The informational content of insider trading

disclosures: Empirical results for the Polish

stock market

Henryk Gurgul *, Paweł Majdosz**

* Department of Applied Mathematics, University of Science and Technology,

ul. Al. Mickiewicza 30, 30-059 Krakow, Poland (e-mail: h.gurgul@neostrada.pl) ** Department of Quantitative Methods, School of Economics and Computer Science,

ul. Św. Filipa 17, 31-150 Kraków, Poland (e-mail: pmajdosz@go2.pl)

Abstract In this paper we try to answer the question as to whether insider

trading disclosures convey valuable information to market participants, valuable

in the sense of the profitability of an investment strategy that faithfully mirrors

insider behaviour. Our interest in this subject is limited to the case of announce-

ments concerning insider transactions issued over a six-year period on the War-

saw Stock Exchange (WSE). Initially, we use event study methodology to check

whether insider trading disclosures are accompanied by a performance of stock

returns as well as trading volume. Two different models generating expected re-

turns (expected volume) are employed to verify the robustness of our results. The

first of these is the regime switching model, with the results then being recalcu-

lated by using a GARCH-type models which seem to be most useful for dealing

with some of the inconvenient statistical properties of stock return and trading

volume data. Afterwards, a technique based on the reference return strategies is

used to examine whether or not outsiders who imitate insider behaviour are able

to profit from it. The major findings are as follows: Firstly, announcements about

the sale of stocks by insiders convey no information to market participants. Sec-

ondly, a statistically significant market response to insider disclosures of pur-

chases of stocks in their own company can be observed in the three days prior to

the announcement release for both return as well as trading volume series, and

finally, outsiders who purchased stocks previously bought by insiders experience

negative returns whereas outsiders disposing of stocks previously sold by insid-

ers earned a return of 8.57% over the six-month period.

JEL Classification: G14

Keywords: Insider trading, Event study, Switching Regressions, GARCH model

We thank two anonymous referees for many valuable comments and suggestions. All remaining er-

rors are our own responsibility.

1 Introduction

Over recent years, we have been witnessing an increased number of theoretical

discussions devoted to insider activities on the stock market. This topic has been

considered from many different points of view. Economists, lawyers and philos-

ophers carry on disputing insiders’ rights to deal in stocks of their own firms.

Some of them set an important trend in the debate by researching the possible

implications of insider trading for insiders themselves, outsiders and other stock

market observers such as analysts. Many statements on this subject have a moral-

ising tone. It is indisputable that nobody should be able to profit from infor-

mation which is not available to other market participants or, even worse, from

spreading rumours concerning own company which then turn out to be untrue. In

this spirit, an insider trading prohibition is often argued for. On the other hand,

there exist circumstances under which insider trading is accompanied by benefits

for all market participants. For instance, in the case of a market crash (like that of

the autumn of 1987), only insiders fully realize that current stock prices do not

correctly reflect the real value of shares. For this reason, insiders are likely to

buy shares when other investors are, in general, prone to close their long posi-

tions. Without insider trading, panic selling of shares would get out of control.

In standard conditions however, public confidence in the financial market

would be undermined by unscrupulous use of insider knowledge. This is why in

many countries there are stringent government regulations against illegal insider

trading activities. Poland is one of these. According the Act on Trading in Finan-

cial Instruments, any information should be regarded as private concerning a fi-

nancial instrument, its issuer, or trading in a financial instrument which is yet

unpublished if it is possible that this information will influence the prices of a fi-

nancial instrument after its release. A notion of private information is then used

to determine who is an insider. Any person has this status with access to private

information due to being a member of the board as well as supervisory directors,

an employer or a shareholder. The list of insiders also includes auditors, solici-

tors, a brokers and any other person possessing private information, regardless of

its source. Defining the notion of an insider so widely requires so-called primary

insiders to be determined to whom especial legal restrictions are applied in order

to protect the interests of other investors. A primary insider is anyone who has

the closest relationships with the issuer of financial instruments, e.g. members of

the board and supervisory directors, auditors, solicitors, and so forth.

It should be emphasised that trading on the basis of private information is il-

legal in Poland and incurs serious penalties. However, with a few notable excep-

tions, sales and purchases of shares by insiders, even those classified as primary

ones, are generally allowable. There is only a requirement to notify about trans-

actions made by insiders. Such notification should take place no later than five

days after the transaction date if its total value exceeds 5,000 euro. For other

transactions the notification deadline is 31st January of the following year. As

mentioned above, over some periods, so-called ‘closed periods’, primary insiders

are not allowed sell and buy shares in the own companies. These periods are

mainly associated with the dates of publication of annual, 6-monthly as well as

quarterly reports and their lengths are equal to two months, one month and two

weeks immediately before publication, respectively.

In this paper, we do not adopt a judgemental role in such a difficult issue as to

whether insider trading is ethical. We are very reluctant to formulate advice or

suggestions addressed to insiders or any government whose target is to improve

market transparency by changing corporate behaviour and/or the legal system.

Instead, our interest is focused on (i) stock market response to announcements

about buying or selling shares by insiders, (ii) the profitability of investment

strategies based on the imitation of insider behaviour by buying shares which

were previously bought by insiders, and selling shares which were previously

sold by insiders.

