Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models

18 Pages Posted: 24 Jan 2000

See all articles by Mark T. Leung

Mark T. Leung

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

Hazem Daouk

Cornell University - School of Applied Economics and Management

An-Sing Chen

National Chung Cheng University - Department of Finance

Multiple version iconThere are 2 versions of this paper

Date Written: March 1999

Abstract

Despite abundant research which focuses on estimating the level of return on stock market index, there is a lack of studies examining the predictability of the direction/sign of stock index movement. Given the notion that a prediction with little forecast error does not necessarily translate into capital gain, we evaluate the efficacy of several multivariate classification techniques relative to a group of level estimation approaches. Specifically, we conduct time series comparisons between the two types of models on the basis of forecast performance and investment return. The tested classification models, which predict direction based on probability, include linear discriminant analysis, logit, probit, and probabilistic neural network. On the other hand, the level estimation counterparts, which forecast the level, are exponential smoothing, multivariate transfer function, vector autoregression with Kalman filter, and multilayered feedforward neural network. Our comparative study also measures the relative strength of these models with respect to the trading profit generated by their forecasts. To facilitate more effective trading, we develop a set of threshold trading rules driven by the probabilities estimated by the classification models. Empirical experimentation suggests that the classification models outperform the level estimation models in terms of predicting the direction of the stock market movement and maximizing returns from investment trading. Further, investment returns are enhanced by the adoption of the threshold trading rules.

Keywords: Forecasting, Multivariate classification, Stock index, Trading strategy

JEL Classification: G19, C53

Suggested Citation

Leung, Mark T. and Daouk, Hazem and Chen, An-Sing, Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models (March 1999). Available at SSRN: https://ssrn.com/abstract=200429 or http://dx.doi.org/10.2139/ssrn.200429

Mark T. Leung (Contact Author)

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

San Antonio, TX
United States

Hazem Daouk

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/

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)

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