Momentum Effect in Indian Stock Market: A Sectoral Study

Abstract

Jegadeesh and Titman (1993) found that when stocks are ranked on the basis of their past returns, then past winners outperform the past loser in the medium-term period. They suggested a zero-investment trading strategy termed momentum trading strategy, consisting of taking long position in the winner portfolio and short position in the loser portfolio, to generate abnormal profit. In this article, we demonstrated that the momentum trading strategy is robust in Indian stock market over the period of 2000–2013. We evaluated a range of trading strategies over alternative backward-looking ranking periods and forward-looking holding horizons in some prominent industries in India and found strong presence of momentum returns in various industries. The same conclusion we have drawn about the market as a whole that provides evidence against weak form of market efficiency.

Keywords

Momentum trading stock market efficiency Jegadeesh and Titman momentum strategy

Introduction

Over the years, numerous strategies have been developed to predict the returns of stocks that could lead the abnormal profits. Predicting returns of assets based on their past returns has gained importance in recent years. Broadly, there are two trading strategies based on prior returns: (i) momentum strategy where returns exhibit continuation in short run and (ii) contrarian strategy where returns have a tendency to revert to fundamental in long run.

Momentum-based investment strategy calls for ‘buying’ today’s winners and ‘selling’ today’s losers with an assumption that past pattern would continue in the future, resulting in past winners generating superior returns compared to past losers. Several behavioural theories have evolved to explain the reasons of the momentum phenomenon. Some of them are reported as follows: initially underreaction and eventually overreaction to firm specific news (Chan et al., 1996; Barberis et al., 1998; Hong and Stein, 1999), low analyst coverage (Hong et al., 2000), post-holding period reversal (Jegadeesh and Titman, 2001), missing risk factors related to valuation (Fama and French, 1996; Antoniou et al., 2007), cross sectional variation in mean returns of individual securities (Conrad and Kaul, 1998), investors’ overconfidence about their own abilities (Daniel et al., 1998), time-varying expected return (Back et al., 1999; Chodia and Shiv Kumar, 2000), time series predictability in stock market (Chan et al., 2000), excess covariance (Lewellen, 2002), non-parametric adjustment of risk (Ahn et al., 2003), disposition effect (Shefrin and Statman, 1985; Grinblatt and Han, 2005). On the contrary, contrarian investment strategy calls for adopting an opposite strategy to buy past losers and sell past winners. This is based on the assumption that market tends to overreact in short run and revert to fundamental in long run. De Bondt and Thaler (1985, 1987) were the first to document contrarian strategy and found individuals overreacting to new, first and subsequent price corrections, leading to return reversal thereafter.

Existence of abnormal profits by following either of the strategies violate the weak form of the market efficiency that implies the absence of abnormal profit for the same risk level. Hence, in the absence of the weak form of market efficiency, the asset pricing anomalies help managers develop trading strategies that provide extra returns. Thus, fund managers chase them to gain from these trading strategies. This article examined the abnormal returns for portfolios on the basis of long-term past returns in Indian industrial sectors.

The article is organized as follows. In Section 2, we review the existing literature. Section 3 contains description of data and the methodology employed along with the empirical tests carried out. Section 4 summarizes the finding of our empirical research and Section 5 presents concluding comments.

Review of Literature

In recent times, a significant body of literature has evolved that examined the significance of the momentum effect in developed as well as developing markets. The theoretical studies differ in their explanation of what might cause creation of momentum, while the empirical studies have focused on testing the effect that momentum has on financial markets. The basic objective of the underlying article is to verify the existence of the momentum trading strategy in Indian stock market. Some similar studies carried by the researcher on different stock markets have been reviewed here.

Levy (1967) claimed the success of trading strategy of buying stock with current prices significantly higher than last 27 weeks’ average price generating significantly abnormal return in US market. Jensen and Bennington (1970) proposed 68 trading strategies based on their past performance and stated that Levi trading rule based on the relative strength was one out of them. Jegadeesh and Titman (1993) documented the momentum trading strategy by evaluating 16 trading strategies based on returns over the past 1–4 quarters and holding them for subsequent 1–4 quarters. They found that these trading strategies that buy past winners and sell past losers realized significant abnormal returns over a period from 1965 to 1989 for US markets. Chan et al. (1996) report comparable profits from momentum strategies based on all stocks listed on NYSE, AMEX and NASDAQ for the period from January 1977 to 1993. Similarly, Foerster et al. (1994), Kan and Kirikos (1996), Cleary and Inglis (1998) found presence of short-run momentum effect in Canadian market. Rouwenhorst (1998) reported significant momentum effects in 11 out of 12 European countries. Conrad and Kaul (1998), Lee and Swaminathan (2000), Chodia and Shivakumar (2002) found significant momentum profits on the NYSE over holding periods ranging from 3 to 12 months.

Chui et al. (2000) reported significant momentum effects in seven out of eight Asian countries (Japan was the exception). Jegadeesh and Titman (2001) performed an out-of-sample test of their earlier findings and showed that momentum strategies continue to be profitable and that past winners outperform past losers by approximately the same magnitude over the time period from 1990 to 1998 as in the earlier period in United States. Nijman et al. (2002) found momentum profits in 18 European countries except for Sweden and Austria. Hurn and Pavlov (2003), Demir et al. (2004) and Stork (2008) found momentum profits in Australia, while Gunasekarage and Kot (2007), Stork (2008), Trethewey and Crack (2010) reported same in New Zealand. Shen et al. (2005) examined the performance of momentum strategies in 18 developed capital markets using country indices instead of individual security returns and found momentum profits for medium-time horizons. Avižinis and Pajuste (2007) studied a sample of stock market returns for seven countries—the Baltic States, Poland, Slovenia, Hungary and Croatia— for time period from January 2002 to December 2006 using the methodology of Jegadeesh and Titman (1993), and most pronounced momentum effect was found for time periods of 6 months of portfolio formation and 6 months for subsequent abnormal returns with average monthly return of 3 per cent for winner-minus-loser portfolio. Leippold and Lohre (2008) reported significant momentum effects in the United States and 14 out of 16 European countries, only Ireland and Austria were exceptions. Wang et al. (2010) used the sample with 539 individual stocks in Taiwan stock market from July 2002 to December 2007 for comparing the performances among these portfolios of institutional net buys/sells by using Jegadeesh and Titman (JT) momentum strategy and George and Hwang (GH) momentum strategy and found short-term momentum with no waiting period in Taiwan’s market. Hu and Chen (2011) examined the performance of momentum investment strategies over the national stock market indices of 48 countries during 1999–2007. Their findings indicated that momentum investment methods exhibit significant continuation of returns over the medium horizon. Pathirawasam et al. (2011) provided empirical evidence for the existence of a momentum anomaly on the Colombo Stock Exchange during the period January 1992–December 2007. Same conclusions have been drawn for the Egyptian market by Ismail (2012).

