Differential Response from Interim Accounting Earnings and Overnight Macro Information

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Differential Responsefrom Interim AccountingEarnings and Overnight MacroInformationAllan Hodgson a & Liddy McCall ba Griffith University , Brisbane , Queenslandb Macquarie Bank , Sydney , New South WalesPublished online: 29 May 2012.

To cite this article: Allan Hodgson & Liddy McCall (1998) Differential Response fromInterim Accounting Earnings and Overnight Macro Information, Asia-Pacific Journal ofAccounting, 5:2, 223-240, DOI: 10.1080/10293574.1998.10510542

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Differential Response from Interim Accounting Earnings and Overnight

Macro Information

Allan Hodgson* Liddy McCall**

ABSTRACT

Previous research in accounting has uncovered an information asymmetry between

large and small firms to the release of earnings news. For smaller firms, earnings

resolve more uncertainty and provide more information which is not already captured

in prices. This paper provides several extensions regarding the impact of information

on firm size using intra-day trading data from the Australian Stock Exchange (ASX).

First, we analyse the impact of overnight macro information as a function of firm

size. This is done by using an intervention series from the US stock market and

transfer function time series model. The research design is necessitated by the lagged

trading hours that occur in Asian-Pacific markets and the possibility that overnight

information, and not earnings news, is driving price changes. After controlling for

the impact of macro information, the distribution of continuations and reversals

test is used as an alternative to the market model to test for the intra-day information

impacts from earnings releases. Our results support a complex and differential

information environment between small and large firms. Macro information has a

greater impact on large firms, whilst earnings news contains relatively greater

information for small firms.

* Professor of Banking and Finance, Griffith University, Brisbane, Queensland ** Associate Director, Macquarie Bank, Sydney, New South Wales

The comments by participants at a OUT workshop, the 1997 European Accounting Conference and the reviewers are greatly appreciated.

Paper received June 1997, revisions September 1997, accepted June 1998.

Allan Hodgson and Liddy McCall, "Differential Response from Interim Accounting Earnings and Overnight Macro Information", Asia-Pacific Journal of Accounting, Vol. 5, No.2, December 1998, pp 223-240.

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224 Asia-Pacific Journal of Accounting December 1998

Introduction

The relationship between accounting earnings and stock returns has long been of

central interest to researchers because it specifically addresses the issue of whether

accounting data provides value relevant information to investors and financial

analysts. There are two main streams in this research program. Association studies

which measure the impact of earnings on stock prices over a long event window

(usually one year or longer), and attempt to identify factors which affect the

determination of prices. For example, the long-run persistence of earnings, the

time-series risk of the earnings stream, the availability of growth options or the

industry location of the firm.

The second stream of research has focussed on the informativeness of

earnings. These studies are usually classified as event studies and examine the short

window effect of a certain event (such as the release of the earnings figure) on

prices. Prior research has indicated that the short term information content of

earnings is related to firm size. Large firms have greater analyst following, more

frequent media disclosure, lower average transaction costs (Atiase, 1985) and

generally more accurate pre-earnings release information (Chaney and Jeter, 1991 ).

On the other hand, small firms have greater uncertainty regarding future earnings

potential, they are subject to more takeover and management change activity

(Collins and De Angelo, 1990), and have higher stock beta's and earnings volatility.

Hence, if small firms have greater outcome uncertainty; (i) relatively more

uncertainty is being resolved by the news of the earnings announcement, and (ii)

earnings provide more information which is not already captured in prices. In short,

there is an asymmetry in the information provided by accounting earnings in short

term event studies.

Empirical research on information asymmetry using monthly, weekly and

daily data has been comprehensive (Shores, 1990; Collins and Kothari, 1989;

Beedles, Dodd and Officer, 1988; Freeman, 1987; and Grant, 1980) and indicates

that market related information is more likely to be contained in the accounting

reports of smaller firms. Moreover, some US research has also examined

information asymmetry at an intra-day level (Woodruff and Senchack, 1988; Patell

and Wolfson, 1984). In Australia, Aitken, Frino and Wong (1993) used a market

model to analyse intra-day share data from the Stock Exchange Automated Trading

System (SEATS) and reported a larger and more prolonged price reaction after

earnings releases for small firms. These studies have supplied further support for a

firm size effect and also provided new insights into the speed and process of market

adjustments.

