# Legal Insider Trading and Stock Market Liquidity

## Abstract

This paper assesses the impact of legal trades by corporate insiders on the liquidity of the firm’s stock. For this purpose, we analyze two liquidity measures and one information asymmetry measure. The analysis allows us to study as well the effect of a change in insider trading regulation, namely the implementation of the Market Abuse Directive (European Union Directive 2003/6/EC) on the Dutch stock market. The first set of results shows that, in accordance with theories of asymmetric information, the intensity of legal insider trading in a given company is positively related to the bid-ask spread and to the information asymmetry measure. We also find that the Market Abuse Directive did not reduce significantly this effect. Secondly, analyzing liquidity and information asymmetry around the days of legal insider trading, we find that small and large capitalization stocks see their bid-ask spread and the permanent price impact increase when insiders trade. For mid-cap stocks, only the permanent price impact increases. Finally, we could not detect a significant improvement of these results following the change in regulation.

## Keywords

Insider trading Financial markets regulation Stock market liquidity Information asymmetry## JEL Classification

G14 G18## 1 Introduction

In this paper, we study stock market liquidity around dates of legal insider trading in the Netherlands. Trades by corporate insiders are highly regulated, especially since the European Union directive called “Market Abuse Directive” (European Union Directive 2003/6/EC) was incorporated into the Dutch national regulation. Even so, insiders are known to have better information than outside investors about the future prospects of their firm (see e.g. Degryse et al. 2014). In this context, we investigate whether legal trades by insiders in shares of their own company have an impact on the stock’s liquidity.

Market liquidity of a firm’s stock is a characteristic that determines partially the risk of the firm’s equity and thus the cost of capital. It also affects trading costs to outsiders. Both have an important effect of corporate finance decisions (see esp. Amihud and Mendelson 2008). For this reason, it is important to understand how corporate insiders affect their company’s stock liquidity when they trade.

The Market Abuse Directive aims at tightening and at expanding stock market regulation regarding market abuse and manipulation, and in particular insider trading. Its goal is also to harmonize the legislation in countries of the European Union. Incorporated into Dutch regulation on October 1st, 2005, the two aspects of this new regulation that are important for our study are, first, the strengthening of monitoring and punishment of illegal insider trading. Therefore, corporate insiders are less likely to trade upon inside information. Second, the new regulation obliges listed companies to disclose through press releases any information that is relevant for the valuation of the stock. As such, if companies disclose more information, then corporate insiders should in principle have less information advantage over outside investors. These two effects together should decrease the information asymmetry of corporate insiders’ trades.

Our contribution on this subject is analyzing whether the effect of insider trading on the stock’s liquidity changed after the implementation of this European Union wide regulation. We tackle this topic by studying data from the Netherlands. The research questions analyzed in this paper are the following. How do legal insider trades affect stock market liquidity? Do the bid-ask spread and price impact increase? Does the implementation of the European Union Market Abuse Directive reduce the effect of insider trading on the liquidity of the stocks?

Two approaches are used to analyze these research questions. A first approach looks at the effect of the intensity, or the prevalence, of insider trading on the average stock liquidity and permanent price impact. This is a cross-sectional approach. The second approach proceeds by looking more closely at the short-term effect around dates of legal insider trading. More specifically, we calculate the impact on a stock’s illiquidity induced by the presence of corporate insiders on the market, by subtracting illiquidity measures on the date of the trading by the same measure obtained during a benchmark period. We label this as “abnormal illiquidity”. This is repeated for each insider trading event and for each firm. This abnormal illiquidity is then analyzed in a regression framework. The goal is to assess whether the presence of informed traders creates abnormal illiquidity, be it by enlarging the bid-ask spread or the adverse selection component of the spread (as measured by the permanent price impact of trades).

Our findings are that insider trading is important to explain liquidity both on the cross-section and around event dates. On the cross-section, stocks with more insider trading have wider spreads and a larger permanent price impact. Also, we show that the Market Abuse Directive helped to reduce this effect. On a finer time frame, we found that on days of insider trading, the spreads and price impact are larger than on other days (this is true mostly for small and large companies, but it is less the case for medium-sized stocks). This analysis did not allow us to identify a positive effect of the Market Abuse Directive.

This paper brings together three strands of literature, the first of which is about legal insider trading. Several papers show that legal insider trading helps to predict future stock price performance (see e.g. Seyhun 1998; Lakonishok and Lee 2001; Jeng et al. 2003; Fidrmuc et al. 2006; Degryse et al. 2014). This is consistent with trades by corporate insiders being perceived by other stock market participants as based partly on private information. The current paper extends this literature by analyzing whether a stock’s liquidity measures on dates of insider trading are different compared to non insider trading days.

The second strand of literature related to our study includes papers that analyze market liquidity around information events. For example, Lee et al. (1993), Krinsky and Lee (1996) and Kavajecz (1999) study the bid-ask spread and adverse selection costs around earnings announcements. Aktas et al. (2007) study the probability of informed trading (PIN) around merger and acquisition announcements. This literature, like the present paper, aims at testing empirical implications of theoretical market microstructure models of adverse selection (see e.g. Kyle 1985; Glosten and Milgrom 1985; O’Hara 1995 or de Jong and Rindi 2009 provide a review of these models).

