Performance Evaluation of Long Short-Term Memory and Autoregressive Integrated Moving Average Time Series Models for Stock Price Prediction
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Abstract: Stock price prediction is a critical task in financial markets, often complicated by the challenges of modeling highly volatile, non-linear, and dynamic time series data. This study evaluates the performance of two prominent forecasting models: Long Short-Term Memory (LSTM) networks, known for their ability to capture long-term dependencies and non-linear patterns, and the Auto-Regressive Integrated Moving Average (ARIMA), a traditional statistical model adept at linear trend modeling.
Historical stock price data from the Nigerian Exchange Limited (NGX) was utilized. Both models were implemented in a Python environment, and their predictive accuracy was assessed using performance metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). Additionally, the Diebold-Mariano (DM) test was employed to statistically compare the models’ predictive accuracies.
This study highlights the potential of LSTM models for robust stock price forecasting and provides valuable insights for selecting predictive models based on data complexity and market dynamics.
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Abubakar S. M, Adeboye K. R (2014) An intense Nigerian stock exchange market prediction using logistic with back-propagation ANN model. Science World Journal.
Adebiyi A. A, Ayo C. K, Adebiyi M. O, Otokiti S. O (2012) Stock prediction using neural network with hybridized market indicators. Journal of Emerging Trends in computing and Information Science. 3:1.
Adebola K., and Ifechukwude Jude Okafor (2024) “Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique”. Journal of Advances in Mathematics and Computer Science 39 (7):45-54. DOI: https://doi.org/10.9734/jamcs/2024/v39i71911
Adetunji A. B, Ganiyu A. A, Omidiora E. O and Adigun A. A (2013) Forecasting Movement of the Nigerian Stock Exchange All Share Index using Artificial Neural and Bayesian Networks. Journal of Finance and Investment Analysis, vol. 2, no.1, 2013, 41-59 ISSN: 2241-0998 (print version), 2241-0996(online) Scienpress Ltd, 2013
Appel, G (2005) Technical analysis: power tools for active investors: FT Press
Biondo AE, Pluchino A, Rapisarda A, Helbing D (2013) Are random trading strategies more successful than technical ones? PloS one 8(7):e68344 Applying machine learning algorithms to predict DOI: https://doi.org/10.1371/journal.pone.0068344
Borovkova S, Tsiamas I (2019) An ensemble of LSTM neural networks for high frequency stock market classification. J Forecast 38(6):600–619 DOI: https://doi.org/10.1002/for.2585
Brown S. J, Goetzmann WN, Kumar A (1998) The Dow theory: William Peter Hamilton’s track record reconsidered. J Financ 53(4):1311–1333 DOI: https://doi.org/10.1111/0022-1082.00054
Chanddrika P. V, Sreenvasan K. S (2020) Application of deep learning techniques on stock market indices. Journal of scientific Engineering. 7:10.
El-Nagar A. M, Zaki A. M, Soliman FAS, El-Bardini M (2022) Hybrid deep learning diagonal recurrent neural network controller for nonlinear systems. Neural Comput Appl 34(24):22367–22386. https://doi.org/10.1007/s00521-022-07673-9 DOI: https://doi.org/10.1007/s00521-022-07673-9
Fromlet, H. (2001) Behavioral finance-theory and practical application: Systematic analysis of departures from the homo oeconomicus paradigm are essential for realistic financial research and analysis. Business Economics, 63–69
Ibidapo I, Adebiyi A, Okesola O (2017). Soft computing technique for stock market prediction: A literature survey. Covenant Journal of Informatics and Communication Technology.
Le TTH, Nguyen V. C, Phan THN (2022) Foreign Direct Investment, Environmental Pollution and Economic Growth—An Insight from Non-Linear ARDL Co-Integration Approach. Sustainability 14(13):8146. https://doi.org/ 10.3390/su14138146. Retrieved from learning a review paper. Int J Computer Appl 163(5):36–43 DOI: https://doi.org/10.3390/su14138146
Neenwi S, Asagba P. O, Kabari L. G (2013) Predicting the Nigerian stock market using artificial neural network. European Journal of Computer Science and Information 1(1):30-39.
Nelson, D. M, Pereira, A. C, and de Oliveira, R. A (2017) Stock market’s price movement prediction with LSTM neural networks. Paper presented at the 2017 International joint conference on neural networks (IJCNN) of Vietnam DOI: https://doi.org/10.1109/IJCNN.2017.7966019
Phuoc, T., Anh, P.T.K., Tam, P.H. et al., Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam. Humanit Soc Sci Commun 11, 393 (2024). https://doi.org/10.1057/s41599-024-02807-x DOI: https://doi.org/10.1057/s41599-024-02807-x
Roman J, Jameel A (1996) Backpropagation and recurrent neural networks in financial analysis of multiple stock market returns. Proc HICSS-29: 29th Hawaii Int Conf Syst Sci 2:454–460 vol.2. https://doi.org/10.1109/HICSS.1996.495431. DOI: https://doi.org/10.1109/HICSS.1996.495431
Trinh H. H, Nguyen C. P, Hao W, Wongchoti U (2021) Does stock liquidity affect bankruptcy risk? DID analysis from Vietnam. Pac-Basin Financ J 69:101634. DOI: https://doi.org/10.1016/j.pacfin.2021.101634
Zhuge Q, Xu L, Zhang G (2017) LSTM Neural Network with Emotional Analysis Stock Price - Definition, Price Changes, How to Determine (corporatefinanceinstitute.com)
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