momentum and contrarian investment strategies...

CHAPTER VI

MOMENTUM AND CONTRARIAN INVESTMENT

STRATEGIES

The Efficient Market Hypothesis (EMH) (Fama, 1970) suggests that

prices are theoretically unpredictable and therefore there is no extra profit

existing in the market. However in the real world situation EMH is strongly

challenged by many financial anomalies. Many studies conducted by

academicians and practitioners have recognised that average stock returns are

related to past performance and the stock returns are predictable based on past

returns. Number of researchers report that past losers (negative or lowest return-

stocks) outperform past winners. De Bondt and Thaler (1985) document those

stocks with extreme capital losses in the past; the so-called losers will outperform

those with extreme capital gains, the so-called winners in the future 3-5 years.

Jegadeesh and Titman (1993) also finds that stocks that perform the best (worst)

over a 3 to 12 months period tend to continue to perform well (poorly) over the

subsequent 3 to 12 months.

Many studies like (Barberish, Shleifer & Vishny, 1998) also concluded

with the opinion that when return autocorrelations are positive and statistically

significant, investors could generate positive and significant profits by using the

momentum strategy. If return autocorrelations are negative and statistically

significant, investors could earn profits by using the contrarian strategy (Daniel,

Hirshleifer & Subramahnyam, 1998). Thus, the variance ratio may also suggest

153

that there exists profit opportunities for contrarian and momentum strategies

(Pan, Liano, & Huang, 2004).

Momentum and contrarian strategies are two opposite investment

strategies which try to make excess returns by investigating historical price or

return data in order to forecast the future trend of stock performance. Momentum

strategy believes that stocks which have performed well in past will be doing so,

also in the future. It buys stocks with good historical performance and sells stocks

which have done worse.(Figure 6.1)

Fig. 6.1

Momentum Strategy

Source: Narasimhan M, Agarwal and Jain, (2004), “An Analysis of Contrarian and Momentum

Strategies in Indian Stock Market”, NIBM, Vol: XXIII, No.1

Under reaction Under reaction Bad Good

Price falls less than

Justified

Sell

Price Correction

Buy again

Price rises less than

Justified

Buy

Price Correction

Books profits

News

154

Contrarian strategy on the other hand believes that stocks whose historical

performance is bad are going to do better in the future because they would be

under priced and historical winner stocks are going to come down because

majority of them would be over-priced, so it suggests buying losers and selling

winners based on historical data. (Conrad and Kaul 1998) (Figure 6.2)

Fig. 6.2

Contrarian Strategy

Source: Narasimhan M, Agarwal and Jain, (2004), “An Analysis of Contrarian and Momentum

Strategies in Indian Stock Market”, NIBM, Vol: XXIII, No.1

Overreaction Overreaction Bad Good

Price falls more than

Justified

Buy

Price Correction

Book Profits

Price rises more than

Justified

Sell

Price Correction

Buy Again

News

155

The empirical evidence of success of both these strategies is strong and

extensive, and these results have created a lot of interest in this area among

institutional investors. For example, the contrarian strategy has become a widely

used investment strategy in many funds and other investments.

Number of studies has been conducted to test the effectiveness of these

two strategies in earning superior returns and for determining the factors of their

profitability. De Bondt and Thaler (1985) were one of the first categories of

researchers arguing that contrarian strategy outperforms the market. Ever since

the debate on that area has been strong .e.g. Chan (1988), Lo and McKinlay

(1990),Jegadeesh (1990), Chopra, Lakonishok and Ritter (1992), Lakonishok et

al. (1994),Conrad and Kaul (1993 and 1998) and Larkomaa (1999) all gave

evidence for profitability by using contrarian strategy.

The first main work when momentum strategy is considered is Jegadeesh

and Titman (1993). Many researchers like Chan et al. (1996 and 1999),

Rouwenhorst (1998 and 1999), Conrad and Kaul (1998), Moskowitz and

Grinblatt (1999), Hong et al.(2000), Griffin et al. (2003), and Avramov and

Chordia (2006) studied about the possibility of making profits by using

momentum strategy.

The evidence that both, momentum and contrarian, strategies can earn

abnormal returns is very strong. However, the reasons behind the success of these

strategies are still very controversial. The aim of this study is to demonstrate the

contrarian and momentum investment strategies, their profitability in Indian stock

market and reasons explaining their existence.

156

6.1 Sample and Methodology

The methodology adopted by researcher is similar to the one which is adopted

in studies like Narasimhan, Agarwal and Jain (2005), Moskowitz and Grinblatt (1999)

and many other studies both in Indian markets and other emerging markets.

Steps involved in the formation and evaluation of portfolios for studying

Momentum and Contrarian strategies are as follows:

1. Mean of daily returns of stocks for the formation period was taken and then

ranked them in the descending order. For this study the formation period was

one month. Based on the ranking, two equal weight portfolios were formed-

one comprising of top seven stocks called as winner portfolio and other

comprising of bottom seven stocks called as loser portfolio.

2. The daily returns of these two portfolios over the next H-week holding period

were computed. H takes the value of 2 to 8 weeks.

3. Momentum returns are calculated as the mean of the daily returns arising from

the winner portfolio for the holding period i.e H value

4. Contrarian returns are calculated as the mean of the daily returns arising from

the loser portfolio for the holding period i.e H value

5. The performance of momentum and contrarian portfolios is evaluated by

comparing with the daily index returns during respective periods.

6. The portfolio formation and evaluation process is repeated for the years 2004-

2009 with formation period of one month and holding period of 2,3,4,5,6,7 and

8 weeks respectively.

157

7. To study the profitability of momentum and contrarian strategies for

various formations and holding periods, t-test was used. It was used to find

out whether momentum and contrarian strategies yield significant positive

returns when compared with the bench mark, i.e. index return.

First step of the researcher for testing the effectiveness of the two

investment strategies namely momentum and contrarian was to establish the

entire study period in to various formation periods of one month each, by taking

daily returns. Daily returns of the shares included in the construction of Nifty

index and whose data which was available for the whole 6 years were taken for

the study. So the sample size was 29 companies shares, the results of which are

given in the following tables. Mean of daily returns had been taken for the study.

6.2 Analysis of momentum and Contrarian Strategies

This section is divided in to three parts. First part explains the

construction of formation period and the selection of the winning portfolio and

Loser Portfolio. Next part of this section discusses on the formation of holding

periods out of the portfolio constructed on equal weight basis and the

computation of average returns. Average returns of winner portfolios are being

compared with the average returns of looser portfolio. Index returns are

calculated to provide a benchmark for all these comparisons.

Researcher also employed T-test to compute the level of significance of

mean returns. The results of the test are given in the third section.

