technical analysis

TESTS OF TECHNICAL ANALYSIS IN INDIA

Sanjay Sehgal and Meenakshi Gupta

The study evaluates the economic feasibility of technical analysis in the Indian stock market. It discussesthat technical indicators do not outperform Simple Buy and Hold strategy on net return basis for individualstocks. Technical indicators seem to do better during market upturns compared to market downturns. However,technical based trading strategies are not feasible vis-à-vis passive strategy irrespective of market cycleconditions. Technical indicators also do not provide economically significant profit for industry as well aseconomy based data. Combining fundamentals with technical information, we find, that technical indicatorsare more profitable for small stocks compared to big stocks and for high value stocks compared to low valuestocks. However, the economic feasibility of fundamentals’ based technical strategies is still questionable.Our results seem to confirm with the efficient market hypothesis.

Key Words : Technical Analysis, Bull Period, Moving Average, Oscillators, Size and ValueStrategies. JEL Classification Codes: C10, C12, G11, G14

INTRODUCTION

THERE have been two main approaches to analysethe securities market - the fundamental approachand the technical approach. The fundamental

approach stresses the influence of a firm’s basic earningsand risk on the market price of its shares, whereas thetechnical approach concentrates on the patterns of stockmarket prices. The technical approach states that pastshare prices and volumes tend to follow a pattern andthey can be used to predict future price movements.Forces of demand and supply determine the share prices;however, the fundamentalists think that they are afunction of rational factors, while technicians attributeit to psychological factors.

The technical analysis approach to capital marketevaluation has received little attention and acceptanceas compared to fundamental analysis. But in recent yearsthe popularity of technical school of thought is increasingamongst academicians and practitioners. There has beensome empirical research on technical analysis, fordeveloped capital markets.1 However similar empiricalwork for developing markets especially India2 is limited.

In this light, an empirical testing of technical indicatorsfor Indian stock market is considered important. It ispossible that the efficacy of technical tools may varyacross mature and emerging market settings owing todifferences in their relative efficiency. There is also aneed to empirically evaluate whether technical analysiscombined with fundamental analysis provides someextra normal returns. Fundamental analysis can beincorporated in the form of forming portfolios on thebasis of certain company characteristics.

The objective of the paper is to evaluate the followingpropositions. Do technical analysis based tradingstrategies are statistically feasible for individual stocks?Are the profits provided by technical indicatorseconomically significant and hence outperform SimpleBuy and Hold (SBH) benchmark? Does the success oftechnical tools vary across different phases of the marketcycles? Does the success of technical analysis vary acrossdifferent industries and old and new economy sectors?Do some of the technical indicators work better forportfolios formed on company characteristics? The presentpaper is divided in six sections including the present one.Data and its sources are described in section two. The

VISION—The Journal of Business Perspective ● Vol. 11 ● No. 3 ● July–September 2007

12 ● Sehgal and Gupta

results of technical indicators for individual securities andmarket cycle conditions are discussed in section three.Section four gives the results of business sectors alongwith an analysis of old and new economy stocks. In sectionfive we discuss the results of technical indicators combinedwith fundamental analysis, while summary andconclusions are provided in section six.

We find that technical analysis gives statisticallysignificant returns for all the nine technical indicators ongross return basis at 5 per cent level during the entire studyperiod. Majority of the technical indicators also do wellon net return basis. However, technical based strategiesdo not seem to be economically feasible for individualsecurities as they are outperformed by SBH strategy overthe study period. Technical indicators tend to do betterduring bull phases of the market compared to marketdownturns. However, they under perform the SBHstrategy irrespective of the market cycle conditions.Technical indicators also do not seem to be economicallyfeasible when used on industry as well as economic data.Combining value and size based strategies with technicalanalysis seem to enhance profits for small firm stocks andhigh value stocks but the superior performance of SBHagain cast a shadow on the applicability of such strategies.

DATA

The data for the study consists of two parts: one to verifythe efficacy of technical analysis based strategies andsecond for trading strategies which combinesfundamental and technical analysis based information.For part one, initially we choose 75 companies (five largecompanies from fifteen major industries). However, sixof the sample companies were dropped, as they did nothave continuous trading record over our study period,which is warranted by study on technical analysis. Thesample selection was balanced across sectors as we alsointend to do technical analysis in industrial data. Thedata comprises of adjusted daily high, low, and closingprices and daily trading volumes for the period January1, 1999 to December 31, 2004. Most of these sharesbelong to BSE-100 Index and are Category A stocks.3

The BSE-100 Index has been used as market surrogate.It is a broad based value weighted stock market indexthat has been constructed on the lines of Standard andPoor USA, and is highly popular amongst investmentresearchers in India.

The data for part two consists of adjusted daily high,low, and closing prices and daily trading volumes of 180companies from BSE-200 Index listed on Bombay Stock

Exchange for the three calendar years 2002, 2003, and2004. The bigger set of data is required to create companycharacteristic sorted portfolios. The sample securities areactively traded. The data also consists of size (marketcapitalisation) and value measures i.e. P/B ratios (inverseof BE/ME) and P/E ratios (inverse of E/P ratio) for 180companies.

The daily stock prices, trading volumes and stockindex data for the sample companies for both the partshave been taken from Capital Market Line software. Thedata for the company characteristics have been collectedfrom CMIE, widely used financial software. The implicityields on 91- day treasury bills have been used as a riskfree proxy and are collected from RBI’s site.

INDIVIDUAL SECURITIES, MARKET CYCLECONDITIONS AND TECHNICAL ANALYSIS

The study includes nine technical indicators broadlyclassified into three categories: trend following priceindicators, price oscillators, and volume indicator, namelyExponential Moving Average (EMA, Trend following,14 days), Moving Average Convergence Divergence(MACD, Trend following, 12-26-9 days), VolumeOscillator (VO, volume indicator,10-25 days), SmoothedRate of Change (ROC, Price oscillator, 14-7 days),Relative Strength Index (RSI, Price oscillator, 7 days ),Commodity Channel Index (CCI, Price oscillator, 7 days),Stochastic (STO, Price oscillator, 7 days), DirectionalIndicator (DI, Trend following, 13 days) and MovingAverage (MA, Trend following, 14 days). Based on theformulae and trading rules of technical analysis (AlexanderElder, 1993) we developed our own programmes fortrading using FoxPro as programming language.

