Comparing the performance of the Auto-Regressive Integrated Moving Average (ARIMA) method with that of the Recursive Neural Network (RNN) of long-short term memory (LSTM) in forecasting stock price
الموضوعات :Ehsan Taieby sani 1 , Hossein Ameri 2
1 - Department of Finance, Faculty of Financial Sciences, Kharazmi University, Tehran, Iran
2 - Department of Finance, Faculty of Financial Sciences, Kharazmi University, Tehran, Iran
الکلمات المفتاحية: Price gaps, Abnormalities, Heteroscedasticity, Patterns,
ملخص المقالة :
In this research, due to the importance of investing and especially investing in the stock market, we predicted the stock price return on the stock exchange through the Auto-Regressive Integrated Moving Average (ARIMA) and Recursive Neural Network (RNN) of long-short term memory (LSTM). Then, to reduce the risk of decision-making, we compared the predictive power of these two models to determine a better model. The research variable is the stock price of the top 20 (in market cap) companies on the stock exchange for the period of the 11th Feb 2015 to 22th Jan 2022. We considered the data of the last 10 days as experimental data and the previous data as educational data. Initially, we calculated the mean and standard deviation of the prediction error of both models; these criteria had less value for the LSTM recursive neural network model than the ARIMA model. To measure the significance of this difference in predictive power, we used Harvey, Liborne, and New Bold tests. The results showed that in predicting the stock prices of the top 20 companies of the stock exchange, the predictive power of the LSTM recursive neural network model was statistically and significantly higher than the ARIMA model which means better predition of stock prices and higher return for investors. In the end, it is believed that the LSTM model may have the best predictive ability, but it is greatly affected by the data processing.
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