The above-mentioned purpose of this contribution is accomplished by means

of an event study framework. This methodology is commonly employed in fi-

nancial and accounting research to investigate abnormal market performance as a

result of an event. Of prime importance as regards the accuracy of obtained re-

sults is the choice of an appropriate model to generate expected levels of any var-

iable under consideration. Therefore, tracking market behaviour in the context of

insider trading disclosures requires different econometric models of expected re-

turns and expected volume to be used, guaranteeing the robustness of findings

and correctness of conclusions. Two models are employed under this study to es-

timate the expected (non-disturbed by event) levels of returns and volume. The

Markov switching regression model enables the variance of variable under con-

sideration to change when the event occurs. This feature may appear to be par-

ticularly useful if it is impossible to eliminate all other (confounding) events tak-

ing place in the period over which stock returns and trading volume are

observed. However, the variance still remains unchanging under each regime. To

model changes in stock return (trading volume) in parallel with changes in its

variance the GARCH model is used.

Our results are as follows: Firstly, announcements of sale of stocks by insiders

bring no information besides that already incorporated in stock prices. Secondly,

a statistically significant market response to insider disclosures of purchases of

stocks their own company can be observed in the three days prior to the an-

nouncement release for both return as well as trading volume series. In parallel

with the analysis of how the market reacts to insider trading disclosures, we also

examine whether or not an investment strategy relying on copying of insider be-

haviour is profit-making. It turned that outsiders who purchased stocks previous-

ly bought by insiders experience negative returns whereas outsiders disposing

stocks previously sold by insiders earned a return of 8.57% over the six-month

period.

The remainder of this article proceeds as follows. In Section 2, a rough survey

of existing contributions which deal with the subject is presented. Section 3 out-

lines our methodology which is then used to identify market responses to an-

nouncements concerning insider trading. A brief data description as well as some

statistics of insider trading on the WSE over the analysed period are reported in

Section 4. Section 5 contains our empirical results and the final section con-

cludes the paper.

2 A brief literature review and main hypotheses

Empirical studies of insider trading can generally be classified into two catego-

ries. Some studies focus on the event of insider trading itself, where the central

issue is insider trading profitability and stock market efficiency. Studies of the

second type usually investigate insider trading behaviour in conjunction with an-

other corporate event, especially buybacks and stock splits.

It is characteristic that the previous contributions do not agree about the prof-

itability of insider trading and the informative content of insider trading. Empiri-

cal evidence provided by Kerr (1980), Lin and Howe (1990) as well as Holder-

ness and Sheehan (1985) seem to support the efficient market hypothesis (EMH)

in its strong form. It is worth remembering here that EMH was formulated by

Fama in 1970 and then extended in the following years (see Fama, 1970, 1991).

The above mentioned strong-form of EMH states that stock prices incorporate all

existing information, i.e. not only public information but also that unpublished,

regardless of information sources. On the other hand, Jaffe (1974), Seyhun

(1986, 1988), Madura and Wiant (1995) showed that insider activity is accompa-

nied by abnormal market performance (a significant positive effect on stock

prices). Raad and Wu (1995) report that insiders increase their buying activities

and decrease their selling activities before stock repurchase offers. While stock-

holders of firms with insider net selling activity earn positive excess returns,

those of firms with insider net buying activities earn larger and more significant

abnormal returns.

More recently, Seyhun (1998) documents the informative value of insider

trading. Seyhun also concludes that transaction volume and the position of the

insider within the company are positively correlated to relative performance. The

results show, on the other hand, that the relative performance negatively depends

on firm market capitalisation. According to Seyhun, this can be attributed to

more efficient pricing for large firms since they are more extensively covered by

analysts.

Ma, Sun and Austin (2000) document a significant increase in insider selling

prior to stock split announcements. They also find indices for a significant corre-

lation between insider sales and stock price run-up prior to stock split an-

nouncements. Their results suggest that portfolio diversification may be the dom-

inant factor behind insider trading activities prior to stock split announcements.

A noteworthy contribution is that of Carter et al. (2002). Based on a sample

obtained from the Washington Service Insider Trade database, the authors pro-

vide empirical evidence that some of the information content of insider transac-

tions leaks out prior to the information becoming public, and that the information

leakage positively depends on the length of the interval between the insider buy-

ing activity and the announcement.

After giving some references to the existing contributions concerning the rela-

tionships between insider trading and stock market performance, we proceed

with formulating the main hypotheses, explicitly. Although the direct identifica-

tion and measurement of impact of insider trading on stock market poses many

difficulties, existing evidence concerning developed stock markets all other the

world seems to indicate that sales and purchases of stocks by insiders induce

stock price changes whose direction and scope depend on many factors, among

which the content of private information possessed by insiders is most promi-

nent. This leads to the first hypothesis:

H1: In the context of insider disclosures about stock purchases (sales), the

prices of this tend to increase (decrease).

If other investors (outsiders) believe that signs derived from announcements

of insider trading are able to help with making profits that exceed those offered

by the market portfolio, they will be prone to buying stocks which insiders

bought first and to sell stocks at the some time as insiders. In this context, it

seems to be particularly interesting to check whether such an imitative invest-

ment strategy is really profitable for outsiders. Therefore, the next hypothesis is

formulated as follows:

H2: Imitating the transactions of insiders gives an extra return which mirrors

the private information underlying insider transactions.

