Please use this identifier to cite or link to this item: https://doi.org/10.4018/978-1-59904-897-0.ch003
Title: Automatically identifying predictor variables for stock return prediction
Authors: Shi, D.
Tan, S.
Ge, S.S. 
Issue Date: 2008
Citation: Shi, D., Tan, S., Ge, S.S. (2008). Automatically identifying predictor variables for stock return prediction. Artificial Higher Order Neural Networks for Economics and Business : 60-78. ScholarBank@NUS Repository. https://doi.org/10.4018/978-1-59904-897-0.ch003
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. © 2009, IGI Global.
Source Title: Artificial Higher Order Neural Networks for Economics and Business
URI: http://scholarbank.nus.edu.sg/handle/10635/129500
ISBN: 9781599048970
DOI: 10.4018/978-1-59904-897-0.ch003
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