A few studies have been carried in Indian context also, for instance, Sehgal and Balakrishnan (2004) evaluated momentum and contrarian effect in stock returns for the Indian equity market and found the presence of momentum returns in India. Joshipura (2011) examined the profitability of the momentum strategy in the Indian market and found a significant momentum return for the post-formation period ranging from 3 to 12 months. Saravanakumar (2011) tried to find out the stock price momentum after dividend announcement and found no indication of the same. Joshipura and Mankar (2012) found the presence of momentum profit in short run. Sehgal et al. (2012) attempted to find various pricing anomalies including momentum effect in Indian stock market and found the presence of short-term momentum.

The motivation of this research is fourfold. First, most of the previous studies of momentum strategies concentrate on developed markets, which is clear from literature review also. The second motivation of this article resides in conflicting results of momentum strategies for emerging capital markets reported by previous authors. The third motivation is to update the existing literature up to recent time with a big and diverse sample size. Fourthly, majority of the studies have been carried on the overall markets indices; it may possible that degree of momentum varies industries to industries, so it is a motivation for this article to find out the momentum in specific sectors also. This article contributes to the existing literature on the subject by examining momentum return during extreme periods in the Indian Stock Market. In particular, we extend the previous studies by providing the evidence using the most recent data ranging from the period May 2000 to April 2013 and finding the momentum effect in some prominent industries.

Data and Methodology

Sample Selection

The objective of the article is to test the presence of momentum profit in some of the prominent sectors of Indian economy. The sample selected for the article consists of the stocks listed on the National Stock Exchange of India, as it is representative stock exchange of India. In order to find out the moment in sectorial indices, banking, information and technology, pharmaceuticals and automobile industries have been selected, as these are four major sectors of Indian economy in terms of their contribution in gross domestic production. All the companies listed at NSE under these four sectors have been taken earlier, but to eliminate the size effect, only large cap companies belonging to these sectors have been considered as final sample. 40 companies in banking sector, 31 companies in pharmaceutical, 20 companies in information and technology and 20 companies in automobile sector qualified the final selection criteria.

To determine the impact of portfolio diversification, we have applied the same methodology on CNX 500 Index too. The CNX 500 is India’s first broad-based benchmark of the Indian capital market. It represents about 95.87 per cent of the free-float market capitalization of the stocks listed on NSE as on March 28, 2013, and total traded value, for the last six months ending on March 2013, of all index constituents is approximately 94.60 per cent of the traded value of all stocks on NSE. It represents 69 industries. The sample selection also seems logical, as NSE 500 index companies are heavily traded and there is less liquidity risk associated with them, which would give reasonably large sample having high liquidity and eliminates stock with small size. This is important to overcome the limitation of results being driven and distorted by small and illiquid stocks that face problems of high risk and bid-ask bounce, respectively.

Data Collection

The monthly adjusted closing price is extracted from Prowess database maintained by the Centre for Monitoring Indian Economy for a period of 13 years, ranging from May 2000 to April 2013.This period covers the strong bull run between 2004 and January 2008, the global financial meltdown of 2008–2009 and then the recovery period thereafter. Thus, this period signifies all the major ups and downs in the Indian equity markets. The sample shares’ adjusted closing price series are converted into logarithmic return series so that these series can be used for further estimation. All stocks with non-missing returns in formation and testing period are considered for analysis.

Methodology

This article used the JT methodology after incorporating the modification suggested by Mankar and Joshipura (2012). We have used the actual returns recommended by Mankar and Joshipura for the analysis on the place of abnormal return. It helps to maintain the robustness of analysis and make analysis easier. We have used ‘J’ months for formation of winner and loser portfolios and ‘K’ months as test period. Such a strategy called ‘J’ × ‘K’ strategy. This article used 36 strategies using various combinations of ‘J’ and ‘K’ using value 1, 2, 3, 6, 9 and 12 months. The estimation procedure for all 36 strategies is similar. A brief explanation of the same is presented here using an example of 6 months formation period (J = 6) and 6 months test period (K = 6).

In case of a 6 × 6 strategy, the analysis is performed using first 6 months data for portfolio formation and next 6 month data for portfolio testing. As our article used 156 months data (from May 2000 to April 2013), there are 144 winners’ (W) and 144 losers’ (L) portfolios each for the testing period. In order to form a winner and loser portfolio, 6 months cumulative return (CR) has been calculated, starting from May 2000 to October 2000, using following formula:

{CR}_{i} \ln (P_{1} {/ P}_{0}) \ln (P_{2} {/ P}_{1}) \ln (P_{3} {/ P}_{2}) \ln (P_{4} {/ P}_{3}) \ln (P_{5} {/ P}_{4}) \ln (P_{6} {/ P}_{5}),

where P₀, P₁, P₂, P₃, P₄, P₅ and P₆ are adjusted, monthly closing prices from April, May, June, July, August, September and October, respectively.

At the end of October, stocks in the samples are ranked using the past six month’s CRs. In the case of the sector-specific study, winner portfolio consists of top five securities based on CR, while loser portfolio consists of bottom five securities that performed worst over the last 6 month in terms of CRs. In the case of CNX 500, top 50 and bottom 50 companies are considered for forming the winner and loser portfolio. The same process is repeated on monthly basis and 144 iterations have been done for the sample period. Use of the overlapping period for the study has an advantage of improving the power of the test.

In the second step, monthly basis average return of the whole winner and loser portfolio is calculated using the following equation:

{AR}_{W, 7} Average {(P}_{i, 7} {/ P}_{i, 6}) {(P}_{j, 7} {/ P}_{j, 6}) {(P}_{k, 7} {/ P}_{k, 6}) {(P}_{l, 7} {/ P}_{l, 6}) {(P}_{m, 7} {/ P}_{m, 6}),

where AR_W,7 is the average return of the winner portfolio for November, P_i_,7, P_i_,6, P_j_,7, P_j_,6, P_k_,7, P_k_,6, P_l_,7, P_l_,6, P_m_,7, P_m_,6 are the adjusted closing price of top five performer stocks (basis of 6 month historical return) for the month November and December, respectively. Likewise, average returns have been calculated for the winner and loser portfolios separately for each of the 144 iterations.

In the third step, monthly average return of the winner and loser portfolios have been used to calculate cumulative average returns (CARs) in each month in the following way:

{CAR}_{W, 12} {AR}_{w, 7} {AR}_{w, 8} {AR}_{w, 9} {AR}_{w, 10} {AR}_{w, 11} {AR}_{w, 12},

where CAR_W,12 is cumulative average return of winner portfolio for month of April, AR_W,7, AR_W,8, AR_W,9, AR_W,10, AR_W,11, AR_W,12, average return of winner portfolio for November, December, January, February, March and April. In the same way, CAR has been calculated for all 144 winner and loser portfolios.

