International Journal of Engineering Technology Management and Applied Sciences

wwwijetmascom April 2015 Volume 3 Issue 4 ISSN 2349-4476

198 Rajat Singla

Prediction from Technical Analysis- A Study of Indian Stock

Market

Rajat Singla

Assistant Professor

Jannayak Chaudhary Devilal Institute of Business Management

Barnala RoadSirsa

Abstract

A lot of academicians and technical analyst are of the opinion that the markets can be predicted by

using proper techniques A lot of controversy always occurs as whether markets can be predicted or

not In order to judge the efficiency of the markets the researcher in this paper has tested the two

most important techniques of technical analysis has used which are RSI (Relative strength index) and

EMA (Exponential Moving Average) The data taken for the study is for five years starting from

January 2010 to December 2014 Conclusion of the study claims that the markets can be predicted

if proper and timely decisions are taken by using EMA and RSI

Key Words - RSI EMA Brock t statistics Alpha ratio Sharpe ratio

Introduction-

Technical Analysis specifically attracts the attention of economists as its successes cast doubt upon

the Efficient Market Hypothesis (EMH) which states that market prices bdquoinstantaneously and fully

reflect all relevant information The EMH maintains that publicly available information such as past

prices should not assist traders in earning unusually high returns Technical Analysis on the other

hand suggests that economic fundamentals such as interest rates and growth are not always the sole

determinant of the exchange rate rather it is driven away from its fair value by the traders‟ irrational

expectations of future exchange rate movements

A trading system is a systematic method for buying and selling financial instruments with a

view to make money consistently As such not only do trading systems require that prices are

predictable but also that the predictable component is financially exploitable Technical analysis is an

approach to predicting future price movements based on identifying patterns in prices volume and

other market statistics Technical analysis usually proceeds by recording market activity in graphical

form and then deducing the probable future trend from the pictured history The premise is that

prices exhibit various geometric regularities which once identified inform the trader what is likely

to happen next This in turn allows the trader to run a profitable trading strategy

Technical analysis is the study of financial market action The technician looks at price

changes that occur on a day-to-day or week-to-week basis or over any other constant time period

displayed in graphic form called charts Hence the name is chart analysis A chartist analyzes price

charts only while the technical analyst studies technical indicators derived from price changes in

addition to the price charts Technical analysts examine the price action of the financial markets

instead of the fundamental factors that (seem to) effect market prices Technicians believe that even

if all relevant information of a particular market or stock was available you still cannot predict a

precise market response to that information There are so many factors interacting at any one time

that it is easy for important ones to be ignored in favor of those which are considered as the flavor

of the day The technical analyst believes that all the relevant market information is reflected (or

discounted) in the price with the exception of shocking news such as natural disasters or acts of God

These factors however are discounted very quickly

International Journal of Engineering Technology Management and Applied Sciences

wwwijetmascom April 2015 Volume 3 Issue 4 ISSN 2349-4476

199 Rajat Singla

Watching financial markets it becomes obvious that there are trends momentum and patterns

that repeat over time not exactly the same way but similarly Charts are self-similar as they show the

same fractal structure (a fractal is a tiny pattern self-similar means the overall pattern is made up of

smaller versions of the same pattern) whether in stocks commodities currencies bonds A chart is a

mirror of the mood of the crowd and not of the fundamental factors Thus technical analysis is the

analysis of human mass psychology Therefore it is also called behavioral finance

The efficient-market hypothesis (EMH) contradicts the basic tenets of technical analysis by

stating that past prices cannot be used to profitably predict future prices Thus it holds that technical

analysis cannot be effective Economist Eugene Fama published the seminal paper on the EMH in

the Journal of Finance in 1970 and said In short the evidence in support of the efficient markets

model is extensive and (somewhat uniquely in economics) contradictory evidence is sparse

