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Selecting characteristics using the Adaptive Group Lasso on U.S. industries

Greve, Henrik Andreas; Maseng, Ivar Gjerstad
Master thesis
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2639406.pdf (1.892Mb)
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https://hdl.handle.net/11250/2687406
Utgivelsesdato
2020
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Samlinger
  • Master of Science [963]
Sammendrag
Throughout the years, hundreds of factors have been proposed to forecast

stock returns. Cochrane (2011) referred to these factors as the "zoo of new

factors." In this thesis, we consider 62 of these factors and analyze which

of them provide incremental value when forecasting stock return in 12 U.S

industries. We apply the Adaptive Group Lasso (AGL) method for model

selection described by Freyberger, Neuhierl, and Weber (2018), and use the

Classical Linear Regression Model (CLRM) as a benchmark. The AGL selects,

on average, approximately three characteristics, while the linear approach

selects 24. The results indicate that the AGL approach generates

more accurate predictions when the sample size increases compared to the

CLRM. Our analysis indicates that there is no superior method for model

selection in our samples.
Beskrivelse
Masteroppgave(MSc) in Master of Science in Business, Finance - Handelshøyskolen BI, 2020
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Handelshøyskolen BI

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