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From Fama-French to LSTM: Rethinking Risk Factors in the Swedish Stock Market

Ritseson, Victor LU and Linde, Johan LU (2025) NEKH02 20242
Department of Economics
Abstract
This paper investigates the application of a Long Short-Term Memory (LSTM) model to construct a forward-looking risk factor for asset pricing. The LSTM model's predictions are utilized to create a novel risk factor, derived from long-short portfolios, offering an innovative approach to decomposing asset returns. For comparative analysis, the Fama-French three-factor (FF3) and Carhart four-factor (C4) models are constructed using data from the Swedish stock market. The evaluation framework employs time-series regression on three 5x5 testing portfolios, with an analysis of intercepts conducted through t-values, and GRS tests. Spanning regressions are performed to assess whether the proposed forward-looking risk factor captures returns not... (More)
This paper investigates the application of a Long Short-Term Memory (LSTM) model to construct a forward-looking risk factor for asset pricing. The LSTM model's predictions are utilized to create a novel risk factor, derived from long-short portfolios, offering an innovative approach to decomposing asset returns. For comparative analysis, the Fama-French three-factor (FF3) and Carhart four-factor (C4) models are constructed using data from the Swedish stock market. The evaluation framework employs time-series regression on three 5x5 testing portfolios, with an analysis of intercepts conducted through t-values, and GRS tests. Spanning regressions are performed to assess whether the proposed forward-looking risk factor captures returns not explained by the FF3 and C4 risk factors. The $R^2$ values increase from smaller to larger portfolios, indicating that the models better explain variation in returns in less volatile, large-cap portfolios. However, the GRS test reveals that while mid-sized portfolios have the lowest unexplained returns, small-cap portfolios struggle with volatility, and large-cap portfolios exhibit persistent unexplained returns despite their higher $R^2$. The predictive accuracy of the LSTM model for stock prices is limited, suggesting areas for further development. However, integrating the LSTM-derived risk factor into the FF3 model slightly enhances explanatory power, as evidenced by lower GRS values. Furthermore, the LSTM-derived factor demonstrates overlap with momentum, indicating a potential improvement when substituting momentum with the forward-looking risk factor. In summary, this study illustrates the potential of a LSTM-derived risk factor to complement traditional models, advancing risk factor frameworks and providing valuable insights for future data-driven financial modeling. (Less)
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author
Ritseson, Victor LU and Linde, Johan LU
supervisor
organization
course
NEKH02 20242
year
type
M2 - Bachelor Degree
subject
keywords
Asset Pricing Models, Fama French Three-Factor Model, Carhart Four-Factor Model, Factor Model, Machine Learning
language
English
id
9184754
date added to LUP
2025-05-16 10:47:17
date last changed
2025-05-16 10:47:17
@misc{9184754,
  abstract     = {{This paper investigates the application of a Long Short-Term Memory (LSTM) model to construct a forward-looking risk factor for asset pricing. The LSTM model's predictions are utilized to create a novel risk factor, derived from long-short portfolios, offering an innovative approach to decomposing asset returns. For comparative analysis, the Fama-French three-factor (FF3) and Carhart four-factor (C4) models are constructed using data from the Swedish stock market. The evaluation framework employs time-series regression on three 5x5 testing portfolios, with an analysis of intercepts conducted through t-values, and GRS tests. Spanning regressions are performed to assess whether the proposed forward-looking risk factor captures returns not explained by the FF3 and C4 risk factors. The $R^2$ values increase from smaller to larger portfolios, indicating that the models better explain variation in returns in less volatile, large-cap portfolios. However, the GRS test reveals that while mid-sized portfolios have the lowest unexplained returns, small-cap portfolios struggle with volatility, and large-cap portfolios exhibit persistent unexplained returns despite their higher $R^2$. The predictive accuracy of the LSTM model for stock prices is limited, suggesting areas for further development. However, integrating the LSTM-derived risk factor into the FF3 model slightly enhances explanatory power, as evidenced by lower GRS values. Furthermore, the LSTM-derived factor demonstrates overlap with momentum, indicating a potential improvement when substituting momentum with the forward-looking risk factor. In summary, this study illustrates the potential of a LSTM-derived risk factor to complement traditional models, advancing risk factor frameworks and providing valuable insights for future data-driven financial modeling.}},
  author       = {{Ritseson, Victor and Linde, Johan}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{From Fama-French to LSTM: Rethinking Risk Factors in the Swedish Stock Market}},
  year         = {{2025}},
}