TAILIEUCHUNG - Forecasting stock indices: a comparison of classification and level estimation models

The advantages of panel-data methods in the macro-panel setting include the use of data for which the spans of individual time series data are insufficient for the study of many hypotheses. Other advantages include better properties of the testing procedures when compared to more standard time series methods, and that many of the issues studied, including the relationship between oil prices and stock markets, naturally lend themselves to these methods. | ELSEVIER International Journal of Forecasting 16 2000 173-190 locate ijforecast Forecasting stock indices a comparison of classification and level estimation models Mark T. Leunga Hazem Daoukb An-Sing Chenc a Department of Operations and Decision Technologies Kelley School of Business Indiana University Bloomington IN 47405 USA bDepartment of Finance Kelley School of Business Indiana University Bloomington IN 47405 USA Department of Finance National Chung Cheng University Ming-Hsiung Chia-Yi 621 Taiwan 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.

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