Individual Securities

The study period is from 1.1.1999 to 31.12.2004 having1504 trading days. Mean return earned using a selectedindicator during the entire period is calculated in twosteps. In the first step the gross return is calculated foreach company using a selected indicator as thepercentage difference between amount realised at the endof the period and the amount invested at beginning ofthe period on the assumption of reinvestment of profitswithout considering the commission and slippage. TheReturn is calculated using the formula:

Return = [(Amtt-Amtt-1)/Amtt-1]*100.

Where Amtt =Amount at the end of the period

Amtt-1=Amount at the beginning of the period


Tests of Technical Analysis in India ● 13

In the second step equally weighted portfolio meanreturn is calculated for all the sample companies using aselected indicator. The net returns are calculated aftertaking into account the commission and slippage.Commissions are paid for entering and exiting a contract.Slippage is the difference in price at which the order isplaced and the price at which it is executed. The slippagedepends on company’s trading activity, size and also onmarket conditions. As the sample stocks are Category Astocks which are characterised by large capitalisation andhigh trading activity, the slippage is low. Slippage ratesfor category A stocks are taken as 0.005 per cent on therecommendations of a survey of ten market practitionersin Delhi including stock brokers and investment analysts.The two-way commissions (both buying and selling) onBSE are .01 per cent for individual investor as suggestedby a sample survey of practitioners. From July 2001 shortselling was not allowed for inter day trading. So tomaintain the uniformity in programming techniques shortselling was ignored.

The details relating to number of recommendations(each buy-sell pair is considered as one recommendationor a trade), and holding period using technical indicatorsare given in Table 1. In all, the study involves 25,379trades for 69 companies using 9 technical indicators overa 6-year total period. The average number of trades forthe total period for each stock using a given technicalindicator varies from 22.83 for DI to 55.39 for MACD.The average holding period for a security varies from19.61 days for MACD to 56.35 days for DI as given inPanel B Table 1. The correlation coefficient betweennumber of trades and holding period is found to be –0.94 and is statistically significant at 5 per cent level,showing an inverse relation between number of tradesand holding period. There is also a negative correlationbetween MR and number of trades (-0.37) showing adecrease in return with large number of trades due tohigh transaction costs.

The results for technical analysis of individual stocksare shown in Table 2. Panel A of the table gives the grossreturns (before deducting trading costs) for the entirestudy period for each of the technical indicator. Panel Bprovides the net returns (after deducting transactions).The t values are calculated at 5 per cent level on one tailbasis, t1 is t statistic that provides statistical feasibilityof the indicators at 5 per cent level i.e. whether theindicators earn a significant positive return or not and t2is t statistic that gives the economic feasibility of theindicators over the Simple Buy and Hold (SBH) strategyi.e. the significance of difference in returns between an

active strategy vis-à-vis a passive strategy. The grossreturn is statistically significant for all the 9 technicalindicators under study. But the return on active strategywhen compared with SBH strategy is not statisticallysignificant even without commission and slippage i.e.the values for t2 are not statistically significant. None ofthe indicators outperforms the SBH strategy. The tradingreality, however does not allow trading withouttransaction costs. But for three indicators i.e. MACD,ROC and MA the difference in returns with SBH strategyis not statistically significant i.e. they are at par with SBHstrategy.

The net returns are statistically significant at 5 percent level for six indicators namely ROC, DI, MA,MACD, STO and VO. Net return is highest for ROCfollowed by DI, MA, MACD, STO and VO. But thevalues for t2 are not significant for any of the indicators.None of the technical indicators outperform the SBHstrategy. As technical analysis is an active strategy, itseems that most of the profits are wiped out by hightransaction costs and slippage.

Market Cycle Conditions

The total sample period has also been divided into 12half-yearly sub-periods. The average number of tradesfor each sub-period for a stock using a given technicalindicator varies from 2.28 for DI to 4.67 for CCI as givenin Panel A Table 1. The average holding period for asecurity varies from 18.21 days for CCI to 42.73 daysfor DI as shown in Panel B Table 1. The Net Returns foreach of the sub-periods are calculated. The results arenot reported for sub-period analysis. The net return ishighest for DI followed by ROC, MACD, MA, VO, andSTO. The t1 values are significant for five indicators.None of the t2 values are significant in sub-periodanalysis, as is also the case for the total period analysis.It means the passive strategy outperformed the activestrategy.

It may be possible that the overall results areinfluenced by different market phases i.e. Bull or Bear.Bull phase in the market is symbolized by prolonged risein the prices of shares sustained by buying pressure frominvestors. In contrast bear phase experiences prolongedperiod of falling prices, dominated by selling pressurein the market place. Thus the 12 sub-periods are classifiedas bull or bear depending upon whether the weekly returnon BSE100 (Rm) is more or less than the weekly returnon 91 days Treasury bill rate (used as risk free rate Rf).If the weekly return for BSE100 index is more than 91



days Treasury bill rate i.e. Rf then the sub-period istermed as bull period otherwise bear period. Using thiscriterion the 12 sub periods are divided into 6 bull periodsand 6 bear periods.

The average number of trades for bull-period for astock using a technical indicator varies from 2.07 forDI to 4.77 for MACD as given in Table 1, panel A. Theaverage holding period for a security for a bull periodvaries from 16.06 days for CCI to 44.59 days for DI asshown in Table 1 panel B. The average number of tradesfor bear period for a stock using a technical indicatorvaries from 2.49 for DI to 4.59 for CCI as given in Table1, panel A. The average holding period for a securityfor a bear period varies from 20.26 days for MACD to42.12 days for DI as shown in Table 1 panel B.

The results for the market phases are given inTable 3. Bull period results are discussed in panel Aand bear period results in panel B. The MR is statisticallysignificant for all the indicators for the average bullperiod, but when compared with SBH strategy thereturns are not significant for any of the indicators. TheMR is not statistically significant either for indicatorsor for the SBH strategy in the bear period. This may bedue to restriction on short selling. The results indicatethat DI gives the best result in Bull period followed byROC, MA, VO, STO, MACD, CCI, RSI, and EMA.The t2 values for some of the indicators in the bear periodare positive but it indicates only smaller losses ascompared to SBH strategy and not the significant valuesas all the MR values are negative by using all thetechnical indicators and also SBH strategy. Thedifference in return in bull period and bear period isstatistically significant for all the indicators under studywhich implies that technical analysis based strategiesare more successful during market up-trends. None ofthe technical indicators outperform the SBH strategyeither in the bull period or in the bear period.

