Forecasting and Trading Financial Time Series with LSTM Neural Network
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
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.
Description
Keywords
Artificial neural networks, time series analysis, forecasting, trading strategy