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Differential Response from Interim Accounting Earnings and Overnight Marco Information 225

The main purpose of this paper is to provide evidence on the relative

informativeness of accounting earnings by placing the research within an Asian

context. However, by doing so an important secondary issue is raised as to how

macro information impacts on different sized firms. This secondary issue is bought

about because Asian markets vary from US markets in that they have different

affects from overnight trading on world markets. Because of their lagged trading

times this means that they are impacted at opening by the bunched arrival of macro

price and trading information, in contrast to the more continuous intra-day arrival

of information in the US. When combined with evidence that almost half of firms

release their accounting earnings in the two hours before opening, then the

information impact of accounting releases may be contaminated by the arrival of

overnight information as well as time of the day price affects (Wood, Mclnish and

Ord, 1985; Harris, 1986; Mclnish and Wood, 1991; Opong 1996).

Given the above scenarios two research agendas are proposed. Initially, the

impact of overnight macro information is assessed as a function of firm size; and

then this impact is pre-filtered before analysing the relative price impact of earnings

information. This is done by utilising a two stage procedure in the statistical tests.

The price influence of the previous day's change on the Dow Jones 65 Stock

Composite Index is first pre-filtered from each stock price series by using a time

series intervention and impulse function. A distribution of reversals and

continuations test is then applied to the residual series to examine price reactions to

the release of interim earnings. This test was first used by Patell and Wolfson

(1984), as an alternate to the market model methodology which may be

inappropriate for intra-day data because it assumes consecutive price changes are

independent and identically distributed.

The study shows that: (i) macro information has a greater impact on price

changes for larger firms, (ii) there is a higher level of price activity and trading

volume around the release of interim earnings for small firms, and (iii) both macro

and firm specific earnings information create a higher level of disagreement for

smaller firms. In general, however, these findings support the asymmetric firm size

hypothesis that there is greater new information in the accounting reports of small

firms.

The remainder of the paper is organised as follows. The next section outlines

the firm size differential information hypothesis and reviews relevant empirical

research. Section three describes the data set and statistical methods. The empirical

results are presented and compared to previous research in section four and the

paper is concluded in section five.

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Background

The Differential Information Hypothesis

The literature analysing the information content of earnings releases is extensive

(see Brown, Clinch and Foster, 1992; and Jeter and Chaney, 1992 for reviews). The

studies directly related to our research are those concerned with the differential

information hypothesis which states that the amount of information available from

sources, other than financial reports, is an increasing function of firm size. This

hypothesis is largely based on observations related to transaction costs and

incentives for information search.

If it is assumed that the costs of information search are fixed and constant

across firm size, then the incentive to undertake research for mispricing is greater

for large firms than small firms (Atiase, 1985). In combination with the greater

capitalisation oflarge firms, this means that private information ofmispricing, can

be used to make larger profits than is the case with insider knowledge ofmispricing

of a small firm. The assumption of constant information search costs across firms is

supported by Freeman (1987) who argued that the possibility of increasing search

costs because of the increasing complexity associated with larger firms is offset by:

(i) larger firms providing a greater variety of information than smaller firms 1; (ii)

larger firms having a higher degree of exposure by constant reporting in the

financial press; and (iii) by the search activities of financial analysts. The

proposition of an increased number of information sources for large firms is

empirically supported by Atiase (1985) and Grant (1980).

Another explanation for the differential information hypothesis is that trading

by private information investors reveals public information (Grossman and Stiglitz,

1976). This observation led Atiase (1985) to suggest that the release of private

information by trading, to a greater extent, reduces the potential for profits in small

firms. This is because trading on private information is more noticeable in thinly

traded stocks, which again, reduces the potential to exploit the knowledge of a

mispriced stock in small stocks.