This paper also relates to the literature about financial market regulation, and specifically about insider trading regulation. It thus contributes to the debate about the efficiency of insider trading regulations (see e.g. Bainbridge 2000; Bhattacharya and Daouk 2002). For this purpose, we also analyze the effect of a change in regulation concerning insider trading on the Dutch stock market. A similar analysis of a change in insider trading regulation is done by Frijns et al. (2008) for the New Zealand Stock Exchange. The authors study whether the average adverse selection component of the spreads of all stocks on the exchange changed when new regulations about insider trading were adopted in New Zealand. They find a reduction of adverse selection after the implementation of the regulation. While their paper only investigates liquidity measures around the change in regulation, the present paper goes one step further by also looking specifically on dates where insiders traded.

The literature studying legal insider trading and effects on market liquidity is scarce. The existing papers use a cross-sectional approach to compare the impact of insider trading on spreads across firms. Chung and Charoenwong (1998) study the bid-ask spread of US stocks and relate it to legal insider trading. They find that in average, firms with larger extent of legal insider trading have larger spreads, but contrary to our results, they do not find evidence that spreads increase in the period around legal insider trading. Khan et al. (2005) study the impact of insider trading on market liquidity in the NASDAQ market. They obtain mixed results and provide the interpretation that dealers are unable to detect informed investors on the market. We improve upon these studies by analyzing more sophisticated measures of the spread including the adverse selection component. In addition, we extend the results for a different market structure, which is, in our case, a fully automated limit order book market. Also related to this strand of literature, Aktas et al. (2008) study the contribution to price discovery that is due to legal insider trading. Garfinkel and Nimalendran (2003) and Gleason (2007) study the difference in response to insider trading by market makers between NASDAQ and NYSE.

A recent analysis similar to ours was done in the Hong Kong stock exchange (Cheng et al. 2006) using a different methodology from ours. They perform a pooled cross-section and time series regression of daily liquidity measures (calculated using intraday data) on explanatory variables and on a dummy variable to identify insider trading days. Consistent with our finding, they find that liquidity and depth is negatively affected by insider trades.

The rest of this paper is organized as follows. Section 2 introduces the data and the methodology, and provides descriptive statistics about different liquidity measures. Section 3 presents results of a cross-sectional analysis of the effect of intensity of legal insider trading on the average liquidity of stocks. Section 4 presents a day-by-day analysis of the effect of legal insider trading on the stock’s liquidity. Finally, Sect. 5 concludes.

## 2 Data and Methodology

Our data on legal insider trading is obtained from the AFM (Authority for the Financial Markets, the Dutch financial markets supervisor). The dataset consists of the days for which a corporate insider traded shares, for each stock. Unfortunately, the trade time during the day is not included in the dataset, rendering it impossible to study intraday changes in a stock’s liquidity. We use only trades in shares by Directors and Board members (top executives). Trades related to option exercise are excluded.^{1} When many insiders from the same company trade during a given day, we aggregate the order flow, so that for each company-date pair we can determine whether it is an insider net purchase date or an insider net sale date.

Prior to October 2005, the main article of law that regulated insider trading by directors and board members was the Act of the Disclosure of Major Holdings of 1996 (Wmz 1996). In 1999, an important change to this regulation was introduced: all corporate insiders had to notify their trades in their own company’s stock to a public registry. The reporting delay was set to 10 days after the end of the month in which the trade occurred. In 2002, this reporting delay was reduced to one business day for top executives, i.e. the insiders analyzed in this paper. In October 2005, the Market Abuse Directive (European Union Directive 2003/6/EC) was implemented into the Dutch law. This new regulation increased substantially the punishment to illegal insider trading and made it mandatory for listed firms to disclose price-sensitive information through press releases. One of the concerns that led to this regulation was that insiders were incited to trade in advance of important company news announcements (AFM 2007). Raising the penalty to this behavior and requiring every company to publish news announcements on a timely manner aimed at reducing information leakage and, therefore, increasing market cleanliness. According to AFM (2007), the new regulation reduced information leakage prior to press releases mainly for small firms, as well as technological firms. These firms have typically lower governance structures and thus are more prone to suspicious insider trading. In contrast, large firms often have very stringent rules about their executives’ trades (e.g. executives are allowed to trade only during a certain window after earnings announcements). Therefore, we hypothesize that small firms will have a larger effect of insider trading on their stock’s liquidity and price impact. Also, we expect to find a stronger effect of the new regulation for these firms. We refer to Degryse et al. (2014) for more detail about the dataset and the institutional and regulatory details concerning insider trading in the Netherlands.

In order to compute liquidity measures and the price impact, we use high frequency data from Euronext Amsterdam. The database contains all executed trades and best quotes, and covers the period from July 2004 to December 2007. We use also Datastream to obtain the market capitalization of stocks in the sample.

