Exploiting Investor Sentiment for Portfolio Optimization

Master's Thesis from the year 2018 in the subject Statistics, grade: 1.0, University of Augsburg (Wirtschaftswissenschaftliche Fakultät, Lehrstuhl für Statistik), language: English, abstract: In efficient financial markets, there is no room for sentimental investors. Any new information would be immediately absorbed and any mispricing ...

Master's Thesis from the year 2018 in the subject Mathematics - Statistics, grade: 1.0, University of Augsburg (Wirtschaftswissenschaftliche Fakultät, Lehrstuhl für Statistik), language: English, abstract: In efficient financial markets, there is no room for sentimental investors. Any new information would be immediately absorbed and any mispricing immediately corrected by the forces of rational arbitrageurs doing the maths with the fundamentals. But why should financial markets be different from any other market where humans interact and are subject to psychological biases? There is strong empirical evidence that investor sentiment, broadly defined as "a belief about future cash flows and investment risks that is not justified by the facts at hand", plays an important role in financial markets. It can lead to significant overpricing/underpricing, particularly of assets prone to subjective valuations. With limits/risks to arbitrage in the short term, prices rather correct over the medium to long term as sentimental beliefs mean-revert. Building on the studies by Baker and Wurgler 2006 and Baker, Wurgler, and Y. Yuan 2012, measures of investor sentiment for international markets are constructed. Using the Copula Opinion Pooling approach developed by Attilio Meucci, this thesis shows how to incorporate these sentiment measures into portfolio optimization. Thereby, a sentiment-based trading strategy that exploits the medium-term reversal effect of sentiment is developed and empirically tested. The results are promising as they provide strong evidence that sentiment contains beneficial information that should not be neglected by quantitative portfolio managers.