Abstract
Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. Researchers have also worked on technical analysis of stocks with a goal of identifying patterns in the stock price movements using advanced data mining techniques. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records from December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. Using the grid-searching technique, the hyperparameters of the LSTM models are optimized so that it is ensured that validation losses stabilize with the increasing number of epochs, and the convergence of the validation accuracy is achieved. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 open values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week’s open value of the NIFTY 50 time series is the most accurate model.
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References
Sen, J., Datta Chaudhuri, T.: An alternative framework for time series decomposition and forecasting and its relevance for portfolio choice - a comparative study of the Indian consumer durable and small cap sector. J. Econ. Libr. 3(2), 303–326 (2016)
Sen, J., Datta Chaudhuri, T.: An investigation of the structural characteristics of the indian IT sector and the capital goods sector - an application of the R programming language in time series decomposition and forecasting. J. Insur. Financ. Manag. 1(4), 68–132 (2016)
Sen, J., Datta Chaudhuri, T.: Understanding the sectors of indian economy for portfolio choice. Int. J. Bus. Forecast. Mark. Intell. 4(2), 178–222 (2018)
Sen, J., Datta Chaudhuri, T.: A robust predictive model for stock price forecasting. In: Proceedings of the 5th International Conference on Business Analytics and Intelligence, Bangalore, India, 11–13 December 2017 (2017)
Sen, J.: Stock price prediction using machine learning and deep learning frameworks. In: Proceedings of the 6th International Conference on Business Analytics and Intelligence, Bangalore, India, 20–22 December 2018 (2018)
Mehtab, S., Sen, J.: A robust predictive model for stock price prediction using deep learning and natural language processing. In: Proceedings of the 7th International Conference on Business Analytics and Intelligence, Bangalore, India, 5–7 December 2019 (2019)
Mehtab, S., Sen, J.: Stock price prediction using convolutional neural network on a multivariate time series. In: Proceedings of the 3rd National Conference on Machine Learning and Artificial Intelligence (NCMLAI), New Delhi, India, 1 February 2020 (2020)
Mehtab, S., Sen, J.: A time series analysis-based stock price prediction using machine learning and deep learning models. Technical Report, No: NSHM_KOL_2020_SCA_DS_1 (2020). https://doi.org/10.13140/RG.2.2.14022.22085/2
Enke, D., Grauer, M., Mehdiyev, N.: Stock market prediction with multiple regression, fuzzy type-2 clustering, and neural networks. Proc. Comput. Sci. 6, 201–206 (2011)
Ma, J., Liu, L.: Multivariate nonlinear analysis and prediction of shanghai stock market. Discrete Dyn. Nat. Soc. 2008, 1–9 (2008). Article ID: 526734
Khan, U., et al.: A robust regression-based stock exchange forecasting and determination of correlation between stock markets. Sustainability 10, 3702 (2018)
Sharma, V., Khemnar, R., Kumari, R., Mohan, B.R.: Time series with sentiment analysis for stock price prediction. In: Proceedings of the IEEE International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, India (2019)
Ivanovski, Z., Ivanovska, N., Narasanov, Z.: The regression analysis of stock returns at MSE. J. Mod. Account. Audit. 12(4), 217–224 (2016)
Adebiyi, A.A., Adewumi, A.O., Ayo, C.K.: Stock price prediction using the ARIMA model. In: Proceedings of the International Conference on Computer Modelling and Simulation, Cambridge, UK, pp. 105–111 (2014)
Xiao, Y., Xiao, J., Liu, J., Wang, S.: A multiscale modeling approach incorporating ARIMA and ANNs for financial market volatility forecasting. J. Syst. Sci. Complex. 27(1), 225–236 (2014). https://doi.org/10.1007/s11424-014-3305-4
Jammalamadaka, S.R., Qiu, J., Ning, N.: Predicting a stock portfolio with multivariate Bayesian structural time series model: do news or emotions matter? Int. J. Artif. Intell. 17(2), 81–104 (2019)
Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., Menon, V.K., Soman, K.P.: Stock price prediction using LSTM, RNN, and CNN-sliding window model. In: Proceedings of the IEEE International Conference on Advances in Computing, Communications, and Informatics (ICACCI), Udupi, India, pp. 1643–1647 (2017)
Kim, M., Sayama, H.: Predicting stock market movements using network science: an information-theoretic approach. Appl. Netw. Sci. 2, 1–14 (2017). Article No: 35
Wang, Z., Ho, S-B., Lin, Z.: Stock market prediction analysis by incorporating social and news opinion and sentiment. In: Proceedings of the IEEE International Conference on Data Mining Workshops, Singapore (2018)
Porshnev, A., Redkin, I., Shevchenko, A.: Machine learning in prediction of stock market indicators based on historical data and data from Twitter sentiment analysis. In: Proceedings of the IEEE International Conference on Data Mining Workshops, Dallas, TX, USA (2013)
Tang, J., Chen, X.: Stock market prediction based on historic prices and news titles. In: Proceedings of the International Conference on Machine Learning Technologies (ICMLT), Jinan, China, pp. 29–34 (2018)
Obthong, M., Tantisantiwong, N., Jeamwatthanachai, W., Wills, G.: A survey on machine learning for stock price prediction: algorithms and techniques. In: Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business, FEMIB 2020, Prague, Czech Republic, 5–6 May 2020 (2020)
Zhou, J., Fan, P.: Modulation format/bit rate recognition based on principal component analysis (PCA) and artificial neural networks (ANNs). OSA Continuum 2(3), 923–937 (2019)
Yahoo Finance Website. https://in.finance.yahoo.com
Brownlee, J.: Introduction to Time Series Forecasting with Python (2019)
Geron, A.: Hands-on Machine Learning with Scikit-Learn Keras & Tensorflow. O’Reilly Publications, Sebastopol (2019)
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Mehtab, S., Sen, J., Dutta, A. (2021). Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models. In: Thampi, S.M., Piramuthu, S., Li, KC., Berretti, S., Wozniak, M., Singh, D. (eds) Machine Learning and Metaheuristics Algorithms, and Applications. SoMMA 2020. Communications in Computer and Information Science, vol 1366. Springer, Singapore. https://doi.org/10.1007/978-981-16-0419-5_8
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