Deep learning models for stock prediction on diverse datasets
Keywords:
Stock Prediction, LSTM, CNN, RMSE, Deep LearningAbstract
Market forecasting has attracted the interest of investors all over the world. The investors are looking for an accurate and reliable forecasting model that can fully embrace the extremely volatile and nonlinear market behavior. It is now possible to design effective stock price prediction algorithms due to the abundance of data, the quick advancement of AI and machine learning techniques, and the machine's increased computational capability. Deep learning algorithms are particularly successful in modelling market volatility. To forecast the closing prices of three stocks: Apple (AAPL), Google (GOOG), and Amazon (AMZN), Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) are implemented and compared. The stock data was obtained from yahoo finance for one year, three years and five years. The Root Mean Square Error (RMSE) metric and loss are employed for evaluating the model’s performance.
References
Bao, W., Yue, J., Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLOSONE,12(7), 1-24. https://doi.org/10.1371/journal.pone.0180944
Baek, Y. Kim, H.Y. (2018). ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Syst. Appl., 113, 457–480. https://doi.org/10.1016/j.eswa.2018.07.019
Bhardwaj, G., Swanson, N. R. (2006). An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series. Journal of Econometrics,131(1–2),539–578. RePEc:eee:econom:v:131:y:2006:i:1-2:p:539-578
Cao, J., Li, Z., Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM. Phys. A, Stat. Mech. Appl.,519,127–139. https://doi.org/10.1016/j.physa.2018.11.061
Darrat, F., Zhong, M. (2000). On testing the random-walk hypothesis: A model-comparison approach. Financial Rev., 35(3),105–124. https://EconPapers.repec.org/RePEc:bla:finrev:v:35:y:2000:i:3:p:105-24
Drashti, T., Miral.P., Bhargesh, P. (2022). Stock Market Prediction Using LSTM Technique. International Journal for Research in Applied Science & Engineering Technology,10(6),1820-1828. https://doi.org/10.22214/ijraset.2022.47967
Fischer, T., Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res., 270(2),654–669. Doi: https:// 10.1016/j.ejor.2017.11.054
Ghosh, P., Neufeld, A., Sahoo, J. (2021). Forecasting directional movements of stock prices for intraday trading using LSTM and random forests. Financial Research Letters, 1-8. https://doi.org/10.1016/j.frl.2021.102280
Guo, Z., Wang H., Liu, Q., Yang, J. (2014). A feature fusion-based forecasting model for financial time series. Journal of Public Library of Science, 9, 1-13. https://doi.org/10.1371/journal.pone.0101113
Henrique, B.M., Sobreiro, V.A., Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Syst. Appl.,124, 226–251. https://doi.org/10.1016/j.eswa.2019.01.012
Hiransha, M., Gopalakrishnan, E.A., Menon, V.K., Soman K.P. (2018). NSE stock market prediction using deep-learning models. Procedia Computer Science, 132, 1351–1362. https://doi.org/10.1016/j.procs.2018.05.050
Li, Z., Tam, V. (2018). A machine learning view on momentum and reversal trading,’’ MDPI Journal of Algorithms,11(11),1-16. https://doi.org/10.3390/a11110170
Mehar, V,. Deeksha, C., Vinay, T., Arun, T.(2020). Stock Closing Price Prediction using Machine Learning Techniques. Procedia Computer Science 167 (2020) 599–606. https://10.1016/j.procs.2020.03.326
Nandakumar, R., Uttamraj, K., Vishal, R., Lokeswari, Y. V. (2018). Stock Price Prediction Using Long Short-Term Memory, Computer Science, 5(3), 3342-3348.
Patel, J., Patel, M., Darji, M. (2018). Stock Price Prediction Using RNN and LSTM. Journal of Emerging Technologies and Innovative Research,5(11),1069-1079. http://www.jetir.org/papers/JETIRK006164.pdf
Pramod, B.S., Mallikarjuna, S. P. M. (2020). Stock Price Prediction Using LSTM. The Mattingley Publishing Co., Inc, 83, 5246-5251.
Roondiwala, M., Patel, H., Varma, S. (2017). Predicting stock prices using LSTM. International Journal of Science and Research (IJSR), 6(4), 1754–1756. DOI: 10.21275/ART20172755
Shah, A., Gor, M., Meet, S., Shah, M. (2022). A stock market trading framework based on deep learning architectures. Multimedia Tools and Applications, 81,14153–14171. https://doi.org/10.1007/s11042-022-12328-x
Xiaojian, Z. (2023). Stock price prediction based on CNN model for Apple, Google and Amazon. BCP Business & Management, EMFRM 2022, volume (38).
Zhichao, Z., Zihao, Q. (2020). Using LSTM in Stock prediction and Quantitative Trading. Stanford University, 1-6.
Moghar, A., Mhamed, H. (2020). Stock Market Prediction Using LSTM Recurrent Neural Network. International Workshop on Statistical Methods and Artificial Intelligence,1168-1173. https:// 10.1016/j.procs.2020.03.049
Yulian, W., Peiguang, L., Xiushan, N. (2020). Research of Stock Price Prediction Based on PCA-LSTM Model. IOP Conf. Series: Materials Science and Engineering,1-6. doi:10.1088/1757-899X/790/1/012109
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