Furthermore, institutional investors are likely to concentrate on large firms

due to the liquidity constraints of small firms. For example, institutions cannot hold

a large percentage of the stock of a small capitalisation firm and expect to be able to

sell the stock immediately without price discounts. Because institutions are a major

source of demand for information, financial analysts concentrate their search

activities on larger firms (Arbel, Carvell and Strebel, 1983; Shores, 1990). In

summary, the differential information hypothesis implies that the information

impounded in accounting earnings should be more value relevant for smaller firms

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compared to larger firms; because of the richer information sources available for

large firms and the search activities of financial analysts which impounds this

information into prices.

Empirical Research

Using a market model, Grant (1980) was one ofthe first researchers to empirically

observe that the informational content of annual earnings announcements for

smaller capitalised Over-the-Counter (OTC) firms was greater than larger New

York Stock Exchange (NYSE) listed firms. A more direct test was conducted by

Atiase ( 1985) who decomposed the data into two sub-samples based on market

capitalisation greater than $400 million and less than $20 million. Atiase concluded

that the degree of a firm's price change associated with an earnings announcement

was inversely related to firm size and prices of large firms were more likely to

reflect alternative information prior to earnings release. In addition, Freeman

(1987) concluded that: (i) prices of large firms reflect earnings information earlier

than small firms, and (ii) the magnitude of cumulative abnormal residuals

surrounding earnings news is larger for small firms. These results were reaffirmed

by Shores ( 1990).

· The above research used monthly, weekly or daily observations, but increased

computing capabilities and wider data availability has made it possible to extend

the research by utilising transaction data. This microstructure extension is made

valuable because market behaviour at the intra-day level could vary from longer

term behaviour and decomposition of the market's reaction may disclose features

which were not previously apparent. In particular, the speed at which investors

analyse and incorporate information into prices and the path process of information

transfer, can now be analysed.

Aitken, Frino and Wong (1993) used the market model technique to analyse

all transactions recorded by the Australian Stock Exchange Automated Trading

System (SEATS) data system for the period November 1989 to September 1992

for 78 large and 78 small firms. They found that small and large firms had a similar

initial price reaction to earnings announcements, but small firms had a much longer

adjustment period with evidence of significant price revision taking place the day

following the announcement.

The above studies apply the market model to estimate expected share returns.

Accordingly, they are joint tests of this model and the firm size effect and, hence,

the tests may be misspecified (Roll, 1977; Ball, 1978; Fama and French, 1992). In

addition, Patell and Wolfson (1984) argued the market model is inappropriate for

intra-day data because it relies on the assumption that consecutive price changes

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are independent and identically distributed. Whilst this assumption has generally

not been rejected using daily, weekly and monthly data, a different stochastic

process has been observed at the intra-day level. For example, Niederhoffer and

Osborne ( 1966) first documented a strong negative serial correlation in consecutive

price changes with price reversals outnumbering continuations by about three to

one.

The Distribution of Reversals and Continuations Test

The above observation by Niederhoffer and Osborne gave rise to the distribution of

reversals and continuations test based upon price changes. In this test a price change

is classified by it's direction conditioned upon the direction of the most recent

previous price change. A price change that is in the same direction as the previous

price change is a continuation, a price change in the opposite direction is called a

reversal, and no changes are classified as zero changes. The distribution of reversals

and continuations has been used to measure the impact and speed of adjustment of

prices to information and is based on the notion that new economic information

causes a revision in investors beliefs. Unfavourable information results in

downwards revisions in stock prices and favourable information causes upward

revisions.

Moreover, if revised prices fall outside the current bid-ask range then an

imbalance between buy and sell orders will occur. This imbalance between either

buy or sell orders manifests itself into a series of sequential price changes in one

direction (continuations). This imbalance will continue until bid and ask prices

move so as to incorporate revised expectations and redress the balance between

bids and asks to the level before the earnings release. When the proportion of

reversals and continuations return to pre-earnings announcement levels then it is

concluded that prices have adjusted to any new information. Consequently, prices

in disequilibrium will exhibit a higher proportion of continuations than prices in

equilibrium, and the relative proportion of continuations is an indication of

information flowing into the market.

Patell and Wolfson (1984) used this technique to examine the speed of

adjustment of intraday prices to earnings releases ofNYSE and AMEX firms. They

found the predominant reversal pattern was sharply interrupted with a higher

proportion of price continuations after earnings announcement which remained

significant for up to ninety minutes. Using the same method, Woodruff and

Senchack ( 198 8) researched prices of firms listed on the NYSE during earnings

announcement and non-announcement periods. They found a higher proportion of

price continuations for small stocks compared to large stocks after earnings

announcement.