### 2.1 Methodology

*T*quotes that appear during the trading day are used, and where \(A_t\) and \(B_t\) are the best ask price and the best bid price, respectively, at each quote update. \(m_t\) is the mid-quote defined as \(\left( A_t + B_t\right) /2\).

*T*

*trades*that occur during the trading day. The difference between this measure and the previous quoted spread is that a trade can “walk up” the order book in case the trade size is larger than the outstanding best quote. In that case the trade price might be larger than the best ask in case of a purchase order, or lower than then best bid in case of a sell order. The effective spread takes this into account.

^{2}defined as twice the relative spread between the prevailing quote mid-point at the time of a trade, and the future quote mid-point:

*n*), ranging from 1 min to 1 h (see Sect. 2.2). To compute all three measures, we discard opening and closing auctions, as well as cross-trades. Figure 1 helps to understand the meaning of these measures by showing an example of a purchase.

In order to perform a cross-sectional analysis, we use two different proxies for the intensity, or prevalence, of legal insider trading. The first intensity measure, *INT* \(^1\), is the total number of shares traded by insiders for a given company, scaled by the number of shares outstanding. The second intensity proxy, *INT* \(^2\), is the number of insider trading days over the total number of trading days; in other words, the proportion of trading days with legal insider trading. We hypothesize that the more days with insider trading, the more information asymmetry there is between market participants and insiders, and thus the less liquid the stock will be.

To analyze the behavior of the spread around dates of insider trading, we use a methodology that closely resembles the event study methodology. “Abnormal Illiquidity”, or *AI* for short is a daily measure of change in liquidity compared to a benchmark liquidity.^{3} The idea is to subtract a given measure of illiquidity of a stock on a day by the expected illiquidity, or benchmark illiquidity. The illiquidity measures used are the quoted bid-ask spread, the effective bid-ask spread, and the permanent price impact.^{4}

*i*on event date

*t*is measured as follows:

*Benchmark*illiquidity is computed by averaging the measure of illiquidity on days \(-\)10 until +10 around the insider trading day, excluding days 0 to 4 (i.e. five trading days starting on the date of insider trading). The Abnormal Illiquidity can thus be obtained for five days: the date of insider trading itself, and the following four trading days. The rationale for using a window of five days to measure Abnormal Illiquidity is because it might be possible that market participants detect the presence of insiders with a delay, and they might adjust the bid-ask spread with some lag. This is a similar approach to other papers in the literature that perform short-term event studies on market microstructure effects. Researchers typically use shorter estimation and event windows compared to the traditional event study methodology. Chung and Charoenwong (1998) use as event period a window of five days before and after the event date, and as benchmark period 15 days before and after the estimation period. Garfinkel and Nimalendran (2003) also perform a short-term event study with microstructure measures. They compare liquidity measures on insider trading dates, i.e. a one-day event window, with a benchmark of five trading days. Moreover, Chung and Charoenwong (1998) find that there is a change in the bid-ask spread on date of insider trading and also to some extent on some following days. But no change is detected in liquidity and market conditions before the insider trading date. For these reasons, it is reasonable for us to take an event window of five days starting on the insider trading date, and an estimation window of 16 days around the event window.

The Abnormal Illiquidity measures from day 0 to day 4, for a given stock, are then added up to obtain what we call Cumulative Abnormal Illiquidity (*CAI*). This measure is to be interpreted simply as the sum of the above average illiquidity of the stock over five days.

*CAI*’s are statistically different from zero and closely follows the event study methodology.

^{5}The

*CAI*’s for all the stocks and events of insider trading are then averaged. We thus obtain the average

*CAI*due to the presence of insiders on the market. To test whether the average

*CAI*is different from zero, we first standardize each

*CAI*by its estimation error, then we aggregate these standardized

*CAI*’s to obtain a statistic that has a known distribution, from which we obtain the critical values. The standardized

*CAI*, or

*SCAI*, for event

*i*is:

*CAI*is distributed as a Student

*t*distribution with \(m-1\) degrees of freedom, where

*m*is the number of observations used to compute the benchmark illiquidity (in our case, \(m=16\)). The variance of this distribution is \(\frac{m-1}{m-3}\).

*CAI*:

*z*, is distributed as standard normal:

*z*statistic is used to make inferences about the average cumulative abnormal illiquidity.

