2.1 Research on insider trading
Insider trading has been, and still is, at the epicenter of a wide variety of studies. It has also been examined as a ‘by-product’ or a ‘level-gauge’ in a great number of works in relation, for instance, to the efficient market hypothesis (e.g. Fama [1970]; Fama et al. [1969]; Finnerty [1976a]; Fama [1991]). It has been examined under different contexts, in different financial markets either geographically or functionally. There have been various methodologies applied in various data sets. Insider trading has been examined either by scholars or by legislative and regulating bodies, but on the other hand, it remains at the center of attention of market participants and financial reporters.
Jaffe ([1974]), in a very concise and detailed manner, provides evidence supporting the argument that insiders do profit from their transactions. He concluded that, in fact, there can be a profitable investment strategy based on this report. Something, of course, that was not compliant with the efficient market hypothesis. Finnerty ([1976b]) builds upon previous work (by Jaffe [1974]; Finnerty [1976a] and others) to move one step further. He demonstrates that insiders not only outperform the market, but there is, in fact, a causal relationship between insiders’ trading and future accounting and financial information. To put it another way, insiders do trade based upon their privileged information.
Since it had already been demonstrated that insiders earned abnormal returns, Givoly and Palmon ([1985]) examined whether, and to what degree, those abnormal returns were due to the close monitoring of insider trading by outsiders or to the disclosure of specific information. Although their research did confirm that insiders earned abnormal returns, they found no evidence to support a direct link with specific events.
Despite abundant research work, a causal relationship between insider trading and abnormal returns can, generally, only be implied. That is mainly due to the fact that there is an inherent difficulty in precisely identifying time as an attribute of this relationship. Damodaran and Liu ([1993]), however, managed to time the moment when private information was acquired. They established thus a direct link between the distinct stages of the insider trading process, providing evidence that insiders do exploit their privileged information and earn abnormal returns.
The vast majority of research papers examining insider trading are based on data from US stock markets. Consequently, Del Brio et al. ([2002]) provide valuable insight in their research on insider trading based on data from the Spanish stock markets, the Madrid Stock Exchange (MSE) and the Spanish Continuous Market (CM). The applicability of previous results can, thus, be tested in a less vigorous, less studied and potentially, somehow, less efficient environment than the stock markets in US. They, too, build upon previous research (Jaffe [1974]; Finnerty [1976a] and others) to conclude that insiders do profit from their transactions, consequently rejecting the hypothesis of the strong form efficiency as defined by the EMH. They also suggest that the regulatory framework fails to inhibit insider trading. In their study, they examined daily transaction data and reported transactions by insiders.
The London Stock Exchange (LSE) is the ground for an insightful study by Hillier and Marshall ([2002]). They conduct their study upon the fact that, under the London Stock Exchange Model Code, there is a ban in transactions by insiders two months before the announcement of earnings both interim and final. Although, as shown by previous research, insiders do comply with the ban, they manage to outperform the market. This fact questions the efficacy of the specified ban and reveals the need for more sophisticated regulation or none at all.
2.2 Research approaches
Typically, research into insider trading is performed by conducting an event study. The methodology applied is based on the research by Fama et al. ([1969]). They examined the way stock prices are adjusted to reflect newly arrived information. Their aim was to test market efficiency by studying the impact split announcements have on stock prices through their implied increase in dividends.
In the years that followed the publication of Fama, Fisher, Jensen and Roll’s research, event study methodology was applied in many research works on various subjects. Kothari and Warner ([2004]) reported that up to that moment, more than 500 event studies had been published.
Effectively, in an event study, a model is applied to estimate the difference between the normally Expected Return (ER) and the Return (R) of a stock within a timeframe of a specific event. The estimated residuals or Abnormal Returns (AR) are cumulated to form the Cumulated Abnormal Returns (CAR). Finally, CAR is tested statistically to examine whether the null hypothesis holds () or the alternative hypothesis that CAR is statistically different from zero holds ().
