  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.
