Predicting Stock Price Crash Risk with a Deep Learning Approach from Artificial Intelligence and Comparing its Efficiency with Classical Predicting Methods.
محورهای موضوعی : Financial EconomicsMeysam Rahmati 1 , Ehsan Taieby Sani 2
1 - Department Finance, Faculty of Financial Sciences, Kharazmi University, Tehran, Iran
2 - Department Finance, Faculty of Financial Sciences, Kharazmi University, Tehran, Iran
کلید واژه: Stock Price Crash Risk , Deep Learning Approach, Artificial Intelligence, Comparing its Efficiency, Classical Predicting Methods,
چکیده مقاله :
Purpose of this research is Predicting Stock Price Crash Risk with a Deep Learning Approach from Artificial Intelligence and Comparing its Efficiency with Classical Predicting Methods. This research is post-event correlation type and practical in terms of purpose. The research data were extracted from the website of the Stock Exchange Organization and Codal website. The risk variable of crashing stock prices was introduced as a predictor. 3200 obser-vations were obtained from 10-year data of 320 companies between 2012 and 2021. In the following, 29 variables were identified as variables that can affect the risk of crashing stock prices. Statistical methods such as unit root test, composite data, Hausman test and variance heterogeneity test were used. Next, the top 10 algorithms in the field of deep learning were selected and used to model the mentioned variables with the CNN method. Python, Eviews and Excel software were used in this research. Examining the performance of different deep learning algorithms shows that the convolutional neural network method performs better compared to other algorithms and can improve the prediction accuracy. Therefore, it is suggested to use this algorithm in reviewing econometric data and especially predicting the risk of crashing stock prices.
Purpose of this research is Predicting Stock Price Crash Risk with a Deep Learning Approach from Artificial Intelligence and Comparing its Efficiency with Classical Predicting Methods. This research is post-event correlation type and practical in terms of purpose. The research data were extracted from the website of the Stock Exchange Organization and Codal website. The risk variable of crashing stock prices was introduced as a predictor. 3200 obser-vations were obtained from 10-year data of 320 companies between 2012 and 2021. In the following, 29 variables were identified as variables that can affect the risk of crashing stock prices. Statistical methods such as unit root test, composite data, Hausman test and variance heterogeneity test were used. Next, the top 10 algorithms in the field of deep learning were selected and used to model the mentioned variables with the CNN method. Python, Eviews and Excel software were used in this research. Examining the performance of different deep learning algorithms shows that the convolutional neural network method performs better compared to other algorithms and can improve the prediction accuracy. Therefore, it is suggested to use this algorithm in reviewing econometric data and especially predicting the risk of crashing stock prices.
[1] Abu-Mostafa, Yaser S., and Amir F. Atiya, Introduction to financial forecasting, applied intelligence, 1996; 6(3):205-213. Doi: 10.1007.BF00126626
[2] Almudhaf, F., Predictability, Price bubbles, and efficiency in the Indonesian stock-market, Bulletin of Indonesian Economic Studies, 2018; 54(1):113-124. Doi:10.1080.00074918.2017.1311007
[3] Arévalo, Rubén, et al., A dynamic trading rule based on filtered flag pattern recognition for stock mar-ket price forecasting, Expert Systems with Applications, 2017; 81 :177-192.
[4] Beck, Thorsten, and Ross Levine,Stock markets, banks, and growth: Panel evidence, Journal of Bank-ing and Finance,2004; 28(3 ): 423-442.
[5] Cavalcante, Rodolfo C., et al.,Computational intelligence and financial markets: A survey and future directions, Expert Systems with Applications ,2016; 55:194-211.
[6] Cristelli, Matthieu, Complexity in financial markets: modeling psychological behavior in agent-based models and order book models, Springer Science and Business Media, 2013.
[7] Deng, Li, and Dong Yu.,Foundations and Trends in Signal Processing: DEEP LEARNING–Methods and Applications,2014.
[8] Erdem, Ekrem, and Recep Ulucak, Efficiency of stock exchange markets in G7 countries: bootstrap causality approach, Economics World, 2016; 4(1): 17-24.
[9] Eugene, F. Fama, Efficient capital markets: A review of theory and empirical work, The Journal of Finance, 1970; 25(2): 383.
[10] Faghihi Nezhad, M. T., Minaei, B., Prediction of Stock Market Behavior Based on Artificial Neural Networks through Intelligent Ensemble Learning Approach, Industrial Management Journal, 2018; 10(2): 315-334, (in Persian).
