Forecasting during recession: Comparing the performance of machine learning and autoregressive models on the Swedish stock market
(2024) NEKH01 20232Department of Economics
- Abstract
- As the processing power of computers continuously increase so does the interest for machine learning and artificial intelligence. This thesis evaluates the forecasting performance of both machine learning models and common auto-regressive models on the Swedish stock market index OMXS30 on the Stockholm stock exchange during the 2008 financial crises. Forecasts are performed 3, 6 and 12 months ahead. The results indicate that machine learning models perform noticeably better when forecasting 6 and 12 month ahead, while the result for the machine learning models are comparable to those of the autoregressive models when forecasting 3 months ahead.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9146954
- author
- Skoglund, Jacob LU
- supervisor
- organization
- course
- NEKH01 20232
- year
- 2024
- type
- M2 - Bachelor Degree
- subject
- keywords
- forecasting, machine learning, stock market, Sweden, AI
- language
- English
- id
- 9146954
- date added to LUP
- 2024-04-16 09:24:39
- date last changed
- 2024-04-16 09:24:39
@misc{9146954, abstract = {{As the processing power of computers continuously increase so does the interest for machine learning and artificial intelligence. This thesis evaluates the forecasting performance of both machine learning models and common auto-regressive models on the Swedish stock market index OMXS30 on the Stockholm stock exchange during the 2008 financial crises. Forecasts are performed 3, 6 and 12 months ahead. The results indicate that machine learning models perform noticeably better when forecasting 6 and 12 month ahead, while the result for the machine learning models are comparable to those of the autoregressive models when forecasting 3 months ahead.}}, author = {{Skoglund, Jacob}}, language = {{eng}}, note = {{Student Paper}}, title = {{Forecasting during recession: Comparing the performance of machine learning and autoregressive models on the Swedish stock market}}, year = {{2024}}, }