A hybrid approach to predicting daily stock market returns with deep learning

S. Vinoth; Satish Kumar; Manjula Jain

doi:10.31893/multiscience.2023ss0312

Keywords:

Abstract

It is quite difficult to correctly forecast returns on stock markets because of the economic stock industry' extreme volatility and complex nature. Programming methods of prediction have shown to be increasingly effective at forecasting stock values with the development of artificial intelligence and improved computational capability. Deep learning (DL) algorithms and big data analytics are becoming more and more crucial in a variety of application areas, including stock market investing. However, other research has focused on predicting daily stock market returns, particularly when employing DL approaches to carry out powerful analysis. The DL algorithm is used in this paper's big data analytics approach to forecast the SPDR S&P 500 ETF's daily stock market return direction. The complete dataset was then run through a DL algorithm, such as the MultiDepth NeuroNetwork (MD-NN) technique, to forecast path of the projected index for the stock market daily returns. The simulation results demonstrate that the MD-NN datasets provide much greater classification accuracy than those utilizing the existing approaches.
References
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Vinoth, S., Kumar, S., & Jain, M. (2023). A hybrid approach to predicting daily stock market returns with deep learning. Multidisciplinary Science Journal, 5, 2023ss0312. https://doi.org/10.31893/multiscience.2023ss0312

[1] Aldhyani TH, Alzahrani A (2022) Framework for predicting modelling stock market prices based on deep learning algorithms. Electronics.

[2] Ayala J, García-Torres M, Noguera JLV, Gómez-Vela F, Divina F (2021) Technical analysis strategy optimization using a machine learning approach in stock market indices Knowledge-Based Systems.

[3] Bustos O, Pomares-Quimbaya A (2020) Stock market movement forecast A systematic review. Expert Systems with Applications.

[4] Chandar SK (2019) Stock market prediction using subtractive clustering for a neuro fuzzy hybrid approach Cluster Computing.

[5] Chen W, Jiang M, Zhang WG, Chen Z (2021) A novel graph convolutional feature based convolutional neural network for stock trend prediction. Information Sciences.

[6] Cheng CH, Tsai MC, Chang C (2022) A Time Series Model Based on Deep Learning Integrated Indicator Selection Method for Forecasting Stock Prices Evaluating Trading Profits, Systems.

[7] Ghosh I, Chaudhuri TD, Alfaro-Cortés E, Gámez M, García N (2022) A hybrid approach to forecasting futures prices with simultaneous consideration of optimality in ensemble feature selection advanced artificial intelligence, Technological Forecasting Social Change,

[8] Ghosh I, Jana RK, Sanyal MK (2019) Analysis of temporal pattern, causal interaction predictive modelling of financial markets using nonlinear dynamics, econometric models machine learning algorithms Applied Soft Computing.

[9] Kamalov F (2020) Forecasting significant stock price changes using neural networks, Neural Computing Applications.

[10] Lu H, Ma X (2020) Hybrid decision tree-based machine learning models for short-term water quality prediction, Chemosphere.

[11] Shah D, Isah H, Zulkernine F (2019) Stock market analysis A review taxonomy of prediction techniques, International Journal of Financial Studies.

[12] Shahi TB, Shrestha A, Neupane A, Guo W (2020) Stock price forecasting with deep learning A comparative study, Mathematics.

[13] Singh S, Madan TK, Kumar J, Singh AK (2019) Stock market forecasting using machine learning Today a tomorrow. In (2019) 2nd International Conference on Intelligent Computing, Instrumentation Control Technologies.

[14] Strader TJ, Rozycki JJ, Root TH, Huang YHJ (2020) Machine learning stock market prediction studies review research directions Journal of International Technology Information Management.

[15] Tabar S, Sharma S, Volkman D, (2020). A new method for predicting stock market crashes using classification artificial neural networks, International Journal of Business and Data Analytics.

Abstract

References

How to cite