For many years, economists, statisticians, and teachers
of finance have been interested in developing and testing
models of stock behaviour. One important model that
has emerged from this research is the theory of 'random
walk.' This theory casts serious doubts on many other
methods that describe and predict stock price behaviour
that have considerable popularity outside the academic
world.
Based on an analysis of 70 companies listed in
the 'A' list category on the Bombay Stock
Exchange, this paper by Belgaumi is an
attempt to test the weak form efficiency of
the Indian stock market.
By subjecting the weekly share prices to
Serial Correlation Analysis and Runs Test,
the author finds that the Indian stock
exchanges are efficient in the weak form and
that the independence assumption regarding
the movements of share prices over short
period holds good.
M S Belgaumi is a faculty member in the
Department ofP G Studies and Research in
Commerce, Mangalore University, Mangalore.
Random walk theorists usually start from the premise
that the major security exchange centres are good
examples of 'efficient' markets. An efficient market is
defined as a market where there are a large number of
rational and profit maximizers actively competing with
each other and trying to predict future market values of
individual securities, and where important current
information is almost freely available to all participants.
In an efficient market, competition among the many
intelligent participants leads to a situation where, at any
point of time, the actual prices of individual securities
already reflect the effects of information based both on
the events that have already occurred and on the events
which are, as of now, expected to take place in the future.
In other words, in an efficient market, at any point of
time, the actual price of a security will be a good estimate
of its intrinsic value.
Objectives
The main objectives of this study are as follows:
* To examine the share prices in the Indian stock
market.
* To empirically test whether the random walk
hypothesis or weak form of efficient market
hypothesis holds good in the Indian stock market.
* To test whether share price movements over short
periods such as a week are independent.
Review of Literature
Some of the earlier studies on the behaviour of stock
markets were carried out in the US and most of them
confirmed that the US stock markets were efficient in
'weak as well as semi-strong' form, and that the random
walk hypothesis theory held good (Alexander, 1961;
Cootner, 1962; Granger and Morgenstern, 1963,70; Fama,
Vol. 20, No. 2, April-June 1995
43
1965; King, 1966; Mandelbrot, 1966; Levy, 1967 and
lenson, 1967).
The British contribution to the random walk theory
goes under the name of "Higgledy Piggledy Growth."
Little (1962) made the following statement in his paper:
"I wrongly expected that I would be able to find some
correlation of future and past growth primarily on the
view that some continuity of good and bad management
would establish such a corrleation." Little and Rayner
(1966), after scrutinizing the records of over 400
companies, concluded that there was a high degree of
randomness in relative company earnings growth, at
least within industries and after adjustment for capital
gains in the normal way.
There have been attempts to investigate the
behaviour of stock markets in other parts of the world
too. For instance, Niarchos (1972) studied the behaviour
of shares on the Athens Stock Exchange. Conrad and
Juttner (1973) tested random walk hypothesis by studying
the behaviour of stock prices listed on the Frankfurt
Exchange. Sharma and Kennedy (1977) compared the
stock prices listed on the Bombay, London, and New
York Stock Exchanges.
In India, only a limited number of studies are
available on the random walk theory. Rao and Mukherjee
(1971) supported the evidence of random walk
hypothesis by taking a single company's share prices
during 1950-70. Gupta (1979) employed two sets of data
from the Indian market during 1971-76 and used serial
correlation and runs test to confirm the applicability of
random walk theory to the Indian stock market.
Sharma (1983) studied 23 Indian companies listed
on the Bombay Stock Exchange by using the Integrated
Moving Average Form of random walk and confirmed
that the previous shocks do not apparently influence
future shocks. Ramachandran (1985) studied the impact
of the announcement of bonus share issues by companies
on the share prices and found that market tended to
carry over the new information made available with
other information at its disposal and confirmed that
semi-strong form of efficiency exists in the Indian stock
market. Barua and Raghunathan (1986) carried out a
study on the share of Reliance Industries by taking
actual returns ajid concluded that the Indian stock market
is inefficient in pricing its securities. Gupta (1987)
questioned the validity of the assumptions made by
Barua and Raghunathan in missing out the peculiarities
of the Indian stock market. Srinivasan and Narasimhan
(1988) also questioned the methodology of Barua and
44
Raghunathan and elaborated on the concept of riskreturn parity and efficiency, drawing a distinction
between information efficiency and market efficiency.
