A. Corporate insiders trading

Corporate insiders trade on privileged information about the firms and earn positive (negative) abnormal returns for the buy (sell) transactions [1, 21–24]. A number of studies attempt to explain reasons behind insider trades (purchases vs. sales). It is often argued that insider purchases are more informative and more likely to occur when insiders observe price sensitive information while insider sales are often attributed to non-informational reasons such as liquidity reasons to unwind directors’ stock-based compensation. Studies supporting this argument include Lakonishok and Lee [25] and Jeng et al. [26] documenting that the buy trades of insiders are more informative than sell trades; and Ofek and Yermack [27] and Meulbroek [28] providing evidence that insiders sell for liquidity reason.

Another strand of research in corporate insider distinguishes between different types of insiders based on the information hierarchy hypothesis. Studies supporting information hierarchy hypothesis include Seyhun [23], Lin and Howe [29] and To et al. [14] documenting cumulative abnormal returns following the transactions by executives are significantly higher than those by non-executive directors; Masson and Madhavan [30] reporting lower firm performance when executives actively use insider information in their stock trading; and Aboody et al. [31] providing evidence that executives rely on private information in their sales.

B. Algorithmic trading and information efficiency

AT is another group of informed traders and to some extent can help improve market efficiency by incorporating information into price at the ultra-high speed. Prior studies supporting this argument include Zhang [32] and Chakrabarty et al. [13] documenting that high frequency traders (HFT) are particularly skilled in incorporating hard information; Carrion [33], Hirschey [34], and Brogaard et al. [7] demonstrating that HFT trades can forecast price changes in several seconds ahead and are more likely to have permanent price effects; and Foucault et al. [9] proposing a mechanism by which HFT directly contributes to information gathering by trading on the Brownian news surprises.

C. Hypotheses development

Corporate insiders trade on private information about the firm and report their trading activities with a delay. In Thailand, the SEC requires all insiders to report their transactions within three days after the trading day and the trading information is shown on the SEC website at the end of the day three. On contrary, algorithmic traders trade on all available public information at the ultra-high speed as such their information gathering are short-horizon comparing to corporate insiders. Given the nature of long horizon and delay in reporting of insider trades, it is unlikely that AT could detect insider trades. As noted by Hendershott et al. [6], Brogaard et al. [7] and Boehmer et al. [10], algorithmic trading clearly indicates that AT improves liquidity and informational efficiency. This implies that the presence of AT makes market to be more liquid, allowing one to trade a small quantity at a lower spread or a more significant amount with less price impact. Theoretically, the presence of AT could either enhance or reduce insider trading profits.

According to asymmetric information hypothesis, the higher the information asymmetry, the higher the insider trading profit [35]. The reason is that market makers increase the bid-ask spread to compensate for the risk of trading with an insider who trades with high level of private information. As algorithmic traders reduce degree of information asymmetry in the market, their presence could lower insider trading profits.

On the other hand, attentive insider trading hypothesis proposed by Alldredge and Cicero [36] suggest that corporate insiders are actually the most attentive to the holdings of their own stocks, motivated them to recognize any profitable trading opportunities from the public information. Hence, corporate insiders could take advantage of more liquid and efficient market induced by AT and make profitable trading from the public information. This predicts a higher insider trading profit in presence of the AT. We postulate our first hypothesis as follows:

H1: There is no relation between algorithmic trading (AT) and insider trading profits.

In addition, trade size can influence both corporate insider trading and AT performance. Corporate insiders who have private information prefer to trade large amounts of shares at any given price [37, 38]. Larger trade size usually has more pronounced price impact. However, larger trade size benefits high-frequency trading [39] and HFTs face lower adverse selection costs than non-HFTs when supplying liquidity in larger trades [33]. Hence, it is likely that trade size could influence the relation between AT and insider trading profits. This leads us to our second hypothesis as follows:

H2: Trade size has no influence on the relation between algorithmic trading (AT) and insider trading profits.

