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Testing for Asymmetry in the Price-Volume Relationship of Listed Australian Banks
1
Testing for Asymmetry in the Price-Volume Relationship of
Listed Australian Banks
Darren Henry*
La Trobe University, Australia
Param Silvapulle
Monash University, Australia
Abstract In this paper, we test for the presence of temporal asymmetry in the price-volume relationship for
a unique industry sub-group, comprising the four major listed Australian trading banks, using daily share
price and trading volume series. We have used a current popular methodology to capture the asymmetric
relation between the two series. The major findings are (i) there is evidence of causality from prices to
volumes, but not vice versa; (ii) there is causality from both positive and negative price changes to volume
changes for all four banks, but only very weak evidence of causality from negative volume to price for the
National Australia Bank and Westpac Banking Group; and (iii) negative price and volume changes have
stronger effects than positive price and volume changes. These findings suggest the existence of significant
feedback trading associated with these banking stocks, which can potentially be generalised to the wider
Australian sharemarket. The findings can be explained in terms of short selling action of traders in response
to price falls and the mentality and trading actions of bull and bear traders in response to price or volume
movements.
Keywords Temporal Asymmetry; Causality Testing; Australian Banks.
Introduction
This paper tests for the existence of temporal asymmetry in the relationship between price
and volume movements for major listed Australian banks trading on the Australian Stock
Exchange (ASX). Temporal asymmetry exists if the banks’ share price changes depend on the
direction of lagged volume changes or, alternatively, if changes in the banks’ share volume
traded depend on the direction of lagged share price changes.
Most prior studies have tested the contemporaneous relationship between price and
volume changes, applying the volume to price change ratio for testing purposes. Temporal
asymmetry is different in that it tests for asymmetry in terms of the direction of lagged
volume (price) changes and their effect on price (volume) changes. Temporal asymmetry is
present if the effect of positive lagged volume changes on price changes is significantly
* Corresponding author for communication. Address: Department of Economics and Finance, School of
Business, La Trobe University, Bundoora, Victoria 3086, Australia. Phone: +61 3 94791730. Fax: +61 3
94791654. E-mail: [email protected]
+ The authors acknowledge helpful comments from two anonymous reviewers, Buly Cardak and participants
at the 2000 Australasian Finance and Banking Conference, Sydney.
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Henry and Silvapulle / Journal of Accounting and Finance 2 (2003) 1~13
different from the effect of negative lagged volume changes on price changes. Similarly,
temporal asymmetry is evident if trading volume responds significantly differently to positive
lagged price changes than to negative lagged price changes.
Testing for temporal asymmetry is important due to the implications of its existence on
the trading actions and investment strategies of market participants. This is particularly
prevalent in relation to the sequential information arrival, market efficiency and feedback
trading hypotheses. In terms of the sequential information arrival hypothesis, temporal
asymmetry may cause differential price adjustment and volume trading between optimists and
pessimists or informed and uninformed traders. Also, temporal asymmetry from volume to
price changes is consistent with market inefficiency, and could suggest the potential for an
effective trading strategy using volume movements as a predictor of future price changes. The
finding of temporal causality from lagged price changes to trading volume levels has similar
implications for the feedback trading strategy, with past price movements having predictive
content for future volume trading and its price implications.
The price and volume series of major Australian banks are tested for their symmetrical
properties in this paper as they are heavily traded shares on the ASX, and represent some of
the largest companies, in terms of market capitalisation, on the ASX. The four listed banks
analysed in this paper, the ANZ Banking Group (ANZ), the Commonwealth Bank of
Australia (CBA), the National Australia Bank (NAB) and the Westpac Banking Corporation
(WBC), have consistently been ranked among the ten largest listed companies on the ASX
and all have significant weightings in market benchmark indices such as the All Ordinaries
Index and the S&P/ASX 200. As at June 1998, the weightings of ANZ, CBA, NAB and WBC
in the formation of the ASX All Ordinaries Index were 4.03%, 5.41%, 7.18% and 4.21%
respectively. At this point in time the total All Ordinaries Index contribution of these four
banking stocks was 20.83%, with no other industry grouping in the Australian market having
such a degree of concentration and influence on market index movement as the banking sector.
