MOMENTUM AND CONTRARIAN INVESTMENT STRATEGIES...
Transcript of 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
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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
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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
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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.
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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.
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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.
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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
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Table 6.3
Returns in Formation Period for
29 companies (July 2004)
Table 6.4
Returns in Formation Period
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
Source: Computed from data source Source: Computed from data source
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Table 6.5
Returns in Formation Period for
29 companies (Jan 2005 )
Table 6.6
Returns in Formation Period
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
Source: Computed from data source Source: Computed from data source
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Table 6.7
Returns in Formation Period for
29 companies (Jul 2005 )
Table 6.8
Returns in Formation Period
for 29 companies (Oct 2005 )
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
Source: Computed from data source Source: Computed from data source
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Table 6.9
Returns in Formation Period for
29 companies (Jan 2006 )
Table 6.10
Returns in Formation Period
for 29 companies (Apr 2006 )
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
Source: Computed from data source Source: Computed from data source
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Table 6.11
Returns in Formation Period for
29 companies (Jul 2006 )
Table 6.12
Returns in Formation Period
for 29 companies (Oct 2006 )
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
Source: Computed from data source Source: Computed from data source
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Table 6.13
Returns in Formation Period for
29 companies (Jan 2007 )
Table 6.14
Returns in Formation Period
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
Source: Computed from data source Source: Computed from data source
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Table 6.15
Returns in Formation Period for
29 companies (Jul 2007 )
Table 6.16
Returns in Formation Period
for 29 companies (Oct 2007 )
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
Source: Computed from data source Source: Computed from data source
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Table 6.17
Returns in Formation Period for
29 companies (Jan 2008 )
Table 6.18
Returns in Formation Period
for 29 companies (Apr 2008 )
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
Source: Computed from data source Source: Computed from data source
167
Table 6.19
Returns in Formation Period for
29 companies (Jul 2008 )
Table 6.20
Returns in Formation Period
for 29 companies (Oct 2008 )
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
Source: Computed from data source Source: Computed from data source
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Table 6.21
Returns in Formation Period for
29 companies (Jan 2009 )
Table 6.22
Returns in Formation Period
for 29 companies (Apr 2009 )
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
Source: Computed from data source Source: Computed from data source
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
Source: Computed from data source
17
3
174
Table 6.25
Winner Portfolio Returns from February 2004 to March 2004
Date TATAP MARUTI TATAM GRASIM PNB SBIN M & M Average Index Return
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
Source: Computed from data source
17
4
175
Table 6.26
Winner Portfolio Returns from February 2004 to March 2004
Date TATAP MARUTI TATAM GRASIM PNB SBIN M & M Average Index Return
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
Source: Computed from data source
17
5
176
Table 6.27
Winner Portfolio Returns from February 2004 to March 2004
Date TATAP MARUTI TATAM GRASIM PNB SBIN M & M Average Index Return
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
Source: Computed from data source
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
Indian Capital Market
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
T-test for 3-Week Holding Period
Pairs t df P value
Momentum & Contrarian 0.516 120 0.607
Momentum & Index 0.779 120 0.438
Contrarian & Index 0.257 120 0.798
Source: Computed from data source
Here the p-value is greater than the significance value 0.05; so the results
of three-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
2. Contrarian Strategy does not give superior returns to the investor in the Indian
Capital Market
3. No significant difference is noticed between Contrarian and Momentum
Strategies in making superior returns from Indian Capital Market.
179
The results also reject the possibility of making superior returns by
adopting the momentum and contrarian investment strategies for a three-week
holding period in the Indian stock market during the study period.
Table: 6.30
T-test for 4-Week Holding Period
Pairs t df P value
Momentum &
Contrarian 0.659 86 0.512
Momentum & Index 0.611 86 0.543
Contrarian & Index -0.106 86 0.916
Source: Computed from data source
Here the p-value is greater than the significance value 0.05; so the results
of four-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
2. Contrarian Strategy does not give superior returns to the investor in the
Indian Capital Market
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 four-week
holding period in the Indian stock market during the study period.
180
Table: 6.31
T-test for 5-Week Holding Period
Pairs t df P value
Momentum & Contrarian 0.522 42 0.604
Momentum & Index 0.608 42 0.547
Contrarian & Index 0.102 42 0.919
Source: Computed from data source
Here the p-value is greater than the significance value 0.05; so the results
of five-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
2. Contrarian Strategy does not give superior returns to the investor in the
Indian Capital Market
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 five-week
holding period in the Indian stock market during the study period.
Table: 6.32
T-test for 6-Week Holding Period
Pairs t df P value
Momentum & Contrarian 0.387 42 0.701
Momentum & Index 0.691 42 0.494
Contrarian & Index 0.366 42 0.717
Source: Computed from data source
181
Here the p-value is greater than the significance value 0.05; so the results
of six-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
2. Contrarian Strategy does not give superior returns to the investor in the
Indian Capital Market
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 six-week
holding period in the Indian stock market during the study period.
Table: 6.33
T-test for 7-Week Holding Period
Pairs t df P value
Momentum & Contrarian 0.470 42 0.641
Momentum & Index 0.749 42 0.458
Contrarian & Index 0.330 42 0.743
Source: Computed from data source
Here the p-value is greater than the significance value 0.05; so the results
of seven-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
182
2. Contrarian Strategy does not give superior returns to the investor in the
Indian Capital Market
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 seven-week
holding period in the Indian stock market during the study period.
Table: 6.34
T-test for 8-Week Holding Period
Pairs t df P value
Momentum & Contrarian 0.604 42 0.549
Momentum & Index 0.561 42 0.578
Contrarian & Index 0-.084 42 0.933
Source: Computed from data source
Here the p-value is greater than the significance value 0.05; so the results
of eight-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
2. Contrarian Strategy does not give superior returns to the investor in the
Indian Capital Market
3. No significant difference is noticed between Contrarian and Momentum
Strategies in making superior returns from Indian Capital Market.
183
The results also reject the possibility of making superior returns by
adopting the momentum and contrarian investment strategies for an eight -week
holding period in the Indian stock market during the study period.
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
Source: Computed from data source
Table: 6.36
3- Week holding period result during recession
Period Momentum Contrarian Index
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
Source: Computed from data source
185
Table: 6.37
4- Week holding period result during recession
Period Momentum Contrarian Index
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
Source: Computed from data source
Table: 6.38
5 - Week holding period result during recession
Period Momentum Contrarian Index
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
Source: Computed from data source
Table: 6.39
6- Week holding period result during recession
Period Momentum Contrarian Index
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
Source: Computed from data source
Table: 6.40
7-Week holding period result during recession
Period Momentum Contrarian Index
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
Source: Computed from data source
186
Table: 6.41
8 -Week holding period result during recession
Period Momentum Contrarian Index
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
Source: Computed from data source
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
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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
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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.
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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
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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
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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)
Source: Bloomberg.com
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)
Source: Bloomberg.com
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
Source: Computed from data source
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
Year N Correlation p-value
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
Source: Computed from data source
a Correlation is significant at the 0.01 level.
b Correlation is significant at the 0.05 level.
c Correlation is not significant.
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Table: 6.44
Correlation Result for Nikkei Index
Year N Correlation p-value
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
Source: Computed from data source
a Correlation is significant at the 0.01 level.
b Correlation is significant at the 0.05 level.
c Correlation is not significant.
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.