### 2.2 Descriptive Statistics

Descriptive statistics

Small | Medium | Large | All | |
---|---|---|---|---|

| ||||

Insider trades | 7.2 | 6.3 | 15.3 | 10.1 |

Non insider trades | 19,468 | 65,269 | 343,477 | 162,923 |

Percentage of insider trades | 0.037 % | 0.010 % | 0.004 % | 0.006 % |

| ||||

All periods | ||||

Mean | 10.11 | 2.89 | 0.44 | 3.96 |

Median | 0.303 | 0.274 | 0.020 | 0.114 |

Before MAD | ||||

Mean | 7.91 | 5.21 | 0.80 | 4.02 |

Median | 0.244 | 0.289 | 0.012 | 0.083 |

After MAD | ||||

Mean | 10.91 | 0.75 | 0.23 | 3.92 |

Median | 0.335 | 0.248 | 0.025 | 0.125 |

| ||||

All periods | ||||

Mean | 818.5 | 835.4 | 694.3 | 774.5 |

Median | 51.9 | 111.6 | 78.8 | 82.8 |

Before MAD | ||||

Mean | 557.7 | 1036.5 | 276.3 | 641.6 |

Median | 8.9 | 98.4 | 59.8 | 60.8 |

After MAD | ||||

Mean | 913.7 | 650.2 | 941.7 | 854.7 |

Median | 74.7 | 169.8 | 94.8 | 99.1 |

| ||||

All periods | ||||

Mean | 57.8 | 11.4 | 14.8 | 27.9 |

SD | 131.4 | 39.9 | 48.0 | 85.8 |

Before MAD | ||||

Mean | 34.0 | 11.8 | 16.5 | 21.8 |

SD | 52.6 | 43.9 | 59.4 | 52.6 |

After MAD | ||||

Mean | 85.1 | 11.1 | 13.4 | 33.2 |

SD | 182.7 | 37.7 | 37.8 | 106.6 |

| ||||

All periods | ||||

Mean | 1.53 % | 1.20 % | 1.82 % | 1.52 % |

SD | 2.32 % | 1.19 % | 2.96 % | 2.27 % |

Before MAD | ||||

Mean | 1.86 % | 1.08 % | 1.49 % | 1.51 % |

SD | 2.95 % | 1.09 % | 1.64 % | 2.12 % |

After MAD | ||||

Mean | 1.15 % | 1.29 % | 2.08 % | 1.52 % |

SD | 1.24 % | 1.27 % | 3.71 % | 2.41 % |

| ||||

Number of stock-days | ||||

Non insider trading days | 1981 | 2113 | 2380 | 6474 |

Insider trading days | 259 | 306 | 386 | 951 |

Mean number of trades | ||||

Non insider trading days | 75 | 258 | 1698 | 731 |

Insider trading days | 76 | 273 | 1441 | 694 |

Mean trade size (euros) | ||||

Non insider trading days | 1008 | 1603 | 2497 | 1750 |

Insider trading days | 1346 | 1515 | 2425 | 1838 |

| ||||

Market capitalization | 139 | 620 | 15,400 | 6358 |

Descriptive statistics of the liquidity measures

Small | Medium | Large | All | |
---|---|---|---|---|

| ||||

Before MAD | ||||

Non ins. tr. days | 1.434 | 0.680 | 0.398 | 0.744 |

Ins. tr. days | 1.711 | 0.643 | 0.581 | 0.845 |

All days | 1.454 | 0.677 | 0.412 | 0.752 |

After MAD | ||||

Non ins. tr. days | 0.844 | 0.372 | 0.140 | 0.460 |

Ins. tr. days | 0.891 | 0.369 | 0.518 | 0.602 |

All days | 0.848 | 0.371 | 0.179 | 0.473 |

| ||||

Before MAD | ||||

Non ins. tr. days | 1.334 | 0.605 | 0.370 | 0.681 |

Ins. tr. days | 1.569 | 0.562 | 0.545 | 0.768 |

All days | 1.351 | 0.602 | 0.384 | 0.688 |

After MAD | ||||

Non ins. tr. days | 0.697 | 0.300 | 0.118 | 0.379 |

Ins. tr. days | 0.750 | 0.299 | 0.474 | 0.519 |

All days | 0.701 | 0.300 | 0.155 | 0.392 |

| ||||

Before MAD | ||||

Nns. tr. days | 0.215 | 0.178 | 0.097 | 0.155 |

Ins. tr. days | 0.343 | 0.215 | 0.149 | 0.217 |

All days | 0.225 | 0.181 | 0.101 | 0.160 |

After MAD | ||||

Non ins. tr. days | 0.219 | 0.152 | 0.062 | 0.144 |

Ins. tr. days | 0.296 | 0.188 | 0.103 | 0.190 |

All days | 0.225 | 0.155 | 0.066 | 0.148 |

Panel A of Table 1 shows the incidence of insider trading. We see that companies have from 7.2 to 15.3 insider trades per year, depending on their size, with an overall average of about 10 insider trades per firm per year. These numbers are of comparable magnitude to the sample used by Chung and Charoenwong (1998), which represents the US market during the year 1988 (they have an average of 10 insider trades per firm per year). However, in our sample, insider trades represent a lower proportion of all trades compared to Chung and Charoenwong’s sample (0.006 % in our sample compared to 0.07 % in theirs). Panels B and C show the size of trades by insiders as proportion of shares outstanding (Panel B) and in value (Panel C). In every case, the mean is much larger than the median, indicating a large skewness. Insider trade sizes as a proportion of shares outstanding are smaller for larger companies. In terms of value traded, there is no clear pattern with respect to firm size. Panels D and E show the mean and standard deviation of the intensity measures *INT* \(^1\) and *INT* \(^2\). Both variables are fairly stable between periods (before and after MAD). Panel F shows the total number of observations (stock-day pairs), and the number and size of intraday trades, during days without insider trading as well as days with insider trading. We see that the mean trade size is increasing with market capitalization. We see also that the mean number of trades per day as well as the mean trade size in euro is not largely different on days of insider trading compared to days without insider trading. Finally, Panel G shows the market capitalization of the stocks in our sample.

