Exploiting Investor Sentiment for Portfolio Optimization

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

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.