[11] Fama, Eugene F.,Random walks in stock market prices, Financial analysts journal , 1995; 51(1): 75-80.
[12] Hiransha, Ma, et al.,NSE stock market prediction using deep-learning models , Procedia computer science 132 2018: 1351-1362.
[13] Jarrahi, M. H., Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making, Business horizons, 2018; 61(4): 577-586.
[14] Johnson, Neil F., Paul Jefferies, and Pak Ming Hui., Financial market complexity, OUP Cata-logue 2003
[15] Klashanov, Fedor,Artificial intelligence and organizing decision in construction ,Procedia Engineer-ing ,2016; 165: 1016-1020.
[16] Konak, Fatih, and Yasin Şeker,The efficiency of developed markets: Empirical evidence from FTSE 100,Journal of Advanced Management Science, 2014; 2(1)
[17] Laurent, Alexandre,La guerre des intelligences, Intelligence artificielle versus intelligence humaine, Paris: JC Lattès ,2017; 131-133.
[18] Levine, Ross, Financial development and economic growth: views and agenda, Journal of economic literature, 1997; 35)2(: 688-726.
[19] Lin, Lin, et al.,Random forests-based extreme learning machine ensemble for multi-regime time series prediction, Expert Systems with Applications ,2017; 83: 164-176.
[20] Mallikarjuna, Mejari, and R. Prabhakara Rao,Evaluation of forecasting methods from selected stock market returns,Financial Innovation ,2019; 5)1(: 1-16.
[21] Nguyen, Thien Hai, Kiyoaki Shirai, and Julien Velcin., Sentiment analysis on social media for stock movement prediction, Expert Systems with Applications,2015 ; 42)24): 9603-9611.
[22] Pan, Yunhe,Heading toward artificial intelligence 2.0, Engineering 2016; 2(4): 409-413.
[23] Park, Cheol‐Ho, and Scott H. Irwin,What do we know about the profitability of technical analy-sis?, Journal of Economic surveys ,2007 ;21(4): 786-826.
[24] Rajan, Raghuram, and Luigi Zingales,Financial dependence and growth, 1996
[25] Rousseau, Peter L., and Paul Wachtel,Equity markets and growth: Cross-country evidence on timing and outcomes, 1980–1995, Journal of Banking and Finance ,2000; 24(12): 1933-1957.
[26] Ruan, Qingsong, et al.,A new investor sentiment indicator (ISI) based on artificial intelligence: A powerful return predictor in China, Economic Modelling ,2020; 88: 47-58.
[27] Sharif far, A., Khalili Araghi, M., Raeesi Vanani, I., Fallah, M. ,The Assessment of the optimal Deep Learning Algorithm on Stock Price Prediction (Long Short-Term Memory Approach). Financial Engineer-ing and Portfolio Management, 2021; 12(48): 348-370, (in Persian).
[28] Siva Kiran Guptha, K., and R. Prabhakar Rao.,The causal relationship between financial development and economic growth: an experience with BRICS economies, Journal of Social and Economic Develop-ment, 2018; 20(2): 308-326.
[29] Snow, Charles C., Øystein Devik Fjeldstad, and Arthur M. Langer, Designing the digital organiza-tion, Journal of organization Design, 2017; 6(1): 1-13.
[30] Syam, Niladri, and Arun Sharma,Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice, Industrial marketing manage-ment , 2018; 69: 135-146.
[31] Tkáč, Michal, and Robert Verner,Artificial neural networks in business: Two decades of re-search, Applied Soft Computing , 2016; 38: 788-804.
[32] Tong, Tong, Bin Li, and Omar Benkato,Revisiting the weak form efficiency of the Australian stock market, Corp Ownersh Control, 2014; 11(2): 21-28.
[33] Vijh, Mehar, et al.,Stock closing price prediction using machine learning techniques, Procedia com-puter science , 2020; 167: 599-606.
[34] Wieland, Oxana L.,Modern financial markets and the complexity of financial innovation, Universal Journal of Accounting and Finance ,2015; 3(3): 117-125.
[35] Zhong, Xiao, and David Enke, Forecasting daily stock market return using dimensionality reduction ,Expert Systems with Applications , 2017; 67: 126-139.
[36] Farzad, Malekian, Fakhari and Ghasemi, Predict the Stock price crash risk by using firefly algorithm and comparison with regression, Advances in mathematical finance and applications, 2018; 5: 43-58.