We now determine which intraday time frame is suitable in order to compute the permanent price impact (the index *n* in Eq. 1). In order to do so, it is useful to show a graph of the price impact as a function of intraday time. See Fig. 2.

Using all company-date couples in the sample (insider trading days as well as 10 days prior and after the trading day itself), Fig. 2 shows the realized spread using several time frames: 1, 2, 5, 10 min, and all multiples of 5 min until 1 h. All trades are used (insider trading dates as well as non insider trading dates). The data are separated into three categories of stocks sorted by market capitalization. The graph shows that it takes some time for the information content of a trade to be incorporated into the quotes. We see also that a different time frame should be used for each size category. For large firms, after 10 min the price impact curve is practically flat. This means that after 10 min, the information content of the trade is almost entirely impacted in the quotes. For medium firms, a 30-min time frame seems to be reasonable. For small firms, the price impact increases until at least 1 h after the trade. For this reason, we use 10, 30 and 60 min time frame for large, medium and small firms, respectively, in order to compute the permanent price impact of each trade.

Table 2 shows some descriptive statistics about the spread measures. Consistent with the literature on market liquidity, the quoted spread and effective spread both are decreasing functions of market capitalization. The (permanent) price impact can be seen as the profits to informed traders. We see that the price impact is larger for small firms. This is consistent with the view that small firms suffer more from adverse selection costs than large firms, since they are typically more opaque, and less followed by the press and by analysts.

In the next section, we analyze how the intensity of insider trading affects the average liquidity and price impact of stocks on the cross-sectional level.

## 3 Intensity of Legal Insider Trading

This section answers the question whether, in the cross-section, liquidity providers on the market adjust the spreads depending on the intensity of insider trading. Since insiders are investors that have informational advantages over other market participants, we hypothesize that liquidity measures will be different depending on the intensity of insider trading, i.e. depending on the likelihood of trading against an insider. As in Chung and Charoenwong (1998), we regress the average stock liquidity on the known explanatory variables, and on the intensity of insider trading. Also, we test for the effect of the change in regulation regime, namely the implementation of the Market Abuse Directive, similarly to Frijns et al. (2008).

^{6}Thus, the basis of observation is a stock-period, where each variable is an average over the period. The effect of intensity of insider trading on liquidity and price impact is obtained by adding the intensity measure in the regression. Also, we add a dummy for MAD, to capture the effect of the regulatory regime. Finally, we add an interaction variable between MAD and intensity. The regression equation is the following:

Intensity of insider trading and stock liquidity

QS | ES | PI | ||||
---|---|---|---|---|---|---|

(1) | (2) | (1) | (2) | (1) | (2) | |

Intercept | 1.6680 | 1.7070 | 1.3599 | 1.3867 | 0.4090 | 0.4217 |

( | ( | ( | ( | ( | ( | |

logMCap | \(-\)0.0660 | \(-\)0.0671 | \(-\)0.0630 | \(-\)0.0650 | \(-\)0.0222 | \(-\)0.0238 |

(\(-\) | (\(-\) | (\(-\) | (\(-\) | (\(-\) | (\(-\) | |

InvPrice | 1.0251 | 1.0354 | 1.0336 | 1.0429 | 0.0693 | 0.0694 |

( | ( | ( | ( | ( | ( | |

logVol | \(-\)0.0586 | \(-\)0.0625 | \(-\)0.0413 | \(-\)0.0439 | \(-\)0.0049 | \(-\)0.0045 |

(\(-\) | (\(-\) | (\(-\) | (\(-\) | (\(-\) | (\(-\) | |

Volty | 0.6155 | 0.6128 | 0.5863 | 0.5847 | 0.0964 | 0.0963 |

( | ( | ( | ( | ( | ( | |

\(\hbox {INT}^1\) | 0.0011 | – | 0.0011 | – | 0.0002 | – |

( | ( | ( | ||||

\(\hbox {INT}^2\) | – | 3.0576 | – | 3.5196 | – | 0.6417 |

( | ( | ( | ||||

MAD | 0.2093 | 0.2555 | 0.1952 | 0.2450 | 0.0323 | 0.0345 |

( | ( | ( | ( | ( | ( | |

\(\hbox {MAD}\times \hbox {INT}^1\) | \(-\)0.0005 | – | \(-\)0.0005 | – | \(-\)0.0001 | – |

