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ABSTRACT

In this study, we forecast Korean Stock Price Index (KOSPI) using historical weekly KOSPI data and three forecasting models such as back-propagation neural network model (BPNN), a Bayesian Chiao’s model (BC), and a seasonal autoregressive integrated moving average model (SARIMA). KOSPI are forecasted over three different periods (i.e. short-term, mid-term, & long-term). The performance of the forecasting models is measured by the forecast accuracy metrics such as absolute forecasting errors and square forecasting errors of each model.

The findings are as follows: First, between BPNN and BC, BPNN performs better than BC for mid term and long term forecasting, while BC performs better than BPNN for the short term forecasting. Second, between BPNN and SARIMA, SARIMA performs better than BPNN for mid term and long term forecasting, while BPNN does better than SARIMA the short term forecasting. Between SARIMA and BC, SARIMA performs better than BC for mid term and long term forecasting, while the other way around is true for the short term forecasting.

In sum, the SARIMA performs best among the three models tested for midterm and long term forecasting, while BC performs best for the short term forecasting.

(ProQuest. denotes formulae omitted.)

INTRODUCTION

The ability to forecast the capital market price index is critical to individual investors, institutional investors, and financial analysts. Among many forecasting models for stock prices and market price index, the seasonal autoregressive integrated moving average model (SARIMA) has been one of the most popular forecasting models in capital market studies.

Recently, the neural network model has been frequently used in many capital market studies (e.g. Ansari et. al. (1994), Hamid et. al. (2004), Huang et. al. (2005), Kumar et. al. (2006), Malik et. al. (2006), Stansell et. al. (2004), and Trinkle et. al. (2005)). Major reasons for the neural network model’s popularity in capital market forecast are twofold. First, the neural network model is data driven method which learns from sample data and hence does not require any underlying assumptions about the data. Thus, the model is known as a universal functional approximate without severe model misspecification problems due to wrong assumptions (Hornik et. al. (1989)). The model is also outstanding in processing large amount of fuzzy, noisy, and unstructured data. For example, Hutchinson et. al. (1994) examine stock option price data and show that the neural network model is computationally less time consuming and more accurate non-parametric forecasting method, especially when the underlying asset pricing dynamics are unknown or when the pricing equation cannot be solved analytically. Second, stock price data are large, highly complex and hard to model because the pricing dynamics are unknown, which suits the neural network model.

The Bayesian Chiao’s model (BC) may be another powerful and practical tool to forecast capital market data for the following reasons. First, the main thoughts of the BC is the dynamic way of combining the prior information (i.e. either from historical datasets or from previous subjective experience) with the current observations, during the process of posterior information. Second, most of the traditional statistics applications are based on the assumptions of independent, identical distributed (i.e. i.i.d.) normal random variables. However, the merit of the BC is to assume independence of variables only. No more identical distributions are needed. Third, the pros of BC can be the dynamic adaptive mechanism of integrating prior knowledge and the current information for accurately predicting the immediate future outcomes. However, the con of this model is its high dependence on the quality of the initial values of the estimates. Further, without constantly absorbing realistic datasets, the long term iterative predictions of this model perform poorly, if merely repeatedly applies the BC. …