Deep learning techniques for stock market forecasting

Yifei Chen; Jiaxin Shen; Runyu Tian; Yemin Wang

doi:10.1117/12.2628033

22 April 2022 Deep learning techniques for stock market forecasting

Yifei Chen, Jiaxin Shen, Runyu Tian, Yemin Wang

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121634S (2022) https://doi.org/10.1117/12.2628033
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Stock is a long-term credit tool in the capital market, which can be transferred and traded. Therefore, the stock market has emerged. It is a place for stock issuance and trading, and plays an important role in today's world economy. More and more people are now beginning to pay attention to the stock market and invest, so the stock price prediction has become a concern. People begin to develop various methods to predict the stock price to comply with the market demand. In the stock market, the market changes are related to the national macroeconomic development, the formulation of laws and regulations, the operation of the company, the confidence of shareholders, and so on. This prediction behavior is based only on assumed factors and established preconditions. As a result, stock forecasting is difficult to predict accurately. People focus on developing various systems that can make stock price prediction more accurate and faster. Although there have been many studies on predicting stock prices, there is no systematic review to revise the state-of-the-art research on stock market prediction. Therefore, we should make a new review of stock price prediction methods and make a systematic summary, review, and comparative analysis of the new methods in recent years.

Citation Download Citation

Yifei Chen, Jiaxin Shen, Runyu Tian, and Yemin Wang "Deep learning techniques for stock market forecasting", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121634S (22 April 2022); https://doi.org/10.1117/12.2628033

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