TITLE:
Research on Short-Term Stock Price Trends Based on Machine Learning
AUTHORS:
Lingling Zeng, Yanan Xiao, Shilong Chen
KEYWORDS:
Machine Learning, Stock Price Fitting Prediction, Quantitative Investment, GBDT, FFW
JOURNAL NAME:
iBusiness,
Vol.14 No.2,
June
6,
2022
ABSTRACT: With the increasing emphasis on economic
development, China’s economy is growing steadily and significantly, and the
stock market is becoming more diversified and richer in products and
derivatives, resulting in a larger and larger base of investors in the Chinese
stock market over the years, with more significant changes in the investment
environment of the primary stock market. In the analysis and research around
stocks, the fitting and prediction of stock price changes have been one of the
keys in the field of stock analysis, however, the current models and solutions
for stock price fitting and prediction have not been well received, and are
lacking in terms of realistic operability
and applicability. In recent years, machine learning and its related models and methods have been widely used in the
financial field, which has also promoted the development of stock price
fitting forecasting. In order to further improve the accuracy of stock price
fitting prediction, this paper introduces the
multi-factor prediction model in traditional quantitative stock analysis into
the stock price fitting prediction method and improves the general stock price
fitting prediction method. This paper finds that the screening of factors that
can significantly affect stock prices can indeed be accomplished by using the
methodological properties of GBDT and FFM.