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Titolo:
Multiresolution forecasting for futures trading using wavelet decompositions
Autore:
Zhang, BL; Coggins, R; Jabri, MA; Dersch, D; Flower, B;
Indirizzi:
Univ Sydney, Sch Elect & Informat Engn, CEL, Sydney, NSW 2006, Australia Univ Sydney Sydney NSW Australia 2006 n, CEL, Sydney, NSW 2006, Australia Oregon Grad Inst, Dept Elect & Comp Engn, Beaverton, OR 97006 USA Oregon Grad Inst Beaverton OR USA 97006 omp Engn, Beaverton, OR 97006 USA
Titolo Testata:
IEEE TRANSACTIONS ON NEURAL NETWORKS
fascicolo: 4, volume: 12, anno: 2001,
pagine: 765 - 775
SICI:
1045-9227(200107)12:4<765:MFFFTU>2.0.ZU;2-P
Fonte:
ISI
Lingua:
ENG
Soggetto:
TIME-SERIES; NETWORKS;
Keywords:
autocorrelation shell representation; automatic relevance determination; financial time series; futures trading; multilayer perceptron; relevance determination; wavelet decomposition;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
28
Recensione:
Indirizzi per estratti:
Indirizzo: Zhang, BL Univ Sydney, Sch Elect & Informat Engn, CEL, Sydney, NSW 2006, Australia Univ Sydney Sydney NSW Australia 2006 dney, NSW 2006, Australia
Citazione:
B.L. Zhang et al., "Multiresolution forecasting for futures trading using wavelet decompositions", IEEE NEURAL, 12(4), 2001, pp. 765-775

Abstract

In this paper, we investigate the effectiveness of a financial time-seriesforecasting strategy which exploits the multiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP), To better utilize the detailed information in the lower scales of wavelet coefficients (high frequencies) and general (trend) information in the higherscales of wavelet coefficients (low frequencies), we applied the Bayesian method of automatic relevance determination (ARD) to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales, To form the overall forecast, the individual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representation, or by another perceptron which learns the weight of each scale in the prediction of the original time series. The forecast resultsare then passed to a money management system to generate trades. Compared with previous work on combining wavelet techniques and neural networks to financial time series, our contributions include 1) proposing a three-stage prediction scheme; 2) applying a multiresolution prediction which is strictly based on the autocorrelation shell representation, 3) incorporating the Bayesian technique ARD with MLP training for the selection of relevant inputs; and 4) using a realistic money management system and trading model to evaluate the forecasting performance. Using an accurate trading model, our system shows promising profitability performance, Results comparing the performance of the proposed architecture with an MLP without wavelet preprocessing on 10-year bond futures indicate a doubling in profit per trade ($AUD1753:$AUD819) and Sharpe ratio improvement of 0.732 versus 0.367, as well as significant improvements in the ratio of winning to loosing trades, thus indicating significant potential profitability for live trading.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 12/08/20 alle ore 20:29:47