Forecasting and Trading Financial Time Series with LSTM Neural Network

Abstract

The growing importance of data science and the development of machine learning allows to implementation of the algorithms created in recent decades with new capable technologies. Machine learning methods can challenge statistical methods of forecasting when applied in financial time series, as such data may exhibit nonlinear characteristics. The objective of the thesis is to present a theoretical introduction and practical steps to construct, test, and implement forecasting methods on the stock market index, using artificial intelligence algorithm called long short-term memory (LSTM) neural network. The relevant trading strategy is developed to implement the model predictions. The empirical study focuses on finding the best configuration of the LSTM model to enhance the forecasting ability, using Keras library in Python programming language. The results are assessed in terms of forecast accuracy measures and profitability when applying relevant trading strategy and compared against selected benchmark methods. Results demonstrate that LSTM forecast accuracy is competitive and trading results outperform compared to selected benchmarks methods.