Financial Econometrics Series SWP 2014/14 How Profitable ... · Majumder, 2013; Mishra et al.,...

Faculty of Business and Law School of Accounting, Economics and Finance

Financial Econometrics Series

SWP 2014/14

How Profitable is the Indian Stock Market?

P.K. Narayan, H.A. Ahmed, S.S. Sharma,

K.P. Prabheesh

The working papers are a series of manuscripts in their draft form. Please do not quote without obtaining the author’s consent as these works are in their draft form. The views expressed in this paper are those of the author and not necessarily endorsed by the School or IBISWorld Pty Ltd.

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How Profitable is the Indian Stock Market?

Paresh Kumar Narayan1, Huson Ali Ahmed2, Susan Sunila Sharma3, K P Prabheesh4

ABSTRACT

In this paper, using a range of technical trading and momentum trading strategies, we show

that the Indian stock market is profitable. We find robust evidence that investing in some

sectors is relatively more profitable than investing in others. We show that sectoral

heterogeneity with respect to profitability is a result of the gradual diffusion of information

from the market to the sectors. Specifically, we show that while the market predicts returns of

sectors, the magnitude of predictability varies with sector. Our results are robust to a range of

trading strategies.

Keywords: Momentum; Technical Trading; Profits; Sectors; Stock Market; India;

Predictability.

1 Alfred Deakin Professor, Research Professor, Financial Econometrics Group, School of Accounting, Economics and

Finance, Deakin University. Email: [email protected] 2 Lecturer in Finance, Financial Econometrics Group, School of Accounting, Economics and Finance, Deakin University.

Email: [email protected] 3 Lecturer in Financial Econometrics, Financial Econometrics Group, School of Accounting, Economics and Finance,

Deakin University. Email: [email protected] 4 Assistant Professor of Economics, Department of Liberal Arts, Indian Institute of Technology, Hyderabad. Email:

[email protected]

mailto:[email protected]




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1. Introduction

The growth of the Indian stock market over the last decade has been impressive, particularly

in terms of market capitalization, number of listed companies, and turnover rate. Market

capitalization as a percentage of GDP of the National Stock Exchange (NSE), for instance,

increased from 35% in 2001 to 85% in 2011. Similarly, the number of companies listed on the

NSE more than doubled over the corresponding period, from 720 to 1552. Likewise, the

turnover on the Indian stock market increased from US$621 billion in 2010 to US$1056 billion

in 2011. For all its impressive growth, India achieved a global rank of 7th in terms of market

capitalization, 10th in terms of total value traded, and 22nd in terms of turnover ratio, as of

December 2010 (NSE, 2011).

The impressive growth of the Indian stock market has attracted research on the

efficiency (or otherwise) of the market. There are a number of studies that examine the

efficiency of the Indian stock market (see, inter alia, Dicle et al., 2010; Kumar et al., 2011;

Majumder, 2013; Mishra et al., 2011; Narayan et al., 2014; and Narayan and Ahmed, 2014).

There are two ways in which this literature can be taken forward. First, these studies do not

provide an economic significance analysis with respect to market efficiency or inefficiency.

Therefore, it is unclear how profitable the Indian stock market is. Second, if an investor wants

to invest in the Indian stock market, which sectors should she invest in? This question remains

unanswered in these studies.1

Therefore, from this growing literature on the Indian stock market, there are clearly four

things which are relatively less understood. These are:

1. The Indian stock market may be profitable but do profits vary from sector-to-sector?

1 There is another branch of this literature (Chang et al., 2004), which typically employs simple technical trading rules,

such as the moving average and the trading range break rules. These studies generally find some evidence of

profitability. Similarly, Gunasekarage and Power (2001) and Narayan et al. (2014) find evidence that the Indian stock

market is profitable, although these studies use different approaches compared to what we do in this paper.

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2. Can investors use different trading strategies to make profits from the Indian stock

market? If yes, how much do the profits vary from sector-to-sector as trading strategies

change?

3. Short-selling is a feature of the Indian market; therefore, what effect does short-selling

has on sectoral profits?

4. The most recent global financial crisis has affected stock markets globally; therefore,

has the crisis affected sectoral profits on the Indian stock market?

To the best of our knowledge, none of these questions has been answered in the literature.

Our study provides the first attempt at addressing each of these questions. Our approach is as

follows. We utilise a range of technical trading rules and momentum trading rules to identify

winners and losers and form returns for momentum and zero-cost portfolios. We also consider

ranking stocks based on moving average rules and then undertaking long and short positions.

Taking long and short positions based on a ranking of sectors allows an investor to diversify

risk and consider a portfolio of sectors in her trading strategy. More details on our approach is

provided in the next section.

2. Approach

2.1. Moving average ranking-based trading strategy

In this section, we consider trading strategies based on moving average rules. The moving

average technical trading rules are popular trading strategies, particularly in the foreign

exchange market (see, for instance, Lee and Mathur, 1996a,b, and Szakmary and Mathur, 1997)

and commodity markets (see Narayan, Narayan, and Sharma, 2013; Narayan, Ahmed, and

Narayan, 2014). We begin as follows:

1. We compute the monthly long-run (LR) and short-run (SR) moving averages (MA)

using identified intervals for LR and SR.

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2. Using (SR-LR) moving average for respective intervals, we rank each of the six sectors,

from best (rank 1) to worst (rank 6). The highest positive difference between SRMA

and LRMA is assigned a rank of 1, and the lowest difference between SRMA and

LRMA is ranked last.

3. We initiate long positions in high ranked sector(s) and short the sector with the lowest

rank. In this way, we are able to devise three trading strategies (excluding the naïve

investor strategy), as will be discussed soon.

4. When we do not allow for short-selling, we simply do not short but allow for cash

positions.

Following the Lee and Mathur (1996a,b) and the Okunev and White (2003) studies, we

define the short-run moving average (𝑀𝐴𝑆𝑅) and the long-run moving average (𝑀𝐴𝐿𝑅) at time

𝑡 as:

𝑀𝐴𝑗,𝑡𝑆𝑅 =

𝑅𝑡 + (𝑗 − 1)𝑀𝐴𝑗,𝑡−1𝑆𝑅

𝑗 (1)

𝑀𝐴𝑖,𝑡𝐿𝑅 =

𝑅𝑡 + (𝑖 − 1)𝑀𝐴𝑖,𝑡−1𝐿𝑅

𝑖 (2)

where 𝑗 and 𝑖 are prior months of sectoral returns. The SRMA values range from 1 to 12

months, while the LRMA values range from 2 to 36 months. In all combinations of short-run

and long-run moving average trading rules, 𝑗 < 𝑖. Just to demonstrate how these trading rule

combinations are implemented, consider the sectoral position based on a SRMA of 1 month.

We compute this as 𝑀𝐴1𝑡𝑆𝑅 − 𝑀𝐴2,𝑡

𝐿𝑅 , 𝑀𝐴1,𝑡𝑆𝑅 − 𝑀𝐴3,𝑡

𝐿𝑅 , … , 𝑀𝐴1,𝑡𝑆𝑅 − 𝑀𝐴36,𝑡

𝐿𝑅 . Similarly, using a 2-

month short-term moving average 𝑀𝐴2,𝑡𝑆𝑅 , 𝑀𝐴2,𝑡

𝑆𝑅 − 𝑀𝐴3,𝑡𝐿𝑅 , … , 𝑀𝐴2,𝑡

𝑆𝑅 − 𝑀𝐴36,𝑡𝐿𝑅 , we determine

the investment positions. At the end of each month, for each individual moving average

combination, the six sectors are ranked on a scale of 1 (best-performing) to 6 (worst-

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performing). This ranking is based on the return-based momentum indicator, which is simply

the difference in return between the SR and LR moving averages. The sector that has the largest

return difference (𝑀𝐴𝑆𝑅 − 𝑀𝐴𝐿𝑅), earns a rank of 1, the sector with the second largest return

difference earns a rank of 2, while the sector with the least return difference is the worst-

performing and, thus, earns a rank of 6. We place weights on each of the short-run/long-run

combinations and positions are taken and held for a month, based on the respective strategy.

