Analysis of SME Stock Price Forecasting System Based on MachineLearning

Abstract

In financial market development, various theories and hypotheses have been studied and analyzed through different
methods to summarize stock prices, including random walk theory, efficient market hypothesis, and behavioral finance.
Therefore, it is of great significance to combine the various algorithms of machine learning with the relevant theories
of financial markets in quantitative finance. In the market economy, small and medium-sized enterprises(SMEs) absorb
many workers in society and play a huge role in production, innovation, and entrepreneurship, which determines the
importance of Chinese small enterprises in the financial market and stock market. Machine learning forecasts stock
prices as a reference for SMEs and their investors. In other words, enterprises can adjust the direction and proportion
of business promptly, and investors can also choose whether to invest according to the forecast results. In addition, this
work shows that the forecasting effect of machine learning can meet the needs of investors and SMEs by comparing the
stock price forecasting using the RM machine learning algorithm and comparing the forecasting results.
The machine learning algorithms commonly used in quantitative finance are briefly introduced, and the random forest
algorithm’s application principle in forecasting the stock price direction is described. Specifically, the stock price
forecasting system is built on the platform of Python, the logic of the system is explained, and the feasibility of the
system is explained through experiments and analysis, which reflects the advantage of machine learning in forecasting
the stock price direction, and provides a new path for SMEs to forecast the stock price.