Finally, information hierarchy can also affect the relation between AT and corporate insider trading profit. According to information hierarchy hypothesis, executives have better access to private information than non-executives do [14, 23, 29]. Hence, executives are likely to be true insiders and their trading profits may not be affected by the presence of AT. However, AT could potentially affect trading profits by non-executives. We postulate our last hypothesis as follows:

H3: There is no relation between algorithmic trading (AT) and executives (or non-executives) trading profits.

A. Data

Two sets of data are obtained for this study. First, the algorithmic trading data, computed by using tick data provided by the Stock Exchange of Thailand. Following existing literature, we use two proxies for AT as follows. The ratio of the number of quoted messages to total value trading is called “AT Proxy1” while the ratio of the total volume traded to total volume across all orders is called “AT Proxy2”. All data are collected over the period of January 1, 2010 to December 31, 2016.

Second, the Thai insider trading data is obtained from Thompson Reuters Insider Database. The data is accessible over the period of 2010–2016. We extract detailed information on insider transactions, including personal identification, transaction date, stock, traded volume and whether it is a buy or sell. This database contains legal trading of all board of directors and management reporting in their purchase or sale of their stocks conformed to the rules set forth by the Securities and Exchange Commission (SEC). In every transaction in this report, we count as an event and calculate the cumulated abnormal returns (CARs), using daily stock return and return on the SET100 as a proxy for market returns. The returns on stocks and SET100 are obtained from the Datastream. The final sample includes 6,156 stock-month insider transactions for 409 stocks. All data are collected over the period of January 1, 2010 to December 31, 2016.

B. Construction of variables

Two proxies are adapted to represent AT intensity. First, we follow the procedure detailed in Hendershott et al. [6] and Boehmer et al. [40] to construct the AT proxy. Specifically, using the order-level information, we count the number of electronic messages sent by traders to market centers (including order entry, amendment, cancellation, etc.) using the raw message traffic numbers, but there has been an increase in trading volume over the same interval. Without normalization, a raw message traffic measure may just capture the increase in trading rather than the change in the nature of trading. Therefore, for each stock in each month, we calculate our daily AT, as the number of electronic messages per of trading volume (AT Proxy1) as follows: (1)

The second AT proxy is adapted from Weller [41], trade-to-order volume ratio. Trade volume is the number of shares traded. Order volume is the volume of total orders placed. The trade-to-order volume ratio is one of several measures designed to characterize the nature of order placement and cancellation in equity markets. This ratio is interpreted reversely to the first proxy since the higher ratio means the lower AT intensity. To ease the interpretation of both AT proxies, we inverse the formula to be order-to-trade ratio as follows: (2)

Next, we obtain our monthly change in AT from the change in AT during two adjacent months as follows: (3)

Another key variable of interests is insider trading profits, proxied by cumulative abnormal return based on an event study method. We expect a positive (negative) CAR for purchase (sale) transactions represents a profit. For this reason, the daily abnormal stock returns of insider trades are estimated for purchase and sale separately. The event date (t₀) is taken to be the date of the insider trade, and an event period is spanning 20 days in the interval (t₁ to t₂₀). Abnormal returns are generated from the market model, where ARi,j,t is the risk-adjusted abnormal returns of firm i at day t of transaction j, Ri,t is the return on firm i at day t and Rm,t is the corresponding return on the market index (SET100) at time t. The estimation period to estimate the market model parameters is (t_-50 to t_-5): (4)

To calculate insider profit, we calculate CAR of each insider transaction j by cumulating ARs over the event window of n = 20 days (1 month) after the transaction. As our sample is at monthly interval, we focus on the cumulative return over the 20 trading day window. For robustness checks, we also compute the cumulative return over one week and 3 month windows (e.g. CAR(1,5) and CAR(1,60)) and include them in our baseline regression, reported in Table 2.

(5)

We then average CAR at stock-month level for purchase and sale transactions separately. Following the insider trading literature, we also include a number of control variables for the firm-specific characteristics such as firm size, leverage, return on assets and the book-to-market ratio, all taken from previous year end. To control the momentum effect in stock returns, we include the CAR over the 1-year window prior to a given insider transaction. Finally, we also control for insider trading characteristics by including the average trade size [32, 42]. In same spirit of To et al. [14], we classify insider level into 8 levels and separate this list into two types of insider levels, executive and non-executive, considered by the priority and role to access private information. We generate an executive flag for each transaction, equal to 1 if that transaction is executed by an executive level and 0 otherwise.