Yalawar (1988) conducted an extensive study on the
efficiency of the Bombay Stock Exchange and found the
behaviour of stock prices to be random. However, Rao
and Bhole (1991) questioned Yalawar's findings on the
ground that his sample got restricted to some companies
which caught the fancy of investors and speculators and,
therefore, they were bound to be those companies which
performed well during that period. Venkateswar (1991)
explored the relationship of the Indian stock market, as
reflected by the Bombay Stock Exchange Index, vis-a-vis
other prominent international stock markets. He found
out that the Indian stock market was insulated from
other international stock markets.
Broca (1992) probed the adequacy of the randomness
assumption for Indian share returns; his shady discovered
that share returns exhibit statistically significant
differences across days of the week. Obaidullah (1991)
examined the time series behaviour of corporate earnings.
The empirical evidence contradicts a widely held notion
that accountants use discretionary techniques to
"smooth" reported earnings.
Methodology
This study is envisaged to study the behaviour of all the
'A' list shares listed on the Bombay Stock Exchange,
which are also traded in Calcutta, Madras, and
Ahmedabad Exchanges. Initially, the sample size
contemplated was all the 88 shares of 'A' list category as
appearing in The Economic Times. But the final sample
was whittled down to 70 companies for lack of
information. The period covered for the study was from
1st April 1991 to 31st March 1992.
Two sets of data were collected for the present
study. The first set consisted of the Economic Times All
India Index of ordinary shares, with the base year
1985=100. The second set included individual weekly
share price series of selected companies. The share
prices were collected from the various issues of The
Investment Week. Any abrupt change in share prices due
to announcement of bonus, rights, and splitting of stocks
were adjusted.
For testing the hypothesis whether the Indian stock
markets are efficient in the weak form, two kinds of tests
are conducted. These are parametric test for independence and non-parametric test for randomness. In the
parametric test, serial correlation coefficients have been
Vikalpa
computed for each of the price series for lags extending
from 1 to 10, whereas, in the non-parametric test, runs of
consecutive price changes of the same sign have been
analysed. For testing the hypothesis that successive
price changes are linearly independent or that share
prices follow the random walk approach, the random
walk model has been used.
For null hypothesis to be true, observed serial
correlation should not be statistically significant, i.e., it
should not be greater than three times the standard error
of coefficients.
Random Walk Model
Runs Analysis
Suppose that {Zi} is a discrete, purely random process
with mean M and variance. A process {Xj} is said to
he a random walk if
Runs analysis, a non-parametric test, has been used to
judge the randomness in the behaviour of Indian stock
markets. This test ignores absolute value in a time series
and concerns itself only with the direction of the changes
in a given time series. In the present work, a run may be
defined as a sequence of price changes of the same sign
preceded and followed by the price changes of different
signs.
This would also imply that the stock market has got
no memory and the past price history of a share will not
help predict today's price. The best estimate of today's
price, given yesterday's price, is yesterday's price itself.
Share price on day t = share price on day (t-1) +
random error (Chatfield, 1980, pp 40-41).
Serial Correlation
Serial correlation coefficients provide a measure of
relationship between the value of a random variable in
time (t) and its value (k) periods earlier. They will
indicate whether price change at time (t) is influenced by
the price changes occurring (k) periods earlier. The
serial correlation of a time series is given by autocorrletion function of lag k. rk is estimated (Chatfield,
1980, p 25) by using:
In a given share price series, there are three possible
types of price changes, namely, positive, negative, and
no change, thus implying the types of runs. Therefore, a
plus run of length 'i' may be defined as a sequence of T
positive price changes preceded and succeeded by either
negative or zero price change. Similarly, a minus or no
change price runs can also be defined for the purpose of
runs analysis. The series of change is first replaced by
series of symbols, namely +, - and 0, depending upon
whether such changes are positive, negative or zero and
then runs are counted.
A runs test is performed by comparing the actual
number of runs with the expected number of runs on the
assumption that price changes are independent. If the
observed runs are not significantly different from the
expected number of runs, then it is inferred that
successive price changes are independent. On the
contrary, if this difference is statistically significant, the
series of price change would be regarded as dependent.
Under null hypothesis, the consecutive price changes are independent and it is assumed that sample
proportions of positive, negative, and no price changes
are unbiased estimates of the population proportions;
the expected number of runs of all types can be computed
by using the method suggested by Brownlee (1965, p
231):
Britannia Industries, Eskayef, Grasim Industries,
Mukand Limited, and Tata Engineering are greater than
twice the standard error. The maximum value of these
coefficients is 0.482 for Modi Rubber and the minimum
coefficient is - 0.005 for Asian Paints.
