Abstract
Stock market forecasting plays a vital role in the decision making of financial firms and investors. This paper focuses and details a comparative study for stock price prediction of Indian industries with stock data from National Stock Exchange (NSE). A lot of research is concentrated for stock forecasting from the last decades which got significance with the emergence of deep learning. The deep learning techniques focused are long short-term memory (LSTM), grated recurrent unit (GRU) and recurrent neural network (RNN). Stock data of automobile and financial industries are taken for analysis. This paper compares the results with ARIMA model, a statistical model for stock prediction as baseline. Mean average percentage error (MAPE) is used as a performance criterion. This work reveals how the investors can make use of deep learning techniques to revise their investment decisions and strategies to hone better returns over time. It helps financial analysts and business communities to make informed decisions.
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References
Ariyo, A.A., Adewumi, A.O., Ayo, C.K.: Stock price prediction using the Arima model. In: 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pp. 106–112. IEEE, New York (2014)
Rather, A.M., Agarwal, A., Sastry, V.: Recurrent neural network and a hybrid model for prediction of stock returns. Exp. Syst. Appl. 42(6), 3234–3241 (2015)
Dass, A., Srivastava, S.: On comparing performance of conventional fuzzy system with recurrent fuzzy system. In: Soft Computing: Theories and Applications, pp. 389–403. Springer, Berlin (2018)
Bhasin, H., Gupta, N.: Critical path problem for scheduling using genetic algorithm. In: Soft Computing: Theories and Applications, pp. 15–24. Springer, Berlin (2018)
Giri, J.P., Giri, P.J., Chadge, R.: Neural network-based prediction of productivity parameters. In: Soft Computing: Theories and Applications, pp. 83–95. Springer, Berlin (2018)
Dhamayanthi, B., Vaiz, J.S., Ramaswami, M.: A study of deep neural networks in stock trend prediction using different activation functions. In: International Conference on Recent Trends in Engineering, Computers, Information Technology and Applications (ICRTECITA-2017)
Chen, L., Qiao, Z., Wang, M., Wang, C., Du, R., Stanley, H.E.: Which artificial intelligence algorithm better predicts the chinese stock market? IEEE Access 6, 48625–48633 (2018)
Nivetha, R.Y., Dhaya, C.: Developing a prediction model for stock analysis. In: 2017 International Conference on Technical Advancements in Computers and Communications (ICTACC), pp. 1–3. IEEE, New York (2017)
Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)
Sharma, Y., Agrawal, G., Jain, P., Kumar, T.: Vector representation of words for sentiment analysis using glove. In: 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT), pp. 279–284. IEEE, New York (2017)
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Barai, A.K., Jain, P., Kumar, T. (2020). NSE Stock Prediction: The Deep Learning Way. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_70
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DOI: https://doi.org/10.1007/978-981-15-4032-5_70
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