As such, significant ownership and trading in these banking shares is maintained by the
various classes of institutional investors, as well as by smaller individual investors holding
less-diversified portfolios of blue-chip stocks. Also, of interest are the significant regulatory
and competition (including industry deregulation) changes which have taken place in the
banking sector over the past two decades, and the influence on fundamental bank price and
volume activity of industry-specific as well as economy-wide and global factors such as
interest rate and exchange rate movements.
The remainder of the paper is structured in the following manner. The next section
presents the theoretical background for asymmetry testing of the price-volume relationship.
Following this we present the model development and hypotheses to test for the existence of
asymmetric properties in the price-volume relationship. The description of the trading bank
data used and the discussion and interpretation of the empirical results from the causality and
asymmetry testing form the content of the next section. Lastly, we provide some concluding
remarks and potential implications of the results.
Theoretical Background for Price-Volume Asymmetry Testing
Contemporaneous symmetry can be defined in notational form as:
Testing for Asymmetry in the Price-Volume Relationship of Listed Australian Banks
v
 
 pt  vt  pt
t

3
(1)
where p is the asset price and v is the associated volume of trading and the positive and
negative signs represent the direction of price change. Symmetry, as defined in equation (1),
exists when the average trading volume associated with positive price changes is equal to the
average trading volume in response to price falls. Asymmetry would therefore be present if
average trading volume is significantly different for price increases and decreases, such as
(vt pt+) > (vt pt-) or (vt pt+) < (vt pt-).
A similar representation of symmetry can be made using the volume to price change ratio,
as follows:
S
t
 pt
  S
t
 pt

(2)
where St = vt / pt. Thus, symmetry would prevail if the value of the volume-price change
ratio associated with a positive price change is not significantly different than the absolute
ratio value in the case of a negative price change. Contemporaneous asymmetry in the volume
to price change ratio can be defined in a similar manner to that outlined above.
Temporal symmetry in the influence of volume changes on price would exist if:
p
t
 
vt1 , vt2 , ... , vtn  pt vt1 , vt2 , ..., vtn

(3)
Here, the effect of positive lagged volume changes on price is equal to the effect of
negative lagged volume changes on price. Similarly, temporal symmetry in the effect of
lagged price changes on volume would exist if:
v
t
 
pt1 , pt2 , ... , ptn  vt pt1 , pt2 , ..., ptn

(4)
Ying (1966) was the first to evaluate the existence of asymmetry in the relationship
between prices and volumes and, in particular, differentiated between the signed price change
and the absolute price change in the volume to price change ratio. Ying (1966) suggested that
asymmetry in the volume to price change ratio may not be bi-directional, and that asymmetry
may differ depending on the direction of price movements (in terms of positive or negative
price changes). Ying (1966) concluded that there was a positive correlation between volume
and both price change (p) and absolute price change (p), with small (large) volume being
associated with a fall (rise) in price. The first relationship is analogous to the idea of temporal
asymmetry, where volume changes are greater in the presence of positive price changes
(upticks) than in the situation of price declines (downticks).
The existence of a positive correlation between volume and absolute price change (p)
has been a consistent finding in the literature. This conclusion has been reported by Crouch
(1970), Westerfield (1977), Harris (1986) and Richardson, Sefcik and Thompson (1987) using
daily or weekly data, Epps and Epps (1976) and Jain and Joh (1988) using transactions data
and for futures contracts trading by Cornell (1981), Tauchen and Pitts (1983) and
Grammatikos and Saunders (1986). A positive correlation has also been identified between
volume and price change (p) by Epps (1977), Hanna (1978), Smirlock and Starks (1985),
Harris (1986) and Jain and Joh (1988) for various security forms including stocks, aggregate
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Henry and Silvapulle / Journal of Accounting and Finance 2 (2003) 1~13
portfolios and bonds and for a range of data intervals. No empirical support, however, has
been identified for a similar positive correlation between volume and price change in futures
markets.
