Please use this identifier to cite or link to this item:
|Title:||Automatically identifying predictor variables for stock return prediction|
|Source:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 13, 2018
checked on Feb 17, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.