Post by Sapphire Capital on Jul 16, 2008 20:05:14 GMT 4
Anomalous Returns in a Neural Network Equity-Ranking Predictor
JEFFREY SATINOVER
University of Nice, CNRS
DIDIER SORNETTE
ETH Zurich
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June 14, 2008
Swiss Finance Institute Research Paper No. 08-15
Abstract:
Using an artificial neural network (ANN), a fixed universe of ~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. All three active trading methods result in very high levels of volatility. But the network method exhibits a distinct kind of volatility: Though overall it does the best job of segregating equities in advance into those that will rise and those that will fall relative to one another, there are many quarters when it does not merely fail, but rather inverts: It disproportionately predicts an inverse rank ordering and therefore generates unusually large losses in those quarters. The same phenomenon occurs, but to a greater degree, with the VL system itself and with a one-step Martingale predictor. An examination of the quarter to quarter performance of the actual and predicted rankings of the change in equity prices suggests while the network is capturing, after a delay, changes in the market sampled by the equities in the Value Line index (enough to generate substantial gains), it also fails in large measure to keep up with the fluctuating data, leading the predictor to be often out of phase with the market. A time series of its global performance thus shows 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
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1155427_code623849.pdf?abstractid=1155427&mirid=2
JEFFREY SATINOVER
University of Nice, CNRS
DIDIER SORNETTE
ETH Zurich
--------------------------------------------------------------------------------
June 14, 2008
Swiss Finance Institute Research Paper No. 08-15
Abstract:
Using an artificial neural network (ANN), a fixed universe of ~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. All three active trading methods result in very high levels of volatility. But the network method exhibits a distinct kind of volatility: Though overall it does the best job of segregating equities in advance into those that will rise and those that will fall relative to one another, there are many quarters when it does not merely fail, but rather inverts: It disproportionately predicts an inverse rank ordering and therefore generates unusually large losses in those quarters. The same phenomenon occurs, but to a greater degree, with the VL system itself and with a one-step Martingale predictor. An examination of the quarter to quarter performance of the actual and predicted rankings of the change in equity prices suggests while the network is capturing, after a delay, changes in the market sampled by the equities in the Value Line index (enough to generate substantial gains), it also fails in large measure to keep up with the fluctuating data, leading the predictor to be often out of phase with the market. A time series of its global performance thus shows 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
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1155427_code623849.pdf?abstractid=1155427&mirid=2