Building Long/Short Portfolios Using Rule Induction

			    George H. John
		     Computer Science Department
			 Stanford University
			  Stanford, CA 94305
			gjohn@cs.stanford.edu
		 http://robotics.stanford.edu/~gjohn/

			     Peter Miller
		      Lockheed Martin AI Center
			 3251 Hanover Street
			 Palo Alto, CA 94304
		       pmiller@aic.lockheed.com
	     http://hitchhiker.space.lockheed.com/~recon/

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 financial engineering.  For stock selection we
employed the Recon* 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%, significantly outperforming the
benchmark (S\&P500), which returned 92.5% over the same period**.  We
conclude that rule induction is a valuable tool for stock selection.

*Recon is a trademark and service mark of Lockheed Martin Missiles and
Space, a subsidiary of the Lockheed-Martin Corporation.

**As with human fund managers, past performance is no guarantee of
future returns.

Citation:

John, George H. & Miller, Peter (1996), "Building Long/Short
Portfolios Using Rule Induction", IEEE Conference on Computational
Intelligence in Financial Engineering, New York.