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Differential Response from Interim Accounting Earnings and Overnight Marco Infonnation 229

The Impact of Macro Information

The impact of macro economic information as a function of firm size is not an area

that has received a great deal of research attention. An exception is the paper by

Martikainen, Perttunen and Puttonen ( 1995) who concluded that the prices of large

and small firms in Finland are nonstationary and cointegrated. These results suggest

that the price response to information is not contemporaneous for all firms, that

large firm prices can be used to predict small firm prices, and there is a deviation

from weak form informational efficiency which may be exacerbated on less liquid

stock exchanges. In Australia, Hodgson, Masih and Masih ( 1997), using vector

error correction and variance decomposition techniques, found that the impact of

information significantly changes between bull and bear trading phases. In bear

phases, macro information has relatively little impact on small firms, but becomes

more important in the bull phase. However, macro information has a consistent and

steady impact on large stocks in both phases.

This paper applies the distribution of reversals and continuations test to

compare information impact from earning release between large, medium and

small firms listed on the ASX. But first, we control for the arrival of market wide

information which might contaminate the distribution of price changes. The data

used and the statistical method is outlined in the next section

Data and Method

Description of the Data

The initial price data obtained consisted of all transactions that occurred on the

ASX for sixty five Australian companies who released interim earnings reports

over the time period 15 February 1993 to 27 April1993 2. Details recorded for each

transaction were the date, the time of the trade (to the nearest 1 OOth of a second),

the price paid per share and the volume of shares traded. Transactions were

excluded when the transaction price was deemed to be affected by a factor other

than the earnings announcement, such as odd-lot trades, exercise of options, special

and contingent-special trades and off-market trades3. The date and precise time of

the earnings announcement were obtained from the SEATS reporting system. The

announcement period of each company was also checked to ensure there were no

announcements which could induce a confounding effect. This check resulted in

two firms being excluded from the data set because of a notice of a share split and

a rights issue, leaving sixty three companies available for analysis. The data was

also inspected for industry bias and it was observed that whilst gold producing

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companies made up the greatest proportion (17.4%)4 of the sample, in general,

firms were evenly spread across industry.

Interim earnings are used to assess informativeness because of a number of

reasons. There are difficulties associated with annual earnings in that they are not

as timely as interim earnings releases, due to the time period lag in providing formal

statements caused by audit attesting and director and management confirmation.

This suggests that information about annual earnings has a greater probability of

being leaked to the market compared to interim earnings. Further, annual earnings

are typically contaminated by dividend announcements, directors forecasts and

other price relevant information embedded in the report. Foster ( 197 5), empirically

confirmed a higher information content for interim earnings releases and the results

of Foster have been supported in Australia by Easton and Sinclair (1989). Hence,

because they are more timely and 'cleaner', interim earnings are preferred.

Consistent with earlier studies (Patell and Wolfson, 1984; Woodruff and

Senchack 1988; Lee, 1992 and Aitken, Frino and Wong, 1993) two periods were

analysed. An announcement period of four days: two days pre-announcement and

two days post-announcement, where the day of announcement is signified as zero

( -2, -1, 0, + 1 ); and a non-announcement period of ten days which was prior to the

announcement period ( -3 to -12). The ten day non-announcement period was used

as the control sample in order to determine the expected or normal levels of reversal

and continuation.

The firms were then ranked by market capitalisation and classified into three

equal groups -large, medium and small. Table 1 contains the sample of

companies in order of decreasing market capitalisation along with the time of the

earnings announcement. From Table I it can be noted that almost 50% of

announcements would be directly contaminated by the overnight arrival of macro

information. This is because 40% of earnings announcements were made outside

trading hours and these would affect prices at market opening and about 8%

occurred within the first half hour of trading. Hence, before expected patterns were

determined the impact of overnight macro information was pre-filtered from the

entire fourteen days of each stocks price series. The method applied is outlined in

the next section.