One cannot discern any general pattern in the signs
of these coefficients and thus the results support the
hypothesis of zero correlation. Hence, we conclude that
there is no correlation between price with one week
duration.
where
R = Total observed number of runs of all signs
0.5 = Continuity adjustment
For testing the significance of the difference between
observed and expected number of runs, the test statistic
employed will be 'Z.' It may be mentioned here that in
the absence of an alternative hypothesis about the
direction of the deviation from random series, a twotailed test is applied.
The null hypothesis (randomness hypothesis) will
be rejected (or accepted) at 5 per cent level of significance
in favour of (or against) the alternative hypothesis (nonrandomness hypothesis) depending on whether
observed values of /Z/ is * 1.96.
First-order Differencing
First-order differencing is now widely used in economic
studies to bring about stationarity in time series. For
non-seasonal data, first-order differencing is usually
sufficient to attain apparent stationarity so that the new
series (y17... y J is formed from the original series (X1,...
Xn }
Yt = X t +i - X t = V X t+i
In the present study too, the entire data are subjected
to first-order differencing before carrying out the tests.
Findings of the Study
Table 1 reveals the serial correlation coefficients of all the
70 share prices and ET Index for lags 1 to 10. It is evident
from Table 1 that first-order coefficients are small in
magnitude and statistically insiginificant in almost all
the cases. Out of the 70 serial correlation coefficients for
lag 1, only one share (Modi Rubber) is statistically
significant which means that its computed coefficient is
greater than three times the standard error in absolute
terms. The coefficients of other five companies, viz.,
46
The higher order serial correlation coefficients also
do not depict statistically significant relationship except
for Standard Industries whose coefficient is greater than
three times the standard error for lag 2; and ACC
company's coefficient is greater than twice the standard
error. There are three shares for lag 3, seven shares for
lag 4, and one share for lag 5 whose coefficients are
greater than twice the standard error respectively. There
is no share for lags 6 to 10 whose coefficients are
significant. Table 2 exhibits the list of shares showing
significant values of serial correlation coefficients for
lags 1 to 10.
A look at Table 3 which indicates the results of runs
test confirms that standard normal variate 'Z' is not at all
significant at 5 per cent level for any company. In stark
contrast to all those shares which evinced temporal
dependence as a result of applying serial correlation
test, the runs test results do not show any non-random
behaviour. Further, the average absolute value of 'Z' is
0.2189 and 'Z' value for ET Index is 0.1902. In Table 3, the
percentage divergence of actual runs from expected
runs is shown by K where K=(R-M/M). The average
absolute value of K is 0.0776 which implies that an
aberration of 7.7 per cent has been observed between
actual and expected number of runs. Thus, the results of
runs test suggest that, in general, successive price changes
appear to occur at random in most of the shares analysed.
The results of serial correlation analysis indicate
that price change series do not show any dependence of
any order. This is confirmed by the results of runs test.
Therefore, we can infer that share price behaviour in the
Indian stock market follows the random walk model.
Hence, Indian stock exchanges are weakly efficient.
Conclusion
We can observe from the above analysis that the
behaviour of share prices over a short period does not
display any apparent pattern and it would be difficult to
predict share prices from their historical price
movements. We find that the two tests—serial correlation
and runs test—do support the independent assumption
Vikalpa
of random walk model. Hence, we conclude that the
exchanges are weakly efficient in pricing their shares.
There have been quite a few studies which confirm
the applicability of random walk model to the Indian
stock market. However, much more research is required
on various aspects of the behaviour of stock markets. It
would be a worthwhile exercise to study the behaviour
of less frequently traded shares, ettmpirically testing the
semi-strong and strong form of EMH. This is possible
only when we have real content in the information being
released by the companies at regular intervals. It is not
possible to have a complete picture about the functioning
of stock exchanges unless we have transparency in the
dealings of these exchanges.
References
Alexander, SS (1961). "Price Movementin Speculative Markets:
Trends or Random Walks," Industrial Management
Review, Vol 2, No 2, pp 7-26.
Barua, S K and Raghunathan, V (1986). "Inefficiency of the
Indian Capital Market," Vikalpa, Vol 11, No 3, pp 225229.
Broca, D (1992). "Day of the Week Patterns in the Indian Stock
Market," Decision, Vol 19, No 2, pp 57-64.
Brownlee, K A (1965). Statistical Theory and Methodology in
Science and Engineering. 2nd Edition. New York: John
Wiley and Sons.
Chatfield, C (1980). The Analysis of Time Series: An Introduction.
London: Chapman and Hall.
Conrad, K and Juttner, DI (1973). "Recent Behaviour of Stock
Market Prices in Germany and the Random Walk
Hypothesis," Kyklos, Vol 26.
