Prediction Risk and the Forecasting of Stock Market Indexes

Abstract

In most of the empirical research on capital markets, stock market indexes are used as proxies for the aggregate market development. In previous work we found that a particular market segment might be less efficient than the whole market and hence easier to forecast. In this paper we extend the focus of this investigation by taking a comprehensive look at the Vienna Stock Exchange. We use feedforward networks and linear models to forecast the all share index WBI as well as various subindexes covering the highly liquid, semi-liquid, and initial public offering (IPO) market segment. In order to shed some light on network construction principles, we compare different models as selected by hold-out crossvalidation (HCV), Akaike's information criterion (AIC), and Schwartz' information criterion (SIC). The forecasts are subsequently evaluated on the basis of hypothetical trading in the out-of-sample period.Neural Network Architecture Selection, Information Criteria, Stock Market Indexes, Trading Strategy