158

Table 6.1

Returns in Formation Period for

29 companies (Jan 2004)

Table 6.2

Returns in Formation Period

for 29 companies (April 2004)

Company Average

Return

Company

Average

Return

TATA POWER 0.00853 UNITECH 0.00854

MARUTI 0.00825 CIPLA 0.00835

TATA MOTORS 0.00779 WIPRO 0.00680

GRASIM 0.00507 GRASIM 0.00678

PNB 0.00455 SUN PHARMA 0.00589

SBIN 0.00349 PNB 0.00573

M & M 0.00282 HCL 0.00466

ITC 0.00181 ACC 0.00385

SUN PHARMA 0.00138 RANBAXY 0.00378

ABB 0.00082 MARUTI 0.00303

BHEL 0.00065 RELCAPITEL 0.00268

HDFC 0.00036 ICICI BANK 0.00263

RELCAPITEL 0.00000 SIEMENS 0.00230

ACC -0.00002 SBIN 0.00213

HERO HONDA -0.00018 SAIL 0.00159

RELIANCE -0.00063 JINDAL 0.00124

HCL -0.00069 INFOSYS 0.00065

ICICI BANK -0.00094 ONGL 0.00063

UNITECH -0.00182 GAIL 0.00027

HDFC BANK -0.00292 ABB 0.00013

INFOSYS -0.00361 ITC -0.00027

ONGL -0.00408 TATA POWER -0.00031

RANBAXY -0.00512 M & M -0.00070

CIPLA -0.00558 HDFC BANK -0.00083

WIPRO -0.00580 HERO HONDA -0.00203

SAIL -0.00700 TATA

MOTORS -0.00213

GAIL -0.00813 RELIANCE -0.00271

SIEMENS -0.00852 BHEL -0.00422

JINDAL -0.01047 HDFC -0.00558

Source: Computed from data source Source: Computed from data source

159

Table 6.3


29 companies (July 2004)

Table 6.4


for 29 companies (Oct 2004 )

Company

Average

Return

Company Average

Return

SAIL 0.01877 INFOSYS 0.00615

JINDAL 0.01004 UNITECH 0.00553

ITC 0.00781 WIPRO 0.00462

CIPLA 0.00744 JINDAL 0.00457

GAIL 0.00626 BHEL 0.00356

RELIANCE 0.00536 MARUTI 0.00292

TATA POWER 0.00484 SUN PHARMA 0.00281

INFOSYS 0.00478 SAIL 0.00270

HDFC 0.00422 SIEMENS 0.00192

TATA MOTORS 0.00422 ICICI BANK 0.00184

ICICI BANK 0.00386 HDFC 0.00124

ONGL 0.00379 ONGL 0.00088

MARUTI 0.00246 HDFC BANK 0.00061

BHEL 0.00244 M & M 0.00054

SIEMENS 0.00204 ABB 0.00031

HCL 0.00204 GAIL 0.00014

UNITECH 0.00142 TATA

MOTORS 0.00010

RANBAXY 0.00128 RANBAXY 0.00006

WIPRO 0.00112 RELIANCE -0.00028

ABB 0.00080 HCL -0.00096

M & M 0.00064 ITC -0.00191

HDFC BANK 0.00050 TATA POWER -0.00301

RELCAPITEL 0.00046 CIPLA -0.00326

SBIN 0.00020 GRASIM -0.00338

ACC -0.00083 PNB -0.00338

SUN PHARMA -0.00153 SBIN -0.00343

PNB -0.00185 HERO HONDA -0.00354

GRASIM -0.00246 ACC -0.00358

HERO HONDA -0.00734 RELCAPITEL -0.00446


160

Table 6.5


29 companies (Jan 2005 )

Table 6.6


for 29 companies (Apr 2005 )

Company Average

Return

Company

Average

Return

HDFC BANK 0.00440 UNITECH 0.00430

ACC 0.00286 SUN PHARMA 0.00392

ITC 0.00243 SIEMENS 0.00367

SIEMENS 0.00171 ITC 0.00330

RELCAPITEL 0.00154 CIPLA 0.00123

SAIL 0.00140 HDFC 0.00120

HDFC 0.00097 ABB 0.00024

ABB 0.00046 ACC -0.00043

GRASIM 0.00027 BHEL -0.00049

PNB -0.00035 HDFC BANK -0.00143

SBIN -0.00071 TATA

MOTORS -0.00220

ONGL -0.00076 TATA POWER -0.00221

RELIANCE -0.00108 MARUTI -0.00285

INFOSYS -0.00114 GRASIM -0.00285

TATA POWER -0.00131 WIPRO -0.00303

M & M -0.00138 RELIANCE -0.00337

ICICI BANK -0.00138 GAIL -0.00365

TATA MOTORS -0.00151 HERO HONDA -0.00385

MARUTI -0.00155 ONGL -0.00420

JINDAL -0.00208 RANBAXY -0.00545

UNITECH -0.00272 JINDAL -0.00549

BHEL -0.00284 ICICI BANK -0.00595

HCL -0.00290 HCL -0.00683

WIPRO -0.00341 M & M -0.00686

GAIL -0.00419 SBIN -0.00703

CIPLA -0.00550 PNB -0.00707

HERO HONDA -0.00571 RELCAPITEL -0.00750

SUN PHARMA -0.00588 INFOSYS -0.00868

RANBAXY -0.00755 SAIL -0.01001


161

Table 6.7


29 companies (Jul 2005 )

