Long Short-Term Memory Neural Network for Financial Time Series

Abstract

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.

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“…Long Short-Term Memory (LSTM) [42][43][44] is a specialized deep learning technique designed for analyzing sequential data, addressing issues found in conventional recurrent neural networks (RNNs) [45][46][47] and other machine learning algorithms. It was proposed by Hochreiter and Schmidhuber [48] to overcome the gradient vanishing problem and enhance the effectiveness of RNNs [49][50][51][52][53].…”