Cootner, P H (1962). "Stock Prices: Random vs Systematic
Changes," Industrial Management Review, Vol 3, No 2, pp
24-45.
Fama, E F (1965). "The Behaviour of Stock Market Prices,"
Journal of Business, Vol 38, No 1, pp 34-105.
Granger, C W J and Morgenstern, O (1963). "Spectral Analysis
of New York Stock Market Prices," Kyklos, Vol 16, pp
1-27.
__________ (1970). "What the Random Walk Model does
not Say," Financial Analysts Journal, Vol 26, No 3, pp 9193.
Gupta, O P (1979). "The Random Walk Theory of Stock Market
Price Behaviour: A Survey," Review of Commerce Studies,
Vol 8.
Gupta, R (1987). "Is the Indian Captial Market Inefficient or
Excessively Speculative?" Vikalpa, Vol 12, No 2, pp 2128.
Jenson, M C (1967). "Random Walks: Reality or MythComment," Financial Analysts Journal, Vol 23, No 6.
Vol. 20, No. 2, April-June 1995
King, H F (1966). "Market and Industry Factors in Stock Price
Behaviour," Journal of Business, Vol 39, No 1,139-190.
Little, IM D (1962). "Higgledy Piggledy Growth," Bulletin of
the Oxford University, Institute of Economics and Statistics.
_______ and Rayner (1966). Higgledy Piggledy Growth Again.
Oxford: Basil Blackwell.
Levy, R A (1967). "Random Walk: Reality or Myth," Financial
Analysts Journal, Vol 23, No 6.
Mandelbrot, B (1966). "Forecasts of Future Prices, Unbiased
Markets, and Martingale Model," Journal of Business,
Vol 39, No 1, 242-255.
Narasimhan, M S and Srinivasan, N P (1988). "Testing Stock
Market Efficiency Using Risk-return Parity Rule,"
Vikalpa, Vol 13, No 2, pp 61-66.
Niarchos, N A (1972). "The Stock Market in Greece: A Statistical
Analysis," in Szego, G and Shell, K (eds), Mathematical
Methods in Investment and Finance. Amsterdam: NorthHolland.
Obaidullah, M (1991). "Time Series Behaviour of Corporate
Earnings," 11MB Management Review, January-December,
79-85.
Ramachandran, J (1985). "Behaviour of Stock Prices: Trading
Rules, Information, and Market Efficiency," FPM thesis,
IIM, Ahmedabad.
Rao, N K and Mukherjee, K (1971). "Random Walk Hypothesis— An Empirical Study," Arthaniti, Vol 4, No 1.
Rao, Narayana K V S S and Bhole, L M (1991). "Bombay Stock
Exchange: Rates of Return and Efficiency Comment,"
Indian Economic Journal, Vol 39, No 2, pp 136-139.
Sharma, J L (1983). "Efficient Capital Markets and Random
Character of Stock Price Behaviour in a Developing
Economy," Indian Economic Journal, Vol 31, No 2, pp 5365.
-------------and Kennedy,RE (1977). "A Comparative Analysis
of Stock Price Behaviour on the Bombay, London and
New York Stock Exchanges," Journal of Financial and
Quantitative Analysis, September, pp 183-190.
Venkateswar, S (1991). "The Relationship of Indian Stock
Market to Other International Stock Markets," Indian
Economic Journal, Vol 39, No 2, pp 105-108.
Yalawar, YB (1988). "Bombay Stock Exchange—Rateof Return
and Efficiency," Indian Economic Journal, Vol35, No4, pp
68-121.
Various Issues of Investment Week and The Economic Times from
April 1991 to March 1992.
47
Table 1: Serial Correlations of Lags 1 to 10
Nm11r <!f tlw CMlpnny
.'l.~~oci.11ed
Cement Co Ltd
,.\poilu Tyre~
:\-<hok Lt>vland
:\11,1:< Cclpco
2
-~
4
;;
6
7
8
9
IO
0.1%
"0.416
0.262
•o.J36
0.284
0.082
0.130
0.015
0.205
·0.071
·0.144
-0.017
•o.359
-0.038
-0.013
0.122
0.088
0.020
0.046
0.091
0.222
-0.287
.(1.]08
0.245
0.190
·0.025
0.004
-0.031
-0.091
-0.022
-OJJ05
-0.009
-0.133
0.064
0.057
·0.276
-0.036
-0.063
0.203
0.122
0.150
-0.073
0.157
0.016
..0.120
·0.021
0.023
0.019
-0.016
.().086
·0.029
..0.048
O.IJ10
·0.095
·0.034
0.046
0.074
n.1jaj Au t1,
11.069
·11.041
..j).007
Bomb.1y Dyeing
-0.1136
-0.204
-0.123
0.154
0.187
0.003
·0.074
·0.054
0.233
0.087
IJn tannia lndustril:':o
•o.3ss
0.182
0.110
0.075
·0.057
0.019
0.051
0.017
0.010
·0.058
Hro(lkt• B<md
-O.OIS
-0.259
0.21B
0.144
0.149
0.139
·0.134
0.075
0.169
0.077
0.003
-0.280
0.078
0.026
0.135
-0.103
.0.163
0.004.