Various arguments have been put forward to explain the presence of asymmetry in the
price-volume relationship. The most prominent explanation provided for the existence of
temporal asymmetry lies with the heterogeneity of traders. Differences in investors’
expectations, preferences, information assimilation and other diverse investment
characteristics have been suggested as reasons for differential price (volume) movements in
response to positive or negative lagged volume (price) changes. Time delays between the
arrival of new information and when actions are taken by traders in response to that new
information, in the form of buying and selling activity, have been suggested as one source of
temporal asymmetry. Copeland (1976) proposed a model based on sequential information
arrival and the existence of informed and uninformed traders, with uninformed traders failing
to learn from informed trading actions. This results in different demand functions for
informed (or optimists) and unimformed (pessimists) traders, causing an asymmetrical
volume reaction to price movements. Jennings, Starks and Fellingham (1981) arrived at a
similar conclusion to Copeland (1976) based on the existence of optimistic and pessimistic
traders and costly short sale positions and margin requirements. With short positions being
more costly than long positions, pessimists are less responsive to price movements than
optimists, resulting in higher volumes with price increases (optimists buying) than price
decreases (pessimists selling).
Further support is provided by Wang (1994) on the basis of the diversity associated with
the availability of information to different investors. Similar to the winner’s curse idea with
IPO pricing, uninformed traders require a higher price discount when buying assets from
informed traders due to the risk premium associated with trading against private information.
In a similar vein, Moosa and Korczak (1999) suggest that expectation differences of traders
cause asymmetry in the price-volume relationship, particularly where expectations differ
between bull and bear markets. Such expectation asymmetry is thought to flow through to the
trading actions of these investors.
Other arguments of a similar nature have also been put forward. Morgan (1976) linked
volume with systematic risk, suggesting from this a positive correlation between volume and
return movements. Epps (1975) distinguished between investors on the basis of their risk
profiles. Epps (1975) characterised two groups of investors: those that are ‘bullish’ about a
security (bulls) and those that are bearish about a security (bears). As such, they will respond
differently to the arrival of new information about the security. Epps (1975) suggested that
bulls are relatively more optimistic about asset values and only react to positive information
or price movements, whereas bears only respond to negative information. Any information
increasing the security’s price will be reacted to immediately by bulls, but will be disregarded
by bears due to their negative perception of the security. This relative optimism of bulls
compared to bears is suggested to result in a greater trading volume being associated with a
positive price change than with a price decrease.
Various concerns have been raised regarding the applicability of these models, primarily
based on their assumptions regarding the behaviour and classification of investor classes
(such as optimistic versus pessimistic) and their interpretation of investor heterogeneity (see
Karpoff, 1987, for a discussion of this issue). The differential cost of short sales positions is
suggested as the most plausible explanation for the positive correlation between volume and
price change, particularly in light of the general finding in futures markets of no such
correlation, where the costs of long and short positions are symmetrical.
Testing for Asymmetry in the Price-Volume Relationship of Listed Australian Banks
5
Recent empirical findings have also increasingly documented the application of noninformational or feedback trading strategies by different categories of investors. These studies
support the existence of a dominating influence of past price changes driving volume trading
in various markets, reflecting the prevailing influence of momentum traders and the trading
patterns of institutional investors and the price impact of institutional trading actions.
Grinblatt, Titman and Wermers (1995), Wermers (1999), Nofsinger and Sias (1999) and Sias
and Starks (1999) all report findings consistent with positive feedback trading by institutional
investors in equity markets, and Odean (1998) and Bange (2000) find significant negative and
positive feedback trading, respectively, by small individual investors. Further, Chan, Hameed
and Tong (2000) and Griffin, Ji and Martin (2002) provide evidence of the profitability of
feedback and momentum trading strategies using the All Ordinaries Index and winner-loser
portfolios respectively. In addition, Kodres (1999) and Koutmos (2002) present results
consistent with the existence of positive feedback trading in index futures securities.
Methodology for Testing for Causality in the Presence of Asymmetry
Testing for temporal causality between prices and volumes traded is centred on a bivariate VAR model comprising two stationary series, x and y. The model can be written as:
p
q
i 1
j 1
p
q
i 1
j 1
x t      i x t  i    j y t  j  u x ,t
y t     i y t  i    j x t  j  u y ,t
(5)
(6)
where x and y are stationary variables and p and q are the lag lengths for x and y respectively.