As pointed out above, the standard vehicle for answering this type of question

is the event study approach, under this study extended by the reference return

portfolio approach. The framework of event study analysis is presented in the

next section; a description of the reference return portfolio approach is given in

Section 5.2.

3 Methodology

Since Fama et al. (1969) formalised the event study approach more than thirty

years ago, it has become the standard method used in empirical finance and ac-

counting in order to detect the reaction of certain financial market variables such

as returns and volume to some informative events which are assumed to be unan-

ticipated. The well-known constitutive elements of this methodology, within its

classical framework, are: determination of the event, including its announcement

date, determination of the event window and estimation window, specification of

the return generating model and the estimation of the model parameters based on

the data set from an estimation window, computation of the abnormal return (as

well as the cumulative abnormal return) and finally, construction of a statistical

significance test of the event effect.

A common practice is to define an abnormal return of ith firm for day t (ARi,t)

as the difference between the actual stock return of ith firm for day t (ri,t) and the

expected value of stock return of ith firm for day t (E[ri,t|It-1]) conditional on the

set of information It-1 available one day before the event window. The classical

Market Model, introduced by Sharpe (1963) is, on the other hand, the most fre-

quently applied model to generate the expected value of stock returns required

for calculating abnormal returns. In this model the stock returns of a given firm

depend on market portfolio returns (rm,t):

, , , ,, ~ 0, ,i t i i m t i t i t ir r N (1)

where ε represents the error term.

Following Boehmer et al. (1991), define a standardised abnormal return of ith

firm for day t (SARi,t) as:

,

,

,

.ˆ

i t

i t AR

i t

ARSAR

(2)

The denominator of (2) can be obtained from:

1

0

1 1

0 0

21

, ,

1 0

, 21 1

1 02

, ,

1 0

1

1ˆ ˆ 1 ,

1

t

m t m k

k tAR

i t it t

m k m k

k t k t

r rt t

t tr r

t t

(3)

where ˆi denotes the estimated standard deviation in (1), t0 and t1 denote an indi-

cator of the oldest observation within the estimation window and the event win-

dow, respectively.

Note that by defining a standardised abnormal return as (2), the forecasting

nature of the estimated abnormal returns is taken into account (see Boehmer et

al., 1991 and Salinger, 1992).

For N firms included in the sample, the test statistic takes the following form:

,

1

2

, ,

1 1

1

,

1 1

( 1)

N

i t

it

N N

i t k t

i k

SARN

Z

SAR SARN N N

(4)

and for any sub-period within the event window restricted by tL and tU (tL < tU):

;

.1

U

L

L U

t

k

k t

t t

U L

Z

Zt t

(5)

The statistics (4) and (5) are both normally distributed with mean zero and

unit standard deviation.

As Boehmer et al. (1991) demonstrated, a test based on (4) is still well speci-

fied, even if the cross-sectional variance increases during the event period. A re-

cent study, however, shows that it becomes misspecified when the estimation

window data set is disturbed by confusing events (see Aktas et al., 2003). Aktas

et al. provide a potential remedy for this problem. Using the Markov Switching

Regression approach introduced and developed by Hamilton (1989, 1994) in-

stead of the classical Market Model, the above-mentioned authors were able to

eliminate the test bias.

Following Aktas et al., it is assumed that the return generating process can be

adequately modelled using a two-regime process, one regime with a normal level

of variance and one regime with high variance. On the other hand, the model pa-

rameters are assumed to be the same in both regimes. This can be expressed as:

, , ,1, ,1, ,1 ,

, , ,2, ,2, ,2 ,

,1 ,2

, ~ 0, if 1

, ~ 0, if 2

and ,

i t i i m t i t i t i i t

i t i i m t i t i t i i t

i i

r r N S

r r N S

(6)

where Si,t stands for a not directly observable indicator variable taking value 1 if

we are in the low variance regime, and 2 if we are in the high variance regime.

Regime state variable Si,t is assumed to be described by a first-order Markov pro-

cess (this assumption means that Si,t depends only on its lagged value at lag 1).

For days with no event occurrence variance is assumed to achieve the first

(normal) regime. When an event occurs, there is a shift in the variance from the

lower normal regime to the higher event-induced regime (the second regime in

(6)). Implied in this is the assumption that the extent to which the variance be-

comes higher in the case of event occurrence remains the same in spite of fact

that different events may influence variance in a distinctive manner. However,

even if confounding events lead to shifts in variance of different magnitudes,

model (6) seems still to be superior to the classical Market Model relying on the

assumption of constant variance with respect to time. Moreover, major emphasis

is placed within the event study framework upon guaranteeing the estimated lev-

el of variance of the variable under consideration not to be affected by confound-

ing events (and the event in question as well) since this is necessary if statistical

inferences about the direction and scale of event effects are to be valid. In this

context the promising nature of model (6) is particularly undisputed.

The dates of possible confounding events are unknown. If they were known,

the researcher could make an effort to exclude all confounding events from the

sample in a way other than using the switching regression model. Without in-

formation about when the confounding events take place, we have to assume that

their occurrence can be described as a first-order Markov process. The state of

such a process (1 for low regime and 2 for high regime) on a given day t depends

only on which of the regimes was achieved on the previous day, i.e. in day t–1.

