Automatically Identifying Predictor Variables for Stock Return Prediction

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Author(s): Da Shi (Peking University, China), Shaohua Tan (Peking University, China)and Shuzhi Sam Ge (National University of Singapore, Singapore)
Copyright: 2009
Pages: 19
Source title: Artificial Higher Order Neural Networks for Economics and Business
Source Author(s)/Editor(s): Ming Zhang (Christopher Newport University, USA)
DOI: 10.4018/978-1-59904-897-0.ch003

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference / Neural Networks

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Abstract

Real-world financial systems are often nonlinear, do not follow any regular probability distribution, and comprise a large amount of financial variables. Not surprisingly, it is hard to know which variables are relevant to the prediction of the stock return based on data collected from such a system. In this chapter, we address this problem by developing a technique consisting of a top-down part using an artificial Higher Order Neural Network (HONN) model and a bottom-up part based on a Bayesian Network (BN) model to automatically identify predictor variables for the stock return prediction from a large financial variable set. Our study provides an operational guidance for using HONN and BN in selecting predictor variables from a large amount of financial variables to support the prediction of the stock return, including the prediction of future stock return value and future stock return movement trends.

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Automatically Identifying Predictor Variables for Stock Return Prediction

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