Equations (5) and (6) are valid for testing the causality of lagged volume changes on price
changes (where x is the stationary price series) and lagged price changes on volume changes
(where y is the stationary volume series). They are not suitable, however, for testing for
temporal asymmetry in the price-volume relationship, as they do not distinguish between
rising and falling prices and volumes. In order to capture these temporal asymmetric effects,
we decompose y and x as follows:
 y
y t =  t
0
 y
yt =  t
0
if
yt  0
(7)
otherwise
if
yt  0
(8)
otherwise
and
 xt
xt = 
0
if x t  0
otherwise
(9)
 xt
xt = 
0
if x t  0
otherwise
(10)
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Henry and Silvapulle / Journal of Accounting and Finance 2 (2003) 1~13
Applying the decompositions in equations (7) through (10), equations (5) and (6) can be
rewritten as:
p
q
q
i 1
j 1
j 1
xt      i xt i   a j yt j   b j yt j  u x ,t
p
q
i 1
j 1
yt     i yt i   c j x

t j
(11)
q
  d j xt j  u y ,t
(12)
j 1
For equation (5), the null hypothesis of the Granger causality test, suggesting that y has no
effect on x, can be represented as:
H0 : 1   2     q  0
(13)
and the alternative hypothesis of causality is given by:
H1 :  j  0
for at least one j
(14)
Similar null and alternative hypotheses can be stated for equation (6) to test for causality
of lagged price changes on volume. The associated test statistic has a standard F distribution
with (q, T-p-q-1) degrees of freedom, where T is the sample size of the series. In this paper
results are reported for p = q = 5, 7 and 14 (daily) lags.
To test for temporal causality in equation (11), the null and alternative hypotheses in the
Granger causality test to determine whether y+ has any effects on x are:
H 0 : a j  0
for j  1, 2, ..., q
(15)
and
H1 : a j  0
for at least one j
(16)
where the test statistic has a standard F distribution with (q, T-p-2q-1) degrees of freedom.
Similarly, to test if y- has any effect on x, the null and alternative hypotheses can be
written as:
H 0 : b j  0
for j  1, 2, ..., q
(17)
H1 : b j  0
for at least one j
(18)
and
Similar hypotheses are created to test for causality of x+ and x- on y in equation (12). To


determine whether x has a stronger effect on y than x the following null and alternative
hypotheses are used in conjunction with equation (12).
H a0 : d j  c j
for j  1, 2, ..., q
(19)
Testing for Asymmetry in the Price-Volume Relationship of Listed Australian Banks
7
and
H a1 : d j  c j
for j  1, 2, ..., q
(20)
Now, equation (12), with d j  c j   j , becomes
p
p
q
j 1
j 1
i 1
yt     c j ( xt j  xt j )    j xt j   i yt i  u y ,t
(21)
For equation (21) the hypotheses represented by (19) and (20) are equivalent to
H 00 :  j  0 and H11 :  j  0 respectively. The statistic for testing H 00 against H 11 and its p-
value are described in Silvapulle (1992a) and (1992b). Furthermore, since daily data are used
to test these hypotheses, it is likely that the normality assumption will not hold. For this
reason, we use the robust tests developed by Silvapulle (1992a) and (1992b), which are based
on the M-estimators.
Data and Empirical Results
The data used in this study comprises daily last-sale share prices and corresponding daily
trading volume for the four largest listed trading banks on the ASX, these being the Australian
and New Zealand Banking Group (ANZ), the Commonwealth Bank of Australia (CBA), the
National Australia Bank (NAB) and the Westpac Banking Corporation (WBC). The sample
consists of 4,270 daily observations covering the period from September 1, 1981 to August 31,
1998 for the ANZ, NAB and WBC companies.