BUSINESS SECTORS, OLD AND NEWECONOMY STOCKS AND TECHNICALANALYSIS

The total sample consists of fifteen different industriesnamely Capital Goods, Chemical and Petrochemicals,Consumer Durables, Diversified, Finance, Fast MovingConsumer Goods (FMCG), Healthcare, HousingRelated, Information Technology, Media andPublishing, Metal and Mining, Oil and Gas, Power andTelecom, Tourism, Transport and TransportEquipments.

The market is looking at a classification of new andold economy in the last decade due to rapid change intechnology. New economy companies produce intangibleproducts. These companies create ideas and concepts tomanage intangibles. Creativeness and innovation aremore important than mass production, and earnings areoften reinvested in new ideas and not in new machines.New economy is characterized by globalization,communication, innovation, technology and vision. Oldeconomy stocks represent large, well-establishedcompanies that participate in more traditional industrysectors and have little investment in the technologyindustry. They generally represent manufacturingindustries and core lines of business activity, which isbased on stable technology. Old economy stocks exhibitrelatively low volatility and usually pay consistentdividends as they operate in mature industry sectors. Incontrast the new economy stocks are heavily involved inthe technology sector. Their stocks are generally morevolatile and they do not pay dividends, opting to reinvesttheir cash into business expansions. Old and neweconomy stocks differ not only in their business activitiesbut also in the way they are valued by the market. Whileanalysing new economy stocks more focus is placed ongrowth expectations and earning estimates but for oldeconomy stocks the emphasis is placed on their value.

Business Sectors

The MR for each industry is calculated as an equallyweighted portfolio return consisting of all the securitiesbelonging to that industry for the total study period. Foreach of the industry MR, SD, t1 and t2 values arecalculated. The results are given in Panel A of Table 4.For Capital Goods industry DI and VO gives statisticallysignificant return. MACD gives highest net returnfollowed by ROC, RSI and DI for Chemical andPetrochemical industry. But only RSI providesstatistically significant returns. ROC gives the highestreturn followed by MACD, DI and MA for ConsumerDurables industry. But ROC does not provide statisticallysignificant return and only MACD and DI givestatistically significant return. In absolute terms MACDand ROC generates greater return than SBH strategy forConsumer Durables industry but these returns are notstatistically different from that of SBH strategy. STOindicator performs best for Diversified industry followedby ROC, RSI, DI and VO indicator. But only STO, RSIand VO give statistically significant net returns. Thereturns for these indicators are not statistically differentfrom that of SBH strategy.



MA gives the highest net return for Finance industryto be followed by DI, ROC, STO, MACD and VO.Except MA indicator all of them provide statisticallysignificant return. None of the indicators provide positivereturns for Fast Moving Consumer Durables (FMCG)and Healthcare industries. SBH strategy also does notprovide statistically significant returns for FMCG andHealthcare industries. DI gives the highest net returnsfollowed by STO, ROC and RSI for Housing industry.ROC is the leading indicator followed by MACD forInformation Technology (IT) and Media and Publishingindustry. But only ROC gives statistically significant netreturns for IT industry and MACD provides statisticallysignificant net returns for media and Publishing industry.

For Metal and Mining industry ROC provides thehighest net returns in absolute terms followed by MACDand VO. But MACD and VO provide statisticallysignificant returns. DI provides the highest net returnsto be followed by MACD, ROC and RSI for Oil andGas industry. But only DI gives statistically significantreturns. For Power and Telecom industry DI providesthe highest net returns to be followed by ROC, MACDand VO. But none of the indicators provide statisticallysignificant returns. Only ROC and DI gives the positivereturns for Tourism industry but the returns are notstatistically significant. DI is the wining indicator forTransport industry followed by ROC and VO. But noneof these indicators provide statistically significant returnsfor transport industry.

Out of fifteen industries under study DI providesstatistically significant net returns for 6 industriesfollowed by MACD and VO for 4 industries, RSI andSTO for three industries and ROC for two industries. DIappears to be most suitable indicator for the industriesas it gives less but more powerful signals and hence thelowest probability of whipsaws and also the profits arenot heavily eroded by market fluctuations. In fiveindustries namely FMCG, Healthcare, Power andTelecom, Transportation, and Tourism none of thetechnical indicators could provide statistically feasiblereturns. But as we find for individual security analysisnone of the technical indicators has outperformed theSBH strategy thus implying economical non feasibilityof technical analysis while analysing the industrial data.

Old and New Economy Stocks

The sample companies are also divided into old and neweconomy stocks. The results are given in Table 4. Fourtechnical indicators provide statistically significant net

returns i.e. MACD, VO ROC and DI for the neweconomy sector as given in Panel B of the Table 4. Forold economy ROC and DI gave statistically significantnet returns as shown in Panel C of the Table 4. But noneof the technical indicators outperform the SBH strategyfor old or new economy sectors. The MR for neweconomy sector is more than the MR for old economysector for all the indicators as well as for the SBH strategyand this difference is statistically significant for MACDindicator.

COMPANY CHARACTERISTICS ANDTECHNICAL ANALYSIS

We empirically evaluate whether technical analysiscombined with fundamental analysis provides some extranormal returns. In the contemporary finance literature,certain company characteristics have been found to bearrelationship with average stock returns. Prominentamongst these company characteristics are company size(measured in terms of market capitalization) [See Banz(1981), Cook and Roseff (1982)], Fama and French(1992) and Chui and Wei (1998)], book equity to marketequity ratio [Stattmen (1980) Rosenberg Reid andLanstein (1985), Chan, Hamao and Lakonishok: 1991)]and price-earnings ratio [Ball (1978), Basu (1983)].According to Banz (1981), the small stocks (having lowmarket capitalization) tend to outperform big stocks(having high market capitalization) resulting in a sizeeffect in the stock market. The value effect implies thatvalue stocks (having high BE/ME ratio or high E/P ratio)outperform growth stocks (having low BE/ME ratio orlow E/P ratio). Ball (1978) shows that both BE/ME andE/P ratios can be used as value proxies and hence highBE/ME and high E/P ratios characterise value stockswhile low BE/ME and low E/P ratios imply growthstocks. Basu (1983) confirmed that the common stockof high E/P firms earn, on average higher risk adjustedreturns than the stocks with low E/P ratios.

