Abstract
Two data mining techniques were compared for their ability to improve the prediction of abnormal returns using insider stock trading data. The two were neural networks (NN) and Multivariate Adaptive Regressive Splines (MARS). In the comparison, both analyzed abnormal stock market returns from the same 343 companies over the identical 4\frac{1}{2} year period (1/93-6/97). The major findings were: 1) both NN and MARS generally identified the same industries that had the most predictive abnormal stock returns 2) both found that predictions further in the future (12 and 9 months ahead) were more accurate than predictions closer to the trading date (6 and 3 months ahead) 3) both obtained better predictive accuracy using four - rather than two - months of back aggregated stock data 4) NN identified a substantially greater percentage of stocks in the group with the highest explained variance than did MARS 5) data from small and midsize companies led to higher predictive accuracy than data from large size (S&P 500) companies using NN, but not MARS. The findings illustrate that the very complex interaction between insider trading data and abnormal stock returns can be systematically analyzed using non-linear techniques. Of the two assessed, NN led to comparatively more accurate predictions than did MARS.
Published Version
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