The rankings are revised each month and new positions are taken if rankings do indeed change.

We propose four different strategies to examine the efficiency and profitability of the

sectors in the Indian market. A summary of the trading strategies is provided in Table 1.

INSERT TABLE 1

Strategy I: This strategy assumes a naïve investor who invests equally in all six sectors,

irrespective of the sectors’ rankings.

Strategy II: This strategy invests 100% in the highest ranked sector and shorts the lowest ranked

sector in the case of short-selling, but takes cash positions in the case of no short-selling. This

strategy, thus, provides opportunities to make arbitrage profits from taking two opposite

positions.

Strategy III: This strategy invests in the top-3 ranked sectors. An investor with this strategy

invests 50% in the sector ranked one, and 25% each in the sectors ranked two and three. The

investor will short the worst-ranked sector in the case of short-selling and take cash positions

in the case of no short-selling. This strategy, therefore, allows an investor to build a portfolio

of sectors which also diversifies risks.

Strategy IV: This strategy allows an investor to invest 70% in the highest ranked sector and

30% in the second-highest ranked sector. The investor continues to short the weakest ranked

sector in the case of short-selling, and takes cash positions in the case of no short-selling.

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In all these strategies, where we allow for short-selling, our approach proceeds as

follows. First, we allow for 100% short-selling. Then, in a robustness test of profitability, we

allow for a short-selling of 76% based on our estimate of the magnitude of short-selling in the

Indian market.

2.2. Technical trading based rules

In this section, we discuss three specific technical trading rules that we utilise to

generate buy and sell signals.1 These are the moving average rule, momentum trading rule, and

a filter-based trading rule. Essentially, we generate profits using these buy and sell signals from

each of these three trading rules and take the average of these profits to make a judgement on

the profitability of sectors.

The short-run (j) moving average values range from one to three months, while the

long-run (i) moving average values range from nine to 12 months. In all combinations of short-

run and long-run moving average trading rules, 𝑗 < 𝑖. These combinations are as follows:

(SRMA1-LRMA9), (SRMA1-LRMA12); (SRMA2-LRMA9), (SRMA2-LRMA12); and

(SRMA3-LRMA9), (SRMA3-LRMA12). From these rules, we generate a buy signal, denoted

by a value of 1 in a particular month t, if SRMA1>LRMA9, while a sell signal is generated if

SRMA1<LRMA9. These buy and sell signals are generated for all six combinations of SR and

LR moving averages. Based on these signals, each month, we simultaneously long in the

winner (that is, when SRMA> LRMA) and short in the loser.

We also use momentum trading rules to generate buy and sell signals. With momentum

trading rules, we use two periods (m = 9, 12). Trading signals are generated based on whether

or not the current stock price is higher than the price “m” months ago: if it is, a buy signal is

1 See Neely et al. (2013) for an example of using technical indicators to predict equity risk premium.

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generated (with a value of 1); otherwise, a sell signal (with a value of 0) is generated. Based

on this rule, we simultaneously long in winner and short in losers within an industry/sector.

Finally, we use a filter of 10% price threshold as a trading rule. If current return is 10%

higher than average return a buy signal is generated or else the signal is to sell. Accordingly, a

long position in winner and short position in loser stocks are taken.

Every time we take positions, we keep the investment for 1-month, 3-month and 6-

month holding periods. Using these three trading rules, we form average time-series profits for

winner, loser, momentum (winner – loser) and zero-cost (winner + loser) portfolios. For each

of these portfolios, we compute mean monthly average returns and standard deviation, which

is what we report in Tables presented in the next section.

3. Data and results

3.1. Data

Our data set comes from the National Stock Exchange (NSE) of India. We use the monthly

S&P CNX Nifty, the index of the NSE, which is comprised of 50 stocks from 24 sectors of the

Indian economy; for a nice recent discussion on the Indian stock market, see Narayan et al.

(2014). We use only six sectors in our analysis. We choose these six sectors because they are

the only sectors for which data are available from January 2001 to December 2012. For the rest

of the sectors, data start much later, which is not helpful for our empirical design. This sample

size, constrained by the start date is noted in Narayan et al. (2014). The main implication here

is that at the sector-level there is no historical time-series data for India that goes beyond 2000;

therefore, we, like the empirical literature, utilise what is available. Because our approach

requires a common start date and needs to have as long a time-series as possible, we end up

with only six sectors. These sectors together constitute approximately 60% of total market

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capitalization, led by the banking sector (15.3%), energy sector (11.1%), and the FMCG sector

(10.2%). The sectoral indices are as follows:

1. CNX Bank Index: this index is comprised of the 12 most liquid and biggest

capitalized Indian banking stocks.

2. CNX MNC Index: this index is comprised of 15 listed companies in which foreign

shareholding is over 50% and/or the management control is vested in the foreign

company.

3. CNX IT Index: this index is comprised of 20 Information Technology (IT) stocks,

such as IT-related activities, in particular, IT Infrastructure, IT Education and

Software Training, Telecommunication Services and Networking Infrastructure,

Software Development, Hardware Manufacturers, Vending, Support and

Maintenance.

4. CNX FMCG Index: this index is comprised of 15 stocks from the Fast Moving

Consumer Goods industry (FMCG).

5. CNX Pharmaceutical Index: this is the index of the Pharmaceuticals sector and is

comprised of 10 stocks.

6. CNX Energy Index: this is the index of Petroleum, Gas and Power and is comprised

of 10 stocks.

A brief discussion of the data is in order here. We report descriptive statistics on returns

in Table 2. This table reports descriptive statistics relating to market (CNX Nifty) and sectoral

returns. Returns are computed as (𝑃𝑡 𝑃𝑡−1⁄ − 1) ∗ 100, where 𝑃𝑡 is the price index. Mean

returns, standard deviation, skewness, and kurtosis statistics’ are reported. These statistics are

based on monthly data for the period January 2001 to December 2012. The descriptive statistics

of the data suggest that monthly market returns over the sample period were 1.26% and four

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sectors (banking, pharmaceutical, FMCG, and energy) had average returns in excess of market

returns. The monthly sectoral returns are in the 0.16-2.07% range. The standard deviation

reveals that the IT sector is most volatile; it is almost twice as volatile as the market. The rest

of the sectors are less volatile. In fact, three of the six sectors (FMCG, pharmaceutical, and

MNC) are less volatile than the market. Like mean returns, volatility also varies sector-by-

sector. All sectors have a negative skewness except for the banking sector returns. The IT sector

returns are the most negatively skewed. A similar picture emerges when reading the kurtosis

statistics, suggesting that the return distributions are all leptokurtic and more so for the IT

sector. Clearly, then, sectoral return behaviour is different and the IT sector stands in sharp

contrast to others.

INSERT TABLE 2

3.2. Results from momentum-based trading strategies

3.2.1 Results without short-selling

The main findings on the performance of each of the trading strategies for each of the

six sectors are reported in Table 3. Results in panel A are based on a strategy whereby the

investor is assumed to be naïve and, therefore, invests equally in all six sectors, irrespective of

their ranking. We call this strategy I. In panel B, we have an investor who invests 100% in the

first-ranked sector and has no interest in investing in the other sectors. We call this strategy II.

