92

2015 8285.91 18095.70 8377.88 20282.24 11585.63 19729.69 2155.71 12252.13

Average 5118.78 9449.10 7565.06 10102.81 6160.29 10311.12 2740.72 5016.82

From above line chart we visualias that,

FMCG is rapidly increases seems look like straight line

Metal shows constat behavior in last 10 year

0

5000

10000

15000

20000

25000

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Ind

ex

Val

ue

Average of Index per year

Nifty 50

Bank

Energy

FMCG

IT

Next 50

13

II) Maximum of Indices –

The following table and graph represent the maximum of index of


Maximum

Year Nifty50 Bank Energy FMCG IT Metal Next50 Pharma

2005 2842.6 4692.85 4800.35 4421.43 3925.55 1273 5564.15 2272.45

2006 4015.95 6330.75 6134.2 6275.28 5432.25 2352.65 7213.9 2967.56

2007 6159.3 10090.7 11322.97 6329.6 5830.55 5456.99 12488.25 3173.55

2008 6287.85 10698.35 12012.26 6778.92 4748.2 5493.95 13069.45 3519

2009 5201.05 9526.7 9483.23 7526.72 5829.7 4709.33 10382.7 3850.16

2010 6312.45 13268.7 10195.42 9674.37 7503.65 5017.33 13555.15 5085.38

2011 6157.6 11894.75 9891.45 10762.35 7545.95 4800.65 12261 5172.9

2012 5930.9 12510.25 8318.3 15795.7 6747.2 3429.35 12340.05 6084.65

2013 6363.9 13317.1 8798 19407.85 9579.1 2986.05 12933.25 7731.55

2014 8588.25 18782.85 10603.35 21375.1 11981 3521.85 18970.25 11387.95

2015 8996.25 20555.25 9055.4 22295.15 12855.9 2715.45 21594.05 13831.15

Maximum 8996.25 20555.25 12012.26 22295.15 12855.9 5493.95 21594.05 13831.15


Bank and Next50 shows same behavior

FMCG shows rapid increase in between 2011to 2015

0

5000

10000

15000

20000

25000

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Ind

ex

Val

ue

Maximum of Index per year

Nifty 50

Bank

Energy

FMCG

IT

Metal

Next 50

Pharma

14

III) Minimum of Indices –

The following table and graph represent the maximum of index of


Minimum

Year Nifty 50 Bank Energy FMCG IT Metal Next 50 Pharma

2005 1902.5 3084.6 3351.61 2679.45 2514.1 940.32 4019.65 1765.76

2006 2632.8 3428.15 4181.35 4251.16 3219.35 1166.42 4517.8 2058.4

2007 3576.5 4893.45 5429.74 4376.84 4172.75 1984.79 6295.4 2442.99

2008 2524.2 4053.2 4748.84 4362.96 2126.1 1188.78 3782.1 2126.66

2009 2573.15 3339.7 5364.89 4550.77 2002 1278.37 3595.1 1968.69

2010 4718.65 8223.25 8615.19 6885.71 5449.75 3806.32 9801.65 3481.72

2011 4544.2 7798.55 6968.1 8157.45 5087.65 2464.6 8295.85 4300.25

2012 4636.75 7995.05 6875.8 10103.95 5489.6 2495.1 8312.1 4567.6

2013 5285 8664.2 7058.25 14516.2 5972.7 1628.2 10203.1 5778.2

2014 6000.9 10102.1 7405.95 16336.1 8675.1 2142.55 11729.65 7411.6

2015 7558.8 15790.1 7299.15 18828.45 10798.25 1605.45 18308.85 10604.95

Minimum 1902.5 3084.6 3351.61 2679.45 2002 940.32 3595.1 1765.76


FMCG shows maximum increase in minimum index.

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Ind

ex

Val

ue

Minimumof Index per year

Nifty 50

Bank

Energy

FMCG

IT

Metal

Next 50

Pharma

15

Return from Indices –

Following table and graph shows the return in percentage from each

index for one year. The red colure digit shows negative return and green

colure digit shows positive return.