In the final step, mean cumulative average returns (MCARs) have been calculated by averaging of 144 portfolios’ CAR using following equation:

{MCAR}_{6 6} {(C A R}_{1} {CAR}_{2} {CAR}_{144}) / 144,

where MCAR_{6 × 6}is mean average CR for 6 × 6 months strategy and CAR₁is cumulative average return of first winner portfolio.

MCAR_W (MCAR_L) indicates how much cumulated returns stock in the winner (loser) portfolio earn on an average during 6 months of test period. If the market follows weak form of efficiency, then MCAR_W minus MCAR_L must be equal to zero, suggesting absence of abnormal return. Momentum hypothesis implies that MCAR_W – MCAR_L is positive. In the case of existence of the momentum returns, winner portfolio always beat the loser portfolio, irrespective of the direction of the market movement, thereby generating absolute positive abnormal returns.

Empirical Results

As stated earlier, J and K months investment strategy suggested by Jegadeesh and Titman (1993) has been adopted with certain modifications as suggested by Mankar and Joshipura (2012). Momentum profits are detected for trading strategies that confine both their portfolio formation and portfolio holding windows to 12 months or less.

Table 1 presents the MCAR generated by all 36 strategies in auto sector. The table indicates that keeping the holding period constant as the formation period increases from 1 month to 12 months, MCARs are generated by buying winners; selling losers and buying winners and selling losers simultaneously (momentum portfolio) also increases in case of all 36 strategies. As we increase the holding period by keeping the formation period constant, winner portfolios generated maximum MCAR in the case of 12 month holding period, while MCAR of loser portfolio turned positive after 6 month. The interesting point is that as we increase the formation period for loser portfolio, the number of month for which the portfolio generates negative MCAR also increases. For 1 month formation period, the portfolio started generating positive MCAR 7th month onwards, while in the case of 12 month holding period, portfolio starts generating positive MACR only in the 12th month. This indicates a portfolio that has been a loser portfolio since long period, takes long time to revert to its fundamental value. In all, 12 months formation and 12 months holding period strategy generated the maximum momentum return of 5.589 per cent, and during all 36 strategies, buying winners portfolio always generated a higher return than the selling loser portfolio. Tables 2 and 3 present the momentum returns in banking and pharmaceutical sectors, respectively. The MCAR patterns for winner, loser and momentum portfolios in these industries are more or less similar to auto sector. Again the maximum profit is generated by 12 × 12 strategy.

Table 4 gives the results pertaining to information and technology sector that shows some different pattern of MCAR. Except for the 12 months formation period, for all other formation period, 9 months holding of momentum portfolio gave the maximum return. In the 12 month testing period, the MCAR of loser portfolio remained negative. Table 5 provides the MACR results for CNX 500 Index. The results of market index are similar to auto, banking and pharma sector, but over here, similar to the IT sector, loser portfolio has negative MCAR throughout the 12-month time frame.

In order to check the statistical significance of the momentum return, t – test has been applied on the MCARs’ data of 12 × 12 trading strategies, and momentum return is found highly significant across four sectors and market index.

It may be seen from the Figures 1–5 that the MACRs of winner and loser portfolios increase throughout the testing period of 12 months in all five cases. It may also be seen that the MCAR of winner portfolio is increasing at a faster pace than the MCAR of loser portfolio, resulting in the winner portfolio divergence from the loser portfolios till the last month. Winner portfolios outperformed the loser portfolios throughout in all 36 investment strategies and in all sectors as well as in the stock market too. It proves the existence of momentum profit in Indian stock market. If we review the momentum profit vis-à-vis to sectors and market index, then market index yielded higher momentum profit as compared to the different sectors. Amongst the four sectors, pharma sector yielded the maximum momentum returns, followed by banking and auto sector, while IT sector showed the minimum momentum return and the same can be observed from Figure 6.

Table 1.

