Abstract
The stock market is the act of buying and selling the share of the companies and yield more profits. In order to provide the abnormal returns for the company by share market, the prediction of the stock market is necessary. Using deep learning algorithms, this analytics process predicts the daily return direction of the SPDR S&P 500 ETF (ticker symbol: SPY). The deep learning algorithms such as simple recurrent neural network and long short-term memory algorithm are applied to predict the daily direction of future price of S&P 500 based on the historical price. The performance of different procedures such as simple RNN and LSTM is compared. LSTM algorithm is found to be an accurate algorithm when compared to the other algorithms. The simple RNN and LSTM algorithm is implemented on another dataset such as Bombay Stock Exchange (BSE), and the performance of both algorithms is compared and simulated; the result of LSTM is a better algorithm, to predict a stock market daily return.
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References
B.M. Henrique, V.A. Sobreiro, H. Kimura, Stock price prediction using support vector regression on daily and up to the minute prices. J. Finance Data Sci. 4(3) (2018). https://doi.org/10.1016/j.jfds.2018.04.003
A.A. Adebiyi, A.O. Adewumi, C.K. Ayo, Stock price prediction using the ARIMA model, in 16th International Conference on Computer Modelling and Simulation (Cambridge University, UK, 2014). https://doi.org/10.1109/UKSim.2014.67
X. Zhong, D. Enke, Predicting the daily return direction of a stock market using the hybrid machine learning algorithm. Financ. Innov. 5(1) (2019). https://doi.org/10.1186/s40854-019-0138-0
X. Pang, Y. Zhou, P. Wang, W. Lin, V. Chang, An innovative neural network approach for stock market prediction. J. Supercomput. (2018). https://doi.org/10.1007/s11227-017-2228-y
A. Ghosh, S. Bose, G. Maji, N.C. Debnath, S. Sen, Stock price prediction using LSTM on Indian share market, in Proceedings of 32nd International Conference on Computer Applications in Industry and Engineering, EPiC Series in Computing, vol. 63 (2019). https://doi.org/10.29007/qgcz
J. Wang, J. Wang, Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing 15 (2015). https://doi.org/10.1016/j.neucom.2014.12.084
V.S. Rajput, S.S. Bobde, Stock market prediction using hybrid approach, in International Conference on Computing, Communication and Automation (ICCCA) (2016). ISBN (Online): 978-1-5090-1666-2
M. Qiu, Y. Song, Predicting the direction of stock market index movement using an optimized artificial neural network model. PLoS ONE (2016). https://doi.org/10.1371/journal.pone.0155133.s001
M. Hiransha, E.A. Gopalakrishnan, V.K. Menon, K.P. Soman, NSE stock market prediction using deep-learning models, in International Conference on Computational Intelligence and Data Science (2018). https://doi.org/10.1016/j.procs.2018.05.050
K. Zhang, G. Zhong, J. Dong, S. Wang, Y. Wang, Stock market prediction based on generative adversarial network, in International Conference on Identification, Information and Knowledge in the Internet of Things (2018). https://doi.org/10.1016/j.procs.2019.01.256
W.-C. Chiang, D. Enke, T. Wu, R. Wang, An adaptive stock index trading decision support system. Expert Syst. Appl. 59 (2016). https://doi.org/10.1016/j.eswa.2016.04.025
D. Selvamuthu, V. Kumar, A. Mishra, Indian stock market prediction using artificial neural networks on tick data. Financ. Innov. (2019). https://doi.org/10.1186/s40854-019-0131-7
S.T.A. Niaki, S. Hoseinzade, Forecasting S&P 500 index using artificial neural networks and design of experiments. J. Ind. Eng. Int. (2013). https://doi.org/10.1186/2251-712X-9-1
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Revathi, S., Begam, R., Radhika, Akila, R. (2022). Comparison of Stock Market Prediction Using Deep Learning Algorithms. In: Peter, J.D., Fernandes, S.L., Alavi, A.H. (eds) Disruptive Technologies for Big Data and Cloud Applications. Lecture Notes in Electrical Engineering, vol 905. Springer, Singapore. https://doi.org/10.1007/978-981-19-2177-3_34
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DOI: https://doi.org/10.1007/978-981-19-2177-3_34
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