Lauren Cohen, Christopher Malloy, and Lukasz Pomorski
Uncovering the hidden information in insider trading
Journal of Finance | Volume 67, Issue 3 (Jun 2012), 1009–1043

Corporate insiders have, by definition, considerably more information about their companies than what is publicly available. Their trades are closely followed by investors and the general public, who hope to glean from them new information about an insider's company and its future share price. But are all insider trades equal in their informational content? For example, suppose that you learned that Bill Gates—a savvy and undoubtedly well informed insider—sold 20 million shares of Microsoft in the third quarter of 2008. How would you interpret this bit of data? Did Gates anticipate the brewing crisis and sell his shares ahead of it? Or did he have some privileged information about Microsoft's future? Crucially, could one systematically make money by replicating his trades?

Whatever is your prior on Gates' motives, your evaluation of his actions would probably change when you found out that he sold another 20 million shares in the last quarter of 2008. And another 20 million in the first quarter of 2009. In fact, Bill Gates routinely sold 20 million shares in each subsequent quarter, as seen in Figure 1. It seems very unlikely that this pattern of trades could arise for information reasons. This means that if you wanted to use insider trades to learn about a company's future, you would probably want to ignore the trades of insiders who, like Bill Gates, are likely trading for reasons that have little to do with private information.

1: Gates' shares sold in MSFT each quarter (in million dollars)
figcmp1

 

Are insider buys more informative than insider sells?

The Gates example serves well to illustrate the typical view of insider trading in the academic community. It has been well documented that while insider buys help predict future stock prices, there is little evidence of any price changes following insider sells. For example, Jeng-Metrick-Zeckhauser (REStat 2003) show that controlling for the risk exposures, stock prices go up by over 6% on average in the year following an insider purchase, but remain flat after an insider sale. The usual explanation of such evidence is that insider buys are indeed motivated by information, but that sells are made for other reasons. Insiders such as Bill Gates often have substantial stock holdings in their companies, and thus may want to sell some shares to diversify their overall portfolio. Moreover, insiders may sell their shares because of a specific liquidity need, e.g., they may be buying a house. Because such sells are not based on any firm-specific information, they should not be expected to predict future returns.

This binary view of insider trading (informative buys, uninformative sells) holds on average, but likely masks interesting variation. After all, some of the best-known examples of insider trading feature insiders selling on information: Enron executives liquidating their holdings ahead of their firm's bankruptcy, the selling of ImClone stock that ultimately led to the imprisonment of Samuel Waksal and Martha Stewart, etc. This means that at least some insider sells may be informative. Conversely, some insider purchases may not be based on new information. For example, some companies have stock purchase programs that allow insiders to purchase their company stock at a discount. Insiders who have money to invest (e.g., if they have just received their annual bonus) may want to participate in such programs even though they have no specific private information that would otherwise justify a trade.

The key question, of course, is how to distinguish trades that are likely to be based on information from trades motivated by other considerations. In Cohen-Malloy-Pomorski (JF 2012) we propose a simple way to divide trades into “routine,” or less likely to be information-based, and “opportunistic,” or more likely to carry new information. We show that our classification scheme works well in predicting company returns and news. Interestingly, we also find evidence that insiders limit their opportunistic trading following waves of SEC insider trading enforcement. In this article, we review our approach and our main findings.

We base our identification of opportunistic and routine insiders on the idea that trades based on private information are unlikely to follow predictable calendar patterns. This is the same argument we made earlier when discussing Bill Gates's trades: new information is unlikely to be generated in a regular calendar cycle. So, insiders who trade on information are likely to trade in a more irregular fashion. To test this idea we need to decide which calendar regularities to look for. In our paper we chose a very simple approach: we check whether a given insider trades in the same calendar month year after year. Insiders who trade in the same month in three consecutive years are classified as “routine,” or unlikely to trade on information. Insiders who trade at least once per year for three years in a row but do not have a monthly routine are classified as “opportunistic,” or more likely to act on information. Of course, this simple idea can be easily extended and perhaps refined. What we show is that even this simple identification is enough to gainfully separate informative from relatively less informative trades.