Therefore, one of four different situations may happen, which are as follows: (i)

reaching the low regime from the high regime, (ii) reaching the high regime from

the low regime, (iii) staying in the low regime, and (iv) staying in the high re-

gime. Each of the above-defined situations is assigned the corresponding proba-

bility, pij, referred to as the probability that the process is in ith regime in period t

when in the period t–1 it was in jth regime. Noting that p12=1–p11 and p21=1–p22,

there are six parameters (αi, βi, σi1, σi2, p11, p22) to be estimated. A detailed de-

scription of the estimation procedure of the model parameters can be found in

Hamilton (1989, 1994).

With model (6) used to generate abnormal returns, the standardised abnormal

return form (2) is modified by replacing in (3) ˆi with

,1ˆ

i . Using the standard

deviation estimates for regime 1 (low variance) in (3), we do not automatically

produce a higher cross-sectional Z statistic in the homoscedastic case (see Aktas

et al., 2003).

While the above-presented technique of testing abnormal market performance

deals explicitly with the possibility of increases in variance due to confounding

event occurrences, it does not adjust for the possibility that the variance of the

variable under consideration varies persistently over time. Building on the semi-

nal contribution of Engle (1982) the standard vehicle for this type of analysis is

the (G)ARCH-type model, often the GARCH(1,1) model in empirical imple-

mentation.

An original test for abnormal market performance, proposed by Hilliard and

Savickas (2000), is based on the Market Model and the GARCH(1,1) error term.

In this study we, however, decided to use the generalized ARMA(r, m)-MM-

GARCH(p, q) model given by:

, ,0 , , , , , , ,

1 1

2

, ,0 , ,

1 1

, ~ 0,r m

i t i i j t j i m t i t i j i t j i t i t

j j

q p

i t i i j t j i j t j

j j

R R R h

h h

(7)

The proper length of time-lags in the model is identified using the Akaike In-

formation Criterion. The model parameters are estimated by means of the ML-

method from observations included within the pre-event window.

The test statistic (lt) can be expressed as:

2

,

1

1 ,tt

N

i t t

i

ASRl N

SR ASR

(8)

where , , ,

ˆ1/i t i t i tSR AR h and 1

,

1

.N

t i t

i

ASR N SR

In order to test the implications of insider trading disclosures over any sub-

period of the event window whose boundaries are set as tL and tU (tL < tU), the

standardized cumulative abnormal return can be calculated:

,

, ,

,

.

ˆ

U

L

L UU

L

t

i t

t t

i t tt

i t

t t

AR

SCAR

h

(9)

The corresponding test statistics is given by:

, ,

2

, , ,

1

1,

L U L U

L U L U

CAR

t t t t N

i t t t t

i

N Nl ASCAR

SCAR ASCAR

(10)

where 1

, , ,

1

.L U L U

N

t t i t t

i

ASCAR N SCAR

At the start of this study, we define the pre-event window and the event win-

dow. The symmetrical event window covering eleven days starts the fifth day

prior to the event day (the day when the disclosure took place). The pre-event

window, on the other hand, contains two hundred days prior to the event win-

dow. Transforming price series into returns, we used the continuous form of re-

turns. Market portfolio returns are approximated by returns of the market-

capitalisation weighted stock index called WIG. Then, we generate abnormal re-

turns using model (6), and standardised abnormal returns. Finally, for six differ-

ent sub-periods of the event window, we check the null hypothesis about no sig-

nificant event effect by means of test statistic (5). To verify our results we re-

calculate the tests using model (7) and statistics (10).

As a recent study shows (see Tkac, 2001), a combination of firm-

specific/market adjusted is the best way to generate the measure of firm-specific

normal (non-event related) trading activity. Therefore, analysing abnormal trad-

ing volume, we decided to employ model (6) which, taking into account that

volume series are usually more strong correlated, needs to be now modified by

the inclusion of an autoregressive term in the mean equation, and the same test-

ing procedure as used for returns. Trading activity is measured by the number of

shares traded per day. Next, we use the first differences in the natural logarithm

of firm trading volume (market trading volume), instead of individual stock re-

turns (market portfolio returns). As with returns, model (7) and statistics (10)

were finally employed in order to check whether the testing results for abnormal

trading volume are robust.

4 Sample Description

Our initial data set comprises 406 insider disclosures which took place on the

WSE over the period from June 1998 to June 2004 (six years). The source of

these data is the register of insider transactions which can be accessed free on the

Internet at http://mojeinwestycje.interia.pl. Starting work with the data, we cate-

gorised insider transactions into three groups (member of the board, member of

the supervisory directors and others) and collated the total number of insider dis-

closures over the period under study with the level of WIG (see figures 1 and 2).

