Sparse factor model based on trend filtering

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We present a model based on trend filtering and regularization for performing factor analysis. Furthermore, the trend filtering method proposed in our model allows incorporating views represented as scenarios. Therefore, factor models can be optimized to explain not only trends of a given data series but also trends reflecting outlooks. As an application to finance, the proposed model constructs a sparse factor model on the trend of an index. Factor analyses on trend-filtered series provide factor models for describing trends, which are valuable for modeling long-term horizons. We also analyze the effect of trends on factor model selection in the US stock market. Further analyses include an investigation of factors during crisis periods and a comparison when various scenarios are considered.
Publisher
SPRINGER
Issue Date
2021-11
Language
English
Article Type
Article
Citation

ANNALS OF OPERATIONS RESEARCH, v.306, no.1-2, pp.321 - 342

ISSN
0254-5330
DOI
10.1007/s10479-021-04029-9
URI
http://hdl.handle.net/10203/288749
Appears in Collection
IE-Journal Papers(저널논문)
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