Stock Selection Using Recon

We approach the problem of stock selection from the perspective of knowledge discovery in databases: given a database of several years of quarterly information on over a thousand companies, discover patterns in the data that will allow one to predict which stocks are likely to have exceptional returns in the future. The database includes measures of trends in the stocks' prices as well as fundamental data on the companies. For this task we employed the Recon system, which is able to induce a set of classiication rules or a neural network to model the data it is given. To evaluate Recon's performance in the stock selection task, we paper-traded a portfolio of the fty stocks ranked highest by Recon. When trading costs were taken into account, Recon's portfolio had a total return of 238% over a four-year period, signiicantly outperforming the benchmark, which returned 93.5% over the same period. The performance is not attributable to growth/value or size eeects alone. We conclude that Recon is a valuable tool for stock selection.