MCARS Generated by Various Trading Strategy in Auto Sector

	Formation Period
	1 Month			2 Months			3 Months			6 Months			9 Months			12 Months
	W	L	M	W	L	M	W	L	M	W	L	M	W	L	M	W	L	M
Holding Period	1 Month	0.135	–0.097	0.232	0.200	–0.131	0.331	0.255	–0.152	0.407	0.406	–0.192	0.598	0.548	–0.207	0.755	0.675	–0.212	0.887
	2 Months	0.135	–0.096	0.232	0.201	–0.130	0.331	0.255	–0.152	0.407	0.409	–0.191	0.599	0.550	–0.207	0.756	0.676	–0.212	0.888
		0.146	–0.077	0.223	0.310	–0.173	0.483	0.446	–0.237	0.683	0.770	–0.323	1.093	1.055	–0.366	1.422	1.315	–0.380	1.695
	3 Months	0.135	–0.097	0.232	0.200	–0.131	0.332	0.256	–0.153	0.408	0.410	–0.191	0.601	0.551	–0.207	0.758	0.678	–0.212	0.890
		0.147	–0.076	0.223	0.310	–0.173	0.484	0.446	–0.239	0.685	0.774	–0.323	1.097	1.058	–0.367	1.424	1.318	–0.379	1.697
		0.156	–0.057	0.213	0.342	–0.141	0.483	0.565	–0.262	0.827	1.093	–0.404	1.497	1.525	–0.479	2.004	1.918	–0.508	2.427
	6 Months	0.136	–0.097	0.233	0.203	–0.131	0.334	0.260	–0.152	0.412	0.413	–0.191	0.605	0.554	–0.207	0.760	0.684	–0.208	0.892
		0.148	–0.076	0.224	0.314	–0.174	0.488	0.453	–0.238	0.692	0.782	–0.324	1.106	1.063	–0.366	1.429	1.329	–0.373	1.702
		0.158	–0.058	0.216	0.347	–0.142	0.489	0.575	–0.263	0.837	1.104	–0.406	1.510	1.532	–0.478	2.010	1.933	–0.499	2.432
		0.189	–0.051	0.240	0.391	–0.122	0.513	0.628	–0.227	0.855	1.378	–0.441	1.819	1.963	–0.551	2.514	2.501	–0.594	3.094
		0.200	–0.026	0.226	0.427	–0.087	0.514	0.680	–0.183	0.863	1.605	–0.424	2.029	2.354	–0.580	2.934	3.032	–0.647	3.679
		0.219	–0.011	0.230	0.462	–0.045	0.507	0.741	–0.135	0.876	1.786	–0.357	2.143	2.702	–0.567	3.269	3.524	–0.661	4.185
	9 Months	0.137	–0.098	0.234	0.204	–0.132	0.336	0.260	–0.154	0.414	0.413	–0.192	0.606	0.557	–0.205	0.762	0.683	–0.209	0.892
		0.149	–0.077	0.226	0.316	–0.175	0.491	0.455	–0.240	0.695	0.782	–0.325	1.107	1.068	–0.362	1.431	1.328	–0.373	1.701
		0.160	–0.057	0.217	0.350	–0.142	0.492	0.577	–0.264	0.841	1.105	–0.406	1.511	1.541	–0.471	2.012	1.936	–0.495	2.431
		0.192	–0.050	0.242	0.396	–0.120	0.516	0.632	–0.226	0.858	1.381	–0.440	1.820	1.973	–0.541	2.515	2.508	–0.584	3.092
		0.202	–0.025	0.228	0.432	–0.085	0.517	0.685	–0.180	0.865	1.609	–0.420	2.029	2.365	–0.568	2.933	3.044	–0.632	3.676
		0.221	–0.010	0.231	0.467	–0.043	0.510	0.746	–0.132	0.878	1.792	–0.350	2.142	2.714	–0.552	3.266	3.539	–0.641	4.181
		0.247	0.005	0.242	0.513	–0.009	0.522	0.810	–0.079	0.889	1.923	–0.235	2.158	3.018	–0.493	3.511	4.000	–0.611	4.611
		0.265	0.022	0.244	0.555	0.025	0.530	0.874	–0.022	0.895	2.052	–0.123	2.176	3.282	–0.391	3.673	4.422	–0.540	4.962
		0.283	0.042	0.241	0.589	0.063	0.525	0.932	0.037	0.896	2.178	–0.012	2.190	3.504	–0.251	3.754	4.805	–0.427	5.232
	12 Months	0.137	–0.098	0.235	0.204	–0.133	0.337	0.261	–0.154	0.415	0.417	–0.191	0.608	0.557	–0.206	0.762	0.685	–0.208	0.893
		0.149	–0.077	0.225	0.315	–0.175	0.490	0.454	–0.241	0.696	0.788	–0.323	1.111	1.068	–0.363	1.431	1.332	–0.370	1.702
		0.159	–0.056	0.216	0.349	–0.142	0.490	0.576	–0.265	0.840	1.113	–0.402	1.515	1.543	–0.469	2.012	1.940	–0.492	2.432
		0.190	–0.050	0.240	0.394	–0.120	0.513	0.628	–0.226	0.855	1.388	–0.435	1.824	1.978	–0.536	2.514	2.512	–0.580	3.092
		0.201	–0.024	0.225	0.430	–0.083	0.513	0.681	–0.180	0.861	1.616	–0.415	2.031	2.372	–0.558	2.931	3.048	–0.627	3.675
		0.221	–0.007	0.228	0.466	–0.039	0.505	0.743	–0.129	0.872	1.799	–0.342	2.142	2.724	–0.538	3.262	3.548	–0.630	4.178
		0.248	0.008	0.239	0.513	–0.003	0.516	0.808	–0.073	0.881	1.932	–0.223	2.155	3.031	–0.474	3.504	4.015	–0.592	4.606
		0.265	0.025	0.241	0.555	0.031	0.523	0.873	–0.013	0.886	2.063	–0.108	2.171	3.296	–0.367	3.662	4.444	–0.513	4.957
		0.283	0.045	0.237	0.589	0.071	0.518	0.932	0.046	0.886	2.190	0.007	2.183	3.518	–0.222	3.740	4.833	–0.394	5.227
		0.300	0.062	0.238	0.630	0.112	0.519	0.983	0.104	0.878	2.311	0.125	2.186	3.699	–0.034	3.733	5.185	–0.238	5.423
		0.320	0.082	0.239	0.671	0.142	0.530	1.031	0.151	0.879	2.426	0.237	2.189	3.884	0.158	3.726	5.500	–0.043	5.542
		0.340	0.093	0.247	0.705	0.166	0.539	1.080	0.202	0.878	2.540	0.347	2.194	4.071	0.351	3.720	5.780	0.191	5.589

Source: Produced by the authors. Raw data has been collected from CMIE Database.

Table 2.

