WThis doctoral thesis focuses on the effects of investor sentiment on asset pricing and the challenges of portfolio optimization under parameter uncertainty. The first essay "Sentiment risk premia in the cross-section of global equity" applies a...
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ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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WThis doctoral thesis focuses on the effects of investor sentiment on asset pricing and the challenges of portfolio optimization under parameter uncertainty. The first essay "Sentiment risk premia in the cross-section of global equity" applies a recently developed sentiment proxy to the construction of a new risk factor and provides a comprehensive understanding of its role in sentiment-augmented asset pricing models for international equity indices. We empirically demonstrate the existence of a statistically significant and economically relevant sentiment premium. Differentiating between developed and emerging markets we reveal different patterns of return reversals / persistence. Our results contribute to the explanation of global cross-sectional average excess returns, demonstrating superiority in terms of predictive power when compared to competing definitions of sentiment. The second essay "Does social media sentiment matter in the pricing of U.S. stocks?" finds that the inclusion of micro-grounded, social media-based sentiment significantly improves the performance of the five-factor model from Fama and French (2015, 2017). This holds for different industry and style portfolios such as size, value, profitability, and investment. Applying a robust GMM estimator, the sentiment risk premium provides the missing component in the behavioral asset pricing theory of Shefrin and Belotti (2008) and (partially) resolves the pricing puzzles of small extreme growth, small extreme investment stocks and small stocks that invest heavily despite low profitability. The third essay "Diversifying estimation errors: An efficient averaging rule for portfolio optimization" proposes a combination of established minimum-variance strategies to minimize the expected out-of-sample variance. The proposed averaging rule overcomes the strategy selection problem and diversifies estimation errors of the strategies included in our rule. Extensive simulations show that the contributions of estimation errors to the out-of-sample variances are uncorrelated between the considered strategies. We therefore conclude that averaging over multiple strategies offers sizable diversification benefits.