The sample for the Commonwealth Bank of Australia consists of only 1,713 daily
observations from September 12, 1991 to August 31, 1998. Prior to September 1991 the
Commonwealth Bank of Australia was owned and operated by the Australian Government
and became listed on the ASX initially in September 1991 as part of a segmented privatisation
program. This bank is included in the analysis due to the ‘four pillars’ or ‘four big banks’
market structure in Australia. These data was obtained from the Core Research Database
maintained by the Securities Industry Research Centre of Asia-Pacific (SIRCA). The dilution
and adjustment factors computed and provided by SIRCA were used to adjust the bank share
price series for equity capitalisation changes such as rights or bonus issues and derivative and
note security conversions. Dividend payments by the banks were also incorporated into the
price series to reflect the total return provided by these stocks. This is particularly important
in respect to these listed banking companies, as they are historically very profitable
companies and pay substantial dollar-value dividends and relatively high dividend yields to
investors. The sample period evaluated, particularly for ANZ, NAB and WBC, includes the
deregulation of the financial system in Australia which occurred during the early- to mid1980s and the sharemarket crash in October 1987. Stationarity testing of these data series
(results are not provided in the paper, but are available from the authors on request) suggest
that the series are stationary in returns (or first differences). Graphical examination of the data
did not reveal evidence of structural breaks in the price or volume return series for the banks.
Table 1 provides descriptive information regarding the daily trading activity and size and
share price information for the four banking stocks over the sample analysis period.
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Henry and Silvapulle / Journal of Accounting and Finance 2 (2003) 1~13
Table 1. Descriptive Statistics for the Four Australian Banks over
the Sample Period from September 1981 to August 1998
Variable
National Australia Bank (NAB)
Daily share return
Daily traded volume
Share price
Market cap* ($Mill)
National Australia Bank (NAB)
Daily share return
Daily traded volume
Share price
Market cap* ($Mill)
Westpac Banking Corporation (WBC)
Daily share return
Daily traded volume
Share price
Market cap* ($Mill)
Commonwealth Bank (CBA)**
Daily share return
Daily traded volume
Share price
Market cap* ($Mill)
Mean
Median
Standard
Deviation
Minimum
Maximum
0.012%
1,387,781
$5.223
7,034.76
0.000%
1,068,742
$4.850
5,081.74
1.779%
1,830,368
$1.790
4,508.21
-24.440%
200
$2.520
3,336.96
9.200%
68,849,417
$11.880
17,016.00
0.048%
1,139,483
$8.007
14,387.05
0.000%
924,165
$6.460
14,126.00
1.438%
1,315,254
$4.949
9,131.72
-20.910%
1,200
$2.300
4,461.47
9.200%
29,387,250
$23.350
29,971.00
0.027%
1,477,009
$4.887
8,691.78
0.000%
1,110,183
$4.670
7,046.00
1.580%
1,794,764
$1.725
4,488.89
-27.190%
7,584
$2.430
4,658.67
7.910%
60,336,827
$11.380
17,634.00
0.060%
737,624
$10.556
11,020.00
0.000%
618,500
$9.350
9,608.00
1.124%
547,524
$3.770
4,197.37
-6.140%
45,100
$5.820
7,031.00
7.430%
10,432,900
$20.550
17,383.00
Notes
* Market capitalisation figures are expressed in A$Million and are calculated as the Number of ordinary
shares on issue  Daily closing share price.
** The figures for the Commonwealth Bank are for the period from September 1991 to August 1998.
The mean daily returns for the four banks are all positive, ranging from 0.01% for ANZ to
0.06% for CBA. The large minimum returns observed for ANZ, NAB and WBC in excess of 20.00% all occurred at the time of the 1987 sharemarket crash, with no individual positive
daily return exceeding 10% for any of the four banks. Average daily trading volume exceeded
one million shares for ANZ, NAB and WBC, with lower mean daily trading observed for the
Commonwealth Bank (CBA). Yearly examination of the volume data for the four banks
shows an increase in trading volume levels over time, in line with expansions in the equity
capital bases of the banks and the general increase in sharemarket investment and trading
activity. Consistent with the respective All Ordinaries Index weightings for the four banks
outlined above, NAB is the largest of the listed banks based on mean market capitalisation,
followed in descending order by CBA, WBC and ANZ. The maximum market capitalisation
figures were recorded in either the 1997 or 1998 financial years for the four banks1. As these
banks are among the largest listed companies on the ASX market, they enjoy significant
institutional investor patronage, with institutional shareholdings representing up to 50% of the
total ownership of these companies. Ownership restrictions are also in place within the
1
These individual bank capitalisation figures can be compared to the average market capitalisation of the
Australian Stock Exchange in the 1998 financial year of approximately $480,000 million.
