Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models
International Journal of Forecasting, Vol. 16, pp. 173-190
Posted: 28 Aug 2000
Although there exists a vast number of articles addressing the predictability of stock market return, most of the proposed models rely on accurate forecasting of the level (i.e., value) of the underlying stock index or its return. In most cases, the degree of accuracy and the acceptability of certain forecasts are measured by the estimates' deviations from the observed values. Depending on the trading strategies adopted by investors, forecasting methods based on minimizing forecast error may not be adequate to meet their objectives. In other words, trading driven by a certain forecast with a small forecast error may not be as profitable as trading guided by an accurate prediction of the direction of movement (or sign of return.) Given that, 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.
Note: This is a description of the paper and not the actual abstract.
Keywords: Forecasting, multivariate classification, neural networks, econometric time series analysis, stock index and return, trading strategy
JEL Classification: G19, C53
Suggested Citation: Suggested Citation