With the improvement of people's awareness of financial management, more and more people put their surplus assets into the stock market. In the dynamic and complex stock system, the future rise and fall of each stock are unknown to a certain extent. This unknown has caused discussion among investors and related scholars, and thus stock forecasting has become a hot topic in the research field. By combing the relevant work of literature in the recent three years, we can find that the research topic of stock forecasting is mainly in the field of machine learning, and its research scope is mainly deep learning and data mining. In the process of applying machine learning to stock forecasting, the influencing factors mainly include stock data collection and preprocessing machine learning model, and machine learning algorithm. After analysis, it is found that the final prediction result of the stock is mainly related to stock data and the machine learning model. Aiming at the deficiency of prediction, the summary and prospect of applying machine learning in stock prediction are discussed at the end of this paper.

Shengyin Luo. Review on the Application of Machine Learning in Stock Forecasting. Academic Journal of Business & Management (2021) Vol. 3, Issue 4: 27-30. https://doi.org/10.25236/AJBM.2021.030407.

[1] Mehar V, Deeksha C, Vinay A T, et al. Stock Closing Price Prediction using Machine Learning Techniques [J]. Procedia Computer Science. 2020, 167.

[2] Junze Qu. Constructing stock trading decision-making framework based on machine learning [J]. China High-tech .2019 (7): 54-56.

[3] Bisoi R, Dash PK, Parida A K. Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for the stock price and movement prediction on daily basis [J]. APPLIED SOFT COMPUTING. 2019, 74: 652-678.

[4] Yonghao Yang. Stock price trend prediction method based on optimized MACD model [j]. Journal of Guangxi academy of sciences, 33 (1): 65-70.

[5] Haoran Xu, Bo Xu, Kewen Xu. Overview of the application of machine learning in stock forecasting [J]. Computer Engineering and Application .2020, 56 (12): 19-24.

[6] Nana Lin, Jiangtao Qin. A-share stock price forecast based on random forest [J]. Journal of the University of Shanghai for Science and Technology.2018, 40 (03): 267-273.

[7] Fengxin Deng, Hongliang Wang. Application of LSTM neural network in stock price trend prediction-based on the data of individual stocks in us and Hong Kong stock markets [J]. Finance and economics. 2018 (14): 96-98.

[8] Cao H, Lin T, Li Y, et al. Stock Price Pattern Prediction Based on Complex Network and Machine Learning [J]. COMPLEXITY. 2019 (4132485).

[9] Hongsheng Liu, Huawei Feng, Zhang Li, et al. Application of machine learning in MALDI-TOF MS identification of microorganisms [J]. Acta Microbiologica Sinica, 2020(5): 841-855.

[10] Fujia Gao. Review of stock price model based on BP neural network [J]. Economic Outlook around the Bohai Sea. 2019(12): 164-165.

[11] Sheng Lin, Ke Qi, Jiecong Wei, et al. Research Review of Machine Learning in Stock Price Forecasting [J]. Economist .2019 (3): 71-73, 78.