Using fundamental analysis we form portfolios onthe basis of size (market capitalization) and two valuemeasures (Book equity to market equity ratio and priceearning ratio) and then buy-sell decisions are taken onthe basis of technical indicators. Size and value factorsare of special significance because of their strongpresence in US and other developed markets4 and alsoin Indian Stock Market.5

To study the effect of company characteristics onreturns, stocks are ranked in ascending order at Decemberend of each year t-1 on the basis of size and value



characteristics, while size is measured in terms of marketcapitalization, and the value characteristics are measuredusing P/B ratio (inverse of BE/ME) and P/E ratio (inverseof E/P ratio). Thus low P/B and low P/E ratios willcharacterise value stocks and high P/B and high P/E ratioswill characterise growth stocks. The ranked securitiesare then classified into three equal groups on the basisof size namely S1 (small), S2 (medium) and S3 (big).The same stocks are re-classified on the basis of their P/B ratios into three equal groups i.e., V1 (high value), V2(medium value), and V3 (low value) and P/E ratios viz.PE1 (high value), PE2 (medium value), and PE3 (lowvalue). We formed the equally weighted portfolios onthe basis of size and value variables. The equallyweighted portfolios are more desirable as theirparameters are less loaded with measurement errors(Lakonishok, Shleifer, Vishny (1994)). We estimated thereturns provided by each of these portfolios usingdifferent technical indicators for three calendar years2002, 2003, 2004. These returns are estimated net oftransaction costs.

The returns on company characteristic(s) sortedportfolios based on selected technical indicators are givenin the Table 5 for the years 2002, 2003 and 2004. ROC,DI and MA indicators are found to give significant netreturns at 5 per cent level on portfolios formed on thebasis of company characteristics. These indicatorsprovide positive returns even after adjusting for marketeffect. The net returns on small stock portfolios are veryhigh as compared to big stock portfolios in all the threeyears. Similarly low P/B and low P/E portfolios (highvalue) perform well as compared to high P/Band high P/E portfolios (low value) in all the three years.

In 2002 all of the indicators give losses for low valueportfolios whereas high value stock portfolios gave areturn of 45.7 per cent using ROC indicator. In 2003and 2004 the low P/B portfolio earns more than twice asthat of high P/B portfolio. Similarly, low P/E portfolioearns a net return as high as 29 per cent in 2002 whilehigh P/E portfolio provides a negative return. In 2003and 2004 value stocks earn twice the return as growthstocks. However the magic wanes out when we comparethe returns provided by different technical indicators withthose on a Simple Buy Hold (SBH) strategy as shown inthe table. The SBH strategy seems to outperform almostall technical indicators for all the portfolios.

The returns are statistically significant at 5 per centlevel for most of the indicators for small, low P/B andlow P/E portfolios for the year 2002. For the year 2003the returns on all types of portfolios whether small, big,

low value, high value or low P/E or high P/E arestatistically significant at 5 per cent level. This may bedue to the fact that Indian market entered a bull phase inMarch 2003. However, for the year 2004 the returns arestatistically significant at 5 per cent level for small andmedium size portfolios (but not for big size portfolio)and also for low P/B and P/E ratio portfolios (valuestocks). However the empirical success of size and valuebased technical investing becomes questionable, oncewe compare the returns on technical analysis with SBHstrategy. The superior performance of SBH strategyprobably suggests that technical analysis is more of amyth than a reality. The empirical result suggests thattechnical analysis being transaction extensive does notpay off after adjusting for transaction costs owing tofrequent buying and selling of securities.

Then we formed double sorted equally weightedportfolios based on size and value groupings i.e. S1V1,S2V1, S3V1, S1V2, S2V2, S3V2, S1V3, S2V3, S3V3,S1PE1, S2PE1, S3PE1, S1PE2, S2PE2, S3PE2, S1PE3,S2PE3, and S3PE3, where S1V1 is a portfolio of smallstock with high value and S3V3 is a portfolio of bigstocks with low value. Similarly S1PE1 is a portfolio ofsmall stocks with high E/P ratio and S3PE3 is a portfolioof big stocks with low E/P ratio. The double-sortedportfolios will lead to better stock characterizationcompared to single sort portfolios. This should bereflected by higher return differential between cornerportfolios (S1V1 - S3V3) formed on the basis of doublesorts and compared to either of the criterion taken inisolation i.e. (S1-S3) and (V1-V3).

The returns on double-sorted portfolios are given intable 6 for the years 2002, 2003 and 2004. Panel A forthe Year 2002 shows that portfolio S1V1 provides betternet returns as compared to portfolio S3V3. The netreturns for S1V1 are statistically positive at 5 per centlevel for most of the indicators. Within the same sizeportfolios the net return decreases as we move from highvalue to low value portfolio i.e. from S1V1 to S1V3 orfrom S2V1 to S2V3 and so on. The net return for smallsize and low PE (value stock) portfolios (S1PE1) is muchhigher than the return on big size and high PE (growthstock) portfolios (S3PE3).The S3V3 portfolio and S3PE3portfolios provide losses for all the indicators while theportfolio S1V1 gives a net return of as high as 66.5 percent using ROC indicator and S1PE1 provides a netreturn of 54.9 per cent using Directional Indicator.

Panel B for the year 2003 shows that portfolio S1V1provides almost three times return as compared toportfolio S3V3. The returns for all types of portfolios



for the year 2003 are statistically positive at 5 per centlevel for most of the indicators as is the case with singlesorted portfolios. The tables also point out that withinthe same size portfolios the return decreases as we movefrom high value to low value portfolio i.e. from S1V1 toS1V3 or from S2V1 to S2V3 and so on. The net returnfor small size and low PE portfolio (S1PE1) is muchhigher than the net return on big size and high PEportfolio (S3PE3).

