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

Advances in Mathematical Finance and Applications

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 02 بهمن 1402

نوع مقاله: َApplied-Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2023.1952556.1698

نویسندگان

Ehsan Taieby sani^{* 1}؛ Hossein Ameri²

¹NO26.,31th St Yousef Abad Av

²Graduated, M.A student ,kharazmi University

چکیده

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.

کلیدواژه‌ها

abnormalities؛ price gaps؛ patterns؛ Heteroscedasticity