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A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting

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Abstract

From exchanging budgetary instruments to tracking individual spending plans to detail a business's profit, money-related organisations utilise computational innovation day by day. Here in this paper, we focus on the significance of innovation in accounts such as financial risk management and stock prediction. We discuss two significant algorithms that have a notable role in stock forecasting. Artificial Neural Networks (ANN), as absenteeism of some data points, does not hamper the network functioning. Secondly, Support Vector Machines (SVM) has several features, and due to simple decision boundaries, it avoids over-fitting. The paper first looks at the different technologies applied in stock market prediction. It examines how sentimental analysis, decision trees, moving average algorithm, and data mining is applied in various stock prediction scenarios. The paper covers the recent past studies to explore the concepts and methodologies through which ANN's and SVM's have been used. Additionally, the paper incorporates significant aspects of novel methods and technologies in which ANN as a hybrid model like ANN-MLP, GARCH-MLP, a combination of the Backpropagation algorithm and Multilayer Feed-forward network, yields better results. Simultaneously, SVM's have been successfully applied in stock prediction, giving an accuracy of about 60%–70% for simple SVM, which is further improved by combining methods like Random Forest, Genetic Algorithm more accurate outcomes. Further, we present our thoughts on where SVM's and ANN's stand as prediction algorithms and challenges like the time constraint, current scenarios, data limitation, and cold start problems were raised. Conclusively SVM and ANN played prominent roles in tackling these issue to an extent and can further be enhanced with their integration with other novel techniques resulting in hybrid methodologies. It will lead students, researchers and financial enthusiasts to more potent approaches for Stock forecasting.

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All relevant data and material are presented in the main paper.

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Acknowledgements

The authors are grateful to Department of Computer Science Engineering, Indus University and Department of Chemical Engineering, Pandit Deendayal Energy University for the permission to publish this research.

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Authors and Affiliations

  1. Department of Computer Engineering, Indus University, Ahmedabad, Gujarat, India

    Akshit Kurani

  2. Department of Computer Science and Engineering, Indus University, Ahmedabad, Gujarat, India

    Pavan Doshi & Aarya Vakharia

  3. Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, 382007, India

    Manan Shah

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  1. Akshit Kurani
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  2. Pavan Doshi
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  3. Aarya Vakharia
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  4. Manan Shah
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Contributions

All the authors make a substantial contribution to this manuscript. AK, PD, AV and MS participated in drafting the manuscript. AK, PD, AV and MS wrote the main manuscript. All the authors discussed the results and implication on the manuscript at all stages.

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Correspondence to Manan Shah.

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Kurani, A., Doshi, P., Vakharia, A. et al. A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting. Ann. Data. Sci. 10, 183–208 (2023). https://doi.org/10.1007/s40745-021-00344-x

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