A Comparative Study of the Prediction Stock Crash Risk by using Meta- Heuristic & Regression

Document Type : Original Article

Authors

1 Professor, Faculty of Economics and Administrative Affairs, University of Mazandaran. Babolsar, Iran (Corresponding auther)

2 Associate Professor, Faculty of Economics and Administrative Affairs, University of Mazandaran. Babolsar, Iran

3 Associate Professor, Faculty of Engineering &Technology, University of Mazandaran. Babolsar, Iran

4 Ph.D. Condidate in Accounting . Faculty of Economics and Administrative Affairs, University of Mazandaran.

Abstract

One of the most important methods of opacity accounting information by management is to accelerate the identification of good news versus delaying the identification of bad news on profits, but there is always a final level of accumulation of bad news in the company, and by reaching that its final level, these bad news will be released, which will lead to a Stock Price Crash Risk. In fact, stock price collapse is a phenomenon in which stock prices are subject to severe negative and sudden adjustments. Accordingly, the first purpose of this research is to model the Stock Price Crash Risk of the  listed companies at the  Tehran Stock Exchange by using an optimal algorithm The cumulative particles and comparison with the results of  logistic regression model. To this, a hypothesis was developed for the study of this issue and the data of 101 listed companies of Tehran Stock Exchange for the period between 2010 and 2014 were analyzed. First, 14 independent variables were introduced as inputs of the combined genetic algorithm and artificial neural network, which was considered as a feature selection method, and 7 optimal variables were selected. Then, using particle cumulative algorithm and logistic regression, predicted The Crashs. To calculate the Stock Price Crash Risk, a Stock Price Crash Period criterion has been used.  In The Second Stage, the particle algorithm was used as a feature selection, and this time, to calculate the Crash risk, the NCSKEW criterion was used. Finally, the optimal variables were entered into the Ant Colony algorithm and the results were compared with the multivariable regression. In the second step, MSE and MAE were used to compare the results. The results of the research show that the particle Swarm Optimization and Ant colony are more able than traditional regression (lojestic and multivariable) to predict the Crashs.  Therefore, the research hypothesises are confirmed.

Keywords

References

1)       Ahmadpour A., Zare Behnamiri M., Rostami, R. (2013). Investigating the Impact of Company Characteristics on the Risk of Stock Falling in Companies Accepted in Tehran Stock Exchange. Journal of Securities Exchange, 28(7):45-29
2)       Ahmadi M., Ramadan S. (2016). Investigating the Impact of Corporate Governance Measures on the Risk of Future Stock Falling in Companies Listed in Tehran Stock Exchange. Financial Accounting Research, (3): 42-19
3)       Alborzai M. (2009), Genetic Algorithm, Tehran, Sharif University of Technology Publishing, First Edition.
4)       Amir Hosseini Z., Davar Panah A. (2015). Modeling a model for forecasting gold prices using the algorithm of flying birds and genetic algorithm and providing a hybrid algorithm. Magazine of Finance and Management of Securities, 26: 83-59.
5)       Blanchard O. J., Watson M. W. (1982). Bubbles, Rational Expectations, and Financial Markets, in Paul Wachtel, ed., Crises in Economic and Financial Structure. Lexington MA: Lexington Books: 295-315
6)       Demoei D., Farid D., Ashar M. (2011). Estimation of Tehran Stock Exchange Index using Bird Flight Algorithm and Comparison with Traditional Patterns., Journal of Accounting Knowledge,2(5)
7)       Jafari M., Shojaee Z. (2015). Effect of real profit management and product market competition on the risk of falling future stock prices of listed companies in the stock exchange. First National Conference on Applied Accounting Research, Economics Management, http: // www.civilica.com/
8)       Jane L., Myers S.C. (2006). R2 around the world: New theory and new test. Journal of financial Economics, 79(2), 257-292.
9)       Kim C., Wang K., Zhang L. (2015). Readability of 10-K Reports and Stock Price Crash Risk. Canadian Academic Accounting Association (CAAA) Aual conference. Available at SSR; http:// ssrn.com/ abstract=2378586
10)    Tonani M., Sedighi A., Amiri A. (2015). Investigating the role of corporate governance mechanisms in reducing the stock price risk of companies accepted in Tehran Stock Exchange. Quarterly Journal of Asset and Finance Management, 4(3):1-20
11)    Vorst, P. (2016). Equity Market Competition and Stock Price Crash Risk. Retrived from https://www.researchgate.net/publication/283292515
International Journal of Finance & Managerial Accounting
Volume 3, Issue 9
April 2018
Pages 63-77

Files

Share

How to cite

Statistics