MCARS Generated by Various Trading Strategy in Banking Sector

	Formation Period
	1 Month			2 Months			3 Months			6 Months			9 Months			12 Months
	W	L	M	W	L	M	W	L	M	W	L	M	W	L	M	W	L	M
Holding Period	1 Month	0.157	–0.109	0.266	0.223	–0.142	0.365	0.276	–0.163	0.439	0.419	–0.194	0.613	0.554	–0.203	0.757	0.677	–0.204	0.881
	2 Months	0.157	–0.108	0.265	0.224	–0.140	0.364	0.278	–0.162	0.440	0.421	–0.192	0.613	0.556	–0.201	0.757	0.680	–0.202	0.882
		0.166	–0.090	0.256	0.338	–0.183	0.521	0.468	–0.241	0.709	0.775	–0.318	1.093	1.058	–0.342	1.400	1.311	–0.350	1.661
	3 Months	0.158	–0.108	0.266	0.224	–0.141	0.365	0.278	–0.162	0.440	0.422	–0.191	0.613	0.556	–0.202	0.758	0.681	–0.202	0.883
		0.168	–0.089	0.257	0.339	–0.182	0.521	0.469	–0.240	0.709	0.779	–0.315	1.094	1.060	–0.341	1.401	1.314	–0.348	1.662
		0.175	–0.067	0.242	0.359	–0.135	0.494	0.585	–0.243	0.828	1.078	–0.385	1.463	1.522	–0.435	1.957	1.903	–0.458	2.361
	6 Months	0.160	–0.108	0.268	0.228	–0.139	0.367	0.283	–0.160	0.443	0.425	–0.192	0.617	0.560	–0.203	0.763	0.688	–0.199	0.887
		0.169	–0.089	0.258	0.344	–0.180	0.524	0.477	–0.235	0.712	0.785	–0.316	1.101	1.067	–0.342	1.409	1.326	–0.343	1.669
		0.178	–0.065	0.243	0.364	–0.131	0.495	0.593	–0.237	0.830	1.089	–0.383	1.472	1.531	–0.435	1.966	1.918	–0.450	2.368
		0.195	–0.045	0.240	0.397	–0.081	0.478	0.636	–0.171	0.807	1.344	–0.403	1.747	1.952	–0.490	2.442	2.470	–0.527	2.997
		0.208	–0.026	0.234	0.428	–0.042	0.470	0.692	–0.110	0.802	1.553	–0.379	1.932	2.330	–0.509	2.839	2.987	–0.574	3.561
		0.222	–0.010	0.232	0.457	–0.008	0.465	0.745	–0.057	0.802	1.714	–0.315	2.029	2.669	–0.501	3.170	3.474	–0.592	4.066
	9 Months	0.160	–0.109	0.269	0.228	–0.142	0.370	0.281	–0.164	0.445	0.425	–0.196	0.621	0.563	–0.204	0.767	0.689	–0.203	0.892
		0.170	–0.090	0.260	0.345	–0.183	0.528	0.476	–0.241	0.717	0.785	–0.323	1.108	1.072	–0.344	1.416	1.330	–0.349	1.679
		0.180	–0.065	0.245	0.367	–0.133	0.500	0.595	–0.242	0.837	1.089	–0.391	1.480	1.539	–0.437	1.976	1.928	–0.455	2.383
		0.198	–0.044	0.242	0.402	–0.081	0.483	0.641	–0.174	0.815	1.345	–0.411	1.756	1.962	–0.491	2.453	2.486	–0.529	3.015
		0.212	–0.024	0.236	0.434	–0.040	0.474	0.701	–0.111	0.812	1.557	–0.386	1.943	2.342	–0.509	2.851	3.007	–0.573	3.580
		0.226	–0.007	0.233	0.464	–0.005	0.469	0.756	–0.057	0.813	1.723	–0.320	2.043	2.681	–0.498	3.179	3.499	–0.588	4.087
		0.240	0.007	0.233	0.497	0.031	0.466	0.806	–0.006	0.812	1.834	–0.212	2.046	2.977	–0.462	3.439	3.956	–0.572	4.528
		0.256	0.019	0.237	0.529	0.056	0.473	0.851	0.038	0.813	1.947	–0.122	2.069	3.234	–0.397	3.631	4.380	–0.523	4.903
		0.277	0.030	0.247	0.569	0.079	0.490	0.905	0.079	0.826	2.062	–0.046	2.108	3.455	–0.297	3.752	4.772	–0.445	5.217
	12 Months	0.160	–0.110	0.270	0.229	–0.143	0.372	0.283	–0.165	0.448	0.430	–0.194	0.624	0.566	–0.205	0.771	0.697	–0.200	0.897
		0.169	–0.092	0.261	0.345	–0.186	0.531	0.477	–0.245	0.722	0.793	–0.320	1.113	1.078	–0.346	1.424	1.344	–0.346	1.690
		0.178	–0.068	0.246	0.365	–0.137	0.502	0.593	–0.248	0.841	1.097	–0.389	1.486	1.549	–0.437	1.986	1.944	–0.455	2.399
		0.195	–0.048	0.243	0.399	–0.086	0.485	0.636	–0.183	0.819	1.352	–0.411	1.763	1.975	–0.490	2.465	2.502	–0.533	3.035
		0.210	–0.027	0.237	0.433	–0.045	0.478	0.694	–0.121	0.815	1.564	–0.388	1.952	2.356	–0.508	2.864	3.026	–0.578	3.604
		0.226	–0.009	0.235	0.466	–0.009	0.475	0.751	–0.066	0.817	1.729	–0.321	2.050	2.699	–0.496	3.195	3.522	–0.589	4.111
		0.240	0.007	0.233	0.501	0.030	0.471	0.805	–0.011	0.816	1.841	–0.212	2.053	2.997	–0.459	3.456	3.983	–0.568	4.551
		0.256	0.020	0.236	0.533	0.057	0.476	0.851	0.037	0.814	1.955	–0.117	2.072	3.256	–0.391	3.647	4.409	–0.514	4.923
		0.278	0.031	0.247	0.574	0.080	0.494	0.906	0.082	0.824	2.073	–0.035	2.108	3.477	–0.288	3.765	4.803	–0.430	5.233
		0.297	0.052	0.245	0.620	0.110	0.510	0.963	0.125	0.838	2.190	0.047	2.143	3.653	–0.137	3.790	5.164	–0.309	5.473
		0.315	0.065	0.250	0.662	0.141	0.521	1.022	0.167	0.855	2.305	0.128	2.177	3.837	0.005	3.832	5.494	–0.154	5.648
		0.328	0.081	0.247	0.699	0.170	0.529	1.075	0.206	0.869	2.418	0.209	2.209	4.027	0.143	3.884	5.785	0.036	5.749

Source: Produced by the authors. Raw data has been collected from CMIE Database.

Table 3.