0.075
0.073
CMtrnllndia
..{).185
0.127
-0.121
"0.319
0.136
0.022
0.044
..().075
0.124
..0.013
Ct·.Jt Tyres
0.293
0.006
0.185
#0.380
0.155
-0.003
0.007
0.064
0.069
0.080
C.•ntury TextilE>~
-0.021
-0.106
4).094
·0.1114
0.072
-0.127
-0.052
·0.035
0.132
0.061
C,•ntury Enka
-0.11!6
-0.004
0.124
..0.012
0.107
0.009
0.050
0.049
..0.044
-0.026
(',>!galE:
-0.122
-0.104
0.100
.0.046
0.151
0.109
-0.030
·0.117
·0.098
-0.033
El Hntl!b
·0.225
·0.188
0.104
-0.027
0.185
-0.066
-0.016
-0.050
..().Q28
0.032
E:ol"()rts
O.OIS
0.030
-0.196
0.159
0.177
0.076
0.086
·0.142
0.120
·0.043
E~kavd
•\1.385
0.123
0.008
0.148
0.085
0.028
0.152
0.081
0.015
·0.077
-iii.OS
..0.092
0.267
0.108
0.081
0.067
-0.049
..0.047
0.072
.0.029
Ex\'t'llndustries
<1.076
..0.145
-0.149
•0.349
0.156
0.034
-O.ot8
-0.029
0.059
0.079
C.E5hipping
0.105
0.102
0.293
0.010
-0.155
·0.037
0.029
-0.048
..0.002
0.035
G<~rw.nt- Nylon~
..0.210
·0.164
0.127
0.161
0.087
-0.058
0.049
0.031
..0.080
0.043
G.lrwi'lrt' Polymer~
..0.030
0.154
·0.136
0.072
0.034
..0.062
..().146
0.089
0.025
0.003
-0.055
·0.143
·0.116
•o.316
-0.118
.0.123
0.133
0.173
-0.033
..0.171
•0.340
0.143
0.109
0.197
0.101
0.102
0.112
0.047
..0.016
0.040
Gujm·,Jt Alkali~
0.207
0.012
..().048
0.189
IH40
0.045
0.019
..0.084
0.122
-0.017
Gujar.lt Ambuja
0.2!l0
0.053
..0.008
0.220
0.038
0.046
-0.029
..().045
0.114
-0.036
Cuiamt Narmadi1
-0.016
-0.150
..0.119
0.037
0.032
0.177
-0.085
..0.158
-0.100
0.074
Cujarat Fertiliz<>r!'
..0.047
·0.129
-0.270
0.043
0.047
-0.003
..0.164
-0.153
0.061
0.037
Hindu!<hlll Ciba·G<>igy
-0.1)}4
-0.225
.0.041
0.076
..0.041
-0.047
-0.047
-0.004
·0.139
..0.009
LP\'eor
0.023
·0.208
0.074
0.108
0.253
0.010
-0.111
0.021
0.066
0.009
Hind u~tan Mntor
0.103
0.040
0.265
·0.009
0.079
-0.034
0.065
0.081
0.019
0.010
Hind•llc''
0.0&1
.().276
0.213
0.245
0.161
0.010
..0.074
·0.011
0.045
0.007
IC'Ilndi,,
0.1QJ
..0.054
-0.088
0.046
-0.066
..0.089
0.072
0.243
0.056
0.044
lndi.111 Aluminit•m
0.251
-0.223
0.038
0.290
0.228
0.058
-0.101
0.026
0.081
O.Dl8
lndi.m R.:w<>n
0.049
0.159
0.157
0.093
0.127
-0.154
0.091
..0.041
0.100
..0.035
Cadhury
E~~;u
Shipping
Gr;1~im
Industries
Hindu~tan
Vikalpa
2
4
5
6
7
8
9
-0.021
..0.034
0.281
0.098
0.113
..0.011
-0.104
0.076
0.045
..0.061
IJ...: Industries
0.112
-0.286
0.066
0.237
0.119
0.102
..0.061
-0.055
..0.117
0.062
; f..: S\'ntlwtio:~
-0.012
-0.137
0.157
-0.026
0.026
0.040
0.006
0.079
0.007
0.071
1-.:•rl(>skiw Cummin!>
-ll.l07
0.057
-0.087
0.149
-0.241
0.088
..0.251
0.076
