Predicting the Stock Price Crash Using Bacterial Foraging Algorithms and Bayes Algorithms

Document Type : applied

Authors

1 Associate Prof. Accounting Department, Islamic Azad University, South Tehran Branch, Tehran, Iran

2 PhD Candidate.in Accounting, Islamic Azad University, South Tehran Branch, Tehran, Iran

Abstract

Subject to sudden changes in stock prices in recent years has attracted the attention of many researchers. Stock price crash has negative effect on stock prices is very large and uncommon and usually occurs without inducing a major economic disaster. The aim of this study was to examine the predictability of stock price crash based on models based on machine learning. In this study, predicted the stock price crash based on bacterial foraging algorithms and Bayes algorithms is used. For this purpose 148 companies of Tehran Stock Exchange during the period from 2010 to 2015 were studied. The results show that these two algorithms with high accuracy on the ability to predict stock price crash. In addition to these research findings have shown that bacterial foraging algorithms with an accuracy of 94% more capacity than the Bayes algorithm (with an accuracy rate of 93%) in predicting of stock price crash.
JEL: G11, G14
How to cite this paper: Darabi, R., & Habibzadeh Baygi, S, J. (2016). Predicted the Stock Price Crash Using bacterial foraging algorithms and Bayes algorithms. Quarterly Journal of Risk Modeling and Financial Engineering, 1(2), 185–205. (In Persian)

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Journal of Risk modeling and Financial Engineering
Volume 1, Issue 2 - Serial Number 2
December 2016
Pages 185-205

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