Table 6.8



Company Average

Return

Company

Average

Return

ICICI BANK 0.01302 INFOSYS -0.00047

M & M 0.00936 ABB -0.00099

BHEL 0.00865 UNITECH -0.00121

JINDAL 0.00814 WIPRO -0.00187

UNITECH 0.00712 RELIANCE -0.00203

SBIN 0.00672 MARUTI -0.00230

HDFC BANK 0.00668 HDFC -0.00256

TATA MOTORS 0.00630 HERO HONDA -0.00260

ACC 0.00594 CIPLA -0.00397

RELCAPITEL 0.00590 SIEMENS -0.00408

GRASIM 0.00556 M & M -0.00412

PNB 0.00517 SUN PHARMA -0.00423

ABB 0.00513 RELCAPITEL -0.00451

RELIANCE 0.00512 BHEL -0.00468

SAIL 0.00491 ACC -0.00500

SIEMENS 0.00381 HCL -0.00556

HERO HONDA 0.00365 HDFC BANK -0.00559

HDFC 0.00341 SBIN -0.00596

SUN PHARMA 0.00310 PNB -0.00599

CIPLA 0.00293 ITC -0.00656

HCL 0.00173 ONGL -0.00736

MARUTI 0.00130 TATA

MOTORS -0.00806

TATA POWER 0.00123 GRASIM -0.00870

GAIL 0.00082 ICICI BANK -0.00882

ITC 0.00047 GAIL -0.00917

WIPRO -0.00050 TATA POWER -0.00988

INFOSYS -0.00170 JINDAL -0.01079

ONGL -0.00230 SAIL -0.01280

RANBAXY -0.03262 RANBAXY -0.01801


162

Table 6.9



Table 6.10



Company Average

Return

Company

Average

Return

UNITECH 0.02116 UNITECH 0.04390

ABB 0.01495 ACC 0.01477

BHEL 0.01372 RELIANCE 0.01273

SIEMENS 0.01163 GRASIM 0.01007

MARUTI 0.00880 RELCAPITEL 0.00555

HCL 0.00811 RANBAXY 0.00453

WIPRO 0.00754 HDFC BANK 0.00410

HDFC 0.00648 BHEL 0.00162

TATA MOTORS 0.00558 ITC 0.00150

ITC 0.00538 MARUTI 0.00145

GAIL 0.00536 ABB 0.00119

M & M 0.00516 INFOSYS 0.00098

RANBAXY 0.00496 SUN PHARMA 0.00013

RELCAPITEL 0.00463 SAIL 0.00012

TATA POWER 0.00445 ONGL 0.00001

ACC 0.00428 TATA

MOTORS -0.00089

HDFC BANK 0.00365 M & M -0.00090

GRASIM 0.00306 ICICI BANK -0.00098

SAIL 0.00272 HDFC -0.00114

ONGL 0.00212 JINDAL -0.00134

ICICI BANK 0.00123 WIPRO -0.00196

SUN PHARMA 0.00109 SIEMENS -0.00245

HERO HONDA 0.00080 HERO HONDA -0.00317

CIPLA 0.00031 TATA POWER -0.00318

JINDAL -0.00001 SBIN -0.00421

PNB -0.00060 PNB -0.00427

SBIN -0.00096 GAIL -0.00538

INFOSYS -0.00160 HCL -0.00776

RELIANCE -0.00986 CIPLA -0.03749


163

Table 6.11



Table 6.12



Company Average

Return

Company

Average

Return

PNB 0.00857 ABB 0.01063

ICICI BANK 0.00655 INFOSYS 0.00763

CIPLA 0.00497 SIEMENS 0.00736

GRASIM 0.00495 M & M 0.00726

SBIN 0.00462 UNITECH 0.00644

ACC 0.00406 HCL 0.00635

SUN PHARMA 0.00319 ICICI BANK 0.00587

BHEL 0.00272 SAIL 0.00560

JINDAL 0.00257 HDFC BANK 0.00501

RANBAXY 0.00248 GRASIM 0.00463

HDFC 0.00235 SBIN 0.00321

ONGL 0.00225 RELIANCE 0.00293

HCL 0.00167 JINDAL 0.00291

HDFC BANK 0.00130 WIPRO 0.00204

TATA POWER 0.00094 CIPLA 0.00130

MARUTI -0.00003 BHEL 0.00121

SIEMENS -0.00049 RELCAPITEL 0.00073

WIPRO -0.00070 ITC 0.00070

ABB -0.00115 MARUTI 0.00002

M & M -0.00178 HDFC -0.00040

GAIL -0.00201 PNB -0.00056

TATA MOTORS -0.00258 HERO HONDA -0.00094

RELIANCE -0.00426 ACC -0.00127

ITC -0.00470 GAIL -0.00140

UNITECH -0.00488 TATA POWER -0.00150

HERO HONDA -0.00549 SUN PHARMA -0.00165

SAIL -0.00722 TATA

MOTORS -0.00247

RELCAPITEL -0.00804 RANBAXY -0.00360

INFOSYS -0.02214 ONGL -0.01509


164

Table 6.13



Table 6.14


for 29 companies (Apr2007 )