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Differential Response from Interim Accounting Earnings and Overnight Marco Infonnation 231

ASX

Code

BHP CML PDP FBG WMC AMC BIL LLC BOR FLC PNI GMF BRY MAY SBG BPC NBH GIO WES ANI ACS Mean Large

Table 1 Compaines Grouped by Market Capitalisation with Earnings

Announcement Times

LARGE MEDIUM SMALL Market Cap. Time of ASX Market Cap. Time of ASX Market Cap. Time of

($'000'sJ Release Code ($'000'sJ Release Code ($'000'sJ Release

21,527,517 12.34 AMX 772,283 15.22 ABF 309,422 12.50

6,123,287 16.15 PAS 763,969 10.24 MWB 290,218 12.59

4,535,245 12.36 SMI 746,637 15.42 OPS 272,847 15.49

4,325,131 15.30 AFI 725,821 12.49 LEI 257,490 14.04

4,154,816 15.26 AGL 723,466 14.43 ABC 251,918 17.07

4,081,934 10.08 PMP 674,876 17.42 QNI 250,250 12.14

3,565,161 16.07 QBE 628,528 14.51 BUN 245,852 17.20

2,914,172 15.38 QRL 606,564 13.12 SPS 218,848 15.56

2,756,136 18.28 FHF 593,825 16.50 DGD 208,102 11.38

2,614,897 09.40 NPL 578,096 10.19 PCM 204,310 16.07

2,147,099 16.03 TMA 544,926 14.13 MLG 197,417 16.15

1,949,570 11.42 RGC 528,901 19.10 SPT 163,755 16.17

1,868,756 10.04 BKW 461,020 13.21 DOM 148,900 11.44

1,815,842 12.43 HSG 461,008 13.17 SGW 145,268 13.58

1,633,270 10.26 TNT 457,755 17.14 GCI 141,043 17.45

1,574,100 14.45 ARG 447,447 19.04 CTY 100,178 12.27

1,419,466 12.40 GMK 440,261 13.45 IHL 99,567 19.23

1,097,533 12.28 WSF 437,699 17.47 MRV 87,242 20.07

1,155,791 13.25 ERA 365,925 17.45 CSD 76,553 17.32

993,523 08.59 SCP 331,538 14.11 MAG 57,732 18.11

877,110 21.51 NFM 310,839 17.20 EMP 35,337 15.30

3,486,684 Mean 552,496 Mean 179,155 Medium Small

Pre-filtering Overnight Macro Information

In the analysis of the time series of stock prices the arrival of overnight information

can affect prices in several ways. The impact can change the level, either abruptly

or after some delay, change the trend, and have either a permanent or transient

impact. In the case of intraday research, where an exogenous intervention on the

price series is known, then a transfer function model which takes account of an

macro intervention can be postulated as:

X1 = u(B) I1 + N

1 (I)

Where: I1 is the intervention (input) variable and is a 'dummy' or 'indicator'

sequence taking the values I and 0 to denote the occurrence or non occurrence of

the exogenous intervention; N1 is the noise or residual; X1 is the price series for

individual stocks; and u(B) is a (possibly infinite) polynomial that may admit a

number of rational forms.

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The first step was to specify the nature of the overnight macro information or

input variable. Relying on the research of Ball and Bowers ( 1988), Eun and Shim

(1989), Hamao, Masulis and Ng (1990)5 the previous day's return on the DJ65

Stock Composite Index was determined as the appropriate overnight macro

intervention set. Further, in order to undertake time series analysis the data from

both the input series (DJ65) and individual stock prices were transformed by taking

natural logarithms and the rate of change in prices was calculated as:

dX1 = ln (X

1) -In {X

1_1) (2)

Where X1 is the price observed at time t, ~-I is the previous period's price and ln is

the natural logarithm. This transformation was undertaken after testing for

nonstationarity using augmented Dickey-Fuller tests6.

