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Forecasting during recession: Comparing the performance of machine learning and autoregressive models on the Swedish stock market

Skoglund, Jacob LU (2024) NEKH01 20232
Department 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.
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author
Skoglund, Jacob LU
supervisor
organization
course
NEKH01 20232
year
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}},
}