(\(-\) | (\(-\) | (\(-\) | ||||

\(\hbox {MAD}\times \hbox {INT}^2\) | – | \(-\)3.2653 | – | \(-\)3.4945 | – | \(-\)0.1771 |

(\(-\) | (\(-\) | (\(-\) | ||||

| 135 | 135 | 135 | 135 | 135 | 135 |

Adj. \(R^2\) | 0.7396 | 0.7385 | 0.7369 | 0.7364 | 0.6399 | 0.6394 |

The results show that the intensity of insider trading has an effect on the average stock liquidity, after controlling for other factors. Both specifications (i.e. model (1) with \({ INT}^1\) and model (2) with \({ INT}^2\)) have positive coefficients for intensity of insider trading on the quoted spread, the effective spread, and permanent price impact. The coefficients are statistically significant for model (1). For model (2), intensity of insider trading has a significant effect only for the effective spread.^{7} Economic significance can be measured by multiplying the coefficient by one standard-deviation of the intensity variables. We see from the results that for a one standard-deviation increase in \({ INT}^1\) (i.e. 85.8 from Table 1), the quoted spread increases by roughly 0.1 % compared with an average quoted spread of 0.75 % before MAD and 0.47 % after MAD (from Table 2). This is an economically significant effect. The magnitude of this effect is similar to what is found in Chung and Charoenwong (1998) for the US market. Our results for the effective spread are similar than for quoted spread. For the permanent price impact, the results are weaker: an increase of one standard deviation of \({ INT}^1\) increases PI by about 0.02 %. But this has to be compared with an average price impact of 0.15–0.16 %, which makes it still economically significant. Results for model (2) using \({ INT}^2\), the proportion of days with insider trading, are of similar magnitude, although the regression results show that the statistical significance is lower. Overall, the regression results clearly show that a larger amount of insider trading harms market liquidity and adverse selection.

Care has to be taken in the interpretation of the coefficient for the Market Abuse Directive dummy. This coefficient might capture some differences between the two periods that are not due to the regulation *per se*. For example, it is well known that liquidity increases over time (this can be seen in the descriptive statistics in Table 2), thus a negative coefficient is to be expected even if the new regulation did not have any effect on liquidity. But what is observed is a positive coefficient, although not significant. This is surprising because the result of MAD is to increase the penalty to illegal insider trading as well as obliging companies to publish more information in the form of press releases. This should lower the information asymmetry between insiders and outside investors, and thus reduce the price impact. We do not observe this effect.

The interaction term between *MAD* and \({ INT}^k\) aims at capturing whether the Market Abuse Directive helped to reduce the impact of insider trading on stock liquidity. All models have negative coefficients. These negative coefficients mean that the intensity of insider trading is affecting *less* stock liquidity after the implementation of the new regulation. However, the results are not statistically significant.

The results are similar to Frijns et al. (2008) but in their paper, only the price impact, or the information content of trades, seems to be reduced due to the new regulation.^{8}

This section showed that the average liquidity of stocks is affected by the intensity of insider trading. The next section will investigate whether insiders have an effect on the stock liquidity on days of their trades.

## 4 Stock Liquidity Around Dates of Legal Insider Trading

Table 2 also shows how liquidity and adverse selection changes between days of insider trading and days without insider trading. We can see that quoted spreads (Panel A) and effective spreads (Panel B) are qualitatively larger on days of insider trading compared to days without insider trading. Surprisingly, this is the case only for small firms and large firms. It is not the case for medium firms. For the permanent price impact (Panel C), this adverse selection measure is larger on insider trading days compared to non-insider trading days for all firms sizes.

To give an example of how much this change in liquidity affects trading costs, assume an investor trades for €10,000 of a large stock, after the Market Abuse Directive has passed. The implicit trading cost, not accounting for commissions and fees, is best represented by the effective spread. In our case, it would be 0.118 %/2 (the trader pays half the spread for a single trade), i.e. €5.90. On days of insider trading, this cost would jump to €47.40 / 2= €23.70. This cost could be further decomposed into information asymmetry costs and liquidity costs. The part due to information asymmetry is 0.062 %/2, i.e. €3.10 on days without insider trading, and €5.15 on days of insider trading.

### 4.1 Abnormal Illiquidity

We see from Fig. 4 that for small firms, all Abnormal Illiquidity measures are different from zero at day zero, i.e. on the insider trading day, but not afterwards. The Cumulative Abnormal Price Impact remains statistically different from zero on day 1 and on day 4. This means that insiders induce an increase in illiquidity on the date at which they trade. This increase is important as it is close to 5 basis points for the quoted spread and the effective spread, and close to 10 basis points for the price impact. These numbers can be compared with the average values for the liquidity measure in the sample provided in Table 2, where we see that the average effective spread is about 1.35 % of the share price before MAD and 0.70 % after MAD. So an abnormal effective spread on insider trading day of 0.05 % is an economically non-negligible increase.

