论文标题
股票价格预测的机器学习模型的比较研究
Comparative Study of Machine Learning Models for Stock Price Prediction
论文作者
论文摘要
在这项工作中，我们将机器学习技术应用于历史股票价格，以预测未来的价格。为此，我们使用适合处理时间序列数据的递归方法。特别是，我们将线性卡尔曼过滤器和长期短期记忆（LSTM）体系结构的不同品种应用于10年范围内的历史股票价格（1/1/2011-1/1/2021）。我们通过计算预测值的误差与每个股票的历史值来量化这些模型的结果。我们发现，在我们研究的算法中，一个简单的线性卡尔曼过滤器可以预测具有低挥发性（例如Microsoft）股票的次要股票价值。但是，在高挥发性库存（例如特斯拉）的情况下，更复杂的LSTM算法显着胜过Kalman滤波器。我们的结果表明，我们可以对不同类型的股票进行分类，然后为每种股票类型培训LSTM。此方法可用于自动化目标返回率的投资组合生成。
In this work, we apply machine learning techniques to historical stock prices to forecast future prices. To achieve this, we use recursive approaches that are appropriate for handling time series data. In particular, we apply a linear Kalman filter and different varieties of long short-term memory (LSTM) architectures to historical stock prices over a 10-year range (1/1/2011 - 1/1/2021). We quantify the results of these models by computing the error of the predicted values versus the historical values of each stock. We find that of the algorithms we investigated, a simple linear Kalman filter can predict the next-day value of stocks with low-volatility (e.g., Microsoft) surprisingly well. However, in the case of high-volatility stocks (e.g., Tesla) the more complex LSTM algorithms significantly outperform the Kalman filter. Our results show that we can classify different types of stocks and then train an LSTM for each stock type. This method could be used to automate portfolio generation for a target return rate.
