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.
Table of Contents
1 Introduction
1.1 Studies about Sentiment
1.1.1 Sentiment by Surveys
1.1.2 Sentiment by Financial Variables
1.1.3 Sentiment by News
1.2 Research Questions
2 Empirical Methodology
2.1 Sentiment Data
2.2 Sentiment Index Computation
2.3 Incorporating Sentiment into the Portfolio
2.3.1 Black-Litterman Approach
2.3.2 Copula Opinion Pooling
2.4 Portfolio Optimization
2.5 Empirical Approach
2.6 Performance Measurement and Benchmarks
3 Results
3.1 Training Period Results
3.2 Test Period Results
3.3 Robustness Checks
4 Discussion
4.1 Sentiment Effect by Market
4.2 Sentiment Effect by Sign
4.3 Sentiment Effect by Business Cycle
5 Conclusion
Objectives and Topics
This thesis investigates whether investor sentiment can be systematically exploited to generate superior returns in portfolio optimization. The primary research question addresses the integration of sentiment indices into portfolio management using the Copula Opinion Pooling approach, evaluating if such strategies outperform standard market benchmarks.
- Measurement and aggregation of investor sentiment proxies across international markets.
- Implementation of the Copula Opinion Pooling (COP) mathematical framework for portfolio optimization.
- Empirical evaluation of sentiment-based trading strategies against traditional benchmarks.
- Analysis of the persistence and robustness of sentiment-induced mean-reversion effects.
- Examination of sentiment performance across different market regimes and business cycles.
Excerpt from the Book
Are financial markets efficient?
Nobel prize winner Eugene Fama devoted his PhD work to the so called Random Walk Hypothesis (Fama 1965). He models the series of asset prices by a random walk, which means that the price increments are stochastic, more specifically, identically and independently distributed. This implies that historical price data has no ability to predict future prices. Empirically, using data from the Dow Jones Industrial Average stock market index, Fama 1965 finds support for his random walk hypothesis. Consequently, any change in market prices can only result from changes in fundamentals, for example, changes of a company’s earnings, liabilities or profitability. Consistent with Fama’s Efficient Market Hypothesis, this change would happen instantaneously as soon as the new information about fundamentals become available to the public. Therefore, given the assumption that all investors share the same set of information at all time, there is no room for sentimental investors in efficient markets.
But if there is no noise in information, then how can there be trading? Trade arises from disagreement what the price of an asset should be. As Black 1986, p. 531, argues, disagreement results from differing opinions about the future performance of an asset, which most likely reflects differing information or its differing use. Most famously, Shiller, Fischer, and Friedman 1984 object the efficient market hypothesis on the grounds of observations about social dynamics in the stock market that are consistent with psychological findings about overreaction. In fact, their data support the notion that social dynamics influence the stock market by enforcing price trends that contradict the assumption by Fama 1965 that the asset price time series can be modeled by a random walk.
Summary of Chapters
Introduction: Provides the theoretical motivation, defines investor sentiment in financial markets, and formulates the central research questions.
Empirical Methodology: Details the mathematical frameworks, including Black-Litterman and Copula Opinion Pooling, used to incorporate sentiment into portfolio construction.
Results: Presents the empirical performance of the sentiment-based strategy during training and test periods, including various robustness checks.
Discussion: Analyzes the findings specifically regarding market specificity, the sign of sentiment indices, and business cycle influences.
Conclusion: Summarizes the key findings, confirms the economic value of the proposed sentiment strategy, and suggests paths for future research.
Keywords
Investor sentiment, portfolio optimization, Copula Opinion Pooling, Black-Litterman approach, stock market returns, mean-reversion, market timing, efficient market hypothesis, asset pricing, sentiment indices, risk-adjusted performance, financial modeling, contrarian investment, noise traders, behavioral finance.
Frequently Asked Questions
What is the core objective of this study?
The core objective is to develop a robust trading strategy that utilizes investor sentiment indices and effectively integrates them into portfolio optimization using the Copula Opinion Pooling (COP) methodology to achieve superior risk-adjusted returns.
Which sentiment proxies are utilized?
The thesis builds upon established proxies such as closed-end fund discounts, market turnover, number of IPOs, first-day returns on IPOs, and volatility premiums to construct composite sentiment indices for international markets.
How is sentiment integrated into the portfolio?
Sentiment is integrated primarily through the Copula Opinion Pooling (COP) framework, which allows for the simulation of market scenarios based on investor views, overcoming limitations of traditional mean-variance approaches.
What is the primary scientific method employed?
The study employs a quantitative empirical approach, utilizing principal component analysis to construct indices, followed by backtesting a sentiment-based trading strategy over training and test periods across international stock markets.
What does the main body of the work cover?
The main body covers the theoretical background, the construction of sentiment indices, the mathematical implementation of the COP approach, the empirical optimization process, and an extensive performance evaluation against standard benchmarks like Buy-and-Hold and Markowitz strategies.
Which keywords best characterize the work?
Key terms include investor sentiment, portfolio optimization, Copula Opinion Pooling, mean-reversion, and market timing.
How does this study handle market non-normality?
The study addresses market non-normality by employing the Copula Opinion Pooling approach, which provides the flexibility to work with arbitrary market distributions, unlike traditional models that rely on strict normality assumptions.
What is the role of the business cycle in this analysis?
The business cycle analysis examines whether the sentiment strategy's performance or its optimal portfolio weights fluctuate according to different economic regimes, specifically comparing expansionary versus recessionary phases.
- Quote paper
- Nicolas Banholzer (Author), 2018, Exploiting Investor Sentiment for Portfolio Optimization, Munich, GRIN Verlag, https://www.grin.com/document/425070
