A Hybrid Predicting Stock Return Model Based on Bayesian Network and Decision Tree
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.
Keywordsstock returns financial indicators Bayesian network decision tree
Unable to display preview. Download preview PDF.
- 1.Murphy, J.J.: Technical Analysis of the Financial Markets. Institute of Finance, New York (1999)Google Scholar
- 2.Bernstein, L., Wild, J.: Analysis of Financial Statements. McGraw-Hill (2000)Google Scholar
- 3.Abraham, A., Nath, B., Mahanti, P.K.: Hybrid intelligent systems for stock market analysis. In: Alexandrov, V.N., Dongarra, J. J., Juliano, B.A., Renner, R.S., Tan, C.J.K. (eds.) ICCS 2001. LNCS, vol. 2074, pp. 337–345. Springer, Heidelberg (2001)Google Scholar