Panel C for the year 2004 shows that portfolio S1V1provides better returns as compared to portfolio S3V3.The returns for S1V1 are statistically positive at 5 per centlevel for most of the indicators. The tables also points outthat within the same size portfolios the return decreasesas we move from high value to low value portfolio i.e.from S1V1 to S1V3 or from S2V1 to S2V3 and so on.The return for small size and low PE portfolio (S1PE1) ismuch higher than the return on big size and high PEportfolio (S3PE3).The S3V3 portfolio gives losses formost of the indicators under study and S3PE3 provideslosses for all the indicators while S1V1 give a return of ashigh as 35 per cent using Directional Indicator and S1PE1provides a return of 40 per cent using MA indicator.

The economic feasibility of different investmentstrategies is also evaluated. It shows the difference inreturns of small and big size portfolios, high value andlow value portfolios along with their t statistics. All theindicators give a statistically significant difference inreturn at 5 per cent level for small minus big stocks (S1-S3), high value minus low value stocks (V1-V3) and(PE1-PE3), and also for double sorted portfolios i.e. smalland high value portfolio vs. big and low value portfolio.For the year 2002 the return difference is as high as 46per cent for small minus big size portfolios (S1-S3) forMA and ROC indicator. Similarly the return differenceis 63 per cent for (V1-V3) and 40 per cent for (PE1-PE3) for Moving Average indicator. The returndifference increases to 80 per cent from 46 per cent ifwe compare S1V1 and S3V3 instead of S1 and S3. Butthe return differential does not change if we compareS1PE1 and S3PE3. So we can follow a strategy of buyingsmall size value stocks (S1V1) and selling big sizedgrowth stocks (S3V3).

For the year 2003 the return difference is as high as105 per cent for small size minus big size portfolio (S1-S3) for Directional Indicator. Similarly the returndifference is 90 per cent for (V1-V3) and 45 per cent for(PE1-PE3) for Directional Indicator. The returndifference increases to 142.9 per cent from 105 per centif we compare S1V1 and S3V3 with that of S1 and S3

using Directional Indicator. But the return differentialdecreases if we compare S1PE1 and S3PE3 whichindicate that investors may not prefer small stocks withhigh value using the price earning ratio as value measure.

For the year 2004 the return difference is as high as22 per cent for small size minus big size portfolio (S1-S3) for Directional Indicator. Similarly the returndifference is 21 per cent for (V1-V3) for DirectionalIndicator and 28 per cent for (PE1-PE3) for MovingAverage indicator. The return difference increases to 32per cent from 22 per cent if we compare S1V1 and S3V3with that of S1 and S3. Thus return differences becomemore prominent if we compare size and valuecharacteristics together. But the SBH strategyoutperforms the technical analysis even after sorting theportfolios on size and value parameters together.

SUMMARY AND CONCLUSION

The study evaluates prominent technical tools for 69large Indian companies for the period January 1, 1999to December31, 2004. The empirical results suggest thattechnical analysis provides statistically significant returnsfor all the nine technical indicators on gross return basisduring the entire study period. Six out of nine technicalindicators also give statistically significant net returnsduring the study period. The technical tools performbetter during bull phases compared to bear phases ofmarket cycle; however, they do not beat SBH strategyin either of these phases.

The returns generated by technical analysis are noteconomically feasible for any of the industries as none ofthe technical indicators could outperform the SBHstrategy. However, DI gives statistically significant returnin six industries followed by MACD and VO in fourindustries. The mean return using technical analysis fornew economy sector is more than that for old economysector, however; none of the technical indicators couldprovide superior returns as compared to SBH strategy.

Combining corporate fundaments with technicalanalysis, we generate statistically significant returns forportfolios having small size and value stocks as comparedto big size and growth stocks. However, none of thecharacteristics sorted portfolios were able to beat SBHstrategy.

Thus compared to SBH strategy the technicalanalysis is not found economically feasible during thestudy period, as trading costs erodes most of the profits.Another reason could possibly be the fact that there



was restriction on short selling of equity shares on interday basis for greater part of our study period makingtrading strategies infeasible during the downtrendmarket phases.

There is a need for comprehensive evaluation oftechnical trading systems such as Triple Screen Trading

Table 1: Average Number of Share Recommendations and Holding Period For Each Co. Using a Technical Indicator

Period EMA MACD VO ROC RSI CCI STO DI MA

Panel A: Average Number of Share Recommendations

Total Period 46.86 55.39 45.41 41.43 29.67 54.46 32.45 22.83 39.32

Average Sub-Period 4.14 4.58 4.13 3.43 2.61 4.67 2.92 2.28 3.58

Bull Period 4.03 4.77 4.3 3.5 2.54 4.75 2.98 2.07 3.63

Bear Period 4.25 4.4 3.97 3.35 2.68 4.59 2.86 2.49 3.52

Panel B: Average Holding Period (in days) Per Company

Total Period 22.61 19.61 25.3 26.56 36.38 19.71 37.98 56.35 29.7

Average Sub-Period 20.61 18.22 22.4 23.45 31.77 18.21 31.67 42.73 24.81

Bull Period 17.72 16.65 20.55 26.27 28.94 16.06 29.17 44.59 26.48

Bear Period 23.49 20.26 24.58 20.73 34.85 20.58 35.32 42.12 23.36

system and Channel Trading system in the Indian context.Time series forecasting models such as Auto RegressiveIntegrated Moving Average (ARIMA) and Vector AutoRegression (VAR) may provide better forecast of futurereturns compared to standard tools of Technical Analysis.Economic feasibility of technical trading tools for high

Table 2: Total Period Results for Technical Indicators using Individual Security Data