Review of Literature-

Andrew (1991) tested for long-run memory that is robust to short-range dependence is developed It

is an extension of the range over standard deviation or RS statistic for which the relevant

asymptotic sampling theory is derived via functional central limit theory This test is applied to daily

and monthly stock returns indexes over several time periods and contrary toprevious findings there

is no evidence of long-range dependence in any of the indexes over any sample period or sub-period

once short-range dependence is taken into account Illustrative Monte Carlo experiments indicate

that the modified RS test has power against at least two specific models of long-run memory

suggested that stochastic models of short-range dependence may adequately capture the time series

behavior of stock returns

Brock Lakonishock and Lebaron (1992) tested two trading rules- Moving Average and Trading

Range Break by utlising the Dow Jones Index from 1897 to 1986 This paper resulted strong support

for Technical Strategies The paper concluded that the return obtained from these strategies are not

consistent with four popular null models the random walk the AR (1) the GARCH-M and the

exponential GARCH and buy signals consistently generate higher return than sell signals and the

returns following buy signals are less volatile than return following by sell signals

Blume Easley and Ohara (1994) investigated the information role of volume and its applicability

for technical analysis The authors developed a new equilibrium model in which aggregate supply is

fixed and traders receive signals with differing quality The paper shows that volume provides

information on information quality that cannot be deducted from the price statistic The paper

showed how volume information precision and price movements relate and demonstrate how

sequences of volume and price can be information The authors also showed that traders who use

information contained in market statistics do better than traders who do not Technical analysis thus

arises as a natural component agents learning process As the analysis suggests introducing volume

unrelated to the underlying information structure would survey weakly the ability of uninformed

traders to interpret market information accurately

Elton Gruber and Blake (1995) developed relative pricing (APT) models that were successful in

explaining expected returns in the bond market In this study authors employed publicly available

bond returns indices (passive bond portfolios) as the independent variables utilized in fitting the

equilibrium models The appendix lists the bond indices used These include indices of government

bonds corporate bonds and mortgages The sample period covered the period from February 1980

to 1992(155 monthly observations) Authors compared the four alternatives APT models those that

did not contain the fundamental expectation variables were rejected at the 5 percent level in favour of

models that do contain those variables The return indices were the most important variables in

International Journal of Engineering Technology Management and Applied Sciences

wwwijetmascom April 2015 Volume 3 Issue 4 ISSN 2349-4476

199 Rajat Singla

Watching financial markets it becomes obvious that there are trends momentum and patterns

that repeat over time not exactly the same way but similarly Charts are self-similar as they show the

same fractal structure (a fractal is a tiny pattern self-similar means the overall pattern is made up of

smaller versions of the same pattern) whether in stocks commodities currencies bonds A chart is a

mirror of the mood of the crowd and not of the fundamental factors Thus technical analysis is the

analysis of human mass psychology Therefore it is also called behavioral finance

The efficient-market hypothesis (EMH) contradicts the basic tenets of technical analysis by

stating that past prices cannot be used to profitably predict future prices Thus it holds that technical

analysis cannot be effective Economist Eugene Fama published the seminal paper on the EMH in

the Journal of Finance in 1970 and said In short the evidence in support of the efficient markets

model is extensive and (somewhat uniquely in economics) contradictory evidence is sparse

Review of Literature-

Andrew (1991) tested for long-run memory that is robust to short-range dependence is developed It

is an extension of the range over standard deviation or RS statistic for which the relevant

asymptotic sampling theory is derived via functional central limit theory This test is applied to daily

and monthly stock returns indexes over several time periods and contrary toprevious findings there

is no evidence of long-range dependence in any of the indexes over any sample period or sub-period

once short-range dependence is taken into account Illustrative Monte Carlo experiments indicate

that the modified RS test has power against at least two specific models of long-run memory

suggested that stochastic models of short-range dependence may adequately capture the time series

behavior of stock returns

Brock Lakonishock and Lebaron (1992) tested two trading rules- Moving Average and Trading

Range Break by utlising the Dow Jones Index from 1897 to 1986 This paper resulted strong support

for Technical Strategies The paper concluded that the return obtained from these strategies are not

consistent with four popular null models the random walk the AR (1) the GARCH-M and the

exponential GARCH and buy signals consistently generate higher return than sell signals and the

returns following buy signals are less volatile than return following by sell signals

