Forecasting and Trading Financial Time Series with LSTM Neural Network

Madisson, Vahur

Forecasting and Trading Financial Time Series with LSTM Neural Network

dc.contributor.advisor	Raus, Toomas, juhendaja
dc.contributor.advisor	Kull, Meelis, juhendaja
dc.contributor.author	Madisson, Vahur
dc.contributor.other	Tartu Ülikool. Loodus- ja täppisteaduste valdkond	et
dc.contributor.other	Tartu Ülikool. Arvutiteaduse instituut	et
dc.date.accessioned	2023-09-21T12:06:09Z
dc.date.available	2023-09-21T12:06:09Z
dc.date.issued	2021
dc.description.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.	et
dc.identifier.uri	https://hdl.handle.net/10062/92337
dc.language.iso	eng	et
dc.publisher	Tartu Ülikool	et
dc.rights	openAccess	et
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Artificial neural networks	et
dc.subject	time series analysis	et
dc.subject	forecasting	et
dc.subject	trading strategy	et
dc.subject.other	magistritööd	et
dc.subject.other	informaatika	et
dc.subject.other	infotehnoloogia	et
dc.subject.other	informatics	et
dc.subject.other	infotechnology	et
dc.title	Forecasting and Trading Financial Time Series with LSTM Neural Network	et
dc.type	Thesis	et