Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index

42 Pages Posted: 13 Aug 2001

See all articles by An-Sing Chen

An-Sing Chen

National Chung Cheng University - Department of Finance

Hazem Daouk

Cornell University - School of Applied Economics and Management

Mark T. Leung

University of Texas at San Antonio - Department of Management Science and Statistics

Date Written: July 2001

Abstract

In the last decade, neural networks have drawn noticeable attention from many computer and operations researchers. While some previous studies have found encouraging results with using this artificial intelligence technique to predict the movements of established financial markets, it is interesting to verify the persistence of this performance in the emerging markets. These rapid growing financial markets are usually characterized by high volatility, relatively smaller capitalization, and less price efficiency, features which may hinder the effectiveness of those forecasting models developed for established markets. In this study, we attempt to model and predict the direction of return on the Taiwan Stock Exchange Index, one of the fastest growing financial exchanges in developing Asian countries. Our approach is based on the notion that trading strategies guided by forecasts of the direction of price movement may be more effective and lead to higher profits. The Probabilistic Neural Network (PNN) is used to forecast the direction of index return after it is trained by historical data. The forecasts are applied to various index trading strategies, of which the performances are compared with those generated by the buy and hold strategy, and the investment strategies guided by the forecasts estimated by the random walk model and the parametric Generalized Methods of Moments (GMM) with Kalman filter. Empirical results show that the PNN-based investment strategies obtain higher returns than other investment strategies examined in this study. The influences of the length of investment horizon and the commission rate are also considered.

Keywords: Emerging economy, forecasting, trading strategy, Neural Networks, Generalized Methods of Moments (GMM)

JEL Classification: G15, C45, C53

Suggested Citation

Chen, An-Sing and Daouk, Hazem and Leung, Mark T., Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index (July 2001). Available at SSRN: https://ssrn.com/abstract=237038 or http://dx.doi.org/10.2139/ssrn.237038

An-Sing Chen

National Chung Cheng University - Department of Finance ( email )

Chia-Yi, Taiwan 621
China
+011 886 5 272 0411 (Phone)
+011 886 5 272 0818 (Fax)

Hazem Daouk (Contact Author)

Cornell University - School of Applied Economics and Management ( email )

446 Warren Hall
Ithaca, NY 14853
United States
331-45-78-63-88 (Fax)

HOME PAGE: http://courses.cit.cornell.edu/hd35/

Mark T. Leung

University of Texas at San Antonio - Department of Management Science and Statistics ( email )

San Antonio, TX
United States

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