Figure 1. The number of insider transactions on the WSE

7/19984/1999

2/200012/2000

9/20017/2002

5/20036/2004

8000

10000

12000

14000

16000

18000

20000

22000

24000

26000

0

1

2

3

4

5

6

7

8

Purchases(R) WIG(L)

7/19984/1999

2/200012/2000

9/20017/2002

5/20036/2004

8000

10000

12000

14000

16000

18000

20000

22000

24000

26000

0

2

4

6

8

10

12

Sales(R) WIG(L)

Figure 2. The proportion of three groups of insiders in dealing

Purchases

28%

44%

28%

Member

of the board

Member

of the supervisory

directors

Others

Sales

31%

39%

30%

Member

of the board

Others

Member

of the supervisory

directors

It is a widely documented fact that the level of insider activity on the stock

market is correlated with market changes (see e.g. Biesta et al., 2003). Our data

also seems to support such a statement. One can find that insiders were prone to

buy shares in their own companies when the market started to increase. The most

striking finding, though in a sense expected by us, is that insider transactions

might protect the market from a strong falling tendency. In the second half of

2000, insiders bought shares in defiance of the general trend of stock prices. This

might have a stabilising impact on the market even if amount of such transac-

tions is small when compared to the whole market. A detailed knowledge of the

dates of insider sales (disclosures about such transactions) may also prove useful

to investors. Note that the announcement about selling shares by insiders pre-

cedes the periods over which stock prices were falling. With regard to the pro-

portion of selected categories of insiders in the total dealing, it can be seen that

the members of the board appear to have some prominence and there is little dif-

ference in this matter between purchases and sales.

To be included in our sample, a given firm has to meet the following selection

requirements. Firstly, it should be reliable and large enough to be quoted on the

primary market of the WSE. Secondly, the information of insider trade in shares

of a given firm should be close enough to identify all circumstances of interest,

particularly, the day in which this transaction indeed took place. We excluded

from the sample all cases where insider transactions are related to (granted) op-

tions and warrants. As we pointed out in Section 1, the date of disclosure of in-

sider transaction is not necessarily identical the date of the real insider activity.

Typically, it is five days from the insider transaction to its disclosure but in some

cases this period may covers even a few months. Therefore, we decided to con-

fine our investigation to these of the insider transactions where the corresponding

disclosure took place no later than on the fifth day after the transaction. These

requirements are fulfilled by 164 disclosures. Next, we divided our sample into

two clusters, namely Purchases (71 events included) and Sales (94 events includ-

ed). For more details see Appendix at the end of this paper.

Daily closing prices, as well as the number of shares traded over a day for

firms included in our sample, are derived from the PARKIET database. In a few

cases a single missing piece of data was filled in (as a mean of its direct neigh-

bours).

5 Empirical Results

5.1 Abnormal market performance

We start our investigation by checking residual series from the respective models

of expected returns and expected volume for autocorrelation, non-normality and

ARCH effect. The Ljung-Box Q statistic is used to make sure that the obtained

results are not being markedly affected by a correlation in the model residuals.

The length of time-lag in the above-mentioned statistic is equal to 15. This re-

flects our view that further autocorrelation coefficients, beyond the fifteenth

time-lag, can all be neglected without any detriment to the investigation. We

found that the ratios of the number of cases where the null hypothesis about ran-

domness was rejected at the 5% level of significance to the number of all cases

tested amount to 20% and almost 40% for return and volume series, respectively.

In order to evaluate to what extent the residual series can be distributed as nor-

mal, the Lilliefors test for normality was here employed. It turned out that the re-

siduals from the model of expected returns exhibit strong non-normality; the null

hypothesis of normality had to be rejected 137 times out of 164 cases. With re-

gard to residuals coming from the model of expected volume it should be

stressed that the corresponding null hypothesis was rejected in less than half of

the cases. Finally, we used the ARCH test, up to the fifteenth time-lag, to check

whether or not the residual series suffer from volatility clustering effects. In this

case the ratio of rejection of the null hypothesis, at a 5% level of significance,

achieved almost 40% for both return and volume series.

Summing up the above-reported results, it should be stated that, as we ex-

pected, the switching regression model is not fully designed for dealing with the

well-documented inconvenient properties of financial time series. Despite this,

the results derived from this model still seem valid, at least as a rough approxi-

mation. However, the drawn conclusions need to be confirmed by using model

(7), which deals explicitly with the possibility of autocorrelation as well as vola-

tility clustering of the variable under consideration.

After controlling for model misspecification, we continued to test whether or

not stock returns exhibited an abnormal pattern over the days of the event win-

dow. We decided to examine the stock returns also in several different sub-

periods of the event window to gain insight into trends mirrored in our sample.

Table 1 summarizes our results. We included the cross sectional average

standardised abnormal returns and the corresponding test statistic, as well as p-

value over the entire event window and the selected sub-periods.

Table 1. Testing for abnormal returns in two clusters

Period

,L Ut t

Purchases Sales

Average

SAR ,L Ut tZ p-value

Average

SAR ,L Ut tZ p-value

{–5,–1} 0.125 0.901 0.184 0.038 0.135 0.446

{–3,–1} 0.530* 2.111 0.017 –0.120 –0.135 0.446

{–3,+3} 0.322* 1.815 0.035 –0.465 –0.437 0.331

{+1,+3} 0.099 0.311 0.378 –1.062 –1.075 0.141

{+1,+5} 0.238 1.236 0.108 –0.269 –0.367 0.643

{–5,+5} 0.198 1.624 0.052 –0.078 0.127 0.449

* significant at 5%.

It can be seen that disclosures about insider purchases induce, on average, a

significant increase in stock prices during the period from the third trading day

prior to the announcement to the third trading day after it. In addition, the aver-

age abnormal return statistically differs from zero in the period from the third to

the first trading day before the insider disclosure. On the other hand, insider sales

are not accompanied by a stock price reaction either before or after the day when

the facts about such transactions are disclosed. What can be concluded from this?

