In our paper, Decoding Inside Information, forthcoming in the Journal of Finance, we employ a simple empirical strategy to decode the information in insider trading. Our analysis rests on the basic premise that insiders, while possessing private information, trade for many reasons, and that by identifying ex-ante those insiders whose trades are “routine” (and hence uninformative), one can better isolate the true information that insiders contain about the future of firms. Using simple definitions of routine traders, we are able to systematically and predictably identify insiders as either opportunistic or routine throughout our sample. We show that stripping away the uninformative signals of routine traders leaves a set of information-rich opportunistic trades that are powerful predictors of future firm returns, news, and events.
We show that while the abnormal returns associated with routine traders are essentially zero, a portfolio strategy that instead focuses solely on opportunistic insider trades yields value-weighted (equal-weighted) abnormal returns of 82 basis points per month (180 basis points per month). Similarly, in a regression context the combined differences in the coefficients between opportunistic trades and routine trades translate into an increase of 158 basis points per month in the predictive ability of opportunistic trades relative to routine trades. Further, this effect increases with the strength of the opportunistic signal (as measured by the number of trades or trade-size intensity), but is unrelated to the strength of the routine signal.