Panel C contains results from a strategy which invests 50% in the first-ranked sector and 25%

each in the sectors ranked two and three, leaving the rest of the worst-performing sectors in

cash positions. We call this strategy III. The fourth panel consists of returns from a strategy

that invests 70% and 30% in sectors ranked one and two, respectively. Again, as in previous

strategies, the investor leaves the rest of the sectors in cash positions. We call this strategy IV.

Evidence obtained from strategy I, where a naïve investor invests equally across all six

sectors, suggests that economic profits are statistically insignificant in energy and IT sectors

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only. Monthly profits for the remaining four sectors are in the [0.22, 0.30] percentage range.

When we look at the evidence from strategy II, in all sectors, except banking and

pharmaceutical, profits are statistically different from zero. In other words, profits are

statistically significant at the 10% level or better for energy, IT, MNC, and FMCG sectors.

However, the magnitude of profits varies from sector-to-sector. It is the IT sector which gives

investors the most profits with a monthly average return of 0.815%, while the pharmaceutical

sector gives investors the least profit with a monthly average loss of 0.215%. When we collect

evidence from strategies III and IV, we discover profitability results consistent with strategy II

in terms of the most profitable and least profitable sectors. The most profitable sector

consistently appears to be IT while the least profitable sector is pharmaceutical. When we

consider all sectors as one portfolio, the monthly returns from strategies I to IV fall in the

[1.4%, 2.5%] range.

The main implication of our findings here is that profits across the three strategies,

excluding a strategy that assumes a naïve investor, are generally consistent on two fronts: first,

profits are statistically significant at the 10% level or better for four of the six sectors, although

profits are clearly sector-dependent—that is, the magnitude of profits is different in different

sectors. Second, the IT sector consistently out-performs the other sectors, while the

pharmaceutical sector produces the lowest profits. On the whole, then, the profitable sectors

are IT, energy, FMCG, and MNC.

There are other interesting features of our results which are worth highlighting.

Generally, we notice that with higher profits comes higher risk. An interesting trend here is

that as investors move from a strategy where they take a long position in the most attractive

sector (strategy II) to taking long positions in more than one sector (strategies III and IV) as

part of investment diversification, a decline in risk is experienced. The standard deviation for

the IT sector, for instance, declines from 3.6 in the case of strategy II to 2.1 and 2.7,

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respectively, in the case of strategies III and IV. When we consider the performance of the

other five sectors, we notice a similar trend in that there is a decline in the standard deviation

as an investor moves from strategy II to strategy IV. The implication here is that a strategy

(such as our strategy III), that allows an investor to take a long position in multiple sectors,

leads to diversification of risk although the profits are smaller from such a strategy compared

to strategies II and IV.

INSERT TABLE 3

For each of the strategies, we report some additional statistics to gauge the performance

of the strategies. In particular, we report the probability that mean returns are greater than zero

and greater than the market (Nifty) return. Ignoring the naïve investor (Strategy I), we notice

that in strategy III, where we allow an investor to diversify risk by taking long positions in the

top-3 sectors, the probability that returns are greater than zero is highest for all sectors. For

example, with strategy III, 34% of the time IT sector returns are greater than zero. Moreover,

when we compare sectoral returns with the market return, we find that all three strategies

(strategies II, III, and IV) reveal that for the IT, FMCG, and banking sectors, over 35% of the

time returns exceed the market return. Finally, the paired t-test reveals that for most sectors

returns from each strategy are statistically significant relative to the market, mostly at the 10%

level of significance.

3.2.2. Results with short-selling

Background

Short-selling is the practice of selling shares that the seller does not own at the time of trading.

In India, the Securities and Exchange Board of India (SEBI), the regulator of the Indian capital

market, defines a ‘short sale’ as “selling of the shares without having the physical possession

of the shares unless it is either for squaring-up of an earlier purchase in the same settlement of

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the same stock exchange, or against the pending deliveries from the same stock exchange

pertaining to previous settlements” (SEBI, 1996). There are various phases associated with

short-selling activities in the Indian market. First, in the 1990s, only retail investors were

allowed to short-sell. The Badla system and the weekly settlement system were in place Weekly

settlement with T+5 days ensured that investors made payments and took delivery of securities

within five days after the day of the transaction. The Badla system, by comparison, was useful

when investors preferred to postpone the settlement of a transaction from one settlement period

to another. Both these systems governing short-selling activities were instrumental in

increasing the volume of trading, which was responsible for high stock price volatility. This

needed a response; the SEBI temporarily banned short-selling in 1998 and 2001. Second, a

more stringent measure was taken by SEBI, who abolished the Badla system on 2 July, 2001.

In fact, the Badla system was replaced by a rolling settlement system, with T+3 days settlement

system. Third, from 1 April, 2003, the Indian stock market moved to the T+2 rolling settlement

system. Therefore, the payment and delivery of securities had to be completed within two days

after the day of the transaction (Pathak, 2008).

Fourth, in 2007, the SEBI allowed all classes of investors, including institutional

investors, to short-sell in the Indian stock market. However, naked short-selling was not

permitted; all investors are required to mandatorily honour their obligation of delivering the

securities at the time of settlement. Similarly, in 2007, the SEBI introduced a fully-fledged

Securities Lending and Borrowing Scheme (SLBS) for the effective transaction of short-

selling. Through this scheme, investors can borrow and lend securities through approved

intermediaries (SEBI, 2010). The SLBS is applicable in the cash market.

Table 4 shows the details of settlement of trade in the cash segment of NSE, such as the

traded quantity, deliverable quantity, and the percentage of deliverable quantity to traded

quantity for the period 2001-2002 to 2010-2011. This table reveals that the total traded quantity

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of shares is high during the stock market boom period (2003-2007). However, the delivered

quantity of shares is much low compared to the traded quantity. During 2000-2011, the average

delivered shares was around 24% of total traded quantity. This implies that the remaining 76%

of trading is for speculative purposes for day trading. The day trade can be either short-selling

of securities or long-buying of securities, those who buy (long) will square their long position

or those who sell (short) will cover their short sales before market close. Accordingly, for every

short sale there will be a corresponding long (buy). Since there is limited availability of data

on short- selling in the Indian stock market, we use the above non-delivered quantity of

shares—that is, 76%—as a proxy for the size of short selling.1

INSERT TABLE 4

Findings

In Table 5, we report the results of sectoral profitability by simply assuming a short-selling of

100%, which is close to what we estimate short-selling to be on the Indian stock market. The

results are reported for only those three strategies where an investor is able to take long and

short positions by forming an arbitrage portfolio with zero investment. The key findings from

the short-selling based trading across three different strategies can be summarised as follows:

The IT sector is the most profitable. The monthly average profits are in the 0.65-1.1%

range.

The pharmaceutical sector is the least profitable with profits/loss in the -0.37 to 0.02%

range.

In terms of sectoral profitability, all three strategies produce very consistent results.

However, in terms of risk, we notice a reduction when an investor moves from strategy II

1 While it is true that the magnitude of short-selling is unknown, the impact of short-selling on the India market

has been of interest. In a recent study, for instance, Giannikos and Gousgounis (2012) find that during the period

of short-sale ban (2001-2007) the equity market was overpriced compared to the futures market. They argue that

the main source of overpricing was opinion dispersion. They further document that overpricing declined when the

short-selling ban was lifted.

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to a relatively more diversified strategy. Consider the IT sector, for example. The standard

deviation declines from 4.77% with strategy II to 2.56% with strategy III.