EMA MACD VO ROC RSI CCI STO DI MA SBH

Panel A: Gross Returns

MR(Gross) 40.91 244.76 119.97 302.68 73.91 118.43 122.82 126.24 303.32 341.51

S.D. 122.4 238.6 152.59 371.87 161.25 365.53 220.59 213.41 906.47 492.92

t1 2.78 8.52 6.53 6.76 3.81 2.69 4.63 4.91 2.78 5.76

t2 -4.92 -1.47 -3.57 -0.52 -4.29 -3.02 -3.36 -3.33 -0.31 0

Panel B: Net Returns

MR(Net) -46.72 40.3 27.73 133.1 -0.16 -7.36 31.82 108.57 85.32 337.12

S.D. 43.47 102.25 82.79 226.91 88.09 153.01 128.9 190.48 383.18 488.02

t1 -8.93 3.27 2.78 4.87 -0.02 -0.4 2.05 4.73 1.96 5.74

t2 -6.51 -4.94 -5.19 -3.15 -5.65 -5.59 -5.02 -3.62 -3.37 0

Table 3: Empirical Results for Market Cycle Conditions


Panel A: Bull Period Results

MR 4.39 11.6 16.05 18.47 9.51 9.57 14.8 21.13 17.5 46.24

SD 7.03 18.26 11.2 18.46 9.64 18.78 13.02 20.22 28.51 34.64

t1 5.19 5.28 11.91 8.31 8.2 4.23 9.44 8.68 5.1 11.09

t2 -9.84 -7.35 -6.89 -5.88 -8.48 -7.73 -7.06 -5.2 -5.32 0

Panel B: Bear Period Results

MR -16.79 -3.81 -10.35 -4.65 -11.88 -14.44 -12.31 -7.12 -10.34 -12.04

SD 10.31 9.71 9.83 6.31 9.25 9.9 10.11 10.91 6.29 11.47

t1 -13.52 -3.26 -8.74 -6.13 -10.67 -12.12 -10.12 -5.42 -13.65 -8.72

t2 -2.49 4.42 0.9 4.51 0.09 -1.28 -0.14 2.51 1.04 0



frequency data such as intra day must be tested in the lightof the fact that short selling is allowed in Indian marketonly on the intra day basis. Efficacy of technical tools foralternative financial assets such as Government andCorporate bonds, Currencies, Commodities, and MutualFunds must also be checked. Technical tools should alsobe tested for Financial Derivatives such as Security andIndex Options and Security and Index Futures. A studycan be performed using a large sample of securities andover a longer time horizon to evaluate if the weak-form

efficiency of the Indian capital market has changed onperiod-to-period basis.

The present study contributes to the literature ontechnical analysis as well as investment strategies.However, further research in the area is desirable so thatone can have a better understanding about thedetermination of stock prices and returns and whethersuch information could be used to devise economicallyfeasible trading strategies.

Table 4: Empirical Results for Business Sectors and Old and New Economy Sectors

MR EMA MACD VO ROC RSI CCI STO DI MA SBH

Panel A: Business Sectors

Capital Goods -61.36 9.57 65.61 114.74 -34.98 -33.3 -31.83 57.92 -16.93 254.04

Chem. and Petrochem -42.95 64.87 9.03 61.64 32.73 -18.3 -14.71 22.66 -4.2 256.26

Consumer Durables -76.83 130.73 -56.31 167.02 -44.01 -58.45 -1.78 54.32 19.02 77.99

Diversified -0.37 88.86 122.55 158.09 139.7 103.96 274.28 153.83 74.78 451.81

Finance -20.67 116.87 93.88 171.16 65.07 133.1 135.46 308.34 451.4 793.79

FMCG -59.35 -54.99 -8 -27.2 -29.25 -58.13 -36.02 -4.73 -55.9719.29

Healthcare -43.1 -12.27 -1.24 68.46 14.36 -10.26 66.67 177.8 49.05 489.11

Housing Related -32.11 57.88 34.96 74.15 58.78 -19.45 177.86 193.24 5.26 293.16

IT -65.37 55.05 23.51 316.23 -76.57 -38.39 -65.18 2.18 12.18 330.37

Media and Publishing -91.36 56.61 -29.76 332.27 -90.51 -83.3 -89.48 -47.77 -68.62 33.29

Metal and Mining -67.37 70.75 51.7 565.83 -46.19 -67.56 11.28 17.34 5.79 492.75

Oil and Gas -41.99 86.78 -22.95 57.59 22.27 -33.27 -0.12 118.57 41.56 195.08

Power & Telecom -53.55 41.63 20.42 100.51 -0.71 -30.54 -19.81 175.69 21.12 281.01

Tourism -49.78 -22.71 -32.29 35.23 -49.91 -51.78 -59.61 26.56 -6.08 47.46

Transport &-52.87 -21.82 48.38 57.51 -47.15 -44.6 -12.46 -1.7 117.61 196.02

Transport Equip.