MCARS Generated by Various Trading Strategy in Pharma Sector

	Formation Period
	1 Month			2 Months			3 Months			6 Months			9 Months			12 Months
	W	L	M	W	L	M	W	L	M	W	L	M	W	L	M	W	L	M
Holding Period	1 Month	0.147	–0.113	0.260	0.212	–0.158	0.370	0.268	–0.186	0.454	0.415	–0.247	0.662	0.542	–0.283	0.825	0.665	–0.304	0.969
	2 Months	0.147	–0.113	0.260	0.212	–0.157	0.369	0.268	–0.185	0.453	0.415	–0.246	0.661	0.543	–0.282	0.825	0.665	–0.303	0.968
		0.161	–0.105	0.266	0.329	–0.223	0.552	0.464	–0.296	0.760	0.781	–0.437	1.218	1.049	–0.521	1.570	1.300	–0.567	1.867
	3 Months	0.147	–0.113	0.260	0.211	–0.157	0.368	0.268	–0.185	0.453	0.414	–0.245	0.659	0.542	–0.282	0.824	0.664	–0.304	0.968
		0.161	–0.104	0.265	0.329	–0.222	0.551	0.463	–0.296	0.759	0.781	–0.434	1.215	1.049	–0.519	1.568	1.299	–0.567	1.866
		0.176	–0.093	0.269	0.361	–0.206	0.567	0.585	–0.334	0.919	1.097	–0.571	1.668	1.518	–0.713	2.231	1.902	–0.789	2.691
	6 Months	0.147	–0.112	0.259	0.212	–0.156	0.368	0.268	–0.183	0.451	0.415	–0.244	0.659	0.542	–0.282	0.824	0.662	–0.303	0.965
		0.160	–0.103	0.263	0.329	–0.221	0.550	0.463	–0.292	0.755	0.782	–0.431	1.213	1.049	–0.519	1.568	1.295	–0.565	1.860
		0.175	–0.092	0.267	0.361	–0.204	0.565	0.584	–0.329	0.913	1.099	–0.564	1.663	1.519	–0.710	2.229	1.897	–0.788	2.685
		0.193	–0.086	0.279	0.398	–0.189	0.587	0.637	–0.299	0.936	1.369	–0.643	2.012	1.951	–0.855	2.806	2.468	–0.963	3.431
		0.205	–0.071	0.276	0.432	–0.163	0.595	0.689	–0.265	0.954	1.590	–0.668	2.258	2.345	–0.953	3.298	3.000	–1.093	4.093
		0.223	–0.058	0.281	0.466	–0.133	0.599	0.746	–0.224	0.970	1.761	–0.647	2.408	2.692	–1.009	3.701	3.499	–1.182	4.681
	9 Months	0.147	–0.113	0.260	0.212	–0.157	0.369	0.268	–0.185	0.453	0.414	–0.247	0.661	0.540	–0.284	0.824	0.655	–0.309	0.964
		0.160	–0.104	0.264	0.329	–0.222	0.551	0.464	–0.294	0.758	0.781	–0.436	1.217	1.046	–0.522	1.568	1.282	–0.576	1.858
		0.175	–0.093	0.268	0.361	–0.205	0.566	0.587	–0.329	0.916	1.099	–0.569	1.668	1.515	–0.713	2.228	1.880	–0.799	2.679
		0.194	–0.086	0.280	0.400	–0.188	0.588	0.642	–0.297	0.939	1.371	–0.646	2.017	1.947	–0.858	2.805	2.448	–0.975	3.423
		0.206	–0.071	0.277	0.433	–0.161	0.594	0.695	–0.261	0.956	1.594	–0.668	2.262	2.341	–0.955	3.296	2.978	–1.104	4.082
		0.223	–0.058	0.281	0.467	–0.130	0.597	0.753	–0.217	0.970	1.768	–0.643	2.411	2.689	–1.007	3.696	3.476	–1.193	4.669
		0.246	–0.045	0.291	0.506	–0.102	0.608	0.807	–0.171	0.978	1.890	–0.567	2.457	2.997	–1.008	4.005	3.938	–1.239	5.177
		0.258	–0.029	0.287	0.541	–0.073	0.614	0.865	–0.123	0.988	2.010	–0.486	2.496	3.262	–0.956	4.218	4.357	–1.239	5.596
		0.276	–0.013	0.289	0.573	–0.045	0.618	0.920	–0.080	1.000	2.129	–0.406	2.535	3.485	–0.864	4.349	4.737	–1.195	5.932
	12 Months	0.147	–0.114	0.261	0.211	–0.159	0.370	0.267	–0.187	0.454	0.412	–0.248	0.660	0.534	–0.289	0.823	0.652	–0.313	0.965
		0.160	–0.105	0.265	0.327	–0.225	0.552	0.461	–0.298	0.759	0.778	–0.440	1.218	1.034	–0.531	1.565	1.275	–0.583	1.858
		0.175	–0.094	0.269	0.359	–0.207	0.566	0.583	–0.335	0.918	1.095	–0.575	1.670	1.500	–0.726	2.226	1.867	–0.812	2.679
		0.193	–0.087	0.280	0.398	–0.191	0.589	0.638	–0.303	0.941	1.366	–0.655	2.021	1.929	–0.872	2.801	2.428	–0.993	3.421
		0.206	–0.071	0.277	0.431	–0.164	0.595	0.692	–0.266	0.958	1.588	–0.677	2.265	2.319	–0.970	3.289	2.952	–1.127	4.079
		0.224	–0.057	0.281	0.466	–0.132	0.598	0.750	–0.221	0.971	1.760	–0.651	2.411	2.663	–1.024	3.687	3.444	–1.218	4.662
		0.247	–0.044	0.291	0.506	–0.103	0.609	0.804	–0.172	0.976	1.881	–0.573	2.454	2.967	–1.025	3.992	3.903	–1.264	5.167
		0.260	–0.028	0.288	0.542	–0.074	0.616	0.862	–0.123	0.985	2.001	–0.489	2.490	3.228	–0.973	4.201	4.321	–1.265	5.586
		0.279	–0.012	0.291	0.576	–0.047	0.623	0.919	–0.080	0.999	2.119	–0.407	2.526	3.447	–0.879	4.326	4.700	–1.222	5.922
		0.296	0.001	0.295	0.611	–0.021	0.632	0.975	–0.038	1.013	2.232	–0.320	2.552	3.623	–0.741	4.364	5.046	–1.139	6.185
		0.315	0.018	0.297	0.646	0.008	0.638	1.032	0.001	1.031	2.340	–0.235	2.575	3.791	–0.597	4.388	5.355	–1.015	6.370
		0.331	0.034	0.297	0.683	0.039	0.644	1.087	0.040	1.047	2.447	–0.148	2.595	3.955	–0.449	4.404	5.627	–0.852	6.479

Source: Produced by the authors. Raw data has been collected from CMIE Database.

Table 4.