Company Average

Return

Company

Average

Return

SAIL 0.00986 JINDAL 0.01379

BHEL 0.00501 HCL 0.01118

TATA POWER 0.00371 SAIL 0.01045

RELIANCE 0.00348 ABB 0.00980

GAIL 0.00331 TATA POWER 0.00957

ICICI BANK 0.00284 ACC 0.00944

SUN PHARMA 0.00248 GRASIM 0.00935

ONGL 0.00217 RELIANCE 0.00922

HDFC 0.00192 SBIN 0.00919

JINDAL 0.00173 PNB 0.00856

RANBAXY 0.00140 UNITECH 0.00822

HCL 0.00089 HDFC 0.00818

HDFC BANK 0.00066 RELCAPITEL 0.00792

UNITECH 0.00063 BHEL 0.00777

SIEMENS 0.00043 SIEMENS 0.00696

WIPRO 0.00039 HDFC BANK 0.00685

RELCAPITEL 0.00019 TATA

MOTORS 0.00616

PNB -0.00029 GAIL 0.00578

INFOSYS -0.00051 ONGL 0.00552

ITC -0.00055 WIPRO 0.00532

GRASIM -0.00088 ITC 0.00476

CIPLA -0.00095 M & M 0.00470

ABB -0.00126 RANBAXY 0.00465

MARUTI -0.00225 HERO HONDA 0.00429

M & M -0.00278 ICICI BANK 0.00419

TATA MOTORS -0.00285 MARUTI 0.00401

ACC -0.00338 INFOSYS 0.00353

SBIN -0.00471 SUN PHARMA -0.00048

HERO HONDA -0.00479 CIPLA -0.00276


165

Table 6.15



Table 6.16



Company Average

Return

Company

Average

Return

JINDAL 0.00916 JINDAL 0.04286

SAIL 0.00805 SIEMENS 0.01919

ACC 0.00634 TATA POWER 0.01705

BHEL 0.00619 BHEL 0.01378

GRASIM 0.00592 SAIL 0.01277

RELIANCE 0.00573 ONGL 0.01122

ITC 0.00525 UNITECH 0.00988

TATA POWER 0.00473 RELIANCE 0.00984

GAIL 0.00449 RELCAPITEL 0.00962

MARUTI 0.00447 ABB 0.00937

RELCAPITEL 0.00440 ICICI BANK 0.00868

UNITECH 0.00393 HDFC BANK 0.00801

RANBAXY 0.00350 WIPRO 0.00542

SBIN 0.00301 SBIN 0.00497

HDFC BANK 0.00234 HDFC 0.00485

ABB 0.00186 MARUTI 0.00452

TATA MOTORS 0.00142 SUN PHARMA 0.00427

ONGL 0.00130 HCL 0.00312

INFOSYS 0.00091 GAIL 0.00220

M & M 0.00007 GRASIM 0.00171

HDFC -0.00010 M & M -0.00001

PNB -0.00089 HERO HONDA -0.00044

HERO HONDA -0.00103 TATA

MOTORS -0.00067

ICICI BANK -0.00106 PNB -0.00082

WIPRO -0.00133 INFOSYS -0.00144

HCL -0.00320 RANBAXY -0.00158

SIEMENS -0.00348 ITC -0.00162

CIPLA -0.00433 CIPLA -0.00223

SUN PHARMA -0.00511 ACC -0.00456


166

Table 6.17



Table 6.18



Company Average

Return

Company

Average

Return

HERO HONDA 0.00023

Company Average

Return

PNB -0.00068 RELCAPITEL 0.01193

HDFC -0.00092 INFOSYS 0.01145

SUN PHARMA -0.00180 HERO HONDA 0.01027

ICICI BANK -0.00218 SUN PHARMA 0.01012

TATA MOTORS -0.00347 WIPRO 0.00994

HDFC BANK -0.00382 JINDAL 0.00973

SBIN -0.00387 HDFC 0.00944

ITC -0.00410 TATA POWER 0.00939

CIPLA -0.00444 HCL 0.00887

RELIANCE -0.00550 HDFC BANK 0.00845

SIEMENS -0.00580 ICICI BANK 0.00824

INFOSYS -0.00658 UNITECH 0.00664

MARUTI -0.00664 RELIANCE 0.00591

TATA POWER -0.00670 PNB 0.00536

RANBAXY -0.00839 SBIN 0.00491

WIPRO -0.00870 RANBAXY 0.00426

ONGL -0.00958 SAIL 0.00354

BHEL -0.00970 TATA

MOTORS 0.00307

UNITECH -0.01024 ITC 0.00246

GRASIM -0.01045 GAIL 0.00236

SAIL -0.01052 M & M 0.00183

M & M -0.01124 ONGL 0.00135

GAIL -0.01196 BHEL 0.00075

HCL -0.01229 CIPLA -0.00161

ACC -0.01247 ABB -0.00239

RELCAPITEL -0.01261 SIEMENS -0.00302

ABB -0.01306 GRASIM -0.00396

JINDAL -0.03562 ACC -0.00421


167

Table 6.19



Table 6.20



Company Average

Return

Company

Average

Return

RELCAPITEL 0.02152 INFOSYS -0.00125

SIEMENS 0.02116 HCL -0.00676

SBIN 0.01856 PNB -0.00745

PNB 0.01564 HERO HONDA -0.00783

HDFC 0.01385 BHEL -0.00914

JINDAL 0.01309 HDFC -0.00950

BHEL 0.01276 MARUTI -0.01044

ONGL 0.01087 WIPRO -0.01063

GAIL 0.00841 ITC -0.01071

HDFC BANK 0.00768 HDFC BANK -0.01103

M & M 0.00758 ACC -0.01177

HERO HONDA 0.00746 M & M -0.01195

ACC 0.00666 ICICI BANK -0.01255

RELIANCE 0.00594 SUN PHARMA -0.01276

TATA POWER 0.00593 CIPLA -0.01333

ICICI BANK 0.00571 SBIN -0.01434

UNITECH 0.00352 TATA POWER -0.01436

CIPLA 0.00317 RELIANCE -0.01442

SAIL 0.00310 ABB -0.01759

ABB 0.00277 SAIL -0.01784

WIPRO 0.00266 RANBAXY -0.01850

GRASIM 0.00229 SIEMENS -0.01991

SUN PHARMA 0.00210 ONGL -0.02197

RANBAXY 0.00187 JINDAL -0.02255

ITC 0.00156 RELCAPITEL -0.02654

MARUTI 0.00011 GRASIM -0.02700

TATA MOTORS -0.00082 GAIL -0.02869

INFOSYS -0.00152 UNITECH -0.02934

HCL -0.00960 TATA

MOTORS -0.03334


168

Table 6.21



Table 6.22



Company Average

Return

Company

Average

Return

JINDAL 0.01425 JINDAL 0.02221

GRASIM 0.00982 TATA

MOTORS 0.02158

RANBAXY 0.00879 ICICI BANK 0.02103

PNB 0.00682 RELCAPITEL 0.02065

RELIANCE 0.00560 WIPRO 0.01776

ICICI BANK 0.00503 HCL 0.01724

TATA MOTORS 0.00492 M & M 0.01407

SBIN 0.00486 UNITECH 0.01333

HERO HONDA 0.00468 SIEMENS 0.01191

ACC 0.00314 SUN PHARMA 0.01177

TATA POWER 0.00237 SBIN 0.01136

SAIL 0.00227 PNB 0.01104

ITC 0.00220 HDFC 0.00920

GAIL 0.00166 GRASIM 0.00916

CIPLA 0.00155 ABB 0.00902

SIEMENS 0.00096 RELIANCE 0.00866

BHEL 0.00077 HERO HONDA 0.00832

HDFC BANK -0.00205 TATA POWER 0.00815

UNITECH -0.00270 BHEL 0.00798

HDFC -0.00293 SAIL 0.00791

ABB -0.00298 ACC 0.00713

WIPRO -0.00376 CIPLA 0.00645

RELCAPITEL -0.00476 HDFC BANK 0.00623

ONGL -0.00502 INFOSYS 0.00619

INFOSYS -0.00571 ONGL 0.00496

SUN PHARMA -0.00576 GAIL 0.00218

MARUTI -0.00702 MARUTI 0.00203

M & M -0.01394 ITC 0.00177

HCL -0.01440 RANBAXY -0.00406


169

The formation and evaluation process was repeated for the years 2004-

2009 with formation period of one month.(Table 6.1 to 6.22) Based on the results

of the formation period the 29 companies were ranked accordingly. Two equal

weight portfolios were formed in terms of average reruns -one comprising of top

seven stocks called as winner portfolio and other comprising of bottom seven

stocks called as loser portfolio. Researcher had arranged the mean returns in

descending order.

Top seven companies in the formation period data given in (Table 6.1 to

6.22) was the ones which were included in winning portfolio construction .Last

seven companies in the table constituted the loser portfolio.

The study was conducted taking different holding periods and holding

periods were 2, 3, 4,5,6,7 and 8 weeks respectively. This was to test the possibility of

variation in profit potential of these two investment strategies with time.

Below given is a sample of the work done by the researcher for

computing momentum returns. In the example given here the holding period is

two weeks. Average returns using both the strategies were compared with the

average Index return. Average returns in two-week holding period were

calculated by taking the average returns of 89 different holding periods the details