0.290
0.003
1..1 rs•·n &
-0.150
0.137
-0.091
0.189
-0.035
0.106
0.112
-0.096
0.096
-0.050
-0.193
-0.058
-0.003
0.042
0.196
0.059
0.034
-0.156
0.020
0.073
0.235
0.214
0.104
0.154
0.035
0.070
0.102
..0.054
-0.060
0.001
:\·l,mg,Jion• Chemical~
-0.1!16
0.215
·-0.364
0.204
-0.114
0.085
-0.141
O.ot5
0.111
0.143
'diC(l
-0.147
0.035
0.184
-0.133
0.155
0.043
0.023
0.057
0.040
0.202
..ll.4!12
0.245
0.067
0.077
-0.006
-0.075
-0.028
-0.013
0.027
0.081
1\luk.md
•0.362
0.29.1
0.097
0.063
-0.093
-0.010
-0.042
0.039
0.022
0.226
'\\·~til'
-O.Ot.S
·0.228
0.172
-0.025
-0.280
-0.015
-0.181
0.030
0.000
0.035
:\.:.•cil
0.068
-0.180
-0.117
0.094
0.166
0.063
-0.083
-0.056
0.012
0.012
( 11'k.w Silk
-0.191
-0.007
0.206
0.135
0.049
0.061
0.051
-o.072
-0.019
0.027
l'.lrk(• D.wb
-0.120
o.oss
0.059
-0.142
0.089
-0.117
0.220
-0.044
0.070
0.154
1'11;(.1'1'
0.029
0.017
0.114
0.155
-0.043
0.005
0.032
-0.092
0.000
0.038
l'nnd' India
0.059
-0.193
-0.169
0.133
0.298
0.099
0.032
-0.121
0.059
0,015
l'l'(•mit'r Autom••hilt>~
O.lt.S
0.091
-0.077
-0.054
0.268
0.107
-0.055
-0.135
0.023
0.042
l{.n·nwnd W{>(>lhm
0.143
0.040
0.034
0.208
0.148
0.094
-0.060
-0.124
0.079
0.138
II.U3l'
-0.145
-0.124
0.009
0.065
0.064
0.053
-0.112
0.126
0.184
ll.144
-0.082
0.058
0.038
0.131
0.184
-0.061
-0.127
..0.007
0.146
0.016
-0.057
-0.174
0.158
"0.341
0.045
-0.009
..Q.101
0.062
0.039
0.155
0.054
0.035
0.127
0.024
-0.112
-0.008
0.017
0.030
-0.003
-0.11:14
-0.024
0.044
0.010
11.009
0.077
-0.107
0.091
-0.073
0.102
0.007
0.175
-0.058
-0.011
-0.266
-0.030
..0.216
-0.094
I'IC
Touhrn
I. tpton
\l,lhindril &
M~hindra
\·h•di Rubber
l~o·(·kitl
& Colman
f~··li,lnCt• lndu~tnt>s
~KF
r,.,•,Jrinh:;
~I''~'
~t.Hld.lfd lndu~tries
0.007
~tl',l\\' Produ<:l~
0.()]1
-0.244
0.049
0.029
-0.146
0.064
-0.115
0.035
0.218
-0.065
.(1.1 0:'\
0.133
-0.363•
0.008
-0.086
0.019
-0.002
-0.205
0.024
0.000
0.264
..0.066
0.096
0.094
..0.010
0.044
0.175
-0.026
0.032
0.114
•0.316
-0.02!\
0.028
0.218
0.217
0.035
-0.056
0.051
0.055
0.083
0.242
-0.251
0.034
•0.334
0.192
0.070
0.042
..0.034
-0.044
0.032
0.225
0.049
0.294
0.238
0.094
-0.008
0.111
0.052
-0.154
-0.03''
11.056
-0.168
.(J.022
•o.363
0.064
-0.134
-0.020
0.020
0.054
-0.216
Wimo•
-0.072
0.001
-0.146
-0.083
-0.109
0.199
0.052
-0.013
0.161
..{1,07'0
ZtMri Agn•
-0.070
0.039
-0.139
0.230
-0.158
-0.169
0.069
-0.167
-0.015
-0.4l7!1
0.1192
0.049
0.303
0.194
0.172
0.032
0.031
0.037
0.068
0.:!37
·1,11.1 PtlW('I'
1.11.1
Clwmkals
T.•t<l EngllleE>rinj.;
1,11,1 St•.·•·l
ETindP:-.