Panel B: New Economy Sector

MR -40.94 70.61 49.03 187.7 9.88 34.59 48.19 151.6 169.3 450.3

SD 56.71 116.54 104.16 226.89 115.27 231.23 159.42 220.83 576.32 593.73

t1 -3.82 3.21 2.49 4.38 0.45 0.79 1.6 3.63 1.55 4.01

t2 -4.36 -3.32 -3.52 -2.19 -3.85 -3.45 -3.46 -2.5 -1.8 0

Panel C: Old Economy Sector

MR -50.66 19.6 13.18 95.81 -7.03 -36.01 20.64 79.18 27.96 259.82

SD 31.66 86.73 61.59 221.96 64.08 40.48 103.81 163.05 130.75 389.66

t1 -10.25 1.45 1.37 2.76 -0.7 -5.7 1.27 3.11 1.37 4.27

t2 -5.09 -3.85 -4 -2.34 -4.33 -4.84 -3.8 -2.74 -3.61 0



Table 5: Net Returns for the Single Sorted (Size/Value) Portfolios


Panel A: Year 2002

S1 1.03 16.83 17.76 41.82 24.88 3.51 41.09 42.03 31.93 96.62

S2 -15.2 -3.11 -9.47 2.79 -7.04 -10.05 2.63 4.76 7.72 26.76

S3 -15.48 -7.96 -10.5 -4.03 -7.83 -12.27 -3.89 -0.35 -14.76 15.12

V1 -2.81 24.63 11.35 45.77 20.36 -0.75 40.35 39.53 45.38 92.43

V2 -9.35 -3.43 -3.8 3.08 -2.72 -4.15 4.78 8.98 -1.79 39.86

V3 -17.73 -15.43 -10.01 -8.28 -7.81 -14.16 -5.4 -2.25 -18.29 5.65

PE1 -3.35 9.55 5.71 23.66 11.13 0.26 17.36 29.83 23.86 74.06

PE2 -14.73 -3.52 -7.26 0.22 -4.96 -5.47 4.04 6.31 4.16 27.86

PE3 -13.96 -12.63 -7.64 -5.33 -6.55 -12.74 -3.55 2.39 -16.61 12.16

Panel B: Year 2003

S1 22.33 73.65 58.12 107.86 29.08 22.87 48.46 123.41 102.15 197.46

S2 17.75 45.9 47.95 89.19 25.81 22.41 36.12 50.64 84.39 158.59

S3 11.37 24.51 31.32 53.36 19.3 14.91 25.84 17.88 28.19 102.91

V1 22.7 69.18 61.47 109.05 31.88 21.48 50.88 114.84 104.94 208.7

V2 15.75 59.79 45.97 90.76 23.11 23.53 37.53 52.92 80.43 162.46

V3 13 15.08 29.96 50.6 19.21 15.19 22.01 24.17 29.36 87.8

PE1 21.92 66.88 55.82 95.65 31.26 21.73 46.52 75.49 93.05 201.18

PE2 14.99 45.29 46.93 91.3 23.83 21.8 40.62 74.26 66.79 162.14

PE3 13.48 15.5 30.93 48.59 17.24 15.81 20.88 30 37.29 83.71

Panel C: Year 2004

S1 1.06 22.69 11.21 27.38 15.65 3.11 10.47 32.51 22.33 42.12

S2 -1.99 7.85 1.67 10.91 0.88 1.01 2.79 23.44 6.44 16.14

S3 -10.4 6.95 4.79 2.88 -4.11 -2.01 -3.87 14.17 -0.07 15.74

V1 0.06 21.6 11.22 20.97 7.44 7.12 4.31 36.14 22.09 32.14

V2 -6 7.42 0.37 5.24 3.85 -3.27 1.84 19.22 2.68 17.4

V3 -5.38 8.47 6.08 14.96 1.13 -1.74 3.23 14.76 3.93 24.46

PE1 -5.29 23.22 8.17 22.3 6.81 5.38 0.17 22.39 25.17 31.68

PE2 -0.2 6.62 4.59 7.6 0.54 -0.46 4.82 23.52 3.23 22.58

PE3 -6.59 2.76 4.48 8.46 2.93 -5.16 3.51 17.12 -2.84 16.83



Table 6: Net Returns for the Double Sorted Size and Value Portfolios


Panel A: Year2002

S1V1 3.01 32.55 18.56 66.53 34.97 1.88 59.03 58.34 56.37 121.65

S1V2 -0.26 -0.15 13.05 7.54 9.86 -0.73 20.18 25.62 -1.02 65.24

S1V3 -4.84 -14.25 25.17 10.65 14.55 20.76 9.15 6.96 -1.14 57.24

S2V1 -13.07 16.54 2.78 21.13 -0.79 -4.62 13.34 16.78 31.86 46.22

S2V2 -9.91 -12.78 -12.97 -4.64 -4.3 -4.4 3.62 2.23 10.72 35.75

S2V3 -22.2 -12.51 -17.72 -7.47 -15.49 -20.47 -8.38 -4.17 -17.82 -0.02

S3V1 -1.11 3.58 -3.05 4.24 6.33 -3.02 22.33 3.6 25.22 80.04

S3V2 -15.58 2.04 -8.5 6.29 -10.65 -6.46 -5.59 2.41 -12.88 24.63

S3V3 -17.79 -17.79 -13.33 -13.58 -7.96 -18.42 -6.91 -3.19 -22.9 -3.23

S1PE1 -0.43 18.61 15.72 43.4 22.01 1.78 24.8 54.92 36.13 98.14

S1PE2 -4.86 1.01 -0.65 7.11 5.94 3.45 17.81 25.2 15.5 57.49

S1PE3 2.63 -5.27 17.97 17.9 11.29 12.04 14.78 20.88 -3.02 65.1

S2PE1 -6.82 6.22 0.17 10.19 4.64 2.01 13.08 14.11 17.89 57.46

S2PE2 -20.29 -8.64 -13.21 -0.91 -10.54 -12.83 0.22 -4.34 9.42 13.08

S2PE3 -16.33 -3.9 -14.75 -4.06 -13.2 -17.59 -7.79 0.98 -10.12 7.46

S3PE1 -4.83 -5.77 -8.52 -0.76 -3.66 -5.65 7.03 -3.22 5.22 44.52

S3PE2 -15.71 -0.23 -4.67 -4.5 -6.86 -2.95 -3.1 4.45 -14.01 22.1

S3PE3 -18.5 -18.71 -13.22 -13.6 -9.72 -18.97 -7.9 -3.19 -23.84 -3.53

Panel B: Year 2003

S1V1 26.3 81.2 60.81 110.06 32.35 24.77 51.08 153.48 115.06 212.64

S1V2 19.44 82.38 63.76 127.03 23.84 21.61 45.51 90.9 106.52 207.17

S1V3 11.31 12.42 29.18 42.95 28.08 17.3 44.15 70.18 27.35 96.56

S2V1 19.33 72.74 77.22 138.07 25.63 21.17 53.9 86.66 126.46 238.85

S2V2 16.65 48.6 36.07 74.64 25.9 26.07 37.03 41.77 84.75 144.7

S2V3 17.6 20.68 36.11 63.82 25.87 19.58 20.34 29.99 48.96 106.37

S3V1 15.98 18.93 34.33 50.99 41.88 10.12 44.48 27.6 27.91 137.86

S3V2 10.75 49.09 38.82 71.38 19 22.55 29.66 25.8 47.58 136.05

S3V3 10.4 12.05 26.18 43.74 13.02 11.91 18.34 10.57 17.19 73.98

S1PE1 26.35 70.32 54.37 81.1 36.48 21.21 46.51 106.41 93.9 193.89

S1PE2 15.88 60.43 62.27 128.46 30.13 28.67 55.19 138.95 92.42 220

S1PE3 19.58 44.21 33.51 75.18 19.58 7.14 42.52 118.47 75.34 126.85

S2PE1 17.74 90.39 74.05 141.07 20.11 21.6 48.01 79.89 124.84 252.17

S2PE2 19.32 35.31 47.21 76.59 25.87 19.26 38.2 49.42 69.03 139.64

S2PE3 18.1 14.41 26.58 53.85 21.82 27.97 17.95 15.9 58.72 96.72