of which is given in Table:6.23

170

Table: 6.23

Returns of Momentum & Contrarian Portfolios compared with index

return for 2-Week Holding Period

Period

Average

Momentum

Return

Average

Contrarian

Return

Average

Index

Return

03-Feb-04 To 16-Feb-04 0.00894 0.00514 0.00571

17-Feb-04 To 27-Feb-04 -0.00827 -0.00745 -0.00664

01-Mar-04 To 16-Mar-04 -0.00056 -0.00122 -0.00249

17-Mar-04 To 31-Mar-04 0.00640 -0.00263 0.00125

03-May-04 To 14-May-04 0.01895 -0.00902 -0.01221

17-May-04 To 31-May-04 -0.00571 -0.00244 -0.00456

01-Jun-04 To 15-Jun-04 -0.01260 0.00483 0.00119

16-Jun-04 To 30-Jun-04 -0.00726 -0.00053 0.00035

02-Aug-04 To 16-Aug-04 -0.00052 0.00306 -0.00182

17-Aug-04 To 31-Aug-04 0.00023 0.00336 0.00188

01-Sep-04 To 15-Sep-04 0.00366 0.00203 0.00284

16-Sep-04 To 30-Sep-04 0.00734 0.00109 0.00337

01-Nov-04 To 12-Nov-04 0.00583 0.00395 0.00473

16-Nov-04 To 30-Nov-04 0.00980 0.00863 0.00452

01-Dec-04 To 15-Dec-04 0.00404 0.00552 0.00323

16-Dec-04 To 31-Dec-04 0.00423 0.00622 0.00212

01-Feb-05 To 14-Feb-05 0.00443 0.00248 0.00198

15-Feb-05 To 28-Feb-05 0.00202 -0.00400 0.00027

01-Mar-05 To 15-Mar-05 0.00121 0.00335 0.00113

16-Mar-05 To 31-Mar-05 -0.00201 -0.00456 -0.00399

02-May-05 To 16-May-05 0.00743 0.00519 0.00515

17-May-05 To 31-May-05 0.00315 0.00598 0.00335

01-Jun-05 To 14-Jun-05 0.00154 0.00397 0.00110

15-Jun-05 To 30-Jun-05 0.00404 0.00563 0.00420

01-Aug-05 To 16-Aug-05 0.00283 0.00305 0.00228

17-Aug-05 To 31-Aug-05 -0.00078 -0.00413 0.00062

01-Sep-05 To 15-Sep-05 0.00133 0.00453 0.00571

16-Sep-05 To 30-Sep-05 0.00398 -0.01126 0.00287

01-Nov-05 To 17-Nov-05 0.00811 0.01121 0.00944

18-Nov-05 To 30-Nov-05 0.00464 0.00003 0.00189

01-Dec-05 To 15-Dec-05 0.00468 0.00283 0.00429

16-Dec-05 To 30-Dec-05 0.00398 -0.00011 0.00194

171

01-Feb-06 To 15-Feb-06 -0.00061 0.00105 0.00074

16-Feb-06 To 28-Feb-06 0.00464 0.00168 0.00194

01-Mar-06 To 16-Mar-06 0.01113 0.00523 0.00445

17-Mar-06 To 31-Mar-06 0.00480 0.00450 0.00487

02-May-06 To 16-May-06 0.00383 -0.00007 -0.00076

17-May-06 To 31-May-06 -0.00563 -0.00947 -0.01184

01-Jun-06 To 15-Jun-06 0.01161 -0.01087 -0.00768

16-Jun-06 To 30-Jun-06 0.00273 0.00718 0.00953

01-Aug-06 To 16-Aug-06 0.00628 0.00621 0.00601

17-Aug-06 To 31-Aug-06 0.00383 -0.00037 0.00157

01-Sep-06 To 14-Sep-06 0.00451 0.00819 0.00176

15-Sep-06 To 29-Sep-06 0.00444 0.00534 0.00304

01-Nov-06 To 15-Nov-06 0.00529 0.00362 0.00317

16-Nov-06 To 30-Nov-06 -0.00044 0.00101 0.00184

01-Dec-06 To 14-Dec-06 0.00060 -0.00376 -0.00271

15-Dec-06 To 29-Dec-06 0.00277 0.00494 0.00323

01-Feb-07 To 14-Feb-07 -0.00021 -0.00228 -0.00080

15-Feb-07 To 28-Feb-07 -0.00509 -0.00616 -0.00843

01-Mar-07 To 14-Mar-07 -0.00189 -0.00673 -0.00253

15-Mar-07 To 30-Mar-07 0.00686 0.00169 0.00450

03-May-07 To 16-May-07 0.00362 0.00050 0.00205

17-May-07 To 31-May-07 0.00143 0.00270 0.00272

01-Jun-07 To 14-Jun-07 -0.00121 -0.00400 -0.00293

15-Jun-07 To 29-Jun-07 -0.00368 -0.00041 0.00320

01-Aug-07 To 16-Aug-07 -0.00650 -0.00450 -0.00709

17-Aug-07 To 31-Aug-07 0.00932 0.00145 0.00618

03-Sep-07 To 14-Sep-07 0.00591 -0.00025 0.00121

17-Sep-07 To 28-Sep-07 0.01045 0.00690 0.01070

01-Nov-07 To 15-Nov-07 0.00086 -0.00018 0.00030

16-Nov-07 To 30-Nov-07 0.00006 0.00015 -0.00217

03-Dec-07 To 14-Dec-07 0.00971 0.00680 0.00490

17-Dec-07 To 31-Dec-07 0.00231 0.00084 0.00187

01-Feb-08 To 14-Feb-08 0.00108 0.00400 0.00176

15-Feb-08 To 29-Feb-08 -0.00065 0.00115 0.00046

03-Mar-08 To 14-Mar-08 -0.01175 -0.00807 -0.01023

17-Mar-08 To 31-Mar-08 -0.00216 0.00207 0.00021

02-May-08 To 15-May-08 -0.00121 -0.00181 -0.00091

16-May-08 To 30-May-08 -0.00315 -0.00441 -0.00484

02-Jun-08 To 13-Jun-08 -0.00483 -0.00579 -0.00736

172

16-Jun-08 To 30-Jun-08 -0.00994 -0.01259 -0.00988

01-Aug-08 To 14-Aug-08 0.00099 0.00719 0.00233

18-Aug-08 To 29-Aug-08 0.00087 0.00126 -0.00147

01-Sep-08 To 15-Sep-08 -0.00842 -0.00535 -0.00655

16-Sep-08 To 30-Sep-08 -0.00445 -0.01130 -0.00317

03-Nov-08 To 14-Nov-08 -0.00495 -0.00455 -0.00198

17-Nov-08 To 28-Nov-08 0.00035 -0.01780 -0.00177

01-Dec-08 To 15-Dec-08 -0.00368 0.02191 0.00826

16-Dec-08 To 31-Dec-08 0.00286 0.00137 -0.00045

02-Feb-09 To 13-Feb-09 0.00334 0.00320 0.00269

16-Feb-09 To 27-Feb-09 -0.01181 -0.00552 -0.00705

02-Mar-09 To 17-Mar-09 0.00267 0.00277 0.00003

18-Mar-09 To 31-Mar-09 0.01192 0.00762 0.00941

04-May-09 To 15-May-09 0.01086 0.00510 0.00579

18-May-09 To 29-May-09 0.02608 0.01881 0.02079

01-Jun-09 To 15-Jun-09 0.00773 0.00262 0.00087

16-Jun-09 To 30-Jun-09 -0.00607 -0.00258 -0.00380

Source: Computed from data source

In two week holding period the researcher had repeated the process of

computation of holding periods and evaluation of winning and looser portfolio

returns for more than 89 times, the final results of which by taking the mean

portfolio returns are put in the table given above (Table: 6.23).The detailed

workings (sample) of this study can be found by referring to the Appendix 1, 2, 3

given at the back of this report. The entire data is provided in a soft copy along

with this report.

The whole process can be explained by taking the example of January

2004 as the formation period and the next two months i.e February and March as

the holding periods. During this period the winner portfolio consisted of 7

companies, the results of which are given in the following table (Table 6.24)

173

Table 6.24

Winner Portfolio Returns from February 2004 to March 2004

Date TATAP MARUTI TATAM GRASIM PNB SBIN M & M Average Index Return

03-Feb-04 -0.05276 -0.05860 -0.02075 -0.00314 -0.07899 -0.05142 0.00623 -0.03706 -0.02252

04-Feb-04 0.06175 0.08882 0.06829 -0.01190 0.01657 0.03940 0.06836 0.04733 0.03007

05-Feb-04 -0.02994 -0.02045 -0.05033 -0.02048 0.03321 -0.01747 0.00159 -0.01484 -0.00971

06-Feb-04 0.02335 0.03495 0.02901 0.00750 0.03234 0.02796 0.00216 0.02247 0.01615

09-Feb-04 0.03750 0.03789 0.01898 0.02902 0.06943 0.04576 0.02853 0.03816 0.02566

10-Feb-04 -0.01106 -0.01074 -0.01457 -0.00364 -0.02333 -0.01879 -0.02576 -0.01541 0.00003

11-Feb-04 0.01119 0.02866 0.01535 0.03939 0.04240 0.02651 0.01243 0.02513 0.00572

12-Feb-04 -0.00141 0.01435 -0.00240 -0.01500 -0.02558 -0.01546 0.03437 -0.00159 -0.00328

13-Feb-04 0.01713 0.02840 0.02366 0.00471 0.00893 0.01870 0.00000 0.01450 0.01501

