Predicting abnormal stock returns with a nonparametric nonlinear method

Neural networks (NN) can be applied to the prediction of stock market trends based on information from legal insider trading. These data are available because officers of companies are required by law to submit to the Securities Exchange Commission a record of the sales and purchases of their companies´ stock. Because purchases are more useful in this endeavor than are sales, all smallcap, midcap, and largecap companies that averaged multiple buys made by insiders of companies over a 4½ year period (1993 to the middle of 1997) were assessed in relation to the price fluctuation of the company´s stock in the forthcoming period (3, 6, 9 and 12 months ahead) as related to the index of stocks and individual stock reaction to the market as a whole. The use of NN has advantages over alternative methods as evidenced by its accuracy without using assumptions involved in other techniques