Specifically, first of all, the event that will be examined has to be defined. For different scopes of research, various events are selected. Subsequently, the event may be an earnings announcement, a merger or acquisition announcement, a change in regulations affecting a specific business sector or even a natural disaster. Secondly, a timeframe around the specified event has to be selected. Over this ‘event window,’ (MacKinlay [1997], p. 14) the selected security prices are examined to determine the effect of the event. The event window consists of a pre-event period, the event day and the post-event period. The pre-event period, may vary from 15 months to a few days, 20 days being a reasonable selection in insider trading studies examining daily data. The event day is the announcement day or the day on which the event is made known to the public by any means. The post-event period is typically one or just a few days after the event day.
Next, available data are selected, examined, filtered and prepared in order to become suitable for the scope and requirements of certain research (MacKinlay [1997], p. 15). Research has been conducted using monthly (e.g. Jaffe [1974]), daily and even intraday data. The use of daily data, however, seems to prevail (Kothari and Warner ([2004]), p. 8) and certainly provides more accuracy than monthly data (MacKinlay [1997], p. 35), especially for studying insider trading. Monthly data examination may not reveal insider trading activity that might take place a few days before the announcement day (Keown and Pikerton [1981], p. 856). Intraday data, on the other hand, although much more accurate, add complexity because they have to be combined with bid-ask spreads at the specific moment of insider trading transaction to determine the effect on security prices.
The next task is to select the model, according to which Abnormal Return is estimated. At this stage, the researcher has to assume which model attributes ‘normal’ performance more accurately. MacKinlay ([1997], pp. 17–19) cites the several models that have been used in different event studies and which are very briefly mentioned here. Under the Constant Mean Return Model, the abnormal return is estimated as the residual of the observed security price and the mean return of the security. On the other hand, using the Market Model, the Expected Return is derived from the relation of a certain security with the market portfolio. Abnormal Return, thus, is assessed as the residual of the observed security price and the Expected Return. Capital Asset Pricing Model (e.g. Finnerty [1976a]; Penman [1982]), Arbitrage Pricing theory and various multifactor models have also been applied to estimate the Expected Return. However, as Fama ([1991], pp. 1603 and 1604) proposes, ‘… [one can use] market model or historical average returns, to abstract from normal expected returns without putting unnecessary constraints on the cross-section of expected returns.’ As a result, the Market Model is selected for the purposes of current research to assess the Expected Return of the selected securities. So, Abnormal Returns () of a (i) security in time (t) are estimated as: (MacKinlay [1997], p. 20). is the observed Return of security (i) in time (t) and is the observed Market Return in time (t). and are the Market Model parameters for security (i) estimated by performing linear regression with the Ordinary Least Squares (O∖S) method.
2.3 Methodological issues
The selection of firm-specific events is influenced by the ability to gather adequate event data and relevant precise announcement dates. Additionally, as it is presumably evident from the literature review, insider trading is interchangeably used to describe both reported transactions and illegal insider trading. In the first case, insider trading is supposed to be irrelevant to the studied event. Otherwise, insiders should be prosecuted. The performance of insiders is examined as if it was relevant to the event. By disregarding this anomaly, one can either question the relevance of the results or accept the insinuation that insiders perform disguised transactions through proxies along with the transactions they report.
On the other hand, illegal insider trading has been examined following three approaches. The first one was to examine prosecuted insider trading in regard to a specific security. The second approach was to derive the presence of insider trading by ‘[s]ystematic abnormal movements’ (Keown and Pikerton [1981], p. 855) before the public announcement. Finally, the third approach was to examine the impact based on a set of prosecuted insider trading cases. In fact, that is what Lisa Meulbroek ([1992]) did when she studied a set of 183 ‘insider trading episodes’ spanning a 15-year period from 1974 to 1989.
Another issue that needs to be considered is that the events that may be examined present distinct characteristics. Consequently, results and their interpretation may differ for different types of events. For instance, in an increased earnings announcement, there may be just a few people who might exploit their access to privileged information. On the contrary, in a merger announcement, there may be hundreds of people involved (Keown and Pikerton [1981], p. 857) for a longer period of time. As a result, insider trading activity may be less traceable and extended over a wider time period.