Example from our sample

To better understand our approach, consider the following example from our sample. Electronics Corporation is a large, publicly traded firm, founded in the late 1960s. (The name of the firm and the dates involved have been disguised.) The firm had a number of insiders from 2003–2006. In particular, two of these insiders were actively trading, but in very different ways. The first insider (the routine trader) traded consistently over the time period, routinely trading in each and every March and solely in March. The second insider (the opportunistic trader), who also happened to be the CFO of Electronics Corporation, traded much differently. His trades came at very selective times over the same time period. As can be seen in Table 1, both employees traded 4 times over the 4 years. Further, their trades contained very different information for future prices. As shown in Table 1, the average returns in the month following the routine trader's sells were positive 33 basis points per month (so a –33 basis point return, as the insider was selling each March). In contrast, the average return following the opportunistic insider's sells was –5.69% per month (so a positive 5.69%).

What is important to note here is that both the opportunistic and routine traders were trading in their respective manners throughout their entire trading histories, so one could have predictably identified these traders as either opportunistic or routine traders before the period we have shown here. We exploit this ability to predictably classify insiders into these two classes of traders throughout the universe of traders.

Opportunistic trades move markets

Cohen-Malloy-Pomorski (JF 2012) show that across the entire universe of insiders from 1989- 2007, a portfolio that mimics opportunistic insider trades—long a portfolio of opportunistic buys and short a portfolio of opportunistic sells—in the month following their trades (such that the portfolio is tradable), makes excess returns of 108 points per month (roughly 13% per year). The identical portfolio mimicking the trades of routine insiders earns only 27 basis points per month. The 81 basis point difference between the two (opportunistic–routine) is highly significant, and isolates the extra information in opportunistic trades for firm's future price movements over that of routine insider trades. This can be seen explicitly by rearranging the spread portfolio as (opportunistic buys–routine buys) = the extra information in buys; and equivalently (opportunistic sells–routine sells) = the extra information in opportunistic sells. The helpful aspect of this decomposition is that it's easily seen that if one did not distinguish between types of insiders, viewing all insiders as informed traders trading on that information, then one would be missing the rich information that emerges from stripping away routine trades.

1: Returns in months following insider sales at Electronics Corporation
Routine Trader Opportunistic Trader
Avg Return in Month Following Sales 0.33% –5.69%
# of trades 4 4
Dates of Trades Mar-03, Mar-04 Jun-03, Apr-04
Mar-05, Mar-06 Jul-05, Nov-06

In Table 2, we update this data through 2011. The same pattern emerges. While both returns increase, the difference between the two (opportunistic–routine) is 85 basis points over this most recent 4-year period. We include each leg of the trade to show the incremental information of opportunistic traders over routine traders, separately for insider buying and selling.

2: Monthly value-weighted portfolio returns, averages in percent
2008–2011 2009–2011
Buys Opportunistic 1.40 2.91
Routine 0.91 1.98
  Difference 0.49 0.93
Sells Opportunistic –0.35 0.70
Routine 0.01 1.13
  Difference –0.36 –0.43
Buy – Sells Opportunistic 1.75 2.21
Routine 0.90 0.85
  Difference 0.85 1.37
Routine trades showed much less of a stock-price response than opportunistic trades. The difference actually widened in the second half of the sample. 1.37% per month was both statistically and economically significant.

Summary

The routine-opportunistic classification we have proposed is simple and intuitive. It is also easily extended. One could, for example, use a more complicated pattern in trades to define “routines” or perhaps use additional data to improve the classification. The approach we propose also lends itself to applications in other context, such as routine rebalancing trades that might be done by institutional or pension managers each quarter.

Our work has attracted interest from policymakers, e.g., the SEC and the Ontario Securities Commission. It is also useful for market participants. For instance, Alliance Bernstein Research 2012 has produced a report that discusses, replicates, and confirms our results along with offering a number of extensions of ways that they may be useful in investment practice.

 

 

 

 

 

 

 

 

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Leonardo da Vinci (1452–1519): Mona Lisa. Italy, 1505.. Almost forgotten, this most-famous smile in the world was once stolen from the Louvre Museum in Paris. The patriotic Italian who effortlessly stole it in 1911 from a complacent Louvre just wanted it to be back in Italy—to restore a treasure stolen by Napoleon. Alas, the painting was in fact acquired by King Francis I of France and not by Napoleon. The director of Uffizi in Florence thus disagreed with this personal national restoration plan and returned the painting to the Louvre. The patriotic thief was stored, too.