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
(
Department of Finance, Faculty of Financial Sciences, Kharazmi University, Tehran, Iran
)
Hossein Ameri
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.
[1] ElAal, M.M,, Selim, G., Fakhr, W., Stock market trend prediction model for the Egyptian stock market using neural networks and fuzzy logic, InBio-Inspired Computing and Applications: 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou, China, August 11-14. 2011; Revised Selected Papers 7 2012: 85-90. Springer Berlin Heidelberg. doi: 10.1007/978-3-642-24553-4_13
[2] Adebiyi, A.A., Adewumi, A.O., Ayo, C.K., Comparison of ARIMA and artificial neural networks models for stock price prediction, Journal of Applied Mathematics, 2014 Mar 5; 2014. doi: 10.1155/2014/614342
[3] AhmadKhanBeygi, S., Abdolvand, N., Stock Price Prediction Modeling Using Artificial Neural Network Approach and Imperialist Competitive Algorithm Based On Chaos Theory, Financial Management Strategy, 2017; 5(3): 27-73. doi: 10.22051/jfm.2017.14635.1319
[4] Ariyo, A.A., Adewumi, A.O., Ayo, C.K., Stock price prediction using the ARIMA model, In2014 UKSim-AMSS 16th international conference on computer modelling and simulation, 2014 Mar 26: 106-112. IEEE.
[5] Babajani, J., Taghva, M., Blue, G., Abdollahi, M., Forecasting Stock Prices In Tehran Stock Exchange Using Recurrent Neural Network Optimized by Artificial Bee Colony Algorithm, Financial Management Strategy, 2019; 7(2): 195-228. doi: 10.22051/jfm.2019.21049.1714
[6] Babu, C.N., Reddy, B.E., Performance comparison of four new ARIMA-ANN prediction models on internet traffic data, Journal of telecommunications and information technology, 2015; (1): 67-75.
[7] Bayat, A., Bagheri, Z., The Predication of Stock Price Using Firely Algorithm, Financial Knowledge of Securities Analysis, 2017; 10(35): 135-145.
[8] Becker, R., Clements A.E., Are combination forecasts of S&P 500 volatility statistically superior?. International Journal of Forecasting, 2008 Jan 1; 24(1):122-33. doi: 10.1016/j.ijforecast.2007.09.001
[9] Engle, RF., Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica: Journal of the econometric society, 1982 Jul 1:987-1007. doi: 10.2307/1912773
[10] Freisleben, B., Ripper, K., Volatility estimation with a neural network. InProceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr), 1997 Mar 24, 177-181. IEEE. doi: 10.1109/CIFER.1997.618932
[11] Hafezi, R., Shahrabi, J., Hadavandi, E., A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price, Applied Soft Computing, 2015 Apr 1; 29: 196-210. doi: 10.1016/j.asoc.2014.12.028
[12] Hajizadeh, E., Seifi, A., Zarandi, MF., Turksen, IB., A hybrid modeling approach for forecasting the volatility of S&P 500 index return, Expert Systems with Applications, 2012 Jan 1; 39(1): 431-6. doi: 10.1016/j.eswa.2011.07.033
[13] Hamid, SA., Iqbal, Z., Using neural networks for forecasting volatility of S&P 500 Index futures prices, Journal of Business Research, 2004 Oct 1; 57(10):1116-25. doi: 10.1016/S0148-2963(03)00043-2
[14] Harvey, D., Leybourne, S., Newbold, P., Testing the equality of prediction mean squared errors, International Journal of forecasting, 1997 Jun 1;13(2): 281-91. doi: 10.1016/S0169-2070(96)00719-4
[15] Hochreiter, S., Schmidhuber, J., Long short-term memory, Neural computation. 1997 Nov 15; 9(8):1735-80. doi: 10.1162/neco.1997.9.8.1735
[16] Kaastra, I., Boyd, M., Designing a neural network for forecasting financial and economic time series. Neurocomputing. 1996 Apr 1;10(3):215-36. doi:10.1016/0925-2312(95)00039-9
[17] Khuat, TT., Le, MH., An application of artificial neural networks and fuzzy logic on the stock price prediction problem. JOIV: International Journal on Informatics Visualization. 2017 Apr 17; 1(2): 40-9.doi: 10.30630/joiv.1.2.20
[18] Laboissiere, L.A., Fernandes, R.A., Lage, G.G., Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks, Applied Soft Computing, 2015 Oct 1; 35: 66-74. doi:10.1016/j.asoc.2015.06.005
[19] Lei, L., Wavelet neural network prediction method of stock price trend based on rough set attribute reduction, Applied Soft Computing, 2018 Jan 1;62:923-32. doi: 10.1016/j.asoc.2017.09.029
[20] Ma, Q., Comparison of ARIMA, ANN and LSTM for stock price prediction. InE3S Web of Conferences, 2020; 218: 01026. EDP Sciences.
[21] Monajemi, S.A. H., Abzari, M., Shavazi, R., A., Predicting stock prices in the stock market using fuzzy neural network and genetic algorithms and comparing it with an artificial neural network, Quantitative economics (Economic studies), 2009. doi: 10.22055/jqe.2009.10697
[22] Mpofu, N., Forecasting stock prices using a weightless neural network, Journal of Sustainable Development in Africa, 2006; 8(1): 115-9.
[23] Nasirzadeh, F., Evaluating the ability of data mining models to predict stock prices, Eleventh National Accounting Conference of Iran. 2012
[24] Roh, TH., Forecasting the volatility of stock price index, Expert Systems with Applications. 2007 Nov 1; 33(4): 916-22. doi:10.1016/j.eswa.2006.08.001
[25] Schöneburg, E., Stock price prediction using neural networks: A project report. Neurocomputing. 1990 Jun 1; 2(1): 17-27. doi: 10.1016/0925-2312(90)90013-H
[26] Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., Menon, V.K., Soman, K.P., Stock price prediction using LSTM, RNN and CNN-sliding window model. In2017 international conference on advances in computing, communications and informatics (icacci), 2017 Sep 13:1643-1647. IEEE. doi: 10.1109/ICACCI.2017.8126078
[27] Weng, B., Lu, L., Wang, X., Megahed, F.M., Martinez, W., Predicting short-term stock prices using ensemble methods and online data sources, Expert Systems with Applications. 2018 Dec 1;112: 258-73. doi: 10.1016/j.eswa.2018.06.016
[28] Yu, P., Yan, X., Stock price prediction based on deep neural networks. Neural Computing and Applications. 2020 Mar; 32: 1609-28. doi: 10.1007/s00521-019-04212-x
[29] Heydari Zare, B., Kordloui, H., Stock price prediction using artificial neural network, Researcher Management , Journal of Industrial Strategic Management, 2010; 7(17): 49-56. doi: 10.4108/eai.19-12-2018.156085
[30] Zhang, G.P., Time series forecasting using a hybrid ARIMA and neural network modelm, Neurocomputing, 2003 Jan 1; 50: 159-75.doi: 10.1016/S0925-2312(01)00702-0