Blume Easley and Ohara (1994) investigated the information role of volume and its applicability

for technical analysis The authors developed a new equilibrium model in which aggregate supply is

fixed and traders receive signals with differing quality The paper shows that volume provides

information on information quality that cannot be deducted from the price statistic The paper

showed how volume information precision and price movements relate and demonstrate how

sequences of volume and price can be information The authors also showed that traders who use

information contained in market statistics do better than traders who do not Technical analysis thus

arises as a natural component agents learning process As the analysis suggests introducing volume

unrelated to the underlying information structure would survey weakly the ability of uninformed

traders to interpret market information accurately

Elton Gruber and Blake (1995) developed relative pricing (APT) models that were successful in

explaining expected returns in the bond market In this study authors employed publicly available

bond returns indices (passive bond portfolios) as the independent variables utilized in fitting the

equilibrium models The appendix lists the bond indices used These include indices of government

bonds corporate bonds and mortgages The sample period covered the period from February 1980

to 1992(155 monthly observations) Authors compared the four alternatives APT models those that

did not contain the fundamental expectation variables were rejected at the 5 percent level in favour of

models that do contain those variables The return indices were the most important variables in

International Journal of Engineering Technology Management and Applied Sciences

wwwijetmascom April 2015 Volume 3 Issue 4 ISSN 2349-4476

200 Rajat Singla

explaining the time series of return Authors utilized our fundamental relative pricing models to

examine the performance of bond funds Bond funds underperform the returns predicted by the

relative pricing models by the amount of expenses on adverse and the models using fundamental

variables do a better job than other models in accounting for the difference in performance between

types of bonds funds

Wong Manzurand Chew (2003) focused on the role of technical analysis in signaling the timing of

stock market entry and exit Test statistics are introduced to test the performance of the most

established of the trend followers the Moving Average and the most frequently used counter-trend

indicator the Relative Strength Index Using Singapore data the results indicated that the indicators

can be used to generate significantly positive return It is found that member firms of Singapore

Stock Exchange (SES) tend to enjoy substantial profits by applying technical indicators

Metghalchi Gomez Chen and Monsef (2005) tested three moving average technical trading rules

for the SampP 500 stock index Using daily data from 1954 to 2004 their results indicate that moving

average rules did indeed had predictive power and could discern recurring-price patterns for the

period up to mid-1980s However since mid-1980s technical trading rules do not work and could

not discern recurring-price patterns Their results are consistent with market inefficiency from 1954

to 1984 and market efficiency from 1984 to present

Zhou and Zhu (2010) showed that the probability of a stock market drop of 50 percent from a high

is about 90 percent over a 100-year period based on the popular random walk model of stock prices

On 9 October 2007 the Dow Jones Industrial Average reached a high of 1416453 by 9 March

2009 it had dropped about 54 percent to a low of 654705 Former Fed chairman Alan Greenspan

called this a ldquoonce-in-a-centuryrdquo crisis They concluded that with a broad market index and a more

sophisticated asset pricing model that captures more risks in the economy the probability rises to

above 99 percent They also cocluded that a market drop of 50 percent or more is very likely in long-

term stock market investments and investors should be prepared for it

Chiang KeLiaoand Wang (2012) tested nine common trading strategies including buy and hold

(passive) and eight technical trading strategies (active) The results show that the Relative Strength

Index (RSI) oscillator and parabolic strategies outperform the other technical trading strategies and

all of the eight technical trading strategies beat the buy and hold strategy both before and after

transaction costs In addition investing a portion of investors‟ money in risky assets and a portion in

risk free assets can help distinguish performance among the trading strategies

Research Objectives

To use EMA and RSI as tools of Technical Analysis

To find out the validity of RSI and EMA in Indian stock market

Hypothesis

bullNull Hypothesis (H1) There is no significant difference between return calculated from

Exponential Moving Average and Index Return (REMAltRINDEX)

bullNull Hypothesis (H2) There is no significant difference between return calculated from Relative

strength index and Index Return (RRSIltRINDEX)

