Predicting Stock Price Crash Risk with a Deep Learning Approach from Artificial Intelligence and Comparing its Efficiency with Classical Predicting Methods.
محورهای موضوعی : Financial Economics
Meysam Rahmati
1
(Department Finance, Faculty of Financial Sciences, Kharazmi University, Tehran, Iran)
Ehsan Taieby Sani
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.
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