The next step was to prespecify possible transfer function models. For

example, one form of the lag polynomial:

v(B) = co(B)b 8{B) (3)

allows I to influence X via a distributed lag u(B) which is referred to as the transfer

function and the numerator and denominator polynomials are defined as:

ro(B) =roo- (01 B- ... - rosBS

and

8(B) = I- 81 B- ... - 8TBT

The parameter b signifies the dead-time of the model. If b=O then there is a

contemporaneous relationship, but ifb>O then there is a delay ofb periods before It

begins to influence the price series. A variety of forms may be hypothesised for the

impact of the input series It and the specification ofu(B) at timeT. Three models

which have theoretical antecedents in the financial literature, however, were chosen

as follows: (i) information is immediately impounded into prices, (ii) there is a lag

effect and information slowly leaks into prices, and (iii) there is an overreaction to

information. These models can be formally stated as follows:

where

[ r JnJ l-aB 1 (4)

• DJt is the previous day's log relative return on the DJ65 Composite

Index

• y is the coefficient of impact of the DJ65 return on opening

Australian prices

• (1- uB)-1 is a polynomial in lag B.

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Setting y DJt equal to It and noting that:

(1- aB)-1 = 1 + aB + a 2B2 + a3B3 ••.

with lal < 1, then identity (4) can also be expressed as:

(5)

Equation (5) allows the price impact of macro information to be described in a

number of ways. If the previous day's return on the DJ65 has some information

value at the opening of trading on the Australian market then y should be positive

and significant. In addition if a is equal to zero, then the overnight information

from the US market only impacts on opening price returns and is represented by y

DJt (Modell). This scenario is more likely to occur under complete and perfect

market assumptions where information is instantaneously impounded into security

prices at the earliest opportunity and there is no leakage or transfer across into later

prices.

However, if a is positive and less than one (0 < a < 1) then the information

impact from the US market is not completely impounded into returns at market

opening and has a transfer effect for k periods in the following form:

[1 + aB1 + a2B2 + ....... + akBk] It (6)

That is, an exponential decay transfer function whose rate of transfer into

prices is determined by the a coefficient (Model 2). This scenario is likely to occur

under conditions of less than perfect information transfer when markets take some

time to fully reflect information shocks into prices. Finally, if there is price

overreaction to information shocks then the transfer into intraday prices can be

modelled according to the following lag function (Model3):

[l+aB] 1 + fJB It (7)

Constraining IPI < 1, p >a then it can be shown from equation (7) the initial

macro impact (It) from the US market during the following morning trading in the

Australian market would evolve such that the cumulative shock (B=4 observations)

would be:

[1 +(a- p)B1 + (P2-aP)B2 +(ap2-p3)B3 + (p4-ap3)B4] It (8)

Further, if the coefficient on pis greater than a then the combined effect will

oscillate between a negative and positive impact on the lagged value oflt. In terms

of the reversals and continuations test, this model induces a relatively higher level

of price reversals.

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Results

Price Changes

The application of the intervention and transfer function model to the data for each

firm revealed that the arrival of overnight macro information has a greater impact

on larger stocks. On average, forty eight percent of large stocks, thirty percent of

medium stocks and twenty three percent of small stocks total intraday price

movement is accounted for by overnight information.

Moreover, for the majority of large firms ( 18 of 21 ), macro information leaks

into prices consistent with the diffusion process of model two. This means that

market wide information substantially increases the number of price continuations

for large stocks. On the other hand, overnight information induced a greater number

price transfers consistent with the overreaction behaviour of model three ( 5 for

medium and 8 for small), with the remainder having a price reaction consistent

with model two. Model one, where information is immediately impounded in

prices, was not descriptive for any stock in the sample. Further, large stocks prices

took an average often transactions to adjust and five to six transactions for medium

and small firms. Furthermore, a chi-squared test on an hourly basis during the non

announcement period, revealed there were no significant time of the day

differences in price continuations.

In summary, macro information has a greater impact on large stocks in terms

of degree and persistence. For smaller stocks the same information appears to create

some uncertainty as to the implications for pricing. These results have a number of

similarities with Gosnell (1995) who found continuation changes were more

prevalent during the first 90 minutes of trading for high equity value firms -

consistent with the arrival of information. On the other hand, small equity value

firms had a consistent level of continuation changes across the trading day.

