  Using an artificial neural network (ANN), a fixed universe of approximately
1500 equities from the Value Line index are rank-ordered by their predicted
price changes over the next quarter. Inputs to the network consist only of the
ten prior quarterly percentage changes in price and in earnings for each equity
(by quarter, not accumulated), converted to a relative rank scaled around zero.
Thirty simulated portfolios are constructed respectively of the 10, 20,..., and
100 top ranking equities (long portfolios), the 10, 20,..., 100 bottom ranking
equities (short portfolios) and their hedged sets (long-short portfolios). In a
29-quarter simulation from the end of the third quarter of 1994 through the
fourth quarter of 2001 that duplicates real-world trading of the same method
employed during 2002, all portfolios are held fixed for one quarter. Results
are compared to the S&P 500, the Value Line universe itself, trading the
universe of equities using the proprietary ``Value Line Ranking System'' (to
which this method is in some ways similar), and to a Martingale method of
ranking the same equities. The cumulative returns generated by the network
predictor significantly exceed those generated by the S&P 500, the overall
universe, the Martingale and Value Line prediction methods and are not eroded
by trading costs. The ANN shows significantly positive Jensen's alpha, i.e.,
anomalous risk-adjusted expected return. A time series of its global
performance shows a clear antipersistence. However, its performance is
significantly better than a simple one-step Martingale predictor, than the
Value Line system itself and than a simple buy and hold strategy, even when
transaction costs are accounted for.
