股票价格预测的特征学习显示分析师评级的重要作用
Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating
摘要：
为了否定有效市场假说，确定了一组5个技术指标和23个基本指标，以确定股票市场产生超额收益率的可能性。利用这些数据点和各种分类机器学习模型，分析了过去20年美国标准普尔500指数505只股票的交易数据，以开发一个有效的分类器。从任何一天开始，我们都能够预测未来10天内1%的价格变化方向。预测的总体准确率为83.62%，买入信号的准确率为85%，卖出信号的召回率为100%。此外，我们按行业对股票进行分组，并重复实验，以观察将类似资产分组在一起是否会对结果产生积极影响，但得出的结论是，它没有显示出显着的业绩改善，拒绝了基于行业分析的想法。此外，使用特征排名，我们可以识别出一组更小的6个指标，同时保持与原始28个特征相似的准确性，并且还揭示了买入，持有和卖出分析师评级的重要性，因为它们是模型中的主要贡献者。最后，为了评估分类器在现实生活中的有效性，使用适度的交易策略对FAANG股票进行了回测，在测试数据集的期限内，它产生了超过60%的高回报。总之，我们提出的方法与有目的地挑选的特征相结合，显示出比以前的研究有所改进，我们的模型预测了第10天1%的价格变化方向，具有很高的信心，并且有足够的缓冲，甚至可以构建一个机器人交易系统。
英文摘要：
To reject the Efficient Market Hypothesis a set of 5 technical indicators and 23 fundamental indicators was identified to establish the possibility of generating excess returns on the stock market. Leveraging these data points and various classification machine learning models, trading data of the 505 equities on the US S&P500 over the past 20 years was analysed to develop a classifier effective for our cause. From any given day, we were able to predict the direction of change in price by 1% up to 10 days in the future. The predictions had an overall accuracy of 83.62% with a precision of 85% for buy signals and a recall of 100% for sell signals. Moreover, we grouped equities by their sector and repeated the experiment to see if grouping similar assets together positively effected the results but concluded that it showed no significant improvements in the performance rejecting the idea of sector-based analysis. Also, using feature ranking we could identify an even smaller set of 6 indicators while maintaining similar accuracies as that from the original 28 features and also uncovered the importance of buy, hold and sell analyst ratings as they came out to be the top contributors in the model. Finally, to evaluate the effectiveness of the classifier in real-life situations, it was backtested on FAANG equities using a modest trading strategy where it generated high returns of above 60% over the term of the testing dataset. In conclusion, our proposed methodology with the combination of purposefully picked features shows an improvement over the previous studies, and our model predicts the direction of 1% price changes on the 10th day with high confidence and with enough buffer to even build a robotic trading system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