The hypothesis will be tested at 3 levels of significance which are 1 5 and 10

Research Design

The research design has been distinctive described to the objective of the study The present study

involves exploratory research design

International Journal of Engineering Technology Management and Applied Sciences

wwwijetmascom April 2015 Volume 3 Issue 4 ISSN 2349-4476

201 Rajat Singla

Sample Size and source of data

In the present study data is taken only for Indian market form January 2010 to December 2014 The

whole data of daily trade and prices of the markets is taken from the website-

wwwfinanceyahoocom

Tools used

The tool taken for analysis is RSI and EMA

To check the validity of EMA and RSI Brock t- statistics is applied

Analysis and Interpretation-

Table I Results of Technical Trading Rules for Whole Period

Technique Index No (Buy) No (Sell) Long(B) Short(S) Long-Short(B-S)

EMA NIFTY 764

501 00008

137

00017

-0805

-000021

0352

RSI NIFTY 812

453 00018

1947

-000024

0307

000036

0813

represents significant at 1 5 10 level respectively

Source Compiled by researcher on the basis of data

Table I represents the analysis of EMA and RSI for the whole period of study The table shows that

EMA as well as RSI is found to have significant presence in Indian market for the whole period of

data EMA and RSI both show to have significant presence in case of long strategy at 10 and 5

level of significance But when judged for short strategy these techniques are not significant In case

of aggregate strategy too the result are not significant So in short we can say that EMA and RSI

have their prediction power in Indian market but mainly in case of long strategy

Table 2 represents the analysis of EMA and RSI for the whole period of study for risk and return

attached with the techniques When observed for the alpha and sharpe ratio in both cases EMA and

RSI both have given the positive alpha and sharpe ratio It means that using EMA and RSI one can

have the positive returns as compared with the index return

Table-2 Risk Return Analysis Using RSI and EMA for whole period

Technique

Index

No of

trades1

Trade

Repetition

Time2

(in days)

Gross Returns ()

T C ()3 Net Returns ()

Sharpe45

Ratio

()

Alpha6

(uarrIndex

Return)

Aggregate CAGR Rank Aggregate Aggregate CAGR Rank

EMA NIFTY 253 5 3480 615 2 1834 1646 309 1 456 146

RSI NIFTY

302 419 3604 635 1 2027 1577 297 2 225 72

1) Number of trades is reached as follows eg buying ldquoXrdquo quantity on day one to be long and there

after selling ldquo2Xrdquo quantity ie one quantity for becoming neutral and another quantity to be short by

ldquoXrdquo quantity 2) Trade Repetition Time is an average number of days between two consecutive

trades and has direct bearing on the transaction cost 3) T C (transaction cost) is estimated at 001

percent of average trade value (average of INDEX over years) times numbers of trades The transaction

cost is usually variable between clients based on their volume of trade and almost nil for members of

stock exchanges where they buy a seat against one-time payment Hence transaction cost is being

assumed 4) Sharpe Ratio= (Net Returns - Index Return) Standard deviation 5) Annual Standard

International Journal of Engineering Technology Management and Applied Sciences

wwwijetmascom April 2015 Volume 3 Issue 4 ISSN 2349-4476

202 Rajat Singla

Deviation= SD of daily returns multiplied by square root of average numbers of days in a year for the

panel to the given index 6) Alpha Ratio= (Net Returns - Index Return)

Conclusion-

At last it can be concluded that both techniques of technical analysis ie RSI and EMA can play the

positive returns in Indian stock market However as the transaction cost increases with the number of

trades hence it may cut down the returns as earned by the investor But if the investor can make a lot

of trading then the transaction can be minimized as some of the brokering houses are charging a very

nominal fee for a good volume of transactions

References-

Andrew W Lo 1991 Long-Term Memory in Stock Market Prices Econometrica Vol 59 No 5

(September 1991) pp 1279-1313

Brock W J Lakonishockamp B Lebaron 1992 Simple Technical Trading rules and the

Stochastic Properties of Stock Returns The Journal of Finance Vol 47 No 5 ( Dec 1992) pp

1731-1764

Blueme L D Easley amp M O‟Hara 1994 Market statistics and Technical Analysis The role of

volume Journal of Finance Vol 49 No 1 (March 1994) pp 153-181

Elton Edvin J Gruber Martin J and Blake Christopher R(1995) ldquoFundamental Economic