Firstly, our findings provide empirical support for the argument made by

Lakonishok and Lee (1998) that insiders sell their shares for many different mo-

tives, but they buy shares for only one motive – to gain profit. Therefore, a signal

for other market participants is more transparent in the case where an insider

purchases shares. When an insider sells shares in his own company, outsiders

need to solve the problem as to what signal is given to them by the insider and

whether they should follow in the insider’s footsteps or not.

Secondly, the results illustrate the complicated nature of relationships between

an insider and the so-called environment which includes his family, friends, his

broker and others. The significance of the test statistic at a 5% level during the

period from day –3 to day –1 can be seen as evidence supporting the hypothesis

about leakage of information concerning insider transactions prior to the official

disclosure. This problem is discussed in detail in Carter et al. (2002). The above-

mentioned author listed the possible sources of such a leakage. Family, friends

and brokers are, taken together, one of the most important sources of information

leakage before the official disclosure.

An effort has been made to explain the cross sectional differences in the scope

of leakage of information by various factors. Unfortunately, applying the regres-

sion suggested by Carter et al., we find no support for a positive relationship be-

tween the information leakage and the time which elapses between an insider

transaction and on official announcement. Also, none of the other factors em-

ployed by Carter et al. appears to have explanatory power for information leak-

age.

Testing for abnormal volume completes this part of our investigation. Table 2

presents the cross sectional average standardised abnormal volume (SAV) as well

as t-Student statistics and corresponding p-value in two clusters.

Table 2. Testing for abnormal volume in two clusters

Period

,L Ut t

Purchases Sales

Average

SAV ,L Ut tZ p-value

Average

SAV ,L Ut tZ p-value

{–5,–1} 0.921* 1.785 0.037 0.752 0.247 0.402

{–3,–1} 0.985** 2.368 0.009 1.030 1.096 0.136

{–3,+3} 0.321 0.638 0.262 0.471 0.197 0.422

{+1,+3} –0.506 –0.970 0.166 –0.802 –1.029 0.152

{+1,+5} –0.771 –1.339 0.090 –0.660 –0.817 0.207

{–5,+5} –0.009 0.079 0.468 0.334 –0.262 0.397

** significant at 1%

* significant at 5%.

With regard to the context of volume in surroundings of insider trading disclo-

sures, abnormal trading activity occurs during the period of three days prior to

the official announcement only in the first cluster (Purchases). This finding is in

line with those previously given in respect to testing for abnormal returns.

As we pointed out at the beginning of this section, the conclusions drawn

above may appear incorrect due to the fact that the switching regression model

used to generate the expected returns as well as the expected trading volume is

unlikely to give a reliable representation of the variable in question when its var-

iance persistently varies with respect to time. Therefore, to confirm our previous

findings a GARCH-based technique, tracing statistically significant movements

of stock prices and trading volume, is employed. For the sake of comparison

simplicity, the same sub-periods of the event window are applied as when the

expected returns and the expected trading volume were obtained by means of the

switching regression model. The results are reported in the tables 3 and 4.

Table 3. Test results of GARCH-based abnormal returns in two clusters

Period

,L Ut t

Purchases Sales

,L Ut tASCAR ,L U

CAR

t tl p-value ,L Ut tASCAR ,L U

CAR

t tl p-value

{–5,–1} 3.484 1.420 0.161 0.970 1.002 0.319

{–3,–1} 3.607* 2.215 0.031 0.816 1.000 0.320

{–3,+3} 4.999* 2.495 0.015 0.805 0.634 0.528

{+1,+3} 1.225 1.196 0.237 0.667 0.697 0.488

{+1,+5} 1.659 1.617 0.112 0.913 0.784 0.435

{–5,+5} 5.310 1.878 0.065 1.206 0.939 0.350

Note that ASCAR values are expressed in per cent.

* significant at 5%.

Table 4. Test results of GARCH-based abnormal volume in two clusters

Period

,L Ut t

Purchases Sales

,L Ut tASCAR ,L U

CAR

t tl p-value ,L Ut tASCAR ,L U

CAR

t tl p-value

{–5,–1} 0.577** 4.155 0.000 0.716** 3.696 0.000

{–3,–1} 0.418* 2.171 0.034 0.958** 5.180 0.000

{–3,+3} 0.091 0.487 0.628 0.437* 2.033 0.045

{+1,+3} –0.242 –1.707 0.093 –0.331 –1.704 0.092

{+1,+5} –0.429* –2.187 0.033 –0.391* –2.116 0.037

{–5,+5} 0.062 0.360 0.720 0.135 0.917 0.362

** significant at 1%

* significant at 5%.

As one can see from the figures, the test results for the significance of stand-

ardized cumulative abnormal returns fully coincide with those derived from the

switching regression model. Regarding trading volume, some differences be-

tween the results based upon the two applied techniques can be, however, identi-

fied. In addition to significant shifts in the level of market activity during the pe-

riods from the fifth and third trading day before the disclosure release in the case

of purchases of shares by insiders, which corroborates the previous findings,

there are also changes in volume over the second half of the event window for

this cluster. The GARCH-based technique reveals that the days immediately af-

ter the insider disclosure brought a significant offset. The same pattern, i.e. posi-

tive changes in the level of market activity during the first half of the event win-

dow and negative ones over the second half, can be also identified in the case of

disclosures concerning insider sales.

