Dr. Haoting Yuan Profile

Dr. Haoting Yuan

at Affiliated High School of Peking Univ

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Proceedings Article | 17 May 2022 Paper

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

Haoting Yuan

Proceedings Volume 12259, 122595V (2022) https://doi.org/10.1117/12.2639212

KEYWORDS: Data modeling, Machine learning, Analytical research, Principal component analysis, Visualization, Neurons, Neural networks, Data processing, Statistical modeling, Statistical analysis

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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.

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