The application and analysis of stock forecasting methods based on support vector machine and deep learning

Haoting Yuan

doi:10.1117/12.2639212

17 May 2022 The application and analysis of stock forecasting methods based on support vector machine and deep learning

Haoting Yuan

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122595V (2022) https://doi.org/10.1117/12.2639212
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

Stock markets are a symbol of market capitalism, and billions of shares of stock are traded every day. In 2018, stocks worth more than 65 trillion U.S. dollars were traded worldwide, and the market capitalization of domestic companies listed in the U.S. exceeded the country's GDP. Although stock movement prediction is a difficult problem, its solutions can be applied to the industry. Many researchers in both industry and academia have long shown interest in predicting future trends in the stock market. Researchers focused on finding profitable patterns in historical data are known as quants in the financial industry and are generally referred to as data scientists. Regardless of which term is used, such researchers are increasingly using more systematic trading algorithms to automatically make trading decisions. The study conducts the stock prediction by using the two most significant neural networks: the Support Vector Machine and multilayer perception. They are implemented in the way using Python platform and contrasted based on their prediction results on same 9 stocks in terms of their prediction accuracy. In addition, the results of the Support Vector Machine and multilayer perception models are compared and discussed in the study.

Citation Download Citation

Haoting Yuan "The application and analysis of stock forecasting methods based on support vector machine and deep learning", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122595V (17 May 2022); https://doi.org/10.1117/12.2639212

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KEYWORDS

Data modeling

Machine learning

Analytical research

Principal component analysis

Data processing

Neural networks

Neurons

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