Building long/short portfolios using rule induction

We approach stock selection for long/short portfolios from the perspective of knowledge discovery in databases and rule induction: given a database of historical information on some universe of stocks, discover rules from the data that will allow one to predict which stocks are likely to have exceptionally high or low returns in the future. Long/short portfolios allow a fund manager to independently address value-added stock selection and factor exposure, and are a popular tool in nancial engineering. For stock selection we employed the Recon 1 system, which is able to induce a set of rules to model the data it is given. We evaluate Recon's stock selection performance by using it to build equitized long/short portfolios over eighteen quarters of historical data from October 1988 to March 1993, repeatedly using the previous four quarters of data to build a model which is then used to rank stocks in the current quarter. When trading costs were taken into account , Recon's equitized long/short portfolio had a total return of 277%, signiicantly outperforming the benchmark (S&P500), which returned 92.5% over the same period 2. We conclude that rule induction is a valuable tool for stock selection.