Transformer-Based Downside Risk Forecasting: A Data-Driven Approach with Realized Downward Semi-Variance Article Swipe
Yuping Song
,
Yuetong Zhang
,
Peng Ning
,
Jiayi Peng
,
Chunyu Kao
,
Liang Hao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13081260
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/math13081260
Realized downward semi-variance (RDS) has been realized as a key indicator to measure the downside risk of asset prices, and the accurate prediction of RDS can effectively guide traders’ investment behavior and avoid the impact of market fluctuations caused by price declines. In this paper, the RDS rolling prediction performance of the traditional econometric model, machine learning model, and deep learning model is discussed in combination with various relevant influencing factors, and the sensitivity analysis is further carried out with the rolling window length, prediction length, and a variety of evaluation methods. In addition, due to the characteristics of RDS, such as aggregation and jumping, this paper further discusses the robustness of the model under the impact of external events, the influence of emotional factors on the prediction accuracy of the model, and the results and analysis of the hybrid model. The empirical results show that (1) when the rolling window is set to 20, the overall prediction effect of the model in this paper is the best. Taking the Transformer model under SSE as an example, compared with the prediction results under the rolling window length of 5, 10, and 30, the RMSE improvement ratio reaches 24.69%, 15.90%, and 43.60%, respectively. (2) The multivariable Transformer model shows a better forecasting effect. Compared with traditional econometric, machine learning, and deep learning models, the average increase percentage of RMSE, MAE, MAPE, SMAPE, MBE, and SD indicators is 52.23%, 20.03%, 62.33%, 60.33%, 37.57%, and 18.70%, respectively. (3) In multi-step prediction scenarios, the DM test statistic of the Transformer model is significantly positive, and the prediction accuracy of the Transformer model remains stable as the number of prediction steps increases. (4) Under the impact of external events of COVID-19, the Transformer model has stability, and the addition of emotional factors can effectively improve the prediction accuracy. In addition, the model’s prediction performance and generalization ability can be further improved by stacked prediction models. An in-depth study of RDS forecasting is of great value to capture the characteristics of downside risks, enrich the financial risk measurement system, and better evaluate potential losses.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math13081260
- https://www.mdpi.com/2227-7390/13/8/1260/pdf?version=1744367656
- OA Status
- gold
- Cited By
- 1
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409367227
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Raw OpenAlex JSON
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https://openalex.org/W4409367227Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/math13081260Digital Object Identifier
- Title
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Transformer-Based Downside Risk Forecasting: A Data-Driven Approach with Realized Downward Semi-VarianceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-11Full publication date if available
- Authors
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Yuping Song, Yuetong Zhang, Peng Ning, Jiayi Peng, Chunyu Kao, Liang HaoList of authors in order
- Landing page
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https://doi.org/10.3390/math13081260Publisher landing page
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https://www.mdpi.com/2227-7390/13/8/1260/pdf?version=1744367656Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2227-7390/13/8/1260/pdf?version=1744367656Direct OA link when available
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Downside risk, Econometrics, Variance (accounting), Transformer, Computer science, Economics, Engineering, Financial economics, Electrical engineering, Voltage, Portfolio, AccountingTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2025-04-11 |
| publication_year | 2025 |
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