Predict the Stock price crash risk by using firefly algorithm and comparison with regression
محورهای موضوعی : Financial Accounting
Serveh Farzad
1
(
Department of Economy and Administration, University of Mazandaran, Babolsar, Iran
)
Esfandiar Malekian
2
(
Department of Economy and Administration, University of Mazandaran, Babolsar, Iran
)
Hossein Fakhari
3
(
Department of Economy and Administration, University of Mazandaran, Babolsar, Iran
)
Jamal Ghasemi
4
(
Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran
)
کلید واژه: Cumulative motion of particle algorithms, stock price Crash risk, Feature Selection, Firefly Algorithm,
چکیده مقاله :
Stock price crash risk is a phenomenon in which stock prices are subject to severe negative and sudden adjustments. So far, different approaches have been proposed to model and predict the stock price crash risk, which in most cases have been the main emphasis on the factors affecting it, and often traditional methods have been used for prediction. On the other hand, using Meta Heuristic Algorithms, has led to a lot of research in the field of finance and accounting. Accordingly, the purpose of this research is to model the Stock price crash risk of listed companies in Tehran Stock Exchange using firefly algorithm and compare the results with multivariate regression as a traditional method. Of the companies listed on the stock exchange, 101 companies have been selected as samples. Initially, 19 independent variables were introduced into the model as input property of the particle accumulation algorithm, which was considered as a feature selection method. Finally, in each of the different criteria for calculating the risk Stock price crash risk, some optimal variables were selected, then using firefly algorithm and multivariate regression, the stock price crash risk was predicted and results were compared. To quantify the Stock price crash risk, three criteria for negative skewness, high fluctuations and maximum sigma have been used. Two methods of MSE and MAE have been used to compare the methods. The results show that the ability of meta-meta-heuristic methods to predict the risk Stock price crash risk is not generally higher than the traditional method of multivariate regression, And the research hypothesis was not approved.
[1] Afsar, A., Modeling the stock price index forecast using Fuzzy Neural Networks and Combined Techniques. Tehran: Master's thesis, Tarbiat Modares University, 2014 (in Persian)
[2] Azar, A., Afsar, A., Ahmadi, P., Comparison of Classical and Artificial Intelligence Methods in stock price prediction and design of a hybrid model. Management Research in Iran, 2006.
[3] Gard, A., Waqfi, H., Habibzadeh, J., Khaje Zadeh, S., Comparison of earnings forecast prediction using alimentary algorithms and bacterial nutrition. Journal of Empirical Accounting Research, 2014, 4(15), P.181-203.
[4] Nahandi, Y., Taqizadeh, V., The effect of paying dividends and not releasing bad news on the risk Stock price of crash risk with an emphasis on information asymmetry. Accounting and Auditing Reviews, 2016, 24(1), P.40-19.
[5] Sadr S., Sotoudeh Nia, L., Salman, Amiri, A., The study of the relationship between profit smoothing and the risk Stock price of crash risk in companies admitted to the Tehran Stock Exchange, New Research in Management and Accounting, 2016, 21, P. 230-207.
[6] Tanani, M., Sedighi, A., Amiri, A., Investigating the role of corporate governance mechanisms in reducing the stock price risk of companies accepted in Tehran Stock Exchange. Quarterly Journal of Asset & Finance Management, 2014, 4, P.234-255.
[7] Toloui, A., Hagh doost, Sh., Modeling the stock price forecast using a neural network and comparing it with mathematical prediction methods, Economic Research, 2006, 5, P.252-237.
[8] Blanchard, Olivier J. and Watson, Mark W., Bubbles, Rational Expectations, and Financial Markets, in Paul Wachtel, ed., Crises in Economic and Financial Structure. Lexington MA: Lexington Books, 1982, 55, P.295-315.
[9] Basu, S., The conservatism principle and the asymmetric timeliness of earnings. Journal of Accounting. & Economics, 1997, 24, P.3-37.
[10] Bleck, A., Liu. X., Market Transparency and the Accounting Regime. Journal of Accounting Research, 2007, 45 (2), P.229-256.
[11] Bradshaw. Mark T; Hutton; Marcus Alan J; and Hassan Tehranian, Opacity; Stock price of crash risk Risk and Option Smirk Curves, Boston College, 2010.
[12] Chiam, S., Tan, K., and Mamun, A., A memetic model of evolutionary PSO for computational finance applications. Expert Systems with Applications, 2009, 36, P.3695-3711.
[13] Callen Jeffrey L; Fang X., Short interest and stock price stock price of crash risk risk. Journal of Banking and Finance, 2015.