S3PE1 20.1 29.81 33.93 59.35 37.44 22.77 44.54 18.09 49.24 145.34

S3PE2 7.85 39.3 26.49 63.07 12.77 16.31 24.93 24.05 30.18 117.63

S3PE3 9.17 8.4 32.74 38.41 13.96 11.12 16.73 14.33 14.69 64.59




Panel C: Year 2004

S1V1 -4.22 24.68 14.29 28.98 13.27 5.38 2.83 35.45 30.96 32.88

S1V2 3.35 26.85 8.39 26.02 21.56 0.67 19.43 38.76 17.73 50.26

S1V3 8.43 10.76 9.58 26.38 10.09 2.58 10.84 14.76 11.87 47.48

S2V1 10.45 17.68 6.88 21.37 5.78 13.1 10.28 38.54 20.86 37.16

S2V2 -12.41 -7.3 -8.11 -8.2 -7.83 -8.92 -11.82 13.44 -8.44 -10.52

S2V3 -4.71 10.97 4.65 16.51 3.32 -1.78 7.51 18.12 5.41 18.43

S3V1 -6.42 21.33 11.56 5.65 -1.03 2.07 -1.23 34.11 7.4 23.8

S3V2 -10.43 -0.64 -0.94 -5.32 -4.97 -2.67 -5.16 3.52 -3.79 6.32

S3V3 -12.61 5.09 5.71 8.08 -5.15 -3.78 -4.3 11.7 -1.23 18.96

S1PE1 -8.71 30.67 10.73 38.25 16.75 0.17 -2.65 14.51 40.64 41.57

S1PE2 5.91 8.52 10.41 10.9 9.1 3.25 15.61 33.6 2.33 34

S1PE3 8.12 23.62 11.38 32.83 23.54 4.2 22.57 47.16 17.54 54.19

S2PE1 3.46 19.59 4.72 21.18 5.87 13.66 8.72 30.52 24.92 33.29

S2PE2 0.39 8.64 0.34 9.08 -2.27 -6.27 -1.46 29.53 5.95 13.25

S2PE3 -7.58 -0.94 1.02 4.53 -1.17 -6.12 0.68 13.92 -6.65 5.03

S3PE1 -9.75 18.36 8.58 5.64 -3.35 3.36 -4.76 23.47 8.23 19.19

S3PE2 -9.31 2.06 0.42 1.61 -8.81 -0.22 -4.42 3.8 1.94 15.31

S3PE3 -13.2 -4.1 4.66 -0.01 -3.36 -9 -3.37 4.89 -9.35 10.18

NOTES

1. For details refer to Sidney S. Alexander (1961); Paul Cootnerand F.E. James Jr. (1962), Eugene F. Fama and Blume(1966), Robert A. Levy (1967), F.E. James Jr. (1968) SatishN. Neftci and Andrew Policano (1984), Steven M .Dawson(1985), William Brock, Josef Lakonishok and Le Baron(1992), Lawrence Blume, David Easley and Maureen,O’Hara (1994), Jonathan Batten and Craig Ellis (1996),Ryan Sullivan, Allan Timmermann, Halbert White (1999)and Andrew W. Harry Mamaysky and Jiang Wang (2000),Bondt and Thaler (1985), Bondt and Thaler (1987),Narasimhan Jegadeesh (1990), Andrew W. Lo and A CraigMac Kinlay (1990), Jagadeesh and Titman (1993),Lakonishok, Shleifer and Vishny (1994), Chan, Jegadeeshand Lakonishok (1996), Porta, Lakonishok, Shleifer andVishny (1997), Datar, Naik and Radcliffe (1998), Barberis,Shleifer and Vishny (1998), Daniel, Hirshleifer andSubrahmanyam (1998), Jennifer Conrad and Gautam Kaul(1998), K. Geert Rouwenhorst (1998), Hong and Stein(1999), Tobias J Maskowitz and Mark Grinblatt (1999), Leeand Swaminathan (2000), Grundy and Martin (2001),Jegadeesh and Titman (2001), Chordia and Shivakumar(2002), Timothy C Johnson (2002), Jegadeesh and Titman(2002), Thomas J. George and Chuan-yang Hwang (2004).

2. For details refer to Sehgal Sanjay and Garhyan Anurag(2002) and Subrata Kumar Mitra (2002).

3. The BSE-100 is a broad index of 100 companies. The criteriafor selection of companies in BSE-100 had been marketactivity and due representation to various industry groups.BSE authorities have classified the securities traded on theirexchange into various groups such as group A, group B1,Group B2, and group Z. Group A consists of high turnoverand large market capitalization stocks with a proven profit

record. Group B1 includes stocks of quality companies withequity above Rs 3 crore, with high growth potential andtrading frequency. Group B2 consists of low trading volumestocks with equity below Rs. 3 crore. Group Z includes thestocks of those companies that do not meet the rules,regulations, and stipulations lay down by the exchange.

4. BE/ME effect has been documented for stocks trading onTokyo stock exchange (Aggarwal, Rao and Hikaki (1989),Capaul, Rowley and Sharpe(1993), Chan Hamao andLakonishok (1991)), the London Stock exchange (CapualRowley and Sharpe (1993), Strong and Xu (1995)), and alsoon stock exchanges in France, Germany and Switzerland(Capual Rowley and Sharpe (1993)).

5. The size and BE/ME effect for the Indian capital market isdiscussed in Sehgal and Muneesh Kumar (2004) and Sehgaland Tripathi (2004).

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Sanjay Sehgal ([email protected]) is Professor of Finance at the Department of Financial Studies, University of Delhi, SouthCampus. He has a teaching experience of about 18 years in the field of investment management and corporate finance. He has writtena research book and has two major research projects on Mutual Fund and Asset Pricing funded by Government of India. He has severalnational and internationally published papers to his credit.

Meenakshi Gupta ([email protected]) teaches at Sri Aurobindo College, University of Delhi. She is also associated with post-graduate teaching at the University’s department of Commerce. She has recently submitted her doctoral dissertation .

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