Testing for Asymmetry in the Price-Volume Relationship of Listed Australian Banks
9
banking sector in Australia, with legislative prohibition preventing any individual investor
from obtaining ownership of more than 10% of the issued and voting capital of any of these
four listed banks.
Table 2. Results of Causality Testing Between Prices and Volumes using the F-Test
l =5
Lag Length
l=7
l = 14
ANZ Banking Group
vt  pt
pt  vt
vt+  pt
vt-  pt
pt+  vt
pt-  vt
1.804
0.882
1.320
0.633
3.451
8.308
1.516
0.788
0.982
0.626
3.616
6.029
1.072
1.138
1.734
1.043
3.201
3.139
National Australia Bank
vt  pt
pt  vt
vt+  pt
vt-  pt
pt+  vt
pt-  vt
1.891
2.622
0.835
2.584
5.460
9.666
1.367
2.599
0.526
1.849
4.026
7.200
1.073
3.500
0.820
1.244
3.212
3.927
Westpac Banking Corporation
vt  pt
pt  vt
vt+  pt
vt-  pt
pt+  vt
pt-  vt
0.736
0.777
0.215
1.038
8.544
11.808
0.621
0.611
0.229
0.647
5.888
8.579
1.140
1.299
1.199
2.087
2.669
5.751
Commonwealth Bank
vt  pt
pt  vt
vt+  pt
vt-  pt
pt+  vt
pt-  vt
0.642
2.415
0.409
0.306
4.824
7.659
0.433
1.666
0.482
0.364
4.916
6.739
0.406
1.548
0.426
0.418
2.768
3.741
Hypothesis
Note: Five percent critical values of F5,, F7, and F14, are 2.21, 2.01 and 1.67 respectively.
For the purpose of causality testing on the basis of equations (5) and (6) and equations
(11) and (12), x represents pt and y is representative of vt. The results of the causality
testing are provided in Table 2. The results suggest that causality runs primarily from price
changes to volume changes. The results based on equations (5) and (6) indicate no causality
from lagged volume changes to price changes, whereas there is evidence of causality in the
effect of lagged price changes on volume changes for the National Australia Bank and the
Commonwealth Bank. The results based on equations (11) and (12) indicate that causality
10 Henry and Silvapulle / Journal of Accounting and Finance 2 (2003) 1~13
runs from both positive and negative price changes to volume changes. This is a consistent
finding for all four banks, with the evidence in Table 3 suggesting that negative price changes
have stronger effects on volume changes than positive price changes2. This finding of both
positive and negative price causality to volume is intriguing, particularly in the absence of
similarly strong contemporaneous causality from price changes to volume changes, except for
the National Australia Bank. There is also weak evidence of causality from negative volume
changes to price for the National Australia Bank and Westpac Banking Corporation.
Table 3. Results of Testing for Asymmetrical Causality
Test Statistic
pt  vt ( H 00  d i  ci )
ANZ Banking Group
F
RM
RW
1.02 (0.35)
0.91 (0.39)
0.99 (0.36)
National Australia Bank
F
RM
RW
0.98 (0.38)
0.72 (0.48)
0.79 (0.43)
Westpac Banking Corporation
F
RM
RW
0.78 (0.52)
0.70 (0.67)
0.71 (0.66)
Commonwealth Bank
F
RM
RW
1.20 (0.28)
1.13 (0.39)
0.11 (0.43)
Note: The model with 7 lags was used to calculate the test statistics. The p-values, in parentheses, are
computed using the methodology described in Silvapulle (1992a) and (1992b). RM and RW are the
robust statistics when the error distribution is non-normal, which is true in daily financial market data
series. See Silvapulle (1992a) and (1992b) for details.
These results offer some very interesting interpretations. The absence of bi-directional
causality, and particularly no causality from volume changes to price changes, is consistent
with market efficiency, where volume changes cannot be used to determine future share price
movements. The finding of a causal relationship from lagged prices to volume is consistent
with the noise trading model of De Long, Shleifer, Summers and Waldman (1990) where
noise traders employ a positive-feedback strategy, basing trading decisions on past price
movements. This also supports the recent evidence, outlined above, regarding the existence of
2
The results in Table 3 are based on equation 21 employing the model based on seven lags. This lag length
was chosen based on examination of the models’ respective Akaike information criterion (AIC). Only tests
for the difference in the effects of positive and negative price changes on volume changes were undertaken
in Table 3, as there was no strong evidence, from the results in Table 2, of causality from lagged volume
changes to price changes.
