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Financial Data Modeling using a Hybrid Bayesian Network Structured Learning Algorithm
Abstract
In this paper, a group of hybrid incremental learning algorithms for Bayesian network structures are proposed. The central idea of these hybrid algorithms is to use the polynomial-time constraint-based technique to build a candidate parent set for each domain variable, followed by the hill climbing search procedure to refine the current network structure under the guidance of the candidate parent sets. Experimental results show that, the authors’ hybrid incremental algorithms offer considerable computational complexity savings while obtaining better model accuracy compared to the existing incremental algorithms. One of their hybrid algorithms is also used to model financial data generated from American stock exchange markets. It finds out the predictors of the stock return among hundreds of financial variables and, at the same time, the authors’ algorithm also can recover the movement trend of the stock return.
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