Rule inference for financial prediction using recurrent neural networks

C. Lee Giles, Steve Lawrence, Ah Chung Tsoi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

51 Scopus citations

Abstract

This paper considers the prediction of noisy time series data, specifically, the prediction of foreign exchange rate data. A novel hybrid neural network algorithm for noisy time series prediction is presented which exhibits excellent performance on the problem. The method is motivated by consideration of how neural networks work, and by fundamental difficulties with random correlations when dealing with small sample sizes and high noise data. The method permits the inference and extraction of rules. One of the greatest complaints against neural networks is that it is hard to figure out exactly what they are doing - this work provides one answer for the internal workings of the network. Furthermore, these rules can be used to gain insight into both the real world system and the predictor. This paper focuses on noisy time series prediction and rule inference - use of the system in trading would typically involve the utilization of other financial indicators and domain knowledge.

Original languageEnglish (US)
Title of host publicationIEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr)
Editors Anon
PublisherIEEE
Pages253-259
Number of pages7
StatePublished - 1997
EventProceedings of the 1997 IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, CIFEr - New York, NY, USA
Duration: Mar 23 1997Mar 25 1997

Other

OtherProceedings of the 1997 IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, CIFEr
CityNew York, NY, USA
Period3/23/973/25/97

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Economics, Econometrics and Finance(all)
  • Engineering(all)

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    Giles, C. L., Lawrence, S., & Tsoi, A. C. (1997). Rule inference for financial prediction using recurrent neural networks. In Anon (Ed.), IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr) (pp. 253-259). IEEE.