Profits in the four profitable sectors are much higher when short-selling is allowed

compared to profits obtained from strategies that do not allow for short-selling. Taking the

evidence from strategy III, IT profits are almost 172% higher under short-selling compared

to a case of no short-selling. The second largest gain in profits from short-selling is

achieved by the energy sector (80%), followed by the FMCG sector (40%). The smallest

increase in profits with short-selling is experienced by the MNC sector whose profits

increase only by 4.5%.

When all sectors are considered as a portfolio, the portfolio returns from Strategies II to IV

fall in the [1.98%, 3.69%] range.

INSERT TABLE 5

3.2.3. Results in the pre-crisis and crisis periods

Several studies (see, inter alia, Longstaff, 2010) have shown that the performance of stock

markets has been negatively affected by the 2007 global financial crisis. This literature has

analysed the stock markets from different perspectives. We re-estimate sectoral profits by

splitting the sample into a pre-crisis period (30 August 2002 to 31 March 2007) and a period

including the crisis (1 April 2007 to 31 December 2012). The results for the pre-crisis period

are reported in Table 6, while results from the crisis period are reported in Table 7.

INSERT TABLES 6 AND 7

These results are interesting and reveal the following. First, in the pre-crisis period, there is

mixed evidence on profitability. For example, while strategy III reveals profitability for five

out of six sectors (with the exception of the pharmaceutical sector), strategies II and IV reveal

profitability for only energy and FMCG, and MNC and FMCG sectors, respectively. Compare

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this with the crisis period: strategies II, III and IV consistently suggest that energy and IT are

the only profitable sectors. Second, the FMCG and IT sectors remain the most profitable in

pre-crisis and crisis periods, respectively. Third, two sectors, namely, MNC and FMCG, which

were profitable in the pre-crisis period become unprofitable in the crisis period.

3.2.4. A robustness test

One aspect of our empirical analysis which remains unclear is that related to short-selling. The

amount of short-selling allowed on the Indian stock market is not made public. Therefore, in

this paper, we estimate it to be around 76%. This analysis on short-selling is, itself, a

contribution to the literature. We then allowed for short-selling of 100% in our empirical

analysis, as reported in the previous section. We want to test how profits will change if we

reduce the amount of short-selling to something like 76%, which is closest to what we believe

is allowed on the Indian stock market. We do this and report the results in Table 8.

INSERT TABLE 8

The main findings can be summarised as follows:

1. Like with 100% short-selling, four of the six sectoral profits are statistically significant.

2. Like with 100% short-selling, the ranking of sectors does not change much, except that in

two of the three strategies, the energy sector becomes more profitable than the IT sector.

3. Compared with 100% short-selling, there are only very marginal changes in the magnitude

of profits for some sectors, but not all. In three of the four profitable sectors, profits are

higher with 100% short-selling compared to when short-selling is restricted to 76%.

3.3. Results from technical trading rule-based strategies

The results from technical trading profits based on the moving average, momentum and filter

rules, averaged across strategies and over time, are presented in Table 9. Two results are of

particular relevance. First, we notice that unlike the rank-based trading strategy profits, here

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returns from all six sectors are profitable; that is, they are statistically different from zero at the

1% level. This result holds regardless of the month of holding period. Over the six-month

holding period, for example, we notice that both momentum profits (that is, winner minus loser

profits) and the zero-cost portfolio profits (winner plus loser profits) are maximised for the

banking sector followed by the energy sector. Second, the magnitude of profits are higher in

all sectors compared to a rank-based momentum trading strategy as reported earlier.

TABLE 9

For each stock in a sector, winners and losers are identified from buy and sell signals generated

using the three technical trading rules explained earlier. As before, the buy and sell signals are

generated every month and profits are averaged (across the three technical trading strategies)

every month, generating a time-series of winner minus loser and zero-cost portfolios. This is

to say that portfolios are re-balanced every month. The momentum returns are computed as the

difference between returns of winners and losers whereas the portfolio return is computed as

the sum of returns of winners and losers. The figures reported in Panel A of Table 10 are these

time-series averages and their standard deviation. All profits are statistically different from zero

at (at least) the 10% level of significance.

Next we follow Jegadeesh and Titman (1993) and estimate profits based on buying

winners and selling losers. The idea is simple and proceeds as follows. Based on the geometric

mean of returns sectors are ranked from 1 to 6. Sectors ranked 1 and 2 are taken as winner

sectors while the loser sectors are those ranked 5 and 6. The strategy then is to invest equally

in winner sectors and short equally in loser sectors, holding the portfolios for 1-month, 3-

month, and 6-month horizons. Portfolios are rebalanced every month and momentum and zero-

cost portfolio returns are computed. These results are reported in panel B.

This is what we find. First, with respect to individual stock momentum, we find that:

(a) average returns from both winner minus losers and zero-cost portfolios offer positive and

17

statistically significant (at least at the 5% level of significance) returns in all sectors; and (b)

profits, in terms of magnitude, increase with the portfolio holding period. Second, when

portfolios are held for three and six months, zero-cost portfolio returns are maximised for the

IT sector, followed by banking and MNC sectors. Moreover, at the 6-month holding period

average returns from both winner minus loser and zero-cost portfolios are again maximised for

the IT sector, followed by the banking sector. Third, at the industry-level, average returns of

all winner minus loser portfolios are positive and statistically significant at the 1% level. By

comparison, the zero-cost portfolio only offers positive and significant monthly average returns

in 1-month and 6-month holding periods.

In addition, just to compare the performance over different holding periods, we plot in

Figure 1, average (across sectors) cumulative momentum profits for 1-month, 3-month, 6-

month, 12-month, and 24-month horizons. We observe a clear pattern; that is, momentum

profits for holding periods 1-month to 12-month are fairly consistent but profits at the 24-month

horizon are substantially different, and suggest a weaker momentum.

INSERT FIGURE 1

On the whole, there are two clear messages from these individual and industry-based

portfolio returns. The first point is about whether an investor should devise a trading strategy

based on stocks or sectors? There is mixed evidence on this. Consider the 1-month holding

period first. At the industry-level, the momentum portfolio and the zero-cost portfolio offer

investors an average return of 2.97% and 2.57%, respectively, which are in excess of the

corresponding sectoral average returns, except in the case of the banking sector. Therefore, at

a short holding period of 1-month it seems economically significant for an investor to trade on

winner and loser sectors/industry as opposed to winner and loser stocks unless of course the

investor is only keen in investing in the banking sector. At the 3-month horizon, by comparison,

the momentum portfolio of the industry beats the average returns from winner minus loser

18

stocks in each sector, but the average industry zero-cost portfolio makes a loss of 0.41% per

month whereas all zero-cost sectoral portfolios make a statistically significant profit. The

second point relates to the longest holding period—the 6-month portfolio; here, both winner

minus loser and zero-cost portfolios of the industry offer less profits than corresponding profits

from sectoral portfolios. Therefore, where short horizon portfolios favour sectors over the

industry as a whole, long horizon portfolios offer the complete opposite implication for

investors.

Following Barroso and Santa-Clara (2013), we also compute risk-adjusted momentum

returns (Mom*), which is simply a target volatility (which we take as the standard deviation

over the analysis period) multiplied by the actual momentum returns over time normalised by

a time-varying volatility, which we proxy using a rolling-window of six months. We generally

observe that profits from both momentum and zero-cost portfolios are less than the risk-

adjusted profits; however, the results are robust in the sense that average returns from both

portfolios are positive and statistically different from zero.