*denotes rR > 2 S.E and** denotes rR > 3 S.E.
\lsll. 20. No.2, April-/rmc 1995
49
Table 2 : Shares having Significant Serial Correlation Coefficients
l.tlgs
ra t 2 (i)S. E
1
Britannia Industries
Eskayef
Grasim Industries
Modi Rubber
Mukandlron
ACC
Standard Industries
Apollo Tyres
MCF
TataPower
ACC
CastroI
CeatTyres
Excel Industries
Glaxo
Tata Steel
Voltas
Siemens
No Company
2
3
4
5
6to10
r t 3 <1>5. E
•
Modi Rubber
Standard Industries
No Company
Table 3: Results of Runs Analysis
Name of the Comptzny
Actual
n2
I
M
N
z
K
1552
23.1538
9.189
·0.1799
-0.093
3
1270
28.5769
10.8425
.0.2838
·0.1252
19
6
1126
31.3461
10.8424
-0.0780
·0.0429
19
5
1170
30.5000
10.8409
.0.0922
.0.0492
24
20
8
1040
33.0000
10.4874
·0.1430
·0.0606
23
31
21
0
1402
22.0385
10.9023
·0.2328
·0.1167
26
32
17
3
1322
27.5769
10.8025
·0.0997
·0.0572
Britannia Industries
29
33
14
5
1310
27.8CY77
10.7423
+0.1575
+0.0429
Brooke Bond
35
25
21
6
1102
31.8077
10.8599
+0.3399
+0.1004
Cadbury
23
25
23
4
1170
30.5000
10.8784
·0.6435
·0.2459
Castrollndia
19
37
15
0
1594
22.3461
10.7506
·0.2649
·0.1497
CeatTyres
29
29
17
6
1166
30.5769
10.8138
·0.0996
·0.0516
Century Textiles ,
27
32
19
1
1385
26.3653
10.8509
+0.1046
+0.0241
Century Enka
32
26
18
8
1064
32.5385
10.8385
-0.0036
·0.0165
Colgate
29
31
20
1
1361
26.8269
10.8706
.0.1220
·0.0681
EJ Hotels
25
24
1~
11
986
34.0385
10.8471
.0.7872
.0.2655
Runs
n,
nz
A$sociated Cement Co Ltd
21
36
16
Apollo Tyres
30
25
19
Ashok Leyland
30
27
Asian Paints
29
28
AtlasCopco
31
Bajaj Auto
Bombay Dyeing
R
50
n3
Vikalpa
1
2
3
4
5
6
7
8
9
Escorts
32
26
22
4
1176
30.3846
10.8737
+0.1945
-0.0532
Eskayef
27
28
23
1314
27.7308
10.9128
-0.0211
-0.0263
Essar Shipping
31
32
18
2
1352
27.0000
10.8279
+0.4156
+0.1481
Excel Industries
30
31
15
6
1222
29.5000
10.7756
+0.9280
+0.0169
26.8269
10.8706
-0.1220
-0.0681
Name ofthe Company
GEShipping
25
31
20
1
1361
Garware Nylons
34
22
22
8
1032
33.1538
10.8600
+0.1239
·0.0255
Garware Polymers
30
26
23
3
1214
29.6538
10.8875
+0.0777
+0.0117
Glaxo
33
25
22
5
1134
31.1923
10.8682
+0.2123
+0.0579
Gtasim Industries
27
34
16
2
1416
25.7692
10.7769
+0.1606
+0.0478
Gujarat Alkalies
33
28
20
4
1200
29.9231
10.8542
+0.3295
+0.1028
Gujarat Ambuja
27
34
17
1
1445
25.2115
10.8033
+0.2118
+0.0709
Gujarat Narmada
26
24
25
3
1210
29.7308
10.8907
-0.2967
-0.1255
Gujarat Fertilizers
26
30
20
2
1304
27.9231
10.8641
-0.1309
-0.0689
Hindustan Ciba..Ceigy
33
27
18
7
1102
31.8077
10.8336
+0.1562
+0.0375
Hindustan Lever
27
25
25
2
1254
18.8846
10.9055
-0.1269
.{1.0652
Hindustan Motor
28
29
20
3
1250
28.9615
10.8584
-0.0425
·0.0332
Hindalco
30
30
17
5
1214
