The foundation of this contribution will be the verification of the empirical results presented by Liu et al. (2017). We will start by summarizing their findings and explanations in Chapter 2 (Theoretical Background) and build the theoretical foundation upon which the empirical interpretation will rest. We actually view the compilation of the theoretical explanations in an intuitive line of reasoning as our first valuable contribution for our readers, as an understanding of the topic is not as easily to grasp after a first consultation of the papers. In Chapter 3 (Empirical Results) we address the actual calculations and most important summary statistics that will either speak in support or against our interpretations. In 3.1 (Data and Methodology), we present our dataset important technical notes and in Chapter 3.2 (The Beta Anomaly) we summarize and try to replicate the empirical findings of Liu et al. (2017), which will serve as the bedrock of interpretations we will arrive at in following pages. In Chapter 3.3 (Betting-against-Correlation) and its sub-sections on leverage, size and sentiment, we turn our attention to a paper by Asness et al. (2017) which contributed and responded to the findings of Liu et al. (2017), but with often diametrically contradicting views. The reference date of 2017 suggests that both papers belong to a highly new set of research and we believe that by covering those two and adding our own findings we can add significant clarity to actually understand what is going in the space of the beta anomaly. In Chapter 3.4 (BAB vs. BAI), we finalise the empirical analysis by contrasting opposing views on strategies that are designed to exploit the low-beta anomaly and realign the findings that we came across. We will summarize the newly gained insights for our readers in our last Chapter 4.
Table of Contents
1. Introduction
2. Theoretical Background
3. Empirical Results
3.1 Data and Methodology
3.2 The Beta Anomaly
3.3 Betting-Against-Correlation
3.3.1 BAC Leverage Effects
3.3.2 BAC and Size Effects
3.3.3 BAC and Sentiment Effects
3.4 BAB versus BAI
4. Conclusions
Research Objective and Themes
This thesis examines the validity of the beta anomaly, specifically investigating whether the phenomenon is driven by market beta itself or if it is a byproduct of idiosyncratic volatility (IVOL), mispricing, and leverage constraints. By replicating and extending recent empirical findings, the study aims to clarify the drivers of low-risk anomalies and evaluate the effectiveness of different trading strategies.
- Analysis of the relationship between idiosyncratic volatility (IVOL) and stock returns.
- Evaluation of the betting-against-beta (BAB) and betting-against-correlation (BAC) investment strategies.
- Assessment of the impact of investor sentiment on market anomalies.
- Investigation of size effects and leverage-based explanations for observed excess returns.
- Comparison of different asset-pricing model applications (CAPM, FF-3, FF-5) in explaining strategy profits.
Auszug aus dem Buch
1. Introduction
The beta anomaly is most likely one of the most widely known empirical phenomena and observations, not only in the academic world but also in the financial services industry and among financial practitioners and investors. Liu et al. (2017, p.1) even describe it as the longest-standing empirical challenge to the CAPM and other asset-pricing models which followed. Business students around the world learn the concepts and academic explanations of the low-beta anomaly in finance courses, which are based on findings developed over decades. As a prominent example of a practical implementation based on these findings and with a solid academic and empirical background, stands the paper by Frazzini and Pedersen (2014). By going long low-beta assets and short corresponding high-beta assets, one can yield significant positive risk-adjusted returns. With the rise of smart beta strategies in the form of various investment vehicles such as mutual funds, hedge funds and ETFs, investors have now received the opportunity to exploit this anomaly in some form or another as well.
This seemingly easy strategy appears to work because high-beta stocks are observed to return less than predicted by common asset-pricing models, as well known observed by Black et al. (1972) and many other academic contributions which followed and pointed to the same direction. Frazzini and Pedersen (2014) provide an overview on the fundamental explanations for this anomaly which belongs to the consensus line of reasoning. Among them is the main idea that a sufficient number of investors and market participants face funding constraints due to institutional properties, restricted or expensive access to leverage. Constrained investors, who require a higher risk profile, drive up equilibrium prices of high-beta assets through excessive demand or an overweighting of high-beta securities to compensate for the inability to apply leverage as a means of increasing risk. These investors require lower risk-adjusted returns and drive future returns of high-beta assets lower relative to low-beta assets (Frazzini and Pedersen (2014)).
Summary of Chapters
1. Introduction: Introduces the beta anomaly as a challenge to asset-pricing models and outlines the thesis's motivation to re-examine the phenomenon through the lens of recent empirical contributions.
2. Theoretical Background: Summarizes the consensus view of leverage-based explanations for the beta anomaly and introduces the counter-arguments regarding mispricing and idiosyncratic volatility.
3. Empirical Results: Details the dataset and methodology used for replicating studies, followed by a series of empirical tests analyzing beta, correlation, leverage, size, and sentiment effects.
3.1 Data and Methodology: Describes the criteria for stock selection, the construction of mispricing scores, and the statistical methods employed in the analysis.
3.2 The Beta Anomaly: Presents empirical findings on portfolio sorts based on mispricing and beta, confirming the existence of the anomaly primarily among overpriced stocks.
3.3 Betting-Against-