Forecasting Stock Trend by Data Mining Algorithm

محورهای موضوعی : Financial Accounting

Sadegh Ehteshami ¹ (Department of Accounting, Kish International Branch, Islamic Azad University-South Tehran, Tehran, Iran)
Mohsen Hamidian ² (Department of Accounting and Economic, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Zohreh Hajiha ³ (Young Researcher and Elite club, East Tehran Branch, Islamic Azad University, Tehran, Iran)
Serveh Shokrollahi ⁴ (Department of Accounting and Economic, South Tehran Branch, Islamic Azad University, Tehran, Iran)

تاریخ دریافت : 1396/12/28 تاریخ پذیرش : 1396/12/28 تاریخ انتشار : 1396/12/10

کلید واژه: Stock trend forecasting, Random forest algorithm, Decision tree algorithm,

چکیده مقاله :

Stock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock trend in order to make decision about firms’ stock trading. In this study stock trend, forecasting performs by data mining algorithm. It should mention that this research has two hypotheses. It aimed at being practical and it is correlation methodology. The research performed in deductive reasoning. Hypotheses analyzed based on collected data from 180 firms listed in Tehran stock exchange during 2009-2015. Results indicated that algorithms are able to forecast negative stock return. However, random forest algorithm is more powerful than decision tree algorithm. In addition, stock return from last three years and selling growth are the main variables of negative stock return forecasting.

منابع و مأخذ:

[1] Pour Heydar A., Hemati D., Study effect of debt contract, political costs, award plans, and property of earn

ing management in companies listed in Tehran stock exchange, Accounting and Auditing Studies, 2004, 36, P.23-

45.

 

[2] Hosseini, S. M., Rashidi Z., Forecasting bankruptcy of companies listed in Tehran stock Exchange by deci

sion tree and logestic regression, Financial Accounting Researches, 2017, 5(3), P.105-130

 

[3] Barzegari Khanghah J., Jamali Z., Stock return forecasting by financial ratios: investigating recent researches,

Accounting Quarterly Journal, 2017, 6(1), P.71-92

 

[4] Johnny R.J., Khodadadi V., Study relationship between earning and its component and stock return by focus

of earning quality in companies listed in Tehran stock exchange, Financial Accounting Journal, 2015, 3(9), P.84-

113

 

[5] Mehrani S.M., Pishvayi F., Khalatbari H., Assessing earning management in different levels of co

seratism and institutional investors by Bedford law, Journal of Accounting and Auditing, 2016, 5, P.234-

257.

 

[6] Abder Rovf  M.D., The Corporate Social responsibility Disclosure, Business and Economics Research

Journal, 2007, 2(3), P.19-32.

 

[7] Ardia D., Hoogerheide L.F., Bayesian Estimation of the GARCH (1,1) Model with Student-t Innovations in

RMPRA working paper, 2016, URL http://mpra.ub. uni-muenchen.de/17414/.

 

[8] Bheenick, E. B., Brooks, R. D., Does Volume Help in Predicting Stock Return? An Analysis of the

Australian Market, Research in International Business and Finance, 2015, 24, P.146-157.

 

[9] Delen, D., Kuzey, C., Uyar, A., Measuring firm performance using financial ratios: A Decision tree

approach. Expert System with Application, 2017, 40(10), P.3970-3983.

 

[10] Mac Lyons, M., Dolphin deaths, organizational legitimacy and potential employees’ reactions to as

sured environmental disclosures. Accounting Forum, 2005, 34 (1), P.1–19.

 

[11] Yu G., Wenjuan, G., Decision tree method in financial analysis of listed logistics company. In

2010 International conference on intelligent computation technology and automation, 2015.