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Comparison Of Competing Artificial Neural Networks Models For

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comparison of competing artificial neural networks models for

Abstract Stock market forecasting presents numerous challenges due to volatility, complexity, and nonlinear behaviour of financial markets. Recently, many deep learning models have been employed in stock market forecasting, outperforming traditional machine learning techniques. This work examines four artificial neural network models, namely, simple RNN, GRU, LSTM and Bi-LSTM for carrying out comparative study in stock market forecasting. We trained the networks using historical stock data from Standard and Poor’s 500 index and evaluated them based on performance metrics.

Based on the experimental results, the LSTM model outperforms all competing configurations. Access this chapter Tax calculation will be finalised at checkout Purchases are for personal use only References Banik, S., Sharma, N., Mangla, M., Mohanty, S.N., Shitharth, S.: LSTM based decision support system for swing trading in stock market. Knowl. Based Syst. 239, 107994 (2022) Bas, E., Egrioglu, E., Cansu, T.: Robust training of median dendritic artificial neural networks for time series forecasting. Expert Syst. Appl.

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Rights and permissions Copyright information © 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG About this paper Cite this paper Liagkouras, K., Metaxiotis, K. (2026). Comparison of Competing Artificial Neural Networks Models for Stock Market Forecasting. In: Le Thi, H.A., Pham Dinh, T., Le, H.M. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. MCO 2025. Lecture Notes in Networks and Systems, vol 1689. Springer, Cham.

https://doi.org/10.1007/978-3-032-08384-5_10 Download citation DOI: https://doi.org/10.1007/978-3-032-08384-5_10 Published: Publisher Name: Springer, Cham Print ISBN: 978-3-032-08383-8 Online ISBN: 978-3-032-08384-5 eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)Springer Nature Proceedings excluding Computer Science

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