INSERT TABLE 10

In Table 11, we return to our average profits obtained from the three technical trading

rules. All stocks are collected and sorted on the basis of size (market capitalisation), book-to-

market, and liquidity (number of transactions). We do this in order to test the robustness of our

industry-based results reported in panel B of Table 10, motivated by empirical results presented

in Moskowitz and Grinblatt (1999), amongst others. Under each of these three control

measures, quartiles of stocks are created. For example, for size-based stocks, the first quantile

includes the largest 25% of stocks while quantile four includes the smallest 25% of stocks in

our sample. When performance is controlled by book-to-market ratio, the first quantile includes

19

the 25% of stocks which have the highest book-to-market ratio while the fourth quantile

includes the 25% of stocks with the lowest book-to-market ratio.

Finally, when performance is judged on the basis of liquidity, quantile one includes the

25% most liquid stocks while the 25% of stocks with the least liquidity (that is, the 25% most

illiquid stocks) are in quantile four. For each quantile under each control measure, we identify

winners and losers based on buy and sell signals, identified using technical trading rules. We

equally invest in winners in period “t” and short in losers. From this investment strategy, a

time-series of momentum returns is computed (returns of winners minus returns of losers) and

a time-series of portfolio returns is also computed (returns of winners plus returns of losers).

There are two main findings here. First, profits are positive and statistically significant

regardless of how stocks are sorted. Therefore, that the Indian stock market is profitable holds

across a range of widely used control measures. Second, the most highly liquid stocks (25% of

the most liquid stocks) on the Indian market offer the highest average returns from both the

winner minus loser portfolio and the zero-cost portfolio. This is true regardless of the holding

period.

INSERT TABLE 11

The final part of our analysis is with regard to the difference in profitability results we

obtain. Two aspects of the Indian stock market has become clear. First, the market is profitable.

A range of different strategies tend to offer investors statistically significant monthly returns.

Second, when we estimate profits by sector, we observe that while most strategies support

sectoral profitability to hold for all six sectors, profits tend to be sector-specific. This

heterogeneity in sectoral profits is not surprising but is rather expected. This type of sector-

specific results can best be explained by the “information diffusion” hypothesis, proposed by

Hong et al. (2007), and empirically tested by Westerlund and Narayan (2014a), who show that

different types of financial ratios predict returns of some sectors more successfully than others.

20

Moreover, Hong et al. (2007) test whether industry portfolios predict movements in stock

markets. They find that 14 out of 34 industries can predict movements in stock markets,

suggesting support for the information diffusion hypothesis. More specially, they claim, on the

evidence of this predictability, that the stock market reacts with a delay to information

contained in industry returns. Motivated by these findings, we test whether market returns

predict sectoral returns on the Indian stock market. Essentially, our time-series predictive

regression model has the following form:

𝑆𝑅𝑡 = 𝛼 + 𝛽𝑀𝑅𝑡−1 + 𝜀𝑡

Where 𝑆𝑅 is the sectoral stock return and 𝑀𝑅 is the market return lagged one period

over time 𝑡 which is monthly from January 2001 to December 2012. The returns are computed

as the natural log difference of market price and stock price indices. The null hypothesis is that

the market return does not predict sectoral stock returns. To test for the null hypothesis, we

estimate the model using the generalised least squares estimator proposed by Westerlund and

Narayan (2012, 2014b). The two main features relevant for our predictive regression model

are: (a) predictor endogeneity; and (b) heteroskedasticity. Both these issues characterise

predictive regression models where high frequency data are used and where returns appear on

both sides of the regression model. The results, reported in Table 12, are as follows. First, our

predictor variable—market returns—is endogenous. This endogeneity is present in all sectoral

predictive regression models. Second, the ARCH-LM test on filtered returns based on an

autoregressive model with 12 lags, suggests that the null hypothesis of no heteroskedasticity

can be strongly rejected at the 1% level for both the market returns and all the sectoral returns.

Third, the null hypothesis of no predictability is rejected strongly at the 1% level for all six

sectors and the coefficient on predictability is in the range of 0.264 (pharmaceutical sector) and

0.869 (IT sector). This finding suggests that the sectoral returns react to market returns with

different speeds. For example, a 1% increase in market returns predicts a 0.869% increase in

21

IT sector’s returns but the effect of the market is almost four times smaller on the returns of the

pharmaceutical sector. This is suggestive of a gradual diffusion of information from the market

to its sectors.

INSERT TABLE 12

4. Concluding remarks

In this paper we examine whether, using a range of trading strategies, investors can make

profits from the Indian stock market. Our contributions are three-fold. First, unlike the extant

literature, we examine profitability at the sector-level; yet, we do not ignore the market. We

employ a range of trading strategies, allowing an investor to take long and short positions, and

consistently find that the IT sector is the most profitable. Second, a controversial issue about

which not much public information is available relates to the magnitude of short-selling. We

provide an analysis and estimate the magnitude of short- selling on the Indian market.

We undertake a range of robustness tests, including identifying winners and losers from

technical trading rules and past performance, controlling for industry size, book-to-market, and

liquidity factors, and generating risk-adjusted momentum and zero-cost portfolio profits. All

results point to the profitability of the Indian stock market. Importantly, we discover that some

sectors are relatively more profitable than others. We show that this sector-specific profitability

is due to information diffusion—that is, the market returns, while they do predict all six sectoral

returns, the magnitude of predictability varies from sector-to-sector. So, in our analysis, the

source of information diffusion is not only the evidence that market returns predict stock

returns but also that the speed of predictability is different for different sectors.

There are two main implications emerging from our study. Both relate to prospects for

future studies. First, we believe that future studies, not only on the Indian stock market but on

stock markets in general, should show respect to sectoral heterogeneity in any hypothesis

22

testing that involves sectors of a market. Second, in light of lack of concrete information on

short-selling in the Indian stock exchange, our estimate of the degree of short-selling is merely

a prediction. We emphasise on this point strongly, for we hope future studies will improve

upon our prediction error (if any), and, in doing so, our study sets the motivation for future

research on this subject. We are most excited about these future research outcomes.

23

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28

-40

0

40

80

120

160

200

240

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

12 M 1 M 24 M3 M 6 M

%

Year

Fig. 1. A time-series plot of average cumulative momentum profits. This figure plots the cumulative average

momentum profits. These profits are average over time (December 2003 to December 2012) for the six sectors

for 1-month, 3-month, 6-month, 2-month, and 24-month horizons.

29

Table 1

A summary of trading rules and trading strategies.

Each month, from January 2001 through December 2012, each sector is ranked from one to six based on the

difference between the short-run MA and long-run MA of prior returns, using MA combinations for each strategy

under the respective scenarios. Using the differences between the short-run and long-run MA, we take a long

position in the most attractive sector (rank 1) and a short position in the least attractive sector (rank 6) in the case

of short-selling, or simply take a cash position in the case of no short-selling. The short-run MA parameter ranges

from 1 month to 12 months, while the long-run MA parameter runs from 2 months to 36 months.

Panel A: Scenario I: With 100% short-selling allowed

Strategy MA rules (in number of

months)

Long and short positions

Two [1,2] – [12,36] Long position: rank 1 (100%)

Cash position: rank 2,3,4,5 (0%)

Short position: rank 6 (-100%)

Three [1,2] – [12,36] Long position: rank 1 (50%)

Long position: rank 2,3 (25%)

Cash position: rank 4,5 (0%)


Four [1,2] – [12,36] Long position: rank 1 (70%)

Long position: rank 2 (30%)

Cash position: rank 3 (0%)


Panel B: Scenario II: Short-selling not allowed

Strategy MA rules (in number of

months)

Long and short positions

One: Naive strategy [1,2] – [12,36] Invest equally across 6 sectors


Take no position: rank 2,3,4,5,6 (0%)


Long position: rank 2,3 (25% each)

Take no position: rank 4,5,6 (0%)


Long position: rank (30%)

Panel C: Scenario III: Trading strategies based on limited short-selling



Take no position: rank 2,3,4,5, (0%)

Short position: rank 6(-76%)


Long position: rank 2,3,4,5 (25% each)




Long position: rank 3,4 (38% each)

Short Position: rank 6 (-76%)

30

Table 2 Descriptive Statistics.

