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A Hybrid Predicting Stock Return Model Based on Bayesian Network and Decision Tree

  • Shou-Hsiung Cheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8482)

Abstract

This study presents a hybrid model to predict stock returns. The following are three main steps in this study: First, we utilize Bayesian network theory to find out the core of the financial indicators affecting the ups and downs of a stock price. Second, based on the core of the financial indicators coupled with the technology of decision tree, we establish the hybrid classificatory models and the predictable rules that affect the ups and downs of a stock price. Third, by sifting the sound investing targets out, we use the established rules to set out to invest and calculate the rates of investment. These evidences reveal that the average rates of reward are far larger than the mass investment rates.

Keywords

stock returns financial indicators Bayesian network decision tree 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shou-Hsiung Cheng
    • 1
  1. 1.Department of Information ManagementChienkuo Technology UniversityChanghuaTaiwan

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