[14] Chen J., H. Hong, Forecasting stock price of crash risks: trading volume, past returns, and conditional skewness in stock prices. Journal of Financial Economics, 2001, 61(3), P.345-381.
[15] Chen, C.J.P., Su, X. and Wu, X., Auditor Changes Following A Big Merger with a Local Chinese Firm: A Case Study. Auditing a Journal of Practice & Theory, 2010, 29(1), P.41-72.
[16] Chen, C. J., Kim, L., Earning Management Will Exacerbate or Reduce Stock price of crash risk? A Journal of Corporate Finance, 2017, 42, P.36-54.
[17] Dallagnol, V., Vandenberg, F., Mous, L. Portfolio Management Using Value at Risk: A Comparison between Genetic Algorithm and Particle Swarm Optimization. International of intelligent System, 2009, 2(4), P.729-766.
[18] Yang, X-S., Firefly Algorithms for Multimodal Optimization, in: Stochastic Algorithms. Foundations and Applications, SAGA, Lecture Notes in Computer Sciences, Cambridge, UK, 2009, 57, P.169-178.
[19] Hutton, A.P., Marcus, A.J., Tehranian, H., Opaque financial reports, R2, and stock price of crash risk risk. Journal of Financial Economics, 2009, 94, P.67-86.
[20] Hong, H. and J. C. Stein., Differences of opinion, short sales constraints, and market stock price of crash risks. Review of financial studies, 2003, 16(2), P.487-498.
[21] Katsis. C.D., Golets.Y., Boufounou P.V., Using Ants to Detect Fraudulent Financial Statements. A Journal of Applied Finance & Banking, 2012, 2(6), P.73-89.
[22] Khan, M., Watts, R.L., Estimation and empirical properties of a firm-year measure of accounting conservatism. Journal of Accounting and Economics, 2009, 48, P.132-150.
[23] Kim, J.,Zhang, L., Does Acconting Consevatism Reduse Stock Price Stock price of crash risk, 2010, 55, P.24-56.
[24] Koshino, M., Murata, H., and Kimura, H., Improved Particle Swarm Optimization and Application to Portfolio Selection. Electronic and Communications in Japan, 2007, 90, P.35-68.
[25] Kumar S., Thulasiram R.K., Thulasiraman P., On Colony Optimization for Option Pricing. In: Brabazon A., O'Neill M. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, 185, Springer, Berlin, Heidelberg, 2009.
[26] Lazaridis I., Tryfonidis D., The Relationship Between Liquidity Management and Profitability of Listed Companies in the Athens Stock Exchange. University of Macedonia, Department of Accounting & Finance, 2008, 54, P.1-5.
[27] Panayiotis C.A., Constantinos A., Joanne H., and L. Christodoulos, Corporate Governance and Firm-Specific Stock PriceStock price of crash riskes, 2015.
[28] Tehran, R., Khodayar, F., Optimization of Artificial Neural Networks Using Firefly algorithm Algorithm to Predict the Variation of the Stock Price Index. Journal of Applied Sciences, 2010, 10(3), P.345-356.
[29] Vorst, P., Equity Market Competition and Stock Price Stock price of crash risk Risk. Maastricht University, School of Business and Econ, 2016.
[30] White, H., Economic prediction using neuralnetwork:The case of IBM daily stock returns. IEEE International conference on Neural Networks, San Deigo, 1988, 2, P.451-458.
[31] Yang, X-S., Fiefly Algorithm, Stochastic Test Functions and Design Optimization. International Journal of Bio-inspired Computation, 2010, 2(2), P.78-84.
[32] Ebrahimi, S. K., Rahmani Mansesh M., Bahramini Nasab A., Shafiee, N., Tehran Stock Exchange Index Estimation Using Backup Vector Regression Optimized by Firefighting Algorithm Based on Multi-objective Genetic Algorithm with Non-Diffuse Sorting, Second Conference Annual Economics, Management and Accounting, Ahvaz, Shahid Chamran University - Khuzestan Industry, Mine & Trade Organization, https://www.civilica.com/Paper-EMAC02-EMAC02_342.html, 2017
[33] Miley, A., Eivazlou, R, Fallahpour, S., The use of firefighting algorithm to predict the financial distress of companies accepted in Tehran Stock Exchange, Master's thesis, Faculty of Tehran, Tehran University, Financial engineering, 2014,
[34] Shiri, M., Salehi, M., Hamidehpour, K., Estimating the error of predicting stock price index changes in the pharmaceutical and pharmaceutical industry using artificial intelligence algorithms. Health Accounting for the fourth year of issue, 2014.