This table reports the descriptive statistics relating to market (CNX Nifty) and sectoral returns. Returns are

computed as (𝑃𝑡 𝑃𝑡−1⁄ − 1) ∗ 100 , where 𝑃 is the price index. Mean returns, standard deviation, skewness, and

kurtosis statistics are reported. We also report the coefficient from the AR(1) model of returns and it t-statistic in

the parenthesis (see Column III). Mean AR(1) JB_Q-stat

(24)

Std.Dev. Skewness Kurtosis

S&P CNX Nifty 1.2554 0.3516***

(4.4445)

36.183*

(0.053)

6.3394 -0.4282 4.4843

Banking 2.0732 0.3021***

(0.3753)

34.713*

(0.073)

8.3150 0.1257 4.0234

IT 0.1647 0.2221***

(2.6948)

34.2*

(0.081)

11.6485 -3.2839 26.2201

FMCG 1.3337 0.2343***

(2.8501)

20.47

(0.670)

5.0056 -0.4906 3.4735

Pharmaceutical 1.4228 0.3193***

(0.0001)

34.099*

(0.083)

5.4157 -0.5877 4.4917

Energy 1.6408 0.2972***

(3.7316)

31.494

(0.140)

7.0763 -0.0413 4.5763

MNC 1.1504 0.3333***

(0.0001)

34.32*

(0.079)

5.6119 -0.4685 4.3218

31

Table 3

Performance of long and short strategies with short-sell restriction (short-selling not allowed).

This table reports monthly mean returns without short-selling. The row with “Prob>0” denotes the percentage to

total months in which profits from a given strategy exceeds zero. The row with “Prob>market” represents the

percentage of total months in which profits from a given strategy exceeds the market return. Here, we use the S&P

CNX Nifty as a benchmark for the market. The paired t-test indicates whether or not the return from each of the

strategies is significantly greater than the market return. ***, ** and * represent statistical significance at the 1%,

5% and 10% levels, respectively.

Banking Energy IT MNC FMCG Pharmaceutical

Panel A: Strategy I

Mean Rets % 0.303** 0.164 0.175 0.225** 0.294*** 0.217**

Std. Dev. 1.459 1.107 1.150 0.971 0.852 0.904

Prob. > 0 (%) 57.273 61.818 58.182 61.818 68.182 60.000

Prob. > Market (%) 34.545 30.909 33.636 31.818 35.255 32.727

Paired t-test -1.931 -2.211 -2.187 -2.109 -1.989 -2.2128

Panel B: Strategy II

Mean Rets % 0.286 0.628** 0.815** 0.373** 0.587*** -0.215

Std. Dev. 2.871 2.873 3.617 1.929 2.168 2.536

Prob. > 0 (%) 9.091 8.182 20.000 3.636 20.000 6.364

Prob. > Market (%) 34.545 37.273 40.909 35.455 44.545 33.636

Paired t-test -1.903 -1.23 -0.85 -1.735 -1.342 -2.661

Panel C: Strategy III

Mean Rets % 0.297 0.379** 0.418** 0.377*** 0.447*** 0.010

Std. Dev. 1.971 1.701 2.095 1.291 1.447 1.408

Prob. > 0 (%) 27.27 21.82 33.64 35.45 46.36 26.36

Prob. > Market (%) 36.36 33.64 38.18 31.82 37.27 31.82

Paired t-test -1.942 -1.745 -1.640 -1.783 -1.633 -2.461

Panel D: Strategy IV

Mean Rets % 0.283 0.531*** 0.618** 0.390*** 0.556*** -0.129

Std. Dev. 2.225 2.192 2.696 1.481 1.730 1.845

Prob. > 0 (%) 20.00 14.55 30.00 16.36 34.55 12.73

Prob. > Market (%) 35.45 35.45 39.09 32.73 37.27 30.91

Paired t-test -1.887 -1.462 -1.27 -1.772 -1.456 -2.656

32

Table 4

Settlement in Cash Segment of NSE.

This table shows the details of settlement of trade in the cash segment of NSE, such as the traded quantity,

deliverable quantity, and the percentage of deliverable quantity to traded quantity for the period 2000-2001 to

2010-2011.

Year Traded quantity

(lakh)

Deliverable quantity

(lakh)

% of Delivered quantity

to traded quantity

2001-2002 274695 59299 21.59

2002-2003 365403 82353 16.5

2003-2004 704537 175546 24.92

2004-2005 787996 202276 25.67

2005-2006 818438 227239 27.76

2006-2007 850510 239070 28.11

2007-2008 148123 367970 24.84

2008-2009 141893 275270 21.59

2009-2010 220588 47481 21.86

2010-2011 181091 49737 27.47

Average 23.6

Source: SEBI Bullen (various months).

33

Table 5

Performance of long and short strategies with short-selling allowed (100%). This table reports monthly mean returns based on short-selling. The row with “Prob>0” denotes the percentage to

total months in which profits from a given strategy exceeds zero. The row with “Prob>market” represents the






Panel A: Strategy II

Mean Rets % 0.125 0.822** 0.889** 0.398* 0.433** -0.365

Std. Dev. 5.764 4.167 4.770 2.194 3.092 2.901

Prob. > 0 (%) 19.09 14.55 35.45 4.55 27.27 10.00

Prob. > Market (%) 35.45 35.45 41.82 34.55 44.55 32.73

Paired t-test -1.763 -0.824 -0.687 -1.692 -1.532 -2.868

Panel B: Strategy III

Mean Rets % 0.823*** 0.684*** 1.138*** 0.394*** 0.630*** 0.017

Std. Dev. 2.299 2.061 2.562 1.296 1.630 1.503

Prob. > 0 (%) 48.79 34.55 60.91 38.18 57.27 31.82

Prob. > Market (%) 37.27 33.64 41.82 30.91 39.09 32.73

Paired t-test -0.952 -1.208 -0.403 -1.779 -1.332 -2.868

Panel C: Strategy IV

Mean Rets % 0.165 0.686** 0.645* 0.326* 0.402** -0.240

Std. Dev. 5.527 3.710 4.144 1.572 2.793 2.355

Prob. > 0 (%) 30.00 20.00 45.45 16.36 41.82 17.27

Prob. > Market (%) 36.36 34.55 40.00 32.73 38.18 30.91

Paired t-test -1.786 -1.045 -1.69 -1.853 -1.594 -2.74

34

Table 6

Performance of long and short strategies with short-selling allowed (100%): Pre-crisis period. This table reports monthly mean returns from short-selling over the pre-crisis period. The row with “Prob>0”

denotes the percentage to total months in which profits from a given strategy exceeds zero. The row with

“Prob>market” represents the percentage of total months in which profits from a given strategy exceeds the market

return. Here, we use the S&P CNX Nifty as a benchmark for the market. The paired t-test indicates whether or

not the return from each of the strategies is significantly greater than the market return.