2.4 Research design
A recapitulation of literature review indicates that, first of all, numerous studies have demonstrated that insider trading is a market-wide phenomenon occurring around any, though none could claim every, event that has a significant informational value. Event study methodology seems to be the most frequently used and appropriate method to study insider trading. The Market Model is adequate in evaluating the abnormal performance due to a specific event and subsequently insider trading around this event (see Fama [1991]; MacKinlay [1997]; Meulbroek [1992]).
Events having a positive effect on stock performance are expected to have different characteristics than those having a negative effect. Moreover, some events have a greater impact than others (Meulbroek [1992]). As a result, it was decided to narrow the scope of the Event Detection process, which will be later analyzed, focusing on events having a positive impact on stock performance beyond a predefined benchmark.
Research is conceptually divided into four processes: Dataset Construction, Insider Trading Observation process, Insider Trading Observation Evaluation and Event Detection Evaluation.
Dataset Construction, including data selection, retrieval, cleansing and manipulation, is presented first. Market Model parameters are then estimated using the Athens Stock Exchange General Index (GI) as a proxy for normal performance. At the end of this process a data view is prepared to be used further on.
Next, Insider Trading Observation process is presented. One of the main features of this process, and probably one of the significant outcomes of the present study, is the Event Detection process which is analytically presented and documented including the rationale behind it. Next, the Announcements and News Retrieval procedure is presented. This procedure assists the observer in assessing the significance of the Detected Event Dates. The Event Study process which is performed for the selected event date according to a desired set of parameters is then presented. Event Study results are reflected automatically in a graph. The parameters of the Event Detection process, the Detected Event Dates, announcements and news retrieved, the Event Study parameters and results graphically depicted are then integrated into the Insider Trading Observation Window.
Insider Trading Observation Evaluation is presented next. First, the dataset used as the ground of evaluation is described and analyzed. The execution of this procedure generates results for a pre-specified set of Event Detection parameters which are next examined to evaluate the Insider Trading Observation process.
The Event Detection Validity Assessment, which aims to explore whether the Event Detection process actually produces dates which have a high informational value and can thus be characterized as Event Dates, is then presented. In order to perform such an examination, a new dataset is constructed using previous results along with additional data retrieved from the Athens Stock Exchange website (http://www.ase.gr). Hypothesis development is then described, performed and tested. Empirical results are next presented and evaluated. The workflow of each of the four outlined processes is depicted in Fig. 1.
2.5 Dataset construction
Data were obtained through the Athens Stock Exchange website (http://www.ase.gr). They were checked for inconsistencies such as missing or erroneous transaction dates. They were also randomly checked with prices reported on company websites. No errors were found. The main body of data consists of daily transactions spanning time period from 1/1/2006 to 31/8/2008. They include stock code, transaction date, closing price, change (%), Volume, Highest price, Lowest price, Total Value of Transactions, Opening Price and the International Securities Identification Number (ISIN). Transaction data before 31/12/2005 were no longer available on the Athens Stock Exchange website and so they were not included. Data after 31/8/2008 were intentionally left out because fluctuations, caused by the economic crisis which broke out in September 2008, might render transaction data unsuitable for the purposes of the present study. Furthermore, auxiliary data including distributed dividends and General Index quotes were also downloaded from the ASE website.
Next, stocks lacking transaction data during the specified time period, either because they were listed after 1/1/2006 or unlisted before 31/8/2008 were excluded along with stocks with insufficient liquidity or long suspension intervals. Stocks no longer listed in October 2009 were also excluded to avoid continuity concerns reflected on stock prices. Daily changes were adapted for dividends while necessary updates and adjustments were also performed.
Linear regression was performed using the OLS method for estimating alpha (α) and beta (β) coefficients for each stock in relation to the General Index (GI). The model is based on the classical market model and is of the following form: , where is the return on stock i at a period t, is the return on market (here General Index) at a period t, is the zero mean disturbance term, and α and β are the coefficients for each stock.
Transaction data, Market Model coefficients (α) and (β), estimated through linear regression and General Index (GI) data were then combined to form the final dataset of the Data Selection process. Additionally, linear regression coefficients were used to estimate Normal Returns (NR) and Abnormal Returns (AR) based on GI change. Finally, a dataset was formulated by combining transaction data, Market Model parameters and calculating Normal Returns and Abnormal Returns. This dataset consists of 122,517 records of transaction data of 185 stocks.