In the current stock market, computer science and technology are more and
more widely used to analyse stocks. Not same as most related machine learning
stock price prediction work, this work study the predicting the tendency of the
stock price on the second day right after the disclosure of the companies'
annual reports. We use a variety of different models, including decision tree,
logistic regression, random forest, neural network, prototypical networks. We
use two sets of financial indicators (key and expanded) to conduct experiments,
these financial indicators are obtained from the EastMoney website disclosed by
companies, and finally we find that these models are not well behaved to
predict the tendency. In addition, we also filter stocks with ROE greater than
0.15 and net cash ratio greater than 0.9. We conclude that according to the
financial indicators based on the just-released annual report of the company,
the predictability of the stock price movement on the second day after
disclosure is weak, with maximum accuracy about 59.6% and maximum precision
about 0.56 on our test set by the random forest classifier, and the stock
filtering does not improve the performance. And random forests perform best in
general among all these models which conforms to some work's findings.

通过使用不同的机器学习模型和两组金融指标，本研究研究了股市中公司公布年报后第二天的股价趋势，得出结论基于公司新公布的年度报告财务指标，股票价格很难预测。