Mean Rets % 0.125 0.970* 0.159 0.600 0.992* 0.017

Std. Dev. 5.286 3.585 4.358 2.627 3.691 1.785

Prob. > 0 (%) 19.149 12.766 31.915 6.383 31.915 8.511

Prob. > Market (%) 27.660 29.787 29.787 27.660 38.298 23.404

Paired t-test -1.871 -0.778 -1.489 -1.673 -1.151 -2.242


Mean Rets % 0.644*** 0.850*** 1.146*** 0.594*** 1.321*** 0.272

Std. Dev. 2.093 1.692 2.858 1.557 1.758 0.923

Prob. > 0 (%) 19.149 12.766 31.915 6.383 31.915 8.511

Prob. > Market (%) 36.170 23.404 34.043 23.404 31.915 25.532

Paired t-test -1.154 -1.259 -0.857 -1.763 -0.926 -2.209


Mean Rets % -0.167 0.351 0.125 0.517** 0.939* 0.222

Std. Dev. 5.527 3.710 4.144 1.572 2.793 2.355

Prob. > 0 (%) 25.532 19.149 46.809 19.149 46.809 14.894

Prob. > Market (%) 34.043 27.660 27.660 25.532 27.660 23.404

Paired t-test -1.867 -1.194 -1.687 -1.869 -1.265 -2.209

35

Table 7

Performance of long and short strategies with short-selling allowed (100%): Crisis period.

This table reports mean monthly returns from short-selling over the crisis period. The row with “Prob>0” denotes

the percentage to total months in which profits from a given strategy exceeds zero. The row with “Prob>market”

represents the percentage of total months in which profits from a given strategy exceeds the market return. Here,

we use the S&P CNX Nifty as a benchmark for the market. The paired t-test indicates whether or not the return

from each of the strategies is significantly greater than the market return. ***, ** and * represent statistical

significance at the 1%, 5% and 10% levels, respectively.



Mean Rets % 0.017 1.070* 1.327** 0.250 0.036 -0.626

Std. Dev. 6.110 4.533 5.019 1.850 2.562 3.453

Prob. > 0 (%) 19.048 15.873 38.095 3.175 23.810 11.111

Prob. > Market (%) 41.270 39.683 50.794 39.683 49.206 39.683

Paired t-test -0.776 0.389 0.375 -0.781 -1.020 -1.776


Mean Rets % 0.989*** 0.739** 1.145*** 0.248 0.144 -0.161

Std. Dev. 2.431 2.290 2.362 1.072 1.347 1.781

Prob. > 0 (%) 50.794 36.508 60.317 34.921 47.619 30.159

Prob. > Market (%) 38.095 41.270 47.619 36.508 44.444 38.095

Paired t-test 0.049 -0.502 0.213 -0.831 -0.717 -1.197


Mean Rets % 0.121 0.901*** 0.984*** 0.192 0.035 -0.547

Std. Dev. 5.986 4.062 4.454 1.420 2.357 2.677

Prob. > 0 (%) 34.921 22.222 46.032 14.286 38.095 19.048

Prob. > Market (%) 37.089 41.270 49.206 38.095 46.032 36.508

Paired t-test 0.034 0.551 0.465 0.157 -0.176 -0.291

36

Table 8

Performance of long and short strategies with limited short-selling allowed (maximum of 76% short-selling). This table reports mean monthly returns from limited short-selling. The row with “prob>0” denotes the percentage

to total months in which profits from a given strategy exceeds zero. The row with “Prob>market” represents the







Mean Rets % 0.251 0.735** 0.852* 0.370* 0.579** -0.294

Std. Dev. 4.863 3.680 4.580 2.048 2.779 2.817

Prob. > 0 (%) 31.818 17.273 45.455 13.636 40.909 17.273

Prob. > Market (%) 35.455 37.273 42.727 34.545 45.455 34.545

Paired t-test -1.579 0.970 0.742 -1.729 -1.314 -2.740


Mean Rets % 0.320 0.723** 0.705* 0.462** 0.586*** 0.104

Std. Dev. 4.711 3.379 3.836 2.032 2.542 2.525

Prob. > 0 (%) 53.240 56.364 58.256 60.000 67.273 58.182

Prob. > Market (%) 39.091 33.636 40.000 32.165 41.818 32.145

Paired t-test -1.558 -1.008 -1.058 -1.569 -1.329 -1.015


Mean Rets % 0.443 0.727** 0.677* 0.524** 0.645** 0.294

Std. Dev. 4.926 3.538 3.901 2.473 2.671 2.728

Prob. > 0 (%) 54.545 55.455 59.091 60.000 66.364 58.182

Prob. > Market (%) 38.182 32.727 40.909 32.727 40.000 31.818

Paired t-test -1.297 -1.095 -1.064 -1.448 -1.226 -1.805

37

Table 9

Average profits from technical trading rules.

This table reports average profits obtained from three different technical trading rules; namely, moving average

(MA), momentum, and filter rules. The MA rule is based on short-term MA and long-term MA of 1, 2, and 3

months, and 9 and 12 months, respectively. The momentum rule generates a buy signal whenever current stock

price is higher than or equal to “m” months ago, where we set m = 9 and 12, and a sell signal is generated whenever

the current price is less than price “m” months ago. Finally, the filter rule generates a buy signal whenever current

return is greater than or equal to 110% of the average return based on “t-m”, where we set “m” equals to 12.

Conversely, a sell signal is generated whenever current return is less than the 110% of average return based on “t-

m”. For individual stocks in each of the six sectors, we identify winners and losers based on these buy and sell

signals. We equally investment in winners in period “t” and short in losers. Therefore, the momentum (mom)

returns are simply returns of winners minus returns of losers and the portfolio return is simply the returns of

winners plus returns of losers. The corresponding standard deviation of momentum and portfolio returns for each

sector and for each holding period—that is, for 1-month, 3-month, and 6-month—are also reported. ***, ** and

* represent statistical significance at the 1%, 5% and 10% levels, respectively.

1-month 3-month 6-month

Banking

Mom. Portfolio Mom. Portfolio Mom. Portfolio

Return 2.341*** 1.807*** 2.491*** 3.271* 2.569*** 3.613***

SD 8.433 7.405 5.991 5.003 4.433 3.529

Energy


Return 1.773** 1.691*** 1.979* 2.498*** 2.087*** 2.981***

SD 7.058 6.283 5.107 4.423 4.105 3.31

FMCG


Return 1.522*** 1.574*** 1.575*** 2.124*** 1.606*** 2.290***

SD 5.114 4.506 3.471 2.842 2.698 2.048

IT


Return 0.334 1.302* 0.863 2.404*** 1.045* 3.398***

SD 11.26 7.77 7.53 6.036 5.848 4.665

MNC


Return 1.303*** 1.331*** 1.409*** 2.172*** 1.498*** 2.434***

SD 5.792 5.112 4.181 3.42 3.349 2.638

Pharmaceutical


Return 1.477*** 1.075*** 1.586*** 1.897*** 1.655*** 2.010***

SD 5.537 4.549 3.863 3.224 2.884 2.461

38

Table 10

Individual stock momentum by industry and industry momentum.

This table reports two sets of results organised into Panels A and B. In panel A, we have individual stock

momentum return and portfolio return and their standard deviations for 1-month, 3-month and 6-month holding

periods. Using technical trading rules, buy and sell signals are generated and winner and loser stocks are identified.

Based on these winner and loser stocks, momentum (MP) and zero-cost portfolios (ZCP) are generated. Panel B

has the corresponding industry momentum and portfolio returns and their standard deviations. Instead of ranking

stocks, here we rank the six sectors. Sectors are ranked using the geometric average and those ranked 1 and 2 are

taken as winner sectors while the loser sectors are those ranked 5 and 6. The strategy then is investment equally

in winner sectors and shorting equally in loser sectors and holding the portfolios for 1-month, 3-month, and 6-

month horizons. Mom^ is risk-adjusted momentum returns following the proposal of Barroso and Santa-Clara

(2013), which is simply a target volatility (which is a constant and we set this to the sample standard deviation)

multiplied by the actual momentum returns over time normalised by a time-varying volatility, which we proxy

using a rolling-window of six months. ***, ** and * represent statistical significance at the 1%, 5% and 10%

levels, respectively.

