Building unconditional forecast model of Stock Market Indexes using combined leading indicators and principal components: application to Vietnamese Stock Market

Abstract

Objectives: The goal of this paper is to propose a new approach to build unconditional forecast models of stock market indexes in the context of high dimensional input data set. Methods: The methodology of building these models is to combine the method of leading indicators and the Principal Component Analysis techinique (PCA) for dimensionality reduction and to use the multiple regression method on the new data set of reduced dimension. The real data set collected by month of 293 economic-financial variables were used to build a forecast model of Vietnamese stock market index VNINDEX. Findings: The absolute error percentage of the out-of-sample forecasts for the next 4 periods of the model built under the proposed methodology is no more than 1.6%. Applications: The methodology proposed can also be applied to build the unconditional forecast models for the stock prices as well as many other financial-economic indicators. Keywords: Dimensionality Reduction, High Dimension Data, Leading Indicator, PCA, Unconditional Forecast Model, Stock Market