After filtering out the effects of macro information the relative frequencies of

continuations, reversals and zero price changes for all categories during both non

announcement and announcement periods are contained in Table 2. They can be

summarised as follows:

(1) The greatest proportion of prices in the non-announcement period are

traded at the same price (72.5%) and this proportion is slightly higher for

large firms (73.6%). During the four day announcement window period

the no price change ratio is similar (average 70.5%).

(2) For prices which represent a change, price reversals are relatively more

frequent than continuations. For example, during the non-announcement

period continuations represent only 31.6% of all price changes. This

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compares to previous researchers: Neiderhoffer and Osborne (1966)-

28.4% continuations; Patell and Wolfson (1984)- 30.9%

continuations; Woodruff and Senchack ( 1988)- 30.4% continuations;

and Gosnell (1995)-27.1% continuations.

(3) During the announcement period price continuations have increased for

all classes of firms and continuations as a percentage of all price changes

has increased to 34.6%. For small firms, continuation changes are

statistically significant and higher in all days when compared to the non

announcement period. 7 Further, the proportion of continuations for small

firms records a relatively greater increase from 35.0% during the non

announcement period to 42.2% during the announcement period8.

Furthermore, the largest increase in continuations for small firms occurs

in the second day prior to announcement. Earnings news for small firms

has had a significant effect on prices: even to the extent that a significant

amount of information has been acted on in the market place prior to

official announcement. These results strongly support the asymmetric

information hypothesis.

(4) There is an increased prevalence of reversals for small firms following

an earnings announcement, which does not occur for large and medium

firms. Additionally, the proportion of reversals is lower the two days

prior to announcement.

The results are concordant with a number of general propositions. First,

earnings releases are associated with increased price continuations which is

consistent with the release of price sensitive information. Second, small firms have

a greater relative increase in continuations between the non-announcement period

and the announcement period. Further, the observation of large increases in

continuations up to two days prior to announcement suggests information leakage

and provides further evidence of the price sensitivity of the earnings news for small

firms. Third, the increased prevalence of price reversals for small firms shows a

more complex reaction. Whilst, earnings announcements have relatively more

information for small firms there is also a higher level of disagreement on the

precise meaning of that earnings news. This may be caused by a higher noise to

information ratio in earnings or the thinner cluster of analysts and market makers

who invest in small stocks.

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Table 2 Percentages of Reversals, Continuations and

No Changes for all Companies

Firm Size

Category

% Continuations %Reversals %No Change

Panel A- Non announcement periodb

All Firms 8.7 18.8 72.5

Small 10.7 19.9 69.4

Medium 10.3 19.6 70.1

Large 8.2 18.2 73.6

Panel B- Announcement periods 2nd DAY PRE-ANNOUNCEMENT

All Firms 10.4 20.1 69.5

Small 17.1 .. 17.4 67.5

Medium 10.6 20.4 69.0

Large 9.7 20.3 70.0

PaneiC 1st DAY PRE-ANNOUNCEMENT

All Firms 12.0 18.8 69.2

Small 13.4* 17.6 69.0

Medium 12.9 17.8 69.3

Large 11.2 19.6 69.2

PaneiD 1st DAY POST-ANNOUNCEMENT

All Firms 10.1 18.1 71.8

Small 13.4* 22.6 64.0

Medium 13.3* 17.7 69.0

Large 9.0 17.5 73.6

PaneiE 2nd DAY POST-ANNOUNCEMENT

All Firms 9.1 19.1 71.8

Small 13.8* 23.8 62.4

Medium 11.3 17.6 71.1

Large 8.3 18.8 72.9

PaneiF ENTIRE POST-ANNOUNCEMENT PERIOD

All Firms 10.2 19.3 70.5

Small 14.3* 20.4 65.3

Medium 12.0 18.4 69.6

Large 9.6 19.1 71.3

a Announcement period consists of ten trading days before the non-announcement period, which in turn

consists of 48 hours prior and subsequent to the earnings announcement.

b The x2 statistic for goodness-of-fit test of equality of continuations during the announcement period were

not significantly different at the 0.05 level.