16-Feb-04 -0.00076 0.05088 0.01128 -0.00044 -0.03577 0.00159 0.04845 0.01075 -0.00003

Average 0.00894 0.00571


17

3

174

Table 6.25



17-Feb-04 0.00292 -0.01636 -0.00152 0.04217 0.01855 0.00595 0.01070 0.00892 0.00342

18-Feb-04 -0.00619 0.01419 0.00751 -0.00959 -0.02723 -0.00702 0.01965 -0.00124 -0.00190

19-Feb-04 -0.04502 -0.03512 -0.03496 -0.03695 -0.03915 -0.04051 -0.02526 -0.03671 -0.03034

20-Feb-04 -0.00839 0.02490 0.01526 0.00975 0.00216 -0.00240 0.00492 0.00660 -0.00304

23-Feb-04 -0.02041 -0.03434 -0.04555 -0.04489 -0.06127 -0.02216 -0.02610 -0.03639 -0.02399

24-Feb-04 0.01508 0.01243 -0.00190 -0.01088 0.02573 0.02818 0.01476 0.01191 0.00727

25-Feb-04 -0.02714 -0.02585 -0.05324 -0.01479 -0.01611 -0.02897 -0.02940 -0.02793 -0.01897

26-Feb-04 -0.03234 0.01547 -0.00301 -0.00587 -0.02094 -0.02040 -0.04910 -0.01660 -0.01175

27-Feb-04 -0.00459 0.00898 0.02498 0.00953 0.02689 0.01440 0.03890 0.01701 0.01954

Average -0.00827 -0.00664


17

4

175

Table 6.26



01-Mar-04 0.04208 0.02900 0.02781 0.06368 0.02825 0.02695 0.02098 0.03411 0.02911

03-Mar-04 0.02351 0.02867 0.02686 0.01094 -0.00140 -0.00167 0.02993 0.01669 0.00416

04-Mar-04 0.01486 -0.01493 -0.02905 0.00704 0.01888 0.00876 -0.00358 0.00028 -0.00890

05-Mar-04 0.05286 0.01554 0.02225 0.02215 -0.00828 0.01977 0.01376 0.01972 0.01293

08-Mar-04 0.02516 -0.00878 0.00281 0.03561 0.05624 0.01809 0.00203 0.01874 0.00940

09-Mar-04 -0.01493 -0.01572 -0.02638 -0.03838 0.02051 0.00964 -0.00768 -0.01042 -0.01018

10-Mar-04 0.01415 -0.01432 -0.03122 -0.02765 -0.03042 -0.02541 -0.01171 -0.01808 -0.01163

11-Mar-04 -0.02124 -0.00776 -0.03501 -0.03171 0.01065 -0.02526 -0.03525 -0.02080 -0.02112

12-Mar-04 -0.00341 0.00356 0.00565 -0.01473 0.03029 -0.00465 0.02479 0.00593 0.00377

15-Mar-04 -0.07051 -0.04585 -0.03934 -0.02396 -0.04036 -0.03396 -0.03513 -0.04130 -0.02693

16-Mar-04 -0.00191 -0.01178 -0.02383 -0.02520 -0.00381 -0.01045 0.00011 -0.01098 -0.00797

Average -0.00056 -0.00249


17

5

176

Table 6.27



17-Mar-04 0.01419 0.01109 0.01842 -0.01480 0.06476 0.01266 -0.00303 0.01475 0.00029

18-Mar-04 -0.03054 -0.00590 -0.03263 -0.00263 0.01292 -0.03241 -0.02406 -0.01647 -0.01897

19-Mar-04 -0.00222 -0.01831 0.01051 -0.00396 0.02161 0.01123 0.00255 0.00306 0.00492

22-Mar-04 -0.04757 -0.06031 -0.04849 -0.01640 -0.02653 -0.02943 -0.06789 -0.04237 -0.02325

23-Mar-04 0.00073 0.01218 -0.00656 0.02371 -0.00232 0.00472 0.01723 0.00710 0.00677

24-Mar-04 0.00817 0.00345 0.01494 -0.01029 0.03356 -0.01120 0.01740 0.00800 -0.00253

25-Mar-04 0.03445 0.06230 0.02556 -0.01468 0.05527 0.02997 0.01125 0.02916 0.00730

26-Mar-04 0.03512 0.02321 0.03760 0.04435 0.08773 0.05225 0.05506 0.04790 0.02526

29-Mar-04 0.04366 0.00685 0.01415 0.00694 -0.00481 0.02563 -0.00172 0.01296 0.00833

30-Mar-04 -0.03277 -0.02912 -0.00729 -0.01588 0.05640 -0.02598 -0.01832 -0.01042 -0.00675

31-Mar-04 0.01004 0.03982 0.03482 0.03208 -0.04294 0.02262 0.02031 0.01668 0.01243

Average 0.00640 0.00125

Source: Computed from data

17

6

177

It can be studied from the above table that the results of the average

returns of the winner portfolio i.e. one which follows momentum strategy is

compared with the index returns for the similar period. The same process was

also applied for studying the looser portfolio i.e. which follows contrarian

strategy, the sample of test results is put in Appendix. 1.

Researcher had also conducted the t-test to study the profitability of

momentum and contrarian strategies for various formations and holding periods.

It was to find out whether momentum and contrarian strategies yield significant

positive returns when compared with the bench mark, i.e. index return. The

results of the t-tests for various holding periods and formation periods are given

in following tables. (Table: 6.28 to 6.34)

Table: 6.28

T-test for 2-Week Holding Period

Pairs t df P value

Momentum & Contrarian 1.287 174 0.200

Momentum & Index 1.307 174 0.193

Contrarian & Index -0.088 174 0.930


Here the p-value is greater than the significance value 0.05; so the results

of two-week holding periods draw towards accepting the null hypotheses (H0):

1. Momentum Strategy does not give superior returns to the investor in the

Indian Capital Market

178

2. Contrarian Strategy does not give superior returns to the investor in the


3. No significant difference is noticed between Contrarian and Momentum

Strategies in making superior returns from Indian Capital Market.

The results also reject the possibility of making superior returns by

adopting the momentum and contrarian investment strategies for a two-week

holding period in the Indian stock market during the study period.

Table: 6.29


Pairs t df P value



Contrarian & Index 0.257 120 0.798



of three-week holding periods draw towards accepting the null hypotheses (H0):



2. Contrarian Strategy does not give superior returns to the investor in the Indian

Capital Market



179


adopting the momentum and contrarian investment strategies for a three-week


Table: 6.30


Pairs t df P value

Momentum &

Contrarian 0.659 86 0.512


Contrarian & Index -0.106 86 0.916



of four-week holding periods draw towards accepting the null hypotheses (H0):








adopting the momentum and contrarian investment strategies for a four-week


180

Table: 6.31


Pairs t df P value






of five-week holding periods draw towards accepting the null hypotheses (H0):








adopting the momentum and contrarian investment strategies for a five-week


Table: 6.32


Pairs t df P value





181


of six-week holding periods draw towards accepting the null hypotheses (H0):








adopting the momentum and contrarian investment strategies for a six-week


Table: 6.33


Pairs t df P value






of seven-week holding periods draw towards accepting the null hypotheses (H0):



182






adopting the momentum and contrarian investment strategies for a seven-week


Table: 6.34


Pairs t df P value



Contrarian & Index 0-.084 42 0.933



of eight-week holding periods draw towards accepting the null hypotheses (H0):







183


adopting the momentum and contrarian investment strategies for an eight -week


The study results reveals that there does not appear any merits to the

momentum and contrarian strategies as technical analysis tools in Indian Stock

Market. The results are different from many other emerging markets where

empirical studies have revealed the possibility of making superior returns using

these two strategies.