Variables Expected Returns and Bond Fund Performancerdquo The Journal of Finance Volume 2

Number 4 pp 1229-1256

Wong WK M Manzuramp B K Chew 2003 How rewarding is technical analysis Evidence

from Singapore stock market Applied Financial economics 13 (2003) pp 543-551

Metghalchi M X G Gomez C P Chen amp S Monsef 2005 Market Efficiency For SampP 500

1954-2004 International Business amp Economic Research Journal Vol 4 No 7 (July 2005) pp

23-30

Zhou G amp Y Zhu 2010 Perspectives Is the Recent Financial Crisis Really a Once-in-A-

Century EventFinancial Analysts Journal Vol 66 No 1 (2010)

Chiang YC MC Ke T L Liao amp C D Wang 2012 Are technical trading strategies still

profitable Evidence from Taiwan Stock Index Futures Market Applied Financial Economics

(2012) pp- 1-11

International Journal of Engineering Technology Management and Applied Sciences

wwwijetmascom April 2015 Volume 3 Issue 4 ISSN 2349-4476

201 Rajat Singla

Sample Size and source of data

In the present study data is taken only for Indian market form January 2010 to December 2014 The

whole data of daily trade and prices of the markets is taken from the website-

wwwfinanceyahoocom

Tools used

The tool taken for analysis is RSI and EMA

To check the validity of EMA and RSI Brock t- statistics is applied

Analysis and Interpretation-

Table I Results of Technical Trading Rules for Whole Period

Technique Index No (Buy) No (Sell) Long(B) Short(S) Long-Short(B-S)

EMA NIFTY 764

501 00008

137

00017

-0805

-000021

0352

RSI NIFTY 812

453 00018

1947

-000024

0307

000036

0813

represents significant at 1 5 10 level respectively

Source Compiled by researcher on the basis of data

Table I represents the analysis of EMA and RSI for the whole period of study The table shows that

EMA as well as RSI is found to have significant presence in Indian market for the whole period of

data EMA and RSI both show to have significant presence in case of long strategy at 10 and 5

level of significance But when judged for short strategy these techniques are not significant In case

of aggregate strategy too the result are not significant So in short we can say that EMA and RSI

have their prediction power in Indian market but mainly in case of long strategy

Table 2 represents the analysis of EMA and RSI for the whole period of study for risk and return

attached with the techniques When observed for the alpha and sharpe ratio in both cases EMA and

RSI both have given the positive alpha and sharpe ratio It means that using EMA and RSI one can

have the positive returns as compared with the index return

Table-2 Risk Return Analysis Using RSI and EMA for whole period

Technique

Index

No of

trades1

Trade

Repetition

Time2

(in days)

Gross Returns ()

T C ()3 Net Returns ()

Sharpe45

Ratio

()

Alpha6

(uarrIndex

Return)

Aggregate CAGR Rank Aggregate Aggregate CAGR Rank

EMA NIFTY 253 5 3480 615 2 1834 1646 309 1 456 146

RSI NIFTY

302 419 3604 635 1 2027 1577 297 2 225 72

1) Number of trades is reached as follows eg buying ldquoXrdquo quantity on day one to be long and there

after selling ldquo2Xrdquo quantity ie one quantity for becoming neutral and another quantity to be short by

ldquoXrdquo quantity 2) Trade Repetition Time is an average number of days between two consecutive

trades and has direct bearing on the transaction cost 3) T C (transaction cost) is estimated at 001

percent of average trade value (average of INDEX over years) times numbers of trades The transaction

cost is usually variable between clients based on their volume of trade and almost nil for members of

stock exchanges where they buy a seat against one-time payment Hence transaction cost is being

assumed 4) Sharpe Ratio= (Net Returns - Index Return) Standard deviation 5) Annual Standard

International Journal of Engineering Technology Management and Applied Sciences

wwwijetmascom April 2015 Volume 3 Issue 4 ISSN 2349-4476

202 Rajat Singla

Deviation= SD of daily returns multiplied by square root of average numbers of days in a year for the

panel to the given index 6) Alpha Ratio= (Net Returns - Index Return)