Medium firms show a different pattern than small firms on date of insider trading for the Abnormal Quoted Spread and the Abnormal Effective spread. The average Abnormal Illiquidity is negative for these measures, but not statistically different from zero. However, the Abnormal Price Impact is positive and significant: an increase of about 3 to 4 basis points (compared to an average before MAD of 0.19 % and of 0.16 % after MAD).

Figure 4 shows as well that large firms are not systematically affected in their liquidity on days of insider trading. The Abnormal Illiquidity is close to zero and non significant on day zero. However, there is a pattern of increasing Cumulative Abnormal Illiquidity for this size category. For the effective spread and the price impact, the *CAI* reaches a significant 7 basis points on day 4. There is a similar increase for the quoted spread, but it is not statistically significant. This means that for large firms, the liquidity measures react with a delay to insider trading. This pattern can be explained by the fact that there is a large number of trades during a day for this firm size, and thus the insiders can hide better their trades, so that it takes more time for liquidity providers to detect and react to the presence of insiders on the market. This result is similar to the findings in Gleason (2007) where the author observes that NASDAQ stocks with a larger number of dealers (i.e. larger stocks) are less affected by insider trading in terms of bid-ask spread. Also, Khan et al. (2005) finds that for the largest 100 NASDAQ stocks, the bid-ask spread widens with delay to insider sales, and the authors interpret this results as dealers trying to recover their loss over time after insider trading. A similar interpretation can be made for our results.

### 4.2 Cross-Sectional Analysis of CAI

^{9}for insider trading event \(i; BUY_i\) is a dummy variable indicating insider purchases, \(Ret_i\) is the average return of the stock during 10 days prior to (but not including) the insider trading day; \(Ret\times BUY_i\) is an interaction term to analyze the effect of stock return when there is an insider purchase. \(logHolding_i\) and \(logInsTrSz_i\) are the holding of the insider and the size of the trade, respectively. \(Volty3m_i\) is the volatility of the stock computed during the three months prior to the event, and finally, \({ MAD}_i\) is a dummy that equals 1 after the implementation of the Market Abuse Directive (October 2005). In this regression, we use stock fixed-effects, so the coefficients estimated are to be interpreted as within firm effects.

^{10}Results are shown in Table 4.

Regression of cumulative abnormal illiquidity on explanatory variables

CAI-QS | CAI-ES | CAI-PI | |
---|---|---|---|

BUY | \(-\)0.0811 | \(-\)0.0952 | \(-\)0.0667 |

(\(-\) | (\(-\) | (\(-\) | |

Ret | \(-\)3.0327 | \(-\)2.3642 | 2.7150 |

(\(-\) | (\(-\) | ( | |

Ret \(\times \) BUY | 1.0765 | 0.5080 | \(-\)3.1341 |

( | ( | (\(-\) | |

logHolding | 0.0449 | 0.0362 | \(-\)0.0178 |

( | ( | (\(-\) | |

logInsTrSz | \(-\)0.0273 | \(-\)0.0245 | \(-\)0.0118 |

(\(-\) | (\(-\) | (\(-\) | |

Volty3m | \(-\)0.0020 | 0.1827 | \(-\)1.0757 |

(\(-\) | ( | (\(-\) | |

MAD | \(-\)0.0519 | 0.0041 | \(-\)0.0565 |

(\(-\) | ( | (\(-\) | |

| 597 | 597 | 597 |

Adj. \(R^2\) | 0.0138 | 0.0085 | 0.0453 |

The coefficients of the purchase dummies are small, but for the price impact equation, the dummy is significant at the 5 % level. This result shows that when insiders buy shares, they affect price impact to a lesser extent than when they sell. This is surprising given that insider purchases are known to have higher information content (in the sense that insider purchases can predict future stock returns, as shown in the literature). But here, results show that market participants, on the short run, react stronger when insiders sell shares than when they buy.

Looking at the coefficient of *Ret*, we see that it is negative, large and significant for quoted spread and effective spread. Since the coefficients of \(Reg \times BUY\) are insignificant, it means that an insider trade following an increase in stock price affects negatively the Cumulative Abnormal Illiquidity. In other terms, an insider trade after a run-up in price does not affect liquidity of the stock. But on the contrary, an insider trade following a decrease in stock price strongly affects liquidity. It might be that market participants are more nervous and are prone to withdraw liquidity after a decline in prices.

The coefficients on *logHolding* and on *logInsTrSz* are not significant: the size of the trade and the prior holding of the insider are not important to explain the liquidity effects of their trades. *Volty*3*m* is important only for abnormal price impact, with a negative coefficient. This means that when insiders trade in periods of large volatility, they affect less the price impact compared to periods of low volatility. This can be understood as large volatilities can help the insider to hide better his trades to other market participants. Or alternatively, when volatility is high, the price impact is already higher than usual, as can be seen in Table 2, above.

The regression results show also that once we control for the relevant explanatory factors, the effect of the Market Abuse Directive on the Abnormal Illiquidity is not significant. The new regulation does not help to reduce the impact of insider trading on liquidity and price impact.