MCARS Generated by Various Trading Strategy in Information Technology Sector

	Formation Period
	1 Month			2 Months			3 Months			6 Months			9 Months			12 Months
	W	L	M	W	L	M	W	L	M	W	L	M	W	L	M	W	L	M
Holding Period	1 Month	0.113	–0.109	0.222	0.161	–0.157	0.318	0.195	–0.194	0.389	0.289	–0.270	0.559	0.376	–0.321	0.697	0.454	–0.356	0.810
	2 Months	0.113	–0.109	0.222	0.161	–0.158	0.319	0.195	–0.194	0.389	0.289	–0.270	0.559	0.376	–0.322	0.698	0.453	–0.358	0.811
		0.114	–0.111	0.225	0.237	–0.236	0.473	0.323	–0.324	0.647	0.534	–0.491	1.025	0.720	–0.601	1.321	0.882	–0.672	1.554
	3 Months	0.113	–0.110	0.223	0.161	–0.158	0.319	0.195	–0.195	0.390	0.290	–0.271	0.561	0.375	–0.323	0.698	0.451	–0.358	0.809
		0.114	–0.111	0.225	0.237	–0.236	0.473	0.323	–0.324	0.647	0.535	–0.492	1.027	0.719	–0.603	1.322	0.879	–0.674	1.553
		0.114	–0.111	0.225	0.232	–0.239	0.471	0.392	–0.392	0.784	0.742	–0.664	1.406	1.035	–0.838	1.873	1.282	–0.953	2.235
	6 Months	0.114	–0.110	0.224	0.161	–0.159	0.320	0.196	–0.195	0.391	0.289	–0.272	0.561	0.371	–0.327	0.698	0.447	–0.360	0.807
		0.115	–0.111	0.226	0.238	–0.237	0.475	0.325	–0.325	0.650	0.535	–0.495	1.030	0.712	–0.609	1.321	0.871	–0.678	1.549
		0.115	–0.112	0.227	0.234	–0.241	0.475	0.395	–0.394	0.789	0.742	–0.668	1.410	1.025	–0.845	1.870	1.269	–0.960	2.229
		0.118	–0.117	0.235	0.245	–0.248	0.493	0.416	–0.398	0.814	0.916	–0.795	1.711	1.308	–1.036	2.344	1.649	–1.205	2.854
		0.126	–0.117	0.243	0.269	–0.252	0.521	0.451	–0.400	0.851	1.057	–0.874	1.931	1.566	–1.183	2.749	2.009	–1.402	3.411
		0.136	–0.113	0.249	0.288	–0.246	0.534	0.483	–0.388	0.871	1.159	–0.902	2.061	1.789	–1.287	3.076	2.344	–1.554	3.898
	9 Months	0.113	–0.110	0.223	0.161	–0.159	0.320	0.195	–0.196	0.391	0.285	–0.275	0.560	0.367	–0.328	0.695	0.439	–0.366	0.805
		0.114	–0.111	0.225	0.238	–0.238	0.476	0.323	–0.327	0.650	0.527	–0.499	1.026	0.703	–0.613	1.316	0.857	–0.689	1.546
		0.114	–0.112	0.226	0.234	–0.242	0.476	0.393	–0.396	0.789	0.731	–0.675	1.406	1.011	–0.851	1.862	1.252	–0.973	2.225
		0.118	–0.118	0.236	0.246	–0.249	0.495	0.415	–0.399	0.814	0.903	–0.803	1.706	1.290	–1.044	2.334	1.628	–1.220	2.848
		0.126	–0.117	0.243	0.271	–0.253	0.524	0.450	–0.401	0.851	1.043	–0.881	1.924	1.544	–1.192	2.736	1.985	–1.418	3.403
		0.135	–0.114	0.249	0.289	–0.247	0.536	0.482	–0.389	0.871	1.144	–0.909	2.053	1.764	–1.295	3.059	2.315	–1.572	3.887
		0.137	–0.112	0.249	0.298	–0.236	0.534	0.504	–0.374	0.878	1.198	–0.887	2.085	1.944	–1.353	3.297	2.616	–1.679	4.295
		0.147	–0.111	0.258	0.313	–0.227	0.540	0.524	–0.354	0.878	1.254	–0.854	2.108	2.094	–1.360	3.454	2.890	–1.742	4.632
		0.151	–0.109	0.260	0.330	–0.217	0.547	0.542	–0.333	0.875	1.302	–0.813	2.115	2.213	–1.326	3.539	3.135	–1.760	4.895
	12 Months	0.113	–0.111	0.224	0.159	–0.161	0.320	0.192	–0.199	0.391	0.282	–0.277	0.559	0.361	–0.334	0.695	0.432	–0.372	0.804
		0.112	–0.113	0.225	0.234	–0.242	0.476	0.317	–0.332	0.649	0.520	–0.504	1.024	0.691	–0.623	1.314	0.842	–0.700	1.542
		0.112	–0.114	0.226	0.228	–0.247	0.475	0.384	–0.401	0.785	0.721	–0.681	1.402	0.995	–0.863	1.858	1.229	–0.990	2.219
		0.114	–0.118	0.232	0.238	–0.253	0.491	0.403	–0.404	0.807	0.890	–0.811	1.701	1.269	–1.057	2.326	1.596	–1.242	2.838
		0.122	–0.118	0.240	0.262	–0.256	0.518	0.437	–0.404	0.841	1.027	–0.891	1.918	1.519	–1.206	2.725	1.945	–1.443	3.388
		0.132	–0.115	0.247	0.281	–0.250	0.531	0.468	–0.392	0.860	1.126	–0.919	2.045	1.735	–1.312	3.047	2.268	–1.597	3.865
		0.134	–0.113	0.247	0.291	–0.239	0.530	0.491	–0.378	0.869	1.179	–0.897	2.076	1.912	–1.371	3.283	2.563	–1.702	4.265
		0.144	–0.112	0.256	0.306	–0.230	0.536	0.510	–0.358	0.868	1.234	–0.862	2.096	2.058	–1.378	3.436	2.831	–1.763	4.594
		0.147	–0.110	0.257	0.322	–0.219	0.541	0.528	–0.336	0.864	1.281	–0.820	2.101	2.175	–1.341	3.516	3.069	–1.781	4.850
		0.152	–0.105	0.257	0.334	–0.202	0.536	0.549	–0.311	0.860	1.320	–0.766	2.086	2.255	–1.259	3.514	3.284	–1.757	5.041
		0.157	–0.094	0.251	0.350	–0.186	0.536	0.570	–0.288	0.858	1.357	–0.703	2.060	2.331	–1.162	3.493	3.475	–1.686	5.161
		0.166	–0.086	0.252	0.367	–0.175	0.542	0.594	–0.270	0.864	1.396	–0.636	2.032	2.410	–1.054	3.464	3.642	–1.579	5.221

Source: Produced by the authors. Raw data has been collected from CMIE Database.

Table 5.

MCARS Generated by Various Trading Strategy in CNX 500 Stock Index (Indian Stock Market)