C. Summary statistics

The summary statistics of the all variables are tabulated in Table 1 for purchases and sales separately. Table 1 shows the different sign of CARs in the pre and post events for both purchase and sale transactions. This is consistent with findings by Seyhun [43] that the insiders are more likely to sell (purchase) shares following periods of significant price appreciation (declines). Our findings are also consistent with the insiders trading in anticipation of subsequent price reversals documented in Rozeff and Zaman [44] who show that insiders predominantly buy (sell) shares in value (growth) firms and interpret this as evidence of insiders trading against the market’s over-reaction to the past performance. Such trading behavior is consistent with insiders purchasing (selling) securities with high (low) expected returns or the greatest amount of undervaluation (overvaluation) [45, 46]. Further, we find the average insider profit is positive for purchases and negative for sales suggesting that the insiders successfully predict the direction of future returns. This implies that the insiders tend to be contrarian traders, buying after a fall and selling at the back of a rise, thus is another indicator that they are better informed than outside investors.

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Table 1. Summary statistics.

https://doi.org/10.1371/journal.pone.0255057.t001

The correlation coefficients are presented in Panel B of Table 1. We provide two types of correlation analysis: i) Lower-triangular cells report Pearson’s correlation coefficients and ii), Upper-triangular cells are Spearman’s rank correlation. The result of Pearson’s correlation shows that there is no correlation between AT proxy and the cumulative abnormal returns in both proxies. However, the Spearman’s rank correlation shows correlation between AT Proxy1, AT Proxy2 and CAR (-250,-1) in both purchase and sale transactions. Trade size is negatively correlated with both proxies implying that the higher the AT intensity, the smaller the trade size. Our findings are consistent with the work by Aitken (2014) that AT speed can encompass thousands of trades in a second. This reflects higher trade counts along with smaller trade sizes.

A. Baseline regressions

To investigate the impact of AT on the insider trading performance, we estimate the following regression model: (6) where CARi,t is the cumulative abnormal return over 20 days, AT proxy is as described above. The control variables are firm size, book to market ratio, leverage, ROA and trade size.

Table 2 presents results from our baseline regressions with industry-year fixed effects and firm level clustered standard error. The coefficient estimates for both AT proxies are insignificant. Hence, we fail to reject the null hypothesis (H1), indicating that there is no association between both AT proxies and insider trading profits in both purchases and sales. The control variables are significant in the right direction, e.g., firm size and ROA. The coefficient on the cumulative abnormal return over one-year period is negative significant, capturing return momentum before and after insider transaction [47]. As a robustness check, we also compute the cumulative return over one week and 3 months windows (e.g. CAR(1,5) and CAR(1,60)) as proxies for our insider trading profits. Overall, results are robust to different event windows.

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Table 2. Baseline regression.

https://doi.org/10.1371/journal.pone.0255057.t002

B. Effect of trade size on the relationship between AT and insider trading profits

To test our second hypothesis, we separate sample by trade size into 3 portfolios, perform regression analysis for all portfolios, purchase and sale separately and report the results in Table 3. Panel A of Table 3 reports insider purchase transactions, indicating insignificant coefficient for both AT proxies. Hence, for the purchase transactions, we are unable to reject the null hypothesis. As noted by Lakonishok and Lee [25] and Jeng et al. [26], the buy trades of insiders are driven by privy information, not public information. As such the advantage of AT in processing public information at ultra-high speed has no impact on insider purchase transactions.

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Table 3. AT and insider by trade size.

https://doi.org/10.1371/journal.pone.0255057.t003

Panel B of Table 3 reports results for the sale transaction. In the presence of all control variables, the result in sale transaction for large trade size has positive and significant coefficient on both AT proxies suggesting that increased AT reduces the insider sale profitability. One unit increased in AT results in a drop of insider sale profit 0.62% and 0.051% for each AT proxy respectively. These findings support the asymmetric information hypothesis, demonstrating that AT reduces insider trading profits. Further, consistent with O’Hara et al. [39], we are able to reject our second hypothesis such that large trade size benefits AT performance, resulting in lower insider trading profits. AT has advantage over the corporate insider when trade size is large. Corporate insiders often use large trade size, causing more pronounced price impact. However, with ultra-high speed, AT can trade a more significant amount with less price impact. Hence, the presence of AT improves information efficiency and lower insider trading profits. In addition, our findings from both panels of Table 3 are consistent with the difference in trading motivations between corporate insider purchases and sales. The motivation for the insider purchases is typically private information [25, 26] while sales can also occur for public information or non-information reasons such as liquidity and hedging [27, 28].