Panel A: Individual stock momentum


Banking

MP Mom^ ZCP MP Mom^ ZCP MP Mom^ Portfolio

Return 3.392*** 3.7367*** 2.754*** 3.499*** 4.6709*** 4.381*** 3.668*** 6.7656*** 4.774***

SD 13.4150 11.1022 11.3650 8.7190 10.4182 7.2420 6.5610 10.0251 5.5300

Energy

MP Mom^ ZCP MP Mom^ ZCP MP Mom^ ZCP

Return 2.243*** 2.4928*** 1.684*** 2.259*** 2.5613*** 2.872*** 2.364*** 4.0199*** 3.280***

SD 11.8510 9.3465 9.9900 6.8630 6.9064 5.8580 5.2660 8.1776 4.4170

FMCG


Return 2.166*** 3.2973*** 2.107*** 2.209*** 3.2844*** 2.848*** 2.302*** 5.0557*** 3.076***

SD 8.5630 5.6463 7.6250 5.0160 5.5595 4.2460 3.8420 7.4907 3.1410

IT


Return 2.326*** 2.8890*** 2.668*** 2.968*** 4.1617*** 4.397*** 3.784*** 5.0551*** 5.744***

SD 15.2980 10.5348 12.7600 10.9770 11.9279 9.0150 10.7660 18.1241 9.3280

MNC


Return 2.472*** 3.9820*** 2.328*** 2.706*** 5.5097*** 3.523*** 3.020*** 6.4161*** 4.073***

SD 11.2170 8.9614 9.5780 7.3350 11.2295 6.1630 6.2610 9.4742 5.3640

Pharmaceutical


Return 2.462*** 2.9173*** 2.094*** 2.596*** 3.6675*** 3.183*** 2.842*** 6.2759*** 3.662***

SD 10.7040 6.8730 9.1650 6.7720 6.5431 5.8570 5.5040 7.8865 4.8240

Panel B: Industry momentum


MP ZCP MP ZCP MP ZCP

Return 2.972*** 2.568*** 5.723*** -0.4100 1.615*** 2.055***

SD 12.3240 5.4860 11.4670 2.3580 0.4490 0.4490

39

Table 11

Profits sorted on size, book-to-market, and liquidity.

This table reports profits that are controlled for size (Panel A), book-to-market (Panel B) and liquidity (Panel C).

Using combinations of the three technical trading rules, the performance of portfolios were categorised by size

(market capitalisation), book-to-market, and liquidity (number of transactions). Quartiles are formed under each

of the three control variables. For example, for size, the first quantile includes the largest 25% of stocks while

quantile four includes the smallest 25% of stocks in our sample. When performance is controlled by book-to-

market ratio, the first quartile includes the 25% of stocks which have the highest book-to-market ratio while the

fourth quantile includes the 25% of stocks with the lowest book-to-market ratio. Finally, when performance is

judged on the basis of liquidity, quartile one includes the 25% of most liquid stocks while the 25% of stocks with

the least liquidity (that is, the 25% most illiquid stocks) are in quantile four. For each quantile under each control

measure, we identify winners and losers based on buy and sell signals identified using technical trading rules. We

equally invest in winners in period “t” and short in losers. From this investment strategy, a time-series of

momentum returns are computed (returns of winners minus returns of losers) and a time-series of portfolio return

is also computed (returns of winners plus returns of losers). This table reports the average of these returns. The

corresponding standard deviation of momentum and portfolio returns for each quartile for each holding period—

that is, for 1-month, 3-month, and 6-month—are also reported. ***, ** and * represent statistical significance at

the 1%, 5% and 10% levels, respectively.

Panel A: Size-based


Quantile 1 Momentum Portfolio Momentum Portfolio Momentum Portfolio

Return 2.241*** 1.896*** 2.276*** 2.940*** 2.355*** 3.228***

SD 10.299 8.871 6.091 5.133 4.534 3.726


Return 2.667*** 2.240*** 2.858*** 3.526*** 3.102** 4.022***

SD 11.781 9.885 6.949 5.889 1.803 5.820


Return 3.169*** 2.882*** 3.471 4.193 3.898*** 4.884***

SD 12.410 10.844 8.569 7.540 7.409 6.574


Return 2.616*** 2.550*** 2.917*** 3.941*** 3.399*** 4.795***

SD 13.582 11.525 9.078 7.561 8.373 7.174

Panel B: Book-to-market-based



Return 1.885*** 1.927*** 2.051*** 2.944*** 2.258*** 3.445***

SD 10.378 8.691 6.485 5.198 5.267 4.286


Return 2.922*** 2.634*** 3.094*** 3.905*** 3.354*** 4.387***

SD 11.856 10.211 7.747 6.572 6.270 5.420


Return 2.655*** 2.271*** 2.885*** 3.551*** 3.172*** 4.158***

SD 11.867 10.184 7.389 6.496 6.580 5.694


Return 3.015 2.965 3.303 4.336 3.776*** 5.054***

SD 13.802 11.860 8.893 7.661 7.841 6.776

Panel C: Liquidity-based



Return 2.651*** 2.286*** 2.786*** 3.586*** 2.978*** 4.051***

SD 11.640 9.955 7.251 6.131 5.625 4.689


Return 2.390*** 2.125*** 2.514*** 3.416*** 2.733*** 3.950***

SD 12.715 10.603 7.419 6.215 6.278 5.196


40

Return 2.780*** 2.656*** 3.059*** 3.906*** 3.436*** 4.527***

SD 12.205 10.492 8.171 6.979 6.935 5.997


Return 2.768*** 2.641*** 3.052*** 3.850*** 3.513*** 4.564***

SD 11.430 10.006 7.780 6.730 7.289 6.476

41

Table 12

Results on sectoral return predictability.

This table reports three findings. Column 2 contains the FGLS test examining the null hypothesis that market

return predicts sectoral returns for India. Six sectors, namely, banking, energy, FMCG, IT, MNC, and

pharmaceutical, are considered. The coefficient on beta and the FGLS t-test statistic of Westerlund and Narayan

(2012, 2014) are reported in parenthesis. Column 3 is about whether market return for each of the six sectors is

endogenous in our proposed predictive regression model. To test for endogeneity, the residuals from the proposed

predictive regression model is regressed on residuals obtained from the first-order autoregressive model of market

returns. The slope coefficient is reported together in parenthesis with the t-test statistic examining the null

hypothesis that the slope coefficient is zero. The final column reports the null hypothesis of no ARCH effects in

market returns and in sectoral returns. *** denotes statistical significance at the 1% level.

Predictability test:

𝛽 = 0

Endogeneity test Heteroskedasticity test (p-value)

Sectors Market returns Sectoral returns

Banking 0.486***

(4.894)

1.166***

(20.115)

0.000 0.000

Energy 0.281***

(3.186)

0.920***

(20.928)

0.000 0.000

FMCG 0.126**

(1.941)

0.514***

(11.290)

0.000 0.000

IT 0.869***

(3.323)

1.714***

(5.825)

0.000 0.000

MNC 0.269***

(3.772)

0.779***

(22.364)

0.000 0.000

Pharmaceutical 0.264***

(3.880)

0.669***

(13.991)

0.000 0.000

Financial Econometrics Series SWP 2014/14 How Profitable ... · Majumder, 2013; Mishra et al.,...

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Transcript of Financial Econometrics Series SWP 2014/14 How Profitable ... · Majumder, 2013; Mishra et al.,...