The results in this section can be summarized as follows. In a univariate analysis, we find that small firms are the ones for which liquidity reacts the most to legal insider trading: 5 basis points for the quoted and effective spread, and 10 basis points for the price impact. For medium-sized firms, only the price impact increases. For large firms, Fig. 4 suggests that the *CAI* increase with a delay of a couple of days after insider trading. This shows that in average, trades by insiders affect the liquidity of their stocks and the permanent price impact around the dates at which they trade. Next, we investigated what could be the determinants of this short-term change in liquidity and price impact. A regression analysis with stock fixed-effects shows that the size of the insider trade and the insider’s prior holding do not affect abnormal liquidity. The most important determinants of Abnormal Illiquidity is the return of the stock prior to the trade by an insider: when the stock has negative returns prior to the trade, the insider induces a larger effect on the quoted spread and effective spread. And vice versa: when the stock increases before the trade, the effect of insider trading on liquidity is reduced. The determinants of the abnormal price impact are different: sales by insiders have a larger effect on price impact than purchases. Also, stock volatility negatively explains abnormal price impact.

## 5 Conclusion

Insider trading is a regulated activity in most of the countries around the world. Even if insiders are prohibited by law to trade upon private and price-sensitive information, their trades are in average more information driven than those of outside investors. In this paper, we use legal trades by corporate insiders to analyze whether the presence of insiders on the market affects liquidity measures on the stock market as well as adverse selection. This study is done in the context of the implementation of a European-wide regulation, the Market Abuse Directive, that enhanced market surveillance and insider trading regulations. In order to do so, we use legal insider trading data from the Netherlands on Dutch listed companies.

Using a first approach, we perform a cross-sectional regression of the average liquidity and price impact of each stock on their determinants and on a proxy for the intensity, or prevalence, of insider trading. This allows us to estimate whether the average liquidity and price impact are larger when insider trading is high, compared to stocks where insider trading is low. We find that intensity of insider trading is an important determinant of stock market liquidity and adverse selection. Also, we find some evidence that the new regulation helps to reduce the effect of insider trading on liquidity: after the implementation of the regulation, the variable intensity is a less important factor explaining liquidity and price impact.

In a second approach, we focus on the short term, and we measure whether insider trading affects liquidity and price impact on the days around their trades, compared to a normal liquidity and price impact level. We find that for small stocks, quoted spread, effective spread and price impact are all affected immediately on the day of insider trading. For medium firms, only the price impact changes on insider trading dates, not the other liquidity measures. For large firms, we find an impact but with a delay of two to four days after the date of transaction, as if market participants react late to the presence of a potentially informed trader. Finally, we test what could be the determinants of the effect insiders have on liquidity and price impact when they trade. For the quoted and effective spread, we find that it is mostly the return of the stock prior to the insider trading event that counts. For the price impact, we find that the effect induced by insiders depends on whether the trade is a purchase or a sale, and also on the volatility of the stock prior to the trade.

An important contribution of this paper is to test the ability of the European Union directive, implemented in the Netherlands as the Market Abuse Decree, to improve liquidity and reduce adverse selection. We find that stock average liquidity and price impact are less affected by the intensity of insider trading after the implementation of the regulation. This means that even if legal insider trading remains prevalent after the regulation, outside traders do not find it a threat and so liquidity and price impact are less affected by it. This is true in average. Our second approach, looking specifically on dates of insider trading, did not find that liquidity and price impact are less affected by insider trading after the regulation was passed.

Further analyses have to be done on this topic to understand the link between insider trading and stock liquidity. First and foremost, a better understanding of the dynamics of liquidity is called for, from which the specific impact of insider trading could be better identified.

## Footnotes

- 1.
- 2.
- 3.
The concept of Abnormal Illiquidity measure is analogous to Abnormal Returns, used in event studies.

- 4.
When the bid-ask spread increases, the stock is

*less*liquid. This is why we call it a measure of illiquidity. - 5.
The following description of the test statistic is analogous to the \(J_2\) statistic for standard event studies as exposed in Campbell et al. (1997), chapter 4.

- 6.
Note that some companies in the dataset do not appear in both periods. Indeed, some stocks are traded only before MAD, while others appear only after its implementation. This means that the sample of companies is less than doubled using this methodology.

- 7.
Due to the clustering of the residuals, the degrees of freedom is 83, and so the cut-off points of the

*t*statistics are 1.66, 1.99 and 2.64 for the 10, 5 and 1 % confidence levels, respectively. - 8.
The difference between our results and those of Frijns et al. (2008) may be due to methodology. They use model-based methodologies for decomposing the bid-ask spread into its components (Madhavan et al. 1997; Glosten and Harris 1988) while we use a model-free methodology which is feasible thanks to the richness of the data.

- 9.
*CAI*over day 0 and 1 is used as the dependent variable instead of simply the abnormal illiquidity on day zero because if the trade by the insider occurs close to the closing time, or after the closing time, other market participants might react to it the next trading day. - 10.
Note that with this specification, the cross-sectional determinants of the liquidity measures, like firm size or trading volume, are captured by the unobserved effects.

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