{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:07:13Z","timestamp":1774368433760,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T00:00:00Z","timestamp":1629590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Investors in the stock market have always been in search of novel and unique techniques so that they can successfully predict stock price movement and make a big profit. However, investors continue to look for improved and new techniques to beat the market instead of old and traditional ones. Therefore, researchers are continuously working to build novel techniques to supply the demand of investors. Different types of recurrent neural networks (RNN) are used in time series analyses, especially in stock price prediction. However, since not all stocks\u2019 prices follow the same trend, a single model cannot be used to predict the movement of all types of stock\u2019s price. Therefore, in this research we conducted a comparative analysis of three commonly used RNNs\u2014simple RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)\u2014and analyzed their efficiency for stocks having different stock trends and various price ranges and for different time frequencies. We considered three companies\u2019 datasets from 30 June 2000 to 21 July 2020. The stocks follow different trends of price movements, with price ranges of $30, $50, and $290 during this period. We also analyzed the performance for one-day, three-day, and five-day time intervals. We compared the performance of RNN, LSTM, and GRU in terms of R2 value, MAE, MAPE, and RMSE metrics. The results show that simple RNN is outperformed by LSTM and GRU because RNN is susceptible to vanishing gradient problems, while the other two models are not. Moreover, GRU produces lesser errors comparing to LSTM. It is also evident from the results that as the time intervals get smaller, the models produce lower errors and higher reliability.<\/jats:p>","DOI":"10.3390\/a14080251","type":"journal-article","created":{"date-parts":[[2021,8,22]],"date-time":"2021-08-22T21:42:12Z","timestamp":1629668532000},"page":"251","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5054-6835","authenticated-orcid":false,"given":"Polash","family":"Dey","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Port City International University, Chittagong 4209, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6422-1895","authenticated-orcid":false,"given":"Emam","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Port City International University, Chittagong 4209, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2944-4065","authenticated-orcid":false,"given":"Md. Ishtiaque","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9153-5849","authenticated-orcid":false,"given":"Mohammed Armanuzzaman","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0778-754X","authenticated-orcid":false,"given":"Md. Shariful","family":"Alam","sequence":"additional","affiliation":[{"name":"Department of Information & Communication Technology, Chattogram Cantonment Public College, Chittagong 4311, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7473-8185","authenticated-orcid":false,"given":"Mohammad Shahadat","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0244-3561","authenticated-orcid":false,"given":"Karl","family":"Andersson","sequence":"additional","affiliation":[{"name":"Pervasive and Mobile Computing Laboratory, Lule\u00e5 University of Technology, S-931 87 Skellefte\u00e5, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,22]]},"reference":[{"key":"ref_1","unstructured":"(2021, April 24). Statista. Largest Stock Exchange Operators Worldwide as of January 2021, by Market Capitalization of Listed Companies (in Trillion U.S. dollars). Available online: https:\/\/www.statista.com\/statistics\/270126\/largest-stock-exchange-operators-by-market-capitalization-of-listed-companies."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hossain, E., Shariff, M.A.U., Hossain, M.S., and Andersson, K. (2021, January 21\u201322). A Novel Deep Learning Approach to Predict Air Quality Index. Proceedings of the International Conference on Trends in Computational and Cognitive Engineering, Parit Raja, Malaysia.","DOI":"10.1007\/978-981-33-4673-4_29"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Saiful Islam, M., and Hossain, E. (2021, July 10). Foreign Exchange Currency Rate Prediction using a GRU-LSTM Hybrid Network. Available online: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2666222120300083.","DOI":"10.1016\/j.socl.2020.100009"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Islam, M., Hossain, E., Rahman, A., Hossain, M.S., and Andersson, K. (2020). A review on recent advancements in forex currency prediction. Algorithms, 13.","DOI":"10.3390\/a13080186"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., and Tang, X. (2014, January 23\u201328). Deep learning face representation from predicting 10,000 classes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.244"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yong, B.X., Rahim, M.R.A., and Abdullah, A.S. (2017, January 27\u201329). A stock market trading system using deep neural network. Proceedings of the Asian Simulation Conference, Melaka, Malaysia.","DOI":"10.1007\/978-981-10-6463-0_31"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"383","DOI":"10.2307\/2325486","article-title":"Efficient Capital Markets: A Review of Theory and Empirical Work","volume":"25","author":"Fama","year":"1970","journal-title":"J. Financ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1111\/j.1540-6261.1991.tb04636.x","article-title":"Efficient Capital Markets: II","volume":"46","author":"Fama","year":"1991","journal-title":"J. Financ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ferreira, P., Pereira, E.J., and Pereira, H.B. (2020). From Big Data to Econophysics and Its Use to Explain Complex Phenomena. J. Risk Financ. Manag., 13.","DOI":"10.3390\/jrfm13070153"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wu, D., Wang, X., and Wu, S. (2021). A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction. Entropy, 23.","DOI":"10.3390\/e23040440"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ecer, F., Ardabili, S., Band, S.S., and Mosavi, A. (2020). Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction. Entropy, 22.","DOI":"10.3390\/e22111239"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Vin\u021be, C., Ausloos, M., and Furtun\u0103, T.F. (2021). A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model. Entropy, 23.","DOI":"10.3390\/e23040484"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Abraham, C.M., Elayidom, M.S., and Santhanakrishnan, T. (2019). Analysis and Design of an Efficient Temporal Data Mining Model for the Indian Stock Market. Emerging Technologies in Data Mining and Information Security, Springer.","DOI":"10.1007\/978-981-13-1498-8_54"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","article-title":"Deep learning with long short-term memory networks for financial market predictions","volume":"270","author":"Fischer","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Parmar, I., Agarwal, N., Saxena, S., Arora, R., Gupta, S., Dhiman, H., and Chouhan, L. (2018, January 15\u201317). Stock market prediction using Machine Learning. Proceedings of the 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, India.","DOI":"10.1109\/ICSCCC.2018.8703332"},{"key":"ref_17","unstructured":"Engle, R.F., and Granger, C. (2003). Time-series econometrics: Cointegration and autoregressive conditional heteroskedasticity. Advanced information on the Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel, Royal Swedish Academy of Sciences."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cakra, Y.E., and Trisedya, B.D. (2015, January 10\u201311). Stock price prediction using linear regression based on sentiment analysis. Proceedings of the 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), Depok, Indonesia.","DOI":"10.1109\/ICACSIS.2015.7415179"},{"key":"ref_19","first-page":"130","article-title":"Forecasting stock prices through univariate ARIMA modeling","volume":"13","author":"Afeef","year":"2018","journal-title":"NUML Int. J. Bus. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., and Salwana, E. (2020). Deep learning for stock market prediction. Entropy, 22.","DOI":"10.20944\/preprints202003.0256.v1"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"B\u00f6rjesson, L., and Singull, M. (2020). Forecasting Financial Time Series through Causal and Dilated Convolutional Neural Networks. Entropy, 22.","DOI":"10.3390\/e22101094"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.knosys.2018.10.034","article-title":"Deep learning-based feature engineering for stock price movement prediction","volume":"164","author":"Long","year":"2019","journal-title":"Knowledge-Based Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1287\/inte.31.4.112.9662","article-title":"An analysis of the applications of neural networks in finance","volume":"31","author":"Fadlalla","year":"2001","journal-title":"Interfaces"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1007\/s11831-020-09448-8","article-title":"A comprehensive survey on portfolio optimization, stock price and trend prediction using particle swarm optimization","volume":"28","author":"Thakkar","year":"2020","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_25","first-page":"1","article-title":"Impact of political influences on stock returns","volume":"1","author":"Maqbool","year":"2018","journal-title":"Int. J. Multidiscip. Sci. Publ. (IJMSP)"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.cose.2015.12.006","article-title":"The impact of information security events to the stock market: A systematic literature review","volume":"58","author":"Spanos","year":"2016","journal-title":"Comput. Secur."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Baker, S.R., Bloom, N., Davis, S.J., and Kost, K.J. (2019). Policy News and Stock Market Volatility, National Bureau of Economic Research. Technical Report.","DOI":"10.3386\/w25720"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Christiano, L., Ilut, C.L., Motto, R., and Rostagno, M. (2010). Monetary Policy and Stock Market Booms, National Bureau of Economic Research. Technical Report.","DOI":"10.3386\/w16402"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.1016\/j.procs.2020.03.224","article-title":"KBC: Multiple Key Generation using Key Block Chaining","volume":"167","author":"Prajapati","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chaudhari, K., and Prajapati, P. (2020). Parallel DES with Modified Mode of Operation. Intelligent Communication, Control and Devices, Springer.","DOI":"10.1007\/978-981-13-8618-3_84"},{"key":"ref_31","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/72.279188","article-title":"Recurrent neural networks and robust time series prediction","volume":"5","author":"Connor","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mei, F., Chen, H., and Lei, Y. (2021). Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network. Sensors, 21.","DOI":"10.3390\/s21113884"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mateus, B.C., Mendes, M., Farinha, J.T., and Cardoso, A.M. (2021). Anticipating Future Behavior of an Industrial Press Using LSTM Networks. Appl. Sci., 11.","DOI":"10.3390\/app11136101"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sagheer, A., Hamdoun, H., and Youness, H. (2021). Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series. Sensors, 21.","DOI":"10.3390\/s21134379"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xu, L., and Hu, J. (2021). A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks. Appl. Sci., 11.","DOI":"10.3390\/app11146387"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, C., Yang, X., Peng, S., Zhang, Y., Peng, L., and Zhong, R.Y. (2021). Spark Analysis Based on the CNN-GRU Model for WEDM Process. Micromachines, 12.","DOI":"10.3390\/mi12060702"},{"key":"ref_38","unstructured":"(2021, April 24). Honda Motor Company (HMC) Stock Price. Available online: https:\/\/finance.yahoo.com\/quote\/HMC?p=HMC&.tsrc=fin-srch."},{"key":"ref_39","unstructured":"(2021, April 24). Oracle Corporation (ORCL) Stock Price. Available online: https:\/\/finance.yahoo.com\/quote\/ORCL?p=ORCL&.tsrc=fin-srch."},{"key":"ref_40","unstructured":"(2021, April 24). Intuit Inc. (INTU) Stock Price. Available online: https:\/\/finance.yahoo.com\/quote\/INTU?p=INTU&.tsrc=fin-srch."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Swamidass, P.M. (2000). Mean Absolute Percentage Error (MAPE). Encyclopedia of Production and Manufacturing Management, Springer.","DOI":"10.1007\/1-4020-0612-8_580"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Graves, A. (2012). Long short-term memory. Supervised Sequence Labelling with Recurrent Neural Networks, Springer.","DOI":"10.1007\/978-3-642-24797-2"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/8\/251\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:49:16Z","timestamp":1760165356000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/8\/251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,22]]},"references-count":42,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["a14080251"],"URL":"https:\/\/doi.org\/10.3390\/a14080251","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,22]]}}}