Breaking Bad: Parameter Uncertainty caused by Structural Breaks in Stocks

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Projektart und Laufzeit

FFF-Förderprojekt, Januar 2022 bis Dezember 2022

Koordinator

Lehrstuhl für Finance

Forschungsschwerpunkt

Wealth Management

Beschreibung

Estimating parameter inputs for portfolio optimization has been shown to be notoriously diffi-cult resulting in disappointing out-of-sample performance (Michaud, 1989; DeMiguel et al., 2009). The procedure of estimating parameters is further complicated by breaks and regime shifts in financial data caused by, for example, corporate actions such as mergers and acquisi-tions (Ang & Timmermann, 2012; Smith & Timmermann, 2021). These regime shifts ultimately result in parameter uncertainty, to which investors are averse (i.e. “ambiguity aversion”; Gar-lappi et al., 2007). On an aggregate market level, this ambiguity-aversion gives rise to a premium for parameter uncertainty as stocks with high (low) parameter uncertainty are avoided/sold (more attractive/bought). We propose a novel measure called break-(adjusted stock-) age that proxies for parameter uncertainty and is based on detecting structural breaks in stock returns using unsupervised machine learning techniques. Our measure reveals (i) that break age is priced significantly in the cross-section of stock returns and (ii) that break-age is a powerful proxy for parameter uncertainty.