C. Can AT beat non-executive insider?

Executive and non-executive directors access to private and price-sensitive information differently. Executive management have priority to access data and gather all information from all departments. To test our third hypothesis, we conduct regression analysis for purchase and sale transactions separately for both executive and non-executive insiders. For insider purchase transactions, all panels of Table 4 indicate insignificant coefficient for both AT proxies. We fail to reject the third hypothesis for the insider purchase transactions. Again, consistent with Lakonishok and Lee [25] and Jeng et al. [26], we find insider purchase transactions are likely motivated by private information that cannot be detected by the outsiders. Hence, when it comes to purchasing stocks, algorithmic traders with their superior public information cannot beat corporate insiders with private information.

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Table 4. AT and insider’s management level.

https://doi.org/10.1371/journal.pone.0255057.t004

In addition, Panel A of Table 4 indicates significant and negative (positive) coefficient estimate for AT Proxy1 (AT Proxy2) in the case of executive (non-executive) sale transactions. This implies that AT enhances (reduces) executives (non-executives) profits on their sale transactions. Hence, for the insider sale transactions, we are able to reject our third hypothesis and find support to the information hierarchy hypothesis such that executives are true insiders equipped with better access to private information than non-executives do. Findings on executives support attentive insider trading hypothesis which states that corporate insiders are among the most attentive traders of their own stocks and easily recognize profitable trading opportunities when they observe public information related to their firm. That is, AT cannot beat well-informed traders (i.e. executive traders) but rather enhance their trading profits in the sale transactions. Executives benefit from liquidity and public information efficiency induced by AT and make larger profits.

Further, our findings on non-executive sale transactions support the asymmetric information hypothesis, indicating the higher the information asymmetry, the higher the insider trading profits. Consistent with To et al. [14], we find not all insiders are on the inside, non-executive directors appear to be less informative and their trading profits decrease in the presence of AT who has superior public information. Hence, AT can beat non-executive sales.

Finally, Panels B and C of Table 4 show AT cannot beat executive sales such that executive profits more with the increase of AT activities, for small and medium trade size. Seyhun [23] and Ravina and Sapienza [48] also find that top executives outperform other groups of insiders in share trading, most likely due to their privileged position with respect to the corporate information set. Again, we find AT beat non-executive sales with large trade size. This confirm our previous findings that with ultra-high speed information processing, AT can trade large transaction size with less price impact and non-executive sales are less informative and likely to trade for non-informational reason. Hence, AT reduces information asymmetry and non-executive trading profits.

D. Robustness checks

We run a battery of robustness checks. First, we use various event windows in computing insider trading profits. Our results reported in Table 2 are robust to different event windows. Second, we adopt two proxies in constructing algorithmic trading and in general, results for both proxies are relatively similar. Finally, we employ 2SLS instrumental-variable (IV) regression in order to alleviate endogeneity concerns using an industry median of AT as an instrument variable as shown in Table 5. The idea for chosen instrument variable is that it is unlikely that AT in one firm could have any influence over AT in other firms. In other words, any variation at industry level is beyond the control of a firm because there are many firms in an industry. As such, each firm takes any change at the industry level as given. An industry median is therefore likely to be exogenous. Hence, both the relevance and exclusion requirements for an instrumental variable should be satisfied. Findings reported in Table 5, in general, confirms our previous findings that AT enhances executive sales and that when it comes to purchasing stocks, algorithmic traders with their superior public information cannot beat corporate insiders with private information.

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Table 5. AT and insider’s management level– 2SLS instrumental variable approach.

https://doi.org/10.1371/journal.pone.0255057.t005