  Performance forecasting is an age-old problem in economics and finance.
Recently, developments in machine learning and neural networks have given rise
to non-linear time series models that provide modern and promising alternatives
to traditional methods of analysis. In this paper, we present an ensemble of
independent and parallel long short-term memory (LSTM) neural networks for the
prediction of stock price movement. LSTMs have been shown to be especially
suited for time series data due to their ability to incorporate past
information, while neural network ensembles have been found to reduce
variability in results and improve generalization. A binary classification
problem based on the median of returns is used, and the ensemble's forecast
depends on a threshold value, which is the minimum number of LSTMs required to
agree upon the result. The model is applied to the constituents of the smaller,
less efficient Stockholm OMX30 instead of other major market indices such as
the DJIA and S&P500 commonly found in literature. With a straightforward
trading strategy, comparisons with a randomly chosen portfolio and a portfolio
containing all the stocks in the index show that the portfolio resulting from
the LSTM ensemble provides better average daily returns and higher cumulative
returns over time. Moreover, the LSTM portfolio also exhibits less volatility,
leading to higher risk-return ratios.
