A master thesis from Aalborg University
Forecasting Danish stock prices in 2022 under macroeconomic distress using macro indicators - a deep learning approach
Author(s)
Term
4. term
Education
Publication year
2023
Submitted on
2023-10-23
Pages
78 pages
Abstract
This study delves into the unpredictable nature of the stock market, acknowledging the challenges posed by its volatility and instability. Numerous theories exist on forecasting stock prices, yet no universal explanation fits all scenarios due to the diverse nature of stocks. This research takes a novel approach by employing LSTM models, to predict stock prices. It examines whether incorporating external macroeconomic factors into LSTM could improve forecasting accuracy. This analysis specifically focuses on a period marked by macroeconomic distress in autumn 2022. The study explores the optimization of LSTM and LSTM-X models to effectively handle volatility in the data being forecasted. Despite the findings indicating that both LSTM and LSTM-X models did not fully meet their potential, valuable insights were gained. Discussions and arguments presented in this study propose potential adjustments for future forecast models, aiming to simplify the handling of volatility and optimize forecasting accuracy.
This study delves into the unpredictable nature of the stock market, acknowledging the challenges posed by its volatility and instability. Numerous theories exist on forecasting stock prices, yet no universal explanation fits all scenarios due to the diverse nature of stocks. This research takes a novel approach by employing LSTM models, to predict stock prices. It examines whether incorporating external macroeconomic factors into LSTM could improve forecasting accuracy. This analysis specifically focuses on a period marked by macroeconomic distress in autumn 2022. The study explores the optimization of LSTM and LSTM-X models to effectively handle volatility in the data being forecasted. Despite the findings indicating that both LSTM and LSTM-X models did not fully meet their potential, valuable insights were gained. Discussions and arguments presented in this study propose potential adjustments for future forecast models, aiming to simplify the handling of volatility and optimize forecasting accuracy.
Documents
Colophon: This page is part of the AAU Student Projects portal, which is run by Aalborg University. Here, you can find and download publicly available bachelor's theses and master's projects from across the university dating from 2008 onwards. Student projects from before 2008 are available in printed form at Aalborg University Library.
If you have any questions about AAU Student Projects or the research registration, dissemination and analysis at Aalborg University, please feel free to contact the VBN team. You can also find more information in the AAU Student Projects FAQs.