29.6358
10.8086
+0.0783
+0.0117
ICIIndia
27
24
19
9
1018
33.4231
10.8513
-0.5458
-0.1922
Indian Aluminium
27
21
25
6
1102
31.8077
10.8599
-0.3967
-0.1511
Indian Rayon
32
29
20
3
1250
28.9615
10.8584
+0.3259
+0.1049
lTC
31
33
17
4
1266
28.6538
10.8047
+0.4485
+0.1517
10.8336
+0.1560
+0.0375
JI< Industries
JI< Synthetics
28
27
18
7
1102
31.8077
29
27
22
3
1222
29.5000
10.8809
0.0000
-0.0169
Kirloskar Cummins
27
27
23
2
1262
28.7308
10.8988
-0.1129
.{1.0602
Larsen & Toubro
29
29
20
3
1250
28.9615
10.8593
+0.0496
+0.0013
Lipton
28
32
17
3
1322
27.5769
10.8025
+0.0854
+0.0153
Mahindra & Mahindra
27
21
22
9
1006
33.6538
10.8590
..0.5667
-0.1977
Mangalore Chemicals
30
23
20
9
1010
33.5769
10.8560
..0.2834
-0.1065
MICO
30
33
16
3
1354
26.9615
10.7797
+0.3282
+0.1127
Modi Rubber
30
25
18
9
1030
33.1923
10.8437
-0.2483
-0.0962
Mukand
29
27
22
3
1222
29.5000
10.8809
0.0000
-0.0169
Nestle
26
30
14
8
1160
20.6293
10.7778
-0.0889
-0.1529
Nocil
30
25
24
3
1210
29.7308
10.8907
+0.0706
+0.0091
Orkay Silk
31
24
25
3
1210
29.7308
10.8907
+0.1624
+0.0427
Parke Davis
28
32
15
5
1274
28.5000
10.7665
0.0000
+0.0464
Pfizer
34
25
19
8
1050
32.8077
10.8478
+0.1560
+0.0363
Ponds India
30
29
10
9
1118
31.5000
10.7905
-0.0927
·0.0476
Premier Automobiles
23
23
27
2
1262
28.7308
10.8988
-0.2047
-0.0950
Raymond Woollen
29
26
24
2
1256
28.8461
10.9038
+0.0599
+0.0053
Vol. 20, No. 2, April-June 1995
51
:\!:1111<' of tit<' COIIIJ'1111.lf
4
I
2
Rl'l'kitt and Colman
37
25
16
11
Rt•li.llK(;' Ind u~trk~
'27
25
25
:-;lt'lnt.:n~
21'\
3.1
:-'KF o~~arin~,;
30
"Pk
3
5
6
7
8
9
1002
33.7308
10.9798
+0.3433
+0.0969
2
1254
28.8846
10.9055
-0.1269
-0.0652
16
3
1354
26.9615
10.7797
+0.1427
+0.0963
25
21
6
1102
31.8077
10.8599
-0.1204
-0.0568
29
2ll
20
4
1200
29.9231
10.8548
-0.0389
-0.0308
29
2~
20
4
1200
29.9231
10.854R
-0.0389
..0.0308
2H
29
19
4
1210
29.5769
10.8409
-0.0993
-0.0533
'tlt4l Pn\\.t.'''
29
29
21
2
12811
28.2692
10.117118
..{).1131
+0.025!1
1.•1·• t -lwmk-.,1~
:n
29
21
2
1286
28.2692
10.8788
+0.2969
+0.0966
l".ot,l rn~int•t•nng
-·'
31
Ul
3
1294
28.1154
10.8239
-0.4264
-0.1819
lat .• ~kd
I,,,,, ,-,.,,
2-4
;:\3
18
1413
25.8269
10.8285
-0.1225
-0.0707
.25
24
24
4
11611
30.5385
10.8800
-11.4631
-0.1814
Vt•lt''"
32
22
16
14
936
35.0000
10.8586
-0.2302
..{).0857
\Vuun•
27
31
18
3
1294
28.1154
10.8239
-0.4264
-0.1819
h1.1n
33
25
19
8
1050
32.8077
10.8478
+0.6923
+0.0058
v
31\
14
(I
1640
21.4615
10.7176
+0.1902
+0.0717
0.2189
0.0776
:-;tandard
lndustrie~
l'rndurt:--
~tr.ll\'
A~n•
FT -\I lndt•\
.,~
·\n·r,,g,• Ab~l•lut<• V,lltlt'
· li<motes that Z value is insignificant at 5 per cent leveL
Vikalpa