5.2 Is imitation of insider behaviour profitable?

In this section, our attention is focused on the profitability of the investment

principle of copying insider behaviour. In other words – we try to find an answer

as to whether outsiders may profit from signals which are transferred to the stock

market by insiders who buy or sell shares in their own companies. One reason in

favour of the imitation of insider behaviour is based on the argument that insid-

ers such as executives and directors have superior information concerning their

company prospects. They know, for instance, when a new product is going to be

on the market earlier than other people. As a result they are also able to forecast

future stock price movements more accurately than other market participants. Ir-

respective of its theoretical basis, all over the world, investors carefully observe

insider activity because it is still believed that thanks to an imitation of insider

investment decisions they are able to profit from information accessed only by

insiders.

To examine the profitability of investment strategies based on insider disclo-

sures, we use the reference return portfolio approach which is similar to that of

Gervais et al. (2001). Our portfolio formulation procedure can be described as

follows. The test period for each stock position in the portfolio covers 130 trad-

ing days (approximately six months) and starts the day after the insider trading

announcement is released. We form the reference return portfolio by taking a

long (short) position in all stocks which were previously bought (sold) by insid-

ers. One zloty is invested in every stock included in the portfolio. At the same

time, every long (short) position is offset by a short (long) position in a reference

portfolio (here the market portfolio). All positions are held without rebalancing

until the end of the test period. Offsetting each position by the reference portfo-

lio, we are able to test the hypothesis that the average portfolio return is equal to

zero, separately, for all the positions in stocks which were previously bought by

insiders (Purchases) and all the positions in stocks which were previously sold

by insiders (Sales).

Figure 3 contains the average returns of the reference return portfolio formula-

tion strategies over the entire test period in two clusters separately, as well as the

net return for these clusters taken together. Because some of the firms previously

included in our sample had incomplete stock price series over the test period, we

reduced the number of events to one hundred and fifty four (66 purchases and 88

sales).

Figure 3. Average returns of the reference return portfolio formulation strategies

trading day

1 11 21 31 41 51 61 71 81 91 101 111 121 130-8,0%

-6,0%

-4,0%

-2,0%

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

Purchases

Sales

Net

Regarding the profitability of investment strategies based on insider disclo-

sures, it is worth noting that the disaggregated analysis of purchases versus sales

shows that outsiders who copy insider behaviour, resulting in stock purchases,

would receive negative returns. Taking into account the above-mentioned fact

that in the case where an insider buys shares in his own company, the signal is

more transparent for outsiders, this finding seems to be a bit surprising. Howev-

er, at least two other researchers (Brick et al., 1989 and more recently Jeng et al.,

1999) found the same return pattern. Their authors based the investigations upon

data from the U.S. stock market, and in both cases it turned out that outsiders im-

itating insiders who bought stocks in their own companies experienced negative

excess returns.

There are two possible explanations for this phenomenon. Firstly, insiders are

likely to sell shares immediately before negative information concerning their

companies is issued, but they may buy the shares in their own companies long

before the good news becomes known to other investors. Hence, the observed

negative return may be a consequence of the relatively narrow window over

which profitableness of the respective portfolios is examined. Secondly, as

demonstrated by Banz (1981) and Reinganum (1981), among others, there is an

inverse relationship between stock returns and the market value of a given firm

(the so-called size effect). Therefore, it is possible that the negative returns in the

first cluster is a result of the overlapping of two opposing effects. The one asso-

ciated with insider trades is positive, but the other, which is rooted in the high

market value of firms included in the sample, forces returns down, so that the re-

sultant effect is negative.

On the other hand, outsiders who sell stocks which were previously sold by

insiders, profit from it. Note that the average portfolio return in this cluster sys-

tematically increases over time. In spite of the fact that insiders sell their shares

for many different motives, the imitation of their investment decisions appears to

be profitable.

Table 5 summarises test results for the significance of the average portfolio

returns in the respective clusters at six different time-points. The test used here is

based on the t-Student statistic with n – 1 degrees of freedom.

Table 5. Testing for significance of average returns of the reference return portfolio for-

mulation strategies

Test period (in trading days):

1 10 20 50 100 130

Purchases (returns in %)

0.443 (0.806)

–0.767 (–0.735)

–0.312 (–0.187)

–2.982 (–1.243)

–3.960 (–1.218)

–5.528 (–1.341)

Sales (returns in %)

0.331 (0.933)

2.266* (1.748)

3.343* (1.841)

6.200* (2.155)

8.970** (2.356)

8.569* (1.846)

Net (returns in %)

0.379 (1.224)

0.966 (1.109)

1.777 (1.405)

2.265 (1.151)

3.429 (1.305)

2.528 (0.783)

t-Student statistics are shown in the parentheses ** significant at 1%

* significant at 5%.

The results illustrate the promising nature of the investment principle of copy-

ing the insider’s behaviour resulting in stock sales as a tool for gaining profit.

Note that in the second cluster (Sales) the average portfolio return is a positive

and statistically significant event at 1%. Consistent with the conclusions drawn

on the basis of Figure 3, the average portfolio return in the first cluster (Purchas-

es) is negative, though insignificant.