Many academicians and practitioners now hold that stock prices do have

some degree of predictability. By using the most rigorous and credible methods,

it has been recognised that stock returns can deviate from a random walk, which

highlights the potential value in technical analysis or more sophisticated

statistical forecasting methods. But such studies can no way reject, efficient

market hypothesis. Rather, it should be treated as the base case to which

alternatives can be compared.

Of course, stock prices contain some predictability. Results of this study

also support the weak-form inefficiency of Indian stock-market. So Investors are

left with opportunity to make excess return by studying the historical prices,

However, this has to be done in a way that it compensates for the transactions

costs of trading.

6.3 Momentum and Contrarian strategies during recession period

Researcher analysed the efficiency of momentum and contrarian investment

strategies during the time period in which Indian markets were severely affected by

184

the global financial crisis. The period under study was 2007 October to 2008 April.

The results of the study are given below. (Table: 6.35 to 6.41).

Table: 6.35

2- Week holding period result during recession

Period Momentum Contrarian Index

01-Nov-07 To 16-Nov-07 0.00121 -0.00026 -0.00020

19-Nov-07 To 30-Nov-07 0.00016 -0.00198 0.00229

03-Dec-07 To 14-Dec-07 -0.00519 -0.00674 -0.00490

17-Dec-07 To 31-Dec-07 -0.00169 -0.00111 -0.00187

01-Feb-08 To 15-Feb-08 -0.00664 0.00262 -0.00336

18-Feb-08 To 29-Feb-08 0.00207 -0.00441 0.00143

03-Mar-08 To 14-Mar-08 0.01156 0.00786 0.01023

17-Mar-08 To 31-Mar-08 -0.00046 0.00203 -0.00021


Table: 6.36



01-Nov-07 To 23-Nov-07 0.00104 0.00122 0.00284

26-Nov-07 To 14-Dec-07 -0.00356 -0.00740 -0.00511

01-Feb-08 To 22-Feb-08 -0.00254 0.00285 -0.00003

25-Feb-08 To 14-Mar-08 0.00660 0.00071 0.00499


185

Table: 6.37



01-Nov-07 To 30-Nov-07 0.00073 -0.00104 0.00093

03-Dec-07 To 31-Dec-07 -0.00353 -0.00408 -0.00346

01-Feb-08 To 29-Feb-08 -0.00249 -0.00072 -0.00108

03-Mar-08 To 31-Mar-08 0.00555 0.00494 0.00501


Table: 6.38

5 - Week holding period result during recession


01-Nov-07 To 07-Dec-07 -0.00024 -0.00358 -0.00059

01-Feb-08 To 07-Mar-08 0.00251 0.00098 0.00263


Table: 6.39



01-Nov-07 To 14-Dec-07 -0.00112 -0.00282 -0.00089

01-Feb-08 To 14-Mar-08 0.00173 0.00185 0.00231


Table: 6.40

7-Week holding period result during recession


01-Nov-07 To 20-Dec-07 -0.00078 -0.00059 0.00050

01-Feb-08 To 19-Mar-08 0.00359 0.00327 0.00318


186

Table: 6.41

8 -Week holding period result during recession


01-Nov-07 To 31-Dec-07 -0.00124 -0.00244 -0.00110

01-Feb-08 To 31-Mar-08 0.00122 0.00189 0.00173


The results reveals that there does not appear any merits to the momentum

and contrarian strategies as technical analysis tools in Indian Stock Market even

during recession period. So the researcher had to accept the null hypothesis (H0)

that momentum and contrarian strategies do not give superior returns over the

bench mark.

The analysis of momentum and contrarian returns gave evidence that both

these strategies cannot be recommended for deciding on making short term

investment decisions in Indian Stock Market. So the researcher restrained from

selecting the best out of these two technical analysis tools.

The strategies under study, i.e. momentum and contrarian strategies

unfortunately involved high degree of turnover because the portfolios have to be

reconstituted frequently. These strategies also incur substantial transaction costs.

So it remains to be seen whether they would be profitable after such costs are

fully accounted for.

6.4 Interdependency of Indian Stock Market with other Emerging

Markets

In the recent years due to globalization, deregulation and integration

between countries the interdependency among major world stock markets has

187

increased. Equity market of a country very often responds to the equity

movements of other markets from all over the world.

The Indian Stock market has witnessed a major transformation and

structural change from the 10 to 15 years as a result of the ongoing economic and

financial sector reforms initiated by the government of India since 1991.Among

these measures, lifting of barriers and opening up the doors for foreign investors

have promoted integration of Indian markets with other foreign markets,

especially other emerging countries.

For an international investor who is willing to make portfolio investments

in other financial markets would be interested to know if he can achieve desired

results by going for such diversification. The interdependency between financial

markets had been at the focus of interest of academicians even from 1960s. The

majority of studies in that early period reached the conclusion that the degree of

interdependency between markets is quite low, since the prime factors in the

development of financial markets are of domestic nature. Even in those years

some studies that were published supported the existence of limited

interdependency between markets. Agmon (1972), establishes some degree of

interdependency between the markets of the US, UK, Germany and Japan during

1961 until 1966.

Integration is the process by which segmented markets become open and

unified so that participants enjoy unimpeded access to international trade and

finance (Nalini Prava, 2005). Integration of financial markets will encourage flow

of funds from markets having lesser returns to the one which offers higher

188

returns. It can be assumed that financing and investing decisions by investors

across the globe are greatly influenced by the perceived degree of market

integration.

From an investment angle, if stock markets move together then

diversification of portfolio would not generate any return .On the other hand, if

two markets are independent, investors can do effective portfolio diversification

by investing in these markets. Therefore, a comprehensive study on stock market

interdependency will carry a lot of importance for the Indian investors engaged in

such portfolio diversification.

The present study which is included in this chapter had been conducted by

the researcher with the objective of analyzing whether Indian equity market is

integrated with that of the rest of the world, especially among different Asian

markets.

Researcher tested interdependency of Nifty Index movements with

Shanghai Composite Index, Hangseng index and Nikkei Index from 2006-2009

using simple correlation technique.

Shanghai Composite Index is a capitalization-weighted index. The index

tracks the daily price performance of all A-shares and B-shares listed on the

Shanghai Stock Exchange. The index was developed on December 19, 1990 with

a base value of 100.

189

Fig. 6.3

Shanghai Composite Index Movement (May, 2009 to March 2010)

Source: Bloomberg.com

Shanghai Stock Exchange (SSE) was founded on November 26th, 1990

and in operation since December 19th the same year. The exchange is a

membership institution directly governed by the China Securities Regulatory

Commission (CSRC). By end of December 2007, the exchange had over 71.30

million investors and 860 listed companies. The total market capitalization of

SSE was 3.95 trillion U.S dollars with an average daily turnover of 16.8 billion

U.S dollars approximately. In 2007, total capital raised from SSE market was

97.57 billion U.S dollars .It is Asia's second largest stock exchange by market

capitalisation, behind the Tokyo stock exchange.