	Formation Period
	1 Month			2 Months			3 Months			6 Months			9 Months			12 Months
	W	L	M	W	L	M	W	L	M	W	L	M	W	L	M	W	L	M
Holding Period	1 Month	0.204	–0.170	0.374	0.290	–0.240	0.529	0.357	–0.288	0.645	0.531	–0.395	0.927	0.681	–0.470	1.151	0.817	–0.521	1.338
	2 Months	0.204	–0.169	0.373	0.290	–0.238	0.528	0.358	–0.285	0.643	0.533	–0.391	0.925	0.684	–0.464	1.148	0.819	–0.516	1.335
		0.211	–0.165	0.376	0.442	–0.346	0.788	0.607	–0.468	1.075	0.988	–0.706	1.694	1.304	–0.865	2.170	1.584	–0.978	2.562
	3 Months	0.204	–0.168	0.372	0.291	–0.235	0.527	0.360	–0.282	0.641	0.535	–0.386	0.922	0.686	–0.459	1.145	0.822	–0.510	1.332
		0.212	–0.163	0.375	0.444	–0.342	0.785	0.609	–0.462	1.071	0.992	–0.697	1.689	1.309	–0.854	2.163	1.589	–0.967	2.556
		0.218	–0.154	0.372	0.465	–0.333	0.798	0.750	–0.549	1.299	1.375	–0.941	2.316	1.872	–1.193	3.065	2.303	–1.372	3.675
	6 Months	0.205	–0.167	0.372	0.293	–0.234	0.526	0.362	–0.279	0.641	0.541	–0.378	0.918	0.691	–0.450	1.141	0.827	–0.501	1.328
		0.214	–0.160	0.373	0.447	–0.336	0.783	0.614	–0.455	1.068	1.003	–0.676	1.679	1.320	–0.834	2.154	1.600	–0.946	2.546
		0.221	–0.149	0.370	0.471	–0.323	0.793	0.758	–0.534	1.292	1.392	–0.906	2.299	1.891	–1.157	3.048	2.322	–1.336	3.658
		0.237	–0.148	0.385	0.499	–0.316	0.815	0.801	–0.521	1.322	1.710	–1.078	2.788	2.400	–1.429	3.829	2.992	–1.677	4.669
		0.246	–0.141	0.387	0.529	–0.307	0.836	0.845	–0.510	1.356	1.955	–1.186	3.141	2.845	–1.647	4.493	3.608	–1.965	5.573
		0.260	–0.137	0.398	0.556	–0.299	0.855	0.890	–0.503	1.393	2.126	–1.232	3.359	3.229	–1.817	5.046	4.168	–2.203	6.371
	9 Months	0.207	–0.165	0.372	0.296	–0.229	0.525	0.366	–0.273	0.639	0.543	–0.374	0.917	0.694	–0.446	1.140	0.832	–0.495	1.327
		0.216	–0.155	0.372	0.453	–0.327	0.780	0.621	–0.443	1.064	1.010	–0.666	1.676	1.327	–0.824	2.151	1.611	–0.932	2.542
		0.224	–0.144	0.369	0.478	–0.311	0.789	0.769	–0.517	1.286	1.403	–0.888	2.291	1.902	–1.140	3.042	2.338	–1.313	3.651
		0.240	–0.142	0.382	0.507	–0.302	0.809	0.814	–0.500	1.314	1.727	–1.050	2.777	2.416	–1.402	3.818	3.013	–1.644	4.657
		0.250	–0.132	0.382	0.538	–0.289	0.826	0.860	–0.484	1.344	1.979	–1.146	3.125	2.868	–1.608	4.476	3.634	–1.919	5.554
		0.266	–0.126	0.392	0.568	–0.275	0.843	0.907	–0.470	1.378	2.159	–1.179	3.338	3.261	–1.763	5.023	4.202	–2.142	6.344
		0.278	–0.118	0.396	0.595	–0.263	0.858	0.950	–0.458	1.408	2.262	–1.148	3.409	3.589	–1.867	5.456	4.714	–2.313	7.027
		0.288	–0.111	0.399	0.618	–0.252	0.870	0.988	–0.446	1.434	2.360	–1.122	3.481	3.857	–1.917	5.774	5.170	–2.435	7.605
		0.299	–0.105	0.404	0.641	–0.239	0.880	1.027	–0.431	1.457	2.455	–1.096	3.551	4.063	–1.914	5.978	5.572	–2.510	8.082
	12 Months	0.207	–0.165	0.373	0.296	–0.231	0.526	0.365	–0.275	0.640	0.543	–0.374	0.918	0.696	–0.445	1.141	0.833	–0.496	1.329
		0.217	–0.156	0.372	0.452	–0.329	0.781	0.620	–0.445	1.065	1.009	–0.667	1.676	1.331	–0.822	2.152	1.612	–0.934	2.547
		0.226	–0.143	0.369	0.478	–0.311	0.789	0.769	–0.518	1.286	1.403	–0.889	2.292	1.908	–1.137	3.044	2.342	–1.315	3.657
		0.244	–0.139	0.383	0.511	–0.298	0.809	0.817	–0.497	1.314	1.729	–1.048	2.777	2.425	–1.395	3.820	3.023	–1.641	4.664
		0.255	–0.128	0.383	0.545	–0.282	0.827	0.868	–0.476	1.344	1.986	–1.140	3.125	2.881	–1.596	4.477	3.650	–1.909	5.559
		0.271	–0.122	0.393	0.577	–0.266	0.843	0.919	–0.458	1.377	2.171	–1.167	3.338	3.278	–1.745	5.023	4.223	–2.123	6.346
		0.284	–0.113	0.397	0.605	–0.253	0.858	0.964	–0.443	1.407	2.280	–1.128	3.408	3.611	–1.842	5.453	4.741	–2.286	7.027
		0.295	–0.104	0.399	0.631	–0.239	0.869	1.006	–0.427	1.433	2.387	–1.093	3.480	3.887	–1.882	5.769	5.204	–2.397	7.601
		0.309	–0.096	0.405	0.659	–0.222	0.881	1.049	–0.407	1.456	2.491	–1.058	3.549	4.102	–1.866	5.968	5.614	–2.460	8.074
		0.321	–0.086	0.407	0.686	–0.203	0.889	1.092	–0.383	1.476	2.588	–1.020	3.608	4.255	–1.792	6.046	5.971	–2.474	8.445
		0.333	–0.077	0.410	0.711	–0.184	0.895	1.136	–0.363	1.499	2.679	–0.978	3.657	4.403	–1.714	6.117	6.277	–2.441	8.719
		0.345	–0.066	0.412	0.738	–0.167	0.905	1.174	–0.344	1.518	2.766	–0.933	3.699	4.545	–1.631	6.176	6.533	–2.363	8.896

Source: Produced by the authors. Raw data has been collected from CMIE Database.

Concluding Remarks

In our article, we have examined the existence of a momentum effect in the Indian stock market using four sectors of Indian economy covering the period from May 2000 to April 2013. The sample of the study included stock comprised by CNX 500 and large cap stocks of auto, banking, pharma and IT sectors. The article adds some important findings to the existing literature, as there is little evidence on this concept for emerging countries, particularly for India. Financial academicians and practitioners have recognized that the average stock returns are related to past performance and cross-section of stock returns is predictable on the basis of past returns. Our analysis also revealed the existence of momentum effect in the Indian stock market and all sample sectors. Momentum effect is highly significant and has comparatively become more significant with the increase in formation period and testing period that supports the behavioural explanation given by the Daniel et al. (1998) and Hong and Stein (1999) that momentum profit is a result of initial underreaction of the traders followed by subsequent overreaction. It also could be interpreted by the analysis that the major source of momentum profit is return continuation, where the price continuation in the winner portfolios for the entire formation period is higher than the return reversal in the loser portfolios. Highest momentum profit has been seen in the pharma sector, while lowest momentum return is given by IT sector. Sustained growth in pharma sector could be a possible reason for generation of high-momentum profit (Liu et al., 2005). Auto and banking produced more or less similar momentum profit. So, it advisable for investor to explore more in the pharma sector to make additional return by following the momentum trading strategy.

The article also reviewed medium-term (monthly) and long-term (quarterly) trading strategies for the four sectors and market index and found that long-term trading strategy is more profitable than monthly trading strategy. Out of the 36 trading strategies, the most effective time period for superior profit was the formation period of 12 months with holding period of 12 months. Even momentum return kept on increasing with higher holding period, so investors are advised to follow buy and hold strategy in Indian market. Consistent with the findings of Jegadeesh and Titman (1993, 2001), our article also found in the entire sample period that winner portfolio outperformed the loser portfolio. It could also be interpreted by our analysis that a more diversified momentum portfolio can generate better returns rather than industrial concentrated momentum portfolio.

Our findings could be relevant for institutional investors, QFIs, pension fund managers, banks and HNI investors, who are looking for an investment strategy to beat the market and gain abnormal returns. The article also has implications for evaluation the market efficiency. On the basis of the results, we may conclude that there is a strong presence of momentum returns in the Indian stock market, which produce evidence against the weak form of market efficiency. However, this article has not considered the volume effect which can be considered; this gives the scope of the future research. Further, future researchers could investigate the sensitivity of the results reported in this article to different trading frequencies, such as daily and weekly frequencies and different portfolio-weighting schemes.

Footnotes

Acknowledgements

We appreciate the Referee’s critical and constructive comments on the manuscript that helps us to improve the quality of the paper.

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