6 Conclusions

The main aim of this paper is to explore the information content of insider trad-

ing disclosures. Many previous studies have dealt with this subject and a distinc-

tive contribution to our knowledge of insider transactions has been made by their

authors. Our study contributes to this by investigating stock prices as well as

trading volume reaction to insider disclosures by using a data set from a small

emerging stock market.

Consistent with empirical evidence presented in the literature on developed

stock markets, we find significant positive abnormal returns (excess volume) in

the context of the disclosure date of insiders who purchase stock in their own

companies. Insider sales, as opposed to purchases, have no informational content

given that the various possible motives for this remain essentially opaque. Our

results seem to support the hypothesis about leakage of information concerning

insider transactions prior to official disclosure. However, there is no evidence

supporting a positive relationship between information leakage and the time

which elapses between insider transactions and official announcements.

We are also interested in the benefits to outsiders from public information

about insider trading. It turned out that outsiders, who copy the insider’s behav-

iour resulting in stock purchases, received negative returns. In contrast outsiders

who sell stocks previously sold by insiders may profit from it, in spite of the fact

that insiders might sell their shares for many different motives.

Our study is rather tentative due to the low number of events included in the

sample. Therefore, future research should reveal whether the inferences docu-

mented in this study will hold true also in the case of a larger sample.

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Appendix

Table A. Companies included in the sample

Company /ISIN/ Pur-

chases Sales Company /ISIN/

Pur-chases

Sales

YAWAL /PLYAWAL00058/ 1 1 STALEXPORT /PLSTLEX00019/ 1 0 APATOR /PLAPATR00018/ 3 0 SUWARY /PLSUWAR00014/ 1 0 ATLANTIS /PLATLNT00016/ 2 2 SWARZĘDZ /PLSWRZD00017/ 1 0

BUDIMEX /PLBUDMX00013/ 1 3 TELEKOMUNIKACJA POLSKA /PLTLKPL00017/

1 1

BRE /PLBRE0000012/ 1 3 TRAS /PLTRAST00020/ 1 0 BORYSZEW /PLBRSZW00011/ 1 0 WÓLCZANKA /PLWLCZN00013/ 1 2 CERSANIT /PLCRSNT00011/ 1 0 WAWEL /PLWAWEL00013/ 1 0 DROSED /PLDRSED00012/ 2 0 AGORA /PLAGORA00067/ 0 4 ECHO INVESTMENT /PLECHPS00019/

1 1 AMICA /PLAMICA00010/ 0 1

ELDORADO /PLELDRD00017/ 1 0 ELEKTROCIEPŁOWNIA BĘDZIN /PLECBDZ00013/

0 1

EFEKT /PLEFEKT00018/ 1 1 BEEF-SAN /PLBEFSN00010/ 0 1 ELZAB /PLELZAB00010/ 1 1 BAUMA /PLBAUMA00017/ 0 1 ENERGOMONTAŻ POŁUDNIE /PLENMPD00018/

1 0 COMARCH /PLCOMAR00012/ 0 1

ENERGOMONTAŻ PÓŁNOC /PLENMPN00017/

1 1 COMPUTERLAND /PLCMPLD00016/

0 4

INTER GROCLIN AUTO /PLINTGR00013/

1 2 COMPUTER SERVICE SUPPORT /PLCSSUP00012/

0 2

GANT /PLGANT000014/ 1 1 DĘBICA /PLDEBCA00016/ 0 1 HYDROTOR /PLHDRTR00013/ 1 0 FARMACOL /PLFRMCL00066/ 0 1 HUTMEN /PLHUTMN00017/ 1 0 FERRUM /PLFERUM00014/ 0 1

IB SYSTEM /PLBRSTM00015/ 1 0 GARBARNIA BRZEG /PLGRBRN00012/

0 1

INTERNET GROUP /PLARIEL00046/

3 1 GRUPA ONET.PL /PLOPTMS00012/

0 1

IMPEXMETAL /PLIMPXM00019/ 1 1 HOWELL /PLHOWEL00015/ 0 2

JUTRZENKA /PLJTRZN00011/ 1 2 INSTAL KRAKÓW /PLINSTK00013/

0 1

KROSNO /PLKROSN00015/ 1 1 INSTAL LUBLIN /PLINSTL00011/ 0 1 LUBAWA /PLLUBAW00013/ 1 0 IRENA /PLIRENA00018/ 0 1 LENTEX /PLLENTX00010/ 1 2 KRUK /PLKRUK000019/ 0 1 MCI /PLMCIMG00012/ 1 0 KĘTY /PLKETY000011/ 0 1

MILMET /PLMLMET00015/ 1 0 MANOMETRY KFM /PLKFMAN00012/

0 1

MENNICA PAŃSTWOWA /PLMNNCP00011/

1 0 MIESZKO /PLMSZKO00010/ 0 2

MASTERS /PLELPO000016/ 1 1 NETIA /PLNETIA00014/ 0 1 MOSTOSTAL WARSZAWA /PLMSTWS00019/

1 1 OKOCIM /PLOKOCM00018/ 0 1

MUZA /PLMUZA000019/ 1 1 ORBIS /PLORBIS00014/ 0 2

NOVITA /PLNVITA00018/ 2 1 POLLENA EWA /PLFKPEW00018/

0 1

OBORNIKI WFM /PLWFM0000016/

1 0 POLIGRAFIA /PLPLGRF00011/ 0 1