论文标题
预测使用LSTM和随机森林的盘中交易的股票价格的定向运动
Forecasting directional movements of stock prices for intraday trading using LSTM and random forests
论文作者
论文摘要
我们同时采用随机森林和LSTM网络（更精确的Cudnnlstm）作为培训方法，以分析其在预测1993年1月至2018年12月的标准普尔500标准普尔500分数的样本外定向股票中的有效性，直到2018年12月8日。我们介绍了一个多功能设置，不仅包括收盘价有关的收益，而且还包括开盘价格和日内收益。作为交易策略，我们使用Krauss等人。 （2017）和Fischer＆Krauss（2018）作为基准。在每个交易日，我们都会购买10股概率最高的股票，并出售10个股票，其概率最低的10股在日内收益率方面均优于市场 - 所有这些股票的重量都相同。我们的经验结果表明，使用LSTM网络在交易成本之前的每日回报率为0.64％，使用随机森林提供0.54％的回报。因此，我们在Fischer＆Krauss（2018）和Krauss等人中胜过单点设置。 （2017年）仅由每日收益组成，相应的每日收益分别为0.41％和0.39％，分别相对于LSTM和随机森林。
We employ both random forests and LSTM networks (more precisely CuDNNLSTM) as training methodologies to analyze their effectiveness in forecasting out-of-sample directional movements of constituent stocks of the S&P 500 from January 1993 till December 2018 for intraday trading. We introduce a multi-feature setting consisting not only of the returns with respect to the closing prices, but also with respect to the opening prices and intraday returns. As trading strategy, we use Krauss et al. (2017) and Fischer & Krauss (2018) as benchmark. On each trading day, we buy the 10 stocks with the highest probability and sell short the 10 stocks with the lowest probability to outperform the market in terms of intraday returns -- all with equal monetary weight. Our empirical results show that the multi-feature setting provides a daily return, prior to transaction costs, of 0.64% using LSTM networks, and 0.54% using random forests. Hence we outperform the single-feature setting in Fischer & Krauss (2018) and Krauss et al. (2017) consisting only of the daily returns with respect to the closing prices, having corresponding daily returns of 0.41% and of 0.39% with respect to LSTM and random forests, respectively.
