Semiparametric portfolios: Improving portfolio performance by exploiting non-linearities in firm characteristics
João F. Caldeira,
Andre Santos (andreportela@gmail.com) and
Hudson S. Torrent
Economic Modelling, 2023, vol. 122, issue C
Abstract:
Empirical evidence shows that the relationship between firm characteristics and stock returns is non-linear, with a stronger correlation at the extreme deciles of the characteristic values. In this paper, we propose a novel portfolio optimization method that models the portfolio weights as a non-linear function of firm characteristics. Our approach allows the weights to vary non-linearly across percentiles of the cross-sectional distribution of each characteristic. We apply our method to the universe of firms listed in the NYSE, AMEX, and NASDAQ and find that non-linear effects in size, value, and momentum anomalies are important for constructing portfolios that have lower risk and higher risk-adjusted returns. Our results suggest that a flexible relation between portfolio weights and firm characteristics can better capture the empirical patterns observed in the data.
Keywords: Penalized splines; Portfolio turnover; Sharpe ratios (search for similar items in EconPapers)
JEL-codes: B26 C58 G11 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:122:y:2023:i:c:s0264999323000512
DOI: 10.1016/j.econmod.2023.106239
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