Significant at the 0.05 level

.. Significant at the O.D1 level

Volume Response

The use of trading volume, in conjunction with price changes, provides further

evidence on the differential information impact from accounting earnings. Kim and

Verrecchia (1991) argued that if there are differences in the precision of private

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information, then the magnitude of subsequent post-announcement trading volume

will be negatively associated with the quantity and quality of the pre-announcement

information. Further, if volume proxies for disagreement amongst traders then

volume flow proxies for information surprise (Karpoff 1987). Moreover, if price

changes reflect the aggregate demand for a finn, volume reflects the impact of new

information on individual investors. A number of empirical studies have

determined that that trading volume associated with earnings announcements is

inversely related to finn size (see for example: Bamber 1987; Ajinkya and Jain

1989; Woodruff and Senchack 1988; Woodruff and Senchack 1989; and Lee 1992).

Two measures of trading volume are used- actual trading volume and

transaction frequency. Each trading volume measure is then expressed as a ratio of

announcement volume to non-announcement volume per unit of time. Comparative

results for large and small firms are reported in Table 3.

Panel A • Average Volume Ratios

2nd Day Pre-Announcement

SMALL 1.073 LARGE 0.789

Table 3 Trading Volume Ratios

1st Day Pre- 1st Day Post-Announcement Announcement

1.240 2.255 0.789 1.197

Panel B ·Transaction Frequency Ratios

2nd Day Pre- 1st Day Pre- 1st Day Post-Announcement Announcement Announcement

SMALL 1.064 1.252 1.505 LARGE 0.960 0.956 1.282

2nd Day Post· 1/2 Hour Post-Announcement Announcement

1.546 4.200 1.099 2.089

2nd Day Post- 1/2 Hour Post-Announcement Announcement

1.334 2.517 1.176 1.726

The results in Table 3 show that announcement trading volume, using both

ratio metrics, has increased markedly for the small finn category relative to large

firms and has a greater persistence impact. For small firms, increased relative

trading volume occurs on every day during the announcement period with the

highest ratio during the 24-hours directly after the announcement. In addition the

half hour directly after announcement shows a higher relative increase for small

firms. These volume affects are consistent with previous research (Bamber 1987,

and Woodruff and Senchack 1988), but along with the increased prevalence of

reversals, could also signal increased disagreement among investors in small firms.

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Conclusions

The objective of this paper was to contribute to the empirical research on the

asymmetrical impacts of information on different sized firms. The research shows

that macro information is more important to large firms and smaller firms rely to a

greater extent on firm specific information, such as that embedded in accounting

reports. Using an information driven argument this result is not surprising.

However, the increased prevalence of continuations, in conjunction with increased

reversals, heightened trading volume and over-reaction to macro information, may

also mean that the observed asymmetric behaviour is primarily driven by noise

trading among small firm investors reacting to the arrival of information (DeBondt

and Thaler 1985). This interpretation is, of course, far more contentious.

Regardless, this study has uncovered new evidence regarding the effects of

macro information and firm size. Further, it highlights the importance of

controlling for the potential asymmetric and confounding effects of information

when analysing the impact of accounting reports. This is especially important in

Asian-Pacific markets.

Endnotes I For example, through media releases, industry forecasts, management forecasts and litigation. 2 We are grateful to J.B. Were and Sons for providing the data. 3 Off-market transactions were omitted because they are not time accurate and are simply reported

to the market. 4 Gold mining stocks represented approximately 22% of all stocks listed on the ASX in 1993. 5 US equity returns have the most influence, they lead other domestic stock markets, and returns on

the Australian market lag the US market by one day. 6 The null hypothesis of a unit root was accepted at the 'level' form for all series but rejected for the

transformed return series. This result confirms that the raw price series require differencing, are stationary I( 1) series and time series analysis can be applied to the transformed data.

7 A chi-squared statistic was computed to test for equality of the frequency of continuations between each day of the announcement period and the non-announcement period. The chisquared statistics for the small firm category were 4.642 and 10.625 for one and two days preannouncement, and 3.568 and 5.103 for one and two days post-announcement, significant at the 5% level. In contrast, the proportion of continuations between non-announcement and announcement periods was not significantly different for large firms and only significantly different on the first day post-announcement for medium firms.

8 Compared to large (medium) firms which increased from 32.0% (34.6%) to 33.7% (39.5%).

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Differential Response from Interim Accounting Earnings and Overnight Macro Information

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