Conclusion-

At last it can be concluded that both techniques of technical analysis ie RSI and EMA can play the

positive returns in Indian stock market However as the transaction cost increases with the number of

trades hence it may cut down the returns as earned by the investor But if the investor can make a lot

of trading then the transaction can be minimized as some of the brokering houses are charging a very

nominal fee for a good volume of transactions

References-

Andrew W Lo 1991 Long-Term Memory in Stock Market Prices Econometrica Vol 59 No 5

(September 1991) pp 1279-1313

Brock W J Lakonishockamp B Lebaron 1992 Simple Technical Trading rules and the

Stochastic Properties of Stock Returns The Journal of Finance Vol 47 No 5 ( Dec 1992) pp

1731-1764

Blueme L D Easley amp M O‟Hara 1994 Market statistics and Technical Analysis The role of

volume Journal of Finance Vol 49 No 1 (March 1994) pp 153-181

Elton Edvin J Gruber Martin J and Blake Christopher R(1995) ldquoFundamental Economic

Variables Expected Returns and Bond Fund Performancerdquo The Journal of Finance Volume 2

Number 4 pp 1229-1256

Wong WK M Manzuramp B K Chew 2003 How rewarding is technical analysis Evidence

from Singapore stock market Applied Financial economics 13 (2003) pp 543-551

Metghalchi M X G Gomez C P Chen amp S Monsef 2005 Market Efficiency For SampP 500

1954-2004 International Business amp Economic Research Journal Vol 4 No 7 (July 2005) pp

23-30

Zhou G amp Y Zhu 2010 Perspectives Is the Recent Financial Crisis Really a Once-in-A-

Century EventFinancial Analysts Journal Vol 66 No 1 (2010)

Chiang YC MC Ke T L Liao amp C D Wang 2012 Are technical trading strategies still

profitable Evidence from Taiwan Stock Index Futures Market Applied Financial Economics

(2012) pp- 1-11

International Journal of Engineering Technology Management and Applied Sciences

wwwijetmascom April 2015 Volume 3 Issue 4 ISSN 2349-4476

202 Rajat Singla

Deviation= SD of daily returns multiplied by square root of average numbers of days in a year for the

panel to the given index 6) Alpha Ratio= (Net Returns - Index Return)

Conclusion-

At last it can be concluded that both techniques of technical analysis ie RSI and EMA can play the

positive returns in Indian stock market However as the transaction cost increases with the number of

trades hence it may cut down the returns as earned by the investor But if the investor can make a lot

of trading then the transaction can be minimized as some of the brokering houses are charging a very

nominal fee for a good volume of transactions

References-

Andrew W Lo 1991 Long-Term Memory in Stock Market Prices Econometrica Vol 59 No 5

(September 1991) pp 1279-1313

Brock W J Lakonishockamp B Lebaron 1992 Simple Technical Trading rules and the

Stochastic Properties of Stock Returns The Journal of Finance Vol 47 No 5 ( Dec 1992) pp

1731-1764

Blueme L D Easley amp M O‟Hara 1994 Market statistics and Technical Analysis The role of

volume Journal of Finance Vol 49 No 1 (March 1994) pp 153-181

Elton Edvin J Gruber Martin J and Blake Christopher R(1995) ldquoFundamental Economic

Variables Expected Returns and Bond Fund Performancerdquo The Journal of Finance Volume 2

Number 4 pp 1229-1256

Wong WK M Manzuramp B K Chew 2003 How rewarding is technical analysis Evidence

from Singapore stock market Applied Financial economics 13 (2003) pp 543-551

Metghalchi M X G Gomez C P Chen amp S Monsef 2005 Market Efficiency For SampP 500

1954-2004 International Business amp Economic Research Journal Vol 4 No 7 (July 2005) pp

23-30

Zhou G amp Y Zhu 2010 Perspectives Is the Recent Financial Crisis Really a Once-in-A-

Century EventFinancial Analysts Journal Vol 66 No 1 (2010)

Chiang YC MC Ke T L Liao amp C D Wang 2012 Are technical trading strategies still

profitable Evidence from Taiwan Stock Index Futures Market Applied Financial Economics

(2012) pp- 1-11