Testing for Asymmetry in the Price-Volume Relationship of Listed Australian Banks 11
significant non-informational or feedback trading in equity and futures markets and the
profitability of such strategies in the Australian sharemarket.
The findings in this paper of temporal asymmetry are inconsistent, however, with much of
the prior literature and, in particular, the conclusions of Epps (1975) and Jennings, Starks and
Fellingham (1981). This is particularly the case with the conclusion that negative price
(volume) changes have a greater effect on volume (price) changes than positive changes. This
firstly suggests that bears have a steeper demand function than bulls, which contrasts directly
with the conclusion of Epps (1975). This finding, however, is consistent with the proposition
that bears are likely to react quickly to negative price changes by selling securities to
minimise their losses, whereas bulls are unlikely to react to price falls, rather waiting for the
price to increase before they sell. Similar arguments can be applied to the actions of
pessimists or uninformed traders, and it is not unreasonable to conclude that due to the
general risk-averse nature of most investors that such actions are likely to dominate ‘bull’
characteristics.
In terms of traders’ expectations, the findings in this paper suggest that traders’
expectations are stabilising in a bull (rising) market and destabilising in a bear (falling)
market. As such, when prices rise in a bull market, traders may not expect further price
increases and sell (so-called profit-taking), putting downward pressure on prices and
subsequent trading volumes. Alternatively, when prices fall in a bear market, traders may
have expectations of further price falls so they sell, thus increasing the downward pressure on
prices and increasing trading volumes as other traders begin selling.
Another potential explanation for the stronger volume reaction to negative price changes
compared to positive price movements may relate to the regulations governing the shortselling of equity securities (including the four banking stocks analysed in this paper, which
are all approved securities for short-selling purposes) on the ASX. As opposed to United
States sharemarkets, share trading rules on the ASX permit the short sale of approved
securities on a down tick, as long as the short-sale order price is not lower than the last sale
price for the security and the short seller was not involved in the last trade for that particular
security. As such, the predominance of feedback trading and the existence of temporal
asymmetry between negative and positive price changes and volume movements may be
reflective of such short-selling activity in response to negative price changes.
The results also suggest that testing only for contemporaneous causality, without regard
for the sign of volume or price movements, may not fully characterise the symmetrical
properties of equity securities and trading behaviour.
Concluding Remarks
This paper has tested for the presence of temporal causality in the price-volume
relationship for the four major listed Australian banks, namely the ANZ Banking Group, the
Commonwealth Bank of Australia, the National Australia Bank and the Westpac Banking
Corporation. Using daily share price and trading volume data covering the period from
September 1981 to August 1998 (September 1991 to August 1998 for the Commonwealth
Bank of Australia), the major findings are as follows:
1. There is evidence of causality from price changes to volume changes, but not from volume
changes to price changes.
12 Henry and Silvapulle / Journal of Accounting and Finance 2 (2003) 1~13
2. There is causality from both positive and negative price changes to volume changes for all
four banks, but only very weak evidence of causality from negative volume changes to
price changes for the National Australia Bank and Westpac Banking Corporation.
3. Negative price and volume changes have stronger effects than positive price and volume
changes.
These findings have various implications for traders’ investment actions and expectations,
but they are inconsistent with the Epps (1975) hypothesis regarding the relative demand
functions of bull and bear traders. They do support the existence of significant feedback
trading by market participants in major banking stocks on the Australian Stock Exchange, and
the findings can be explained in terms of short-selling activity in response to share price falls
and the mentality and trading actions of bull and bear traders in response to price or volume
movements.
The ability to generalise the findings in this paper to overall trading activity on the
Australian Stock Exchange cannot be confirmed without evaluation across an extended
company sample although, given the characteristics of the banking stocks evaluated in this
paper, similar trading outcomes and the existence of temporal asymmetry and feedback
trading is likely to exist in actively traded companies on the Australian Stock Exchange, and
those stocks having a predominant influence on overall market performance and index
movement.
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