There are two types of stocks being issued in the Shanghai Stock

Exchange: ‘A’ shares and ‘B’ shares. ‘A’ shares are priced in the local renminbi

yuan currency, while B shares are quoted in U.S .dollars. Initially, trading in ‘A’

shares were restricted to domestic investors only while B shares were available to

both domestic (since 2001) and foreign investors. However, after the new reforms

were implemented in December 2002, foreign investors are allowed (with

190

limitations) to trade in A shares under the Qualified Foreign Institutional Investor

(QFII) program which was officially launched in 2003. Currently, there are a

total of 79 foreign institutional investors who have been approved to buy and sell

‘A’ shares under the QFII program. Quotas under the QFII program are currently

US$30 billion. There is a plan to eventually merge the two types of shares in the

future. The listing requirements of the exchange include:

The shares must have been publically issued following the approval of the

State Council Securities Management Department.

The company’s total share capital must not be less than 7.32 million U.S

dollars

The company must have been in business for more than 3 years and have

made profits over the last three consecutive years. In the case of former

state-owned enterprises re-established according to the law or founded

after implementation of the law and if their issuers are large and medium

state owned enterprises, it can be calculated consecutively. Publically

offered shares must be more than 25 percent of the company’s total share

capital. The company must not have been guilty of any major illegal

activities or false accounting records in the last three years.

Shanghai Composite Index (SSE Index) is the most commonly used indicator

to reflect SSE's market performance. The index which was launched on July 15,

1991, reached 2,675.47 at the end of 2006, peaked at 6092.05 in late 2007 and

fell back to 2500 levels in 2009 because of global financial crisis. Other

191

important indexes used in the Shanghai Stock Exchanges include the SSE 50

Index and SSE 180 Index.

The Hang Seng Index (HSI) is a free-float capitalization-weighted index

of selected companies from the Stock Exchange of Hong Kong. The components

of the index are divided into four sub indexes: Commerce and Industry, Finance,

Utilities, and Properties. The index was developed with a base level of 100 as on

July 31, 1964.

Fig. 6.4

Hangseng Index movement (May, 2009 to March 2010)


This index is used to record and monitor daily changes of the largest

companies of the Hong Kong stock market and is the main indicator of the

overall market performance in Hong Kong. The 45 companies involved in the

construction of this index represent about 67 percent of capitalisation of the

Hong Kong Stock Exchange.

Hang Seng Index is maintained by HSI Services Limited, which is a

wholly owned subsidiary of Hang Seng bank, the largest bank registered and

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listed in Hong Kong in terms of market capitalisation. It is responsible for

compiling, publishing and managing the Hang Seng Index and a range of other

stock indexes, such as Hang Seng China AH Index Series, Hang Seng China

Enterprises Index, Hang Seng China H-Financials Index, Hang Seng Composite

Index Series, Hang Seng Free float Index Series and Hang Seng Total Return

Index Series.

The Hong Kong Stock Exchange is the stock exchange of Hong Kong.

The exchange has predominantly been the main exchange for Hong Kong where

listed companies shares are traded. It is Asia’s third largest stock exchange in

terms of market capitalization, behind the Tokyo stock exchange and Shanghai

Stock exchange. As of 31st December 2007, the Hong Kong Stock Exchange had

1,241 listed companies with a combined market capitalisation of $2.7 trillion.

The Nikkei-225 Stock Average is a price-weighted average of 225 top-

rated Japanese companies listed in the First Section of the Tokyo Stock

Exchange. The Nikkei Stock Average was first published on May 16, 1949.

It is a price weighted average (the unit is yen) and the components are

reviewed once a year. Currently, the Nikkei is the most widely quoted average of

Japanese equities, similar to the Dow Jones Industry average. In fact, it was

known as the "Nikkei Dow Jones Stock Average" from 1975 to 1985.

The Nikkei average hit its all-time high on December 29, 1989, during the

peak of the Japanese asset price bubble, when it reached an intra-day high of

38,957.44 before closing at 38,915.87. Its high for the 21st century stands just

above 18,300 points. In January 2010, it was 72.9 percent below its peak.

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Fig. 6.5

Nikkei Index movement (May, 2009 to March 2010)


The Tokyo stock exchange also called as “Tosho” or TSE for short, is

located in Tokyo, Japan and is the fourth largest stock exchange in the world (as

on May, 2009) and the biggest in Asia by aggregate market capitalisation .As of

31st December 2007, the Tokyo Stock Exchange had 2,414 listed companies with

a combined market capitalization of $4.3 trillion.

Markets are said to be integrated when shocks arising in one market gets

easily transmitted to other interrelated markets. Researcher has made an attempt

to examine the empirical relationship of Indian stock market with the top three

stock markets in Asia, namely Shanghai Composite Index, Hangseng index and

Nikkei Index. Researcher tested interdependency of Nifty Index movements with

these three Indexes from 2006-2009 using simple correlation technique.

6.5 Results of the test of Interdependency

Due to voluminous of data researcher had put only the final result of

correlation test in the report. Sample of workings is given in Appendix 4.Detailed

workings are given in the soft copy attached along with this report.

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Table: 6.42

Correlation Result for Shanghai Composite Index

Year N Correlation p-value

2006 - 07 244 0.003 0.957 c

2007 - 08 249 0.197 0.002 a

2008 - 09 239 0.327 0.000 a

2009 - 10 108 0.216 0.025 b


a Correlation is significant at the 0.01 level.

b Correlation is significant at the 0.05 level.

c Correlation is not significant.

Table: 6.43

Correlation Result for Hangseng Index


2006 - 07 239 0.539 0.000 a

2007 - 08 244 0.573 0.000 a

2008 - 09 237 0.673 0.000 a

2009 - 10 091 0.562 0.000 a





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Table: 6.44

Correlation Result for Nikkei Index


2006 - 07 234 0.404 0.000 a

2007 - 08 235 0.506 0.000 a

2008 - 09 229 0.465 0.000 a

2009 - 10 086 0.234 0.030 b





This study investigated the integration of India’s stock market movements

with the three leading stock markets in Asia. The empirical analysis (Table 6.41

to 6.44) provides evidence for correlation among market movements of Nifty

Index returns with the other three markets studied. So from the portfolio

diversification objective, investors cannot benefit from arbitrage activities in the

long run.

The study is the continuation of research on the issue of growing

interdependency among the world stock markets. If the results of this study,

regarding the influence of the other three stock markets are extended and

contrasted with the previous studies included in literature, it can be concluded

that the interdependencies among the stock markets in the emerging countries has

increased over the years.

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These developments in the international markets will pose severe

challenges before the policy makers and regulators of various countries. These

interdependencies will inculcate financial distress or crisis in the domestic

economy from other economies.

Investors also will find it very difficult to do an active portfolio

diversification, i.e to find a market having low correlation with the domestic

economy.

At the end, it is hoped that the results of this study will be useful for

Indian investors as well as other foreign investors looking for Indian market.

momentum and contrarian investment strategies...

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