Return = 𝐸𝑛𝑑𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒−𝑠𝑡𝑎𝑡𝑟𝑡𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒

𝑠𝑡𝑎𝑡𝑟𝑡𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒 × 100

Year Nifty 50 Bank Energy FMCG IT Metal Next 50 Pharma

2006 39.861422 31.87929 19.55762 17.86001 39.85325 92.32865 27.967406 25.62271 2007 53.181614 63.31432 95.42174 21.80459 -12.8136 136.3008 74.206441 14.08794 2008 -51.83949 -49.5061 -48.3737 -21.4194 -53.9404 -73.6483 -63.95465 -25.42643 2009 71.456592 76.47634 58.13267 41.00342 155.1762 210.1549 122.32286 58.343024 2010 17.245136 29.40076 3.545622 29.01356 27.45927 -1.72968 16.407023 35.456616 2011 -24.90094 -32.7866 -28.7365 8.410411 -18.0642 -48.6611 -32.03572 -10.41523 2012 27.354289 56.02467 12.65232 50.19126 -3.10002 16.23783 48.458873 32.126281 2013 5.9344463 -10.0275 0.362435 11.56614 57.75529 -15.9748 3.5384769 26.060001 2014 31.437005 64.56445 8.941337 18.03911 18.53046 6.706632 43.81788 42.811681 2015 -4.07593 -9.75043 -0.54281 0.673311 -0.02809 -32.1693 6.630424 9.5232646

Over all 180.20064 271.4063 78.31792 359.7315 188.6671 54.08914 259.73619 442.59189

From above we visualise that. In year 2008 due to

Economical wide recetion in all over the world affect share market badly

but after that in 2009 share market give outstanding return in some index

such as IT, Metal and Next50 as compare to other.

-100

-50

0

50

100

150

200

250

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015Ret

urn

in p

erce

nta

ge

Return from each Index per year

Nifty 50

Bank

Energy

FMCG

IT

Metal

Next 50

16

2) Share Trading

I) Average Trading-

Above graph shows that, the investors interested in trading in the

Nifty50 as compare to others.

II) Total Trading-

Below graph represent total share traded in per year.

This shows that, Investors are interested in company which is in Nifty50

and Next50 to trading.

0

20000000

40000000

60000000

80000000

100000000

120000000

140000000

160000000

180000000

Total

Average Share Trading In Last 10 Year

Bank

Energy

IT

Metal

Next50

FMCG

Nifty50

Pharma

0

2E+10

4E+10

6E+10

8E+10

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Total Share Trade

Bank

Energy

IT

Metal

Next50

FMCG

Nifty50

17

3) Turnover (Rs.Cr)

I) Average Turnover-

Below graph give us visualisation for average turnover in indices.

II) Maximum Turnover-

Above bar plot give visualisation that, turnover is maximum in Nifty50

index which is 32873.45. (Cr)

0

1000

2000

3000

4000

5000

6000

7000

Total

Average Turnover In Last 10 Year

Bank

Energy

IT

Metal

Next50

FMCG

Nifty50

Pharma

0

5000

10000

15000

20000

25000

30000

35000

Total

MaximumTurnover In Last 10 Year

Bank

Metal

Energy

IT

Next50

FMCG

Nifty50

Pharma

18

II) Minimum Turnover-

Above bar plot visualise that, second maximum turnover pharma here

has lower turnover which is 1.75 (Cr.) among all indices.

III) Total Turnover-

Below graph represent total turnover in per year.

From above graph we conclude that, investors favorite index is Nifty50

to invest because in 10 years total turnover in Nifty50 is more than

others.

0

20

40

60

80

100

120

Total

MinimumTurnover In Last 10 Year

Bank

Metal

Energy

IT

Next50

FMCG

Nifty50

Pharma

0

500000

1000000

1500000

2000000

2500000

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Total Turnover (Rs. Cr)

Bank

Energy

IT

Metal

Next50

FMCG

Nifty50

19

7. Forecasting:

Now in this section we fit the time series model to closing value of each

index. For this analysis we take only one year data (i.e 2015) to

prediction the future value and to find 95% confidence interval.

Nifty Bank

Time series plot of closing value:

Here we see that decreasing trend. We take difference to remove

trend.

Time series plot of difference:

20

ACF and PACF of difference:

For ARIMA (p, d, q) model here ARIMA (0, 1, 0)

Normality graph for difference is:

21

Using R-code I get the following prediction

Output:

ARIMA (0, 1, 0) with drift

Coefficients:

drift

-7.4018

s.e. 16.3120

sigma^2 estimated as 65990: log likelihood=-1720.49

AIC=3444.97 AICc=3445.02 BIC=3451.99

Forecast:

Forecast Lo 95 hi 95 Actual

16914.8 16411.31 17418.29 17039.25

16907.4 16195.36 17619.44 16599.15

16899.99 16027.93 17772.06 16542.5

16892.59 15885.62 17899.57 16433.15

16885.19 15759.36 18011.02 16073.85

Graph:

14500

15000

15500

16000

16500

17000

17500

18000

18500

1 2 3 4 5

Ind

ex

valu

e

Forecast, CI & Actual of Nifty Bank

Forecast

Lo 95

hi 95

Actual

22

Nifty Bank



trend.


23




24


Output:

ARIMA (0, 1, 0) with drift

Coefficients:

drift

0.5468

s.e. 14.8837


AIC=3399.71 AICc=3399.76 BIC=3406.73

Forecast:


20193.2 19733.79 20652.6 20184.95

20193.74 19544.05 20843.44 20087.8

20194.29 19398.58 20990 20047.45

20194.84 19276.03 21113.65 19689.45

20195.38 19168.12 21222.64 19261.55

Graph:

18000

18500

19000

19500

20000

20500

21000

21500

1 2 3 4 5

Ind

ex

Val

ue

Forecast, CI 95% & Actual of Nifty FMCG

Forecast

Lo 95

hi 95

Actual

25

Nifty IT:

Time series plot of Closing value:


trend.


26




27


Output:

ARIMA(0,1,0)


AIC=3096.09 AICc=3096.1 BIC=3099.6

Forecast:


11212.55 10963.58 11461.52 11174.85

11212.55 10860.45 11564.65 11029.25

11212.55 10781.32 11643.78 10997.15

11212.55 10714.61 11710.49 11018.15

11212.55 10655.83 11769.27 10863.2

Graph:

10000

10200

10400

10600

10800

11000

11200

11400

11600

11800

12000

1 2 3 4 5

Ind

ex

Val

ue

Forecast, CI 95% & Actual of Nifty IT

Forecast

Lo 95

hi 95

Actual

28

Nifty Metal:



trend.


29




30


Output:

ARIMA(0,1,0) with drift

Coefficients:

drift

-3.5077

s.e. 2.1576


AIC=2445.66 AICc=2445.71 BIC=2452.68

Forecast:


1823.342 1756.745 1889.94 1830.3

1819.835 1725.651 1914.018 1804.9

1816.327 1700.976 1931.678 1862.7

1812.819 1679.624 1946.015 1829.05

1809.312 1660.395 1958.229 1742.55

Graph:

1500

1550

1600

1650

1700

1750

1800

1850

1900

1950

2000

1 2 3 4 5

Ind

ex

Val

ue

Forecast, CI 95% & Actual of Nifty Metal

Forecast

Lo 95

hi 95

Actual

31

Nifty Next50:


Here we see that normal increasing trend. We take difference to

remove trend.


32




33


Output:


Coefficients:

drift

5.0291

s.e. 14.4869


AIC=3386.36 AICc=3386.41 BIC=3393.38

Forecast:


19982.08 19534.92 20429.24 20169.45

19987.11 19354.73 20619.48 19956.85

19992.14 19217.64 20766.64 20068.75

19997.17 19102.85 20891.48 20008.7

20002.2 19002.32 21002.07 19408.15

Graph:

18000

18500

19000

19500

20000

20500

21000

21500

1 2 3 4 5

Ind

ex

Val

ue

Forecast, CI 95% & Actual of Nifty Next50

Forecast

Lo 95

hi 95

Actual

34

Nifty Pharma:


Here we see that slightly increasing trend. We take difference to

remove trend.


35




36


Output:


Coefficients:

drift

4.2115

s.e. 10.9844


AIC=3249.63 AICc=3249.68 BIC=3256.65

Forecast:


11967.71 11628.67 12306.76 11979.85

11971.92 11492.44 12451.4 11733.7

11976.13 11388.89 12563.38 11741.5

11980.35 11302.26 12658.44 11673.8

11984.56 11226.43 12742.68 11451.65

Graph:

Nifty Energy:

10000

10500

11000

11500

12000

12500

13000

1 2 3 4 5

Ind

ex

Val

ue

Forecast, CI 95% & Actual of Nifty Pharma

Forecast

Lo 95

hi 95

Actual

37


There is one problem has occurred in time series plot. we observe from

above time series plot after some 150 day there market is suddenly

down. From data we observed that on 24 Aug 2015 market was suddenly

down by 664.75 point. If we go through one year data to analyze the

data this variation will affect on analysis.

So we take data from 25th Aug 2015 to 31st Dec 2015 to analysis data as

follows

38


Here we see that normal increasing trend. We take difference to

remove trend.


39




40


Output:

Series: Close

ARIMA(0,1,0)


AIC=1013.49 AICc=1013.54 BIC=1015.93

Forecast:


8584.1 8402.106 8766.094 8594.7

8584.1 8326.722 8841.478 8468.6

8584.1 8268.877 8899.323 8555

8584.1 8220.112 8948.088 8662.45

8584.1 8177.149 8991.051 8454.45

Graph:

7600

7800

8000

8200

8400

8600

8800

9000

9200

1 2 3 4 5

Ind

ex

Val

ue

Forecast, CI 95% & Actual of Nifty Energy

Forecast

Lo 95

hi 95

Actual

41

Nifty Nifty50:



trend.


42




43


Output:


Coefficients:

drift

-1.3670

s.e. 5.3621


AIC=2895.37 AICc=2895.42 BIC=2902.39

Forecast:


7944.983 7779.476 8110.49 7963.2

7943.616 7709.553 8177.679 7791.3

7942.249 7655.582 8228.916 7784.65

7940.882 7609.867 8271.896 7741

7939.515 7569.43 8309.6 7568.3

Graph:

7000

7200

7400

7600

7800

8000

8200

8400

1 2 3 4 5

Ind

ex

Val

ue

Forecast, CI 95% & Actual of Nifty Nifty50

Forecast

Lo 95

hi 95

Actual

44

8. Major Finding:

Investors are interested in Nifty50 and Next50 index sector.

FMCG index shows more return than any other in last decade

Decreasing in index gives opportunity to investors to invest his

money to make profit from decreased share if, investor know when

market will go up.

From return table observe that the return given by all index in 10

years are positive excpet some years in between these year. This

shows that investor should invest his money for long term to get

better result for make some money.

Using time series forecasting we find the future behavior of index

value and can give a 95% confidence interval for that index.

From above time series analysis I found that for all eight index the

time series model is same that is ARIMA (0, 1, 0).

Time series analysis shows that the market will down for few days.

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9. Limitations:

To forecast the future value of index is calculated using only

closing price of index. Analysis does not considore other factores

which affect on the share market that’s why the pridicted value

does noat give you exact future value. There are lots of factors

affecting on share price such as, natural disaster, government

decisions, terror attack, International level activity, global

economical recetion etc.

These time series analyses give you only the future behaviour of

market not exact prediction.

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10. Reference

Peter J. Brockwell, Richerd A. Davis(1987), Introduction to time

series and forecast.

Tsay R. S. Analysis of financial time series

analysis and prediction of nse indices (1)

Data & Analytics