A Comparison of Linear Regression, LSTM model and ARIMA model in Predicting Stock Price A Case Study: HSBC’s Stock Price

Shiyue Kuang

doi:10.54691/bcpbm.v44i.4858

Authors

Shiyue Kuang

DOI:

https://doi.org/10.54691/bcpbm.v44i.4858

Keywords:

Linear regression; LSTM model; ARIMA model; stock price prediction; HSBC.

Abstract

It is widely assumed that the stock market is a key aspect of the financial market and stock price forecasting has become a popular topic of an in-depth study by financial technologists. With the advent of financial markets, stock prices face several analytical and forecasting challenges. In this paper, the stock price trend forecast analysis is carried out and the survey object was the transaction data of HSBC from 2010 to 2019. Linear regression, LSTM model and ARIMA model are used to forecast stock price trends. The models’ prediction accuracy is demonstrated after the cross-validation process by combining error indicators and trading performance. As a consequence of the analysis, we discover that LSTM has the lowest error value, which implies that it is ideally suited to stock market forecasting. On the contrary, linear regression holds the largest mean square error, implying that this method has the weakest fitting for the stock market.

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References

Berry, M. J., & Linoff, G. S. (2004). Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.

Maulud, D., & Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 1 (4), 140 - 147.

Nelson, D. M., Pereira, A. C., & De Oliveira, R. A. (2017, May). Stock market's price movement prediction with LSTM neural networks. In 2017 International joint conference on neural networks (IJCNN) (pp. 1419-1426). IEEE.

Chen, S., Lan, X., Hu, Y., Liu, Q., & Deng, Y. (2014). The time series forecasting: from the aspect of network. arXiv preprint arXiv: 1403.1713.

Mondal, P., Shit, L., & Goswami, S. (2014). Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications, 4 (2), 13.

Seethalakshmi, R. (2018). Analysis of stock market predictor variables using linear regression. International Journal of Pure and Applied Mathematics, 119 (15), 369 - 378.

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.

Roondiwala, M., Patel, H., & Varma, S. (2017). Predicting stock prices using LSTM. International Journal of Science and Research (IJSR), 6 (4), 1754 - 1756.

Jarrett, J. E., & Kyper, E. (2011). ARIMA modeling with intervention to forecast and analyze Chinese stock prices. International Journal of Engineering Business Management, 3 (3), 53 - 58.

Refaeilzadeh, P., Tang, L., Liu, H., Liu, L., & Özsu, M. T. (2009). Encyclopedia of database systems. In Cross-validation (pp. 532-538). Springer.

Gharehchopogh, F. S., Bonab, T. H., & Khaze, S. R. (2013). A linear regression approach to prediction of stock market trading volume: a case study. International Journal of Managing Value and Supply Chains, 4 (3), 25.

Siami-Namini, S., & Namin, A. S. (2018). Forecasting economics and financial time series: ARIMA vs. LSTM. arXiv preprint arXiv:1803. 06386.

Wenjuan Ding. (2021). Comparison of ARIMA Model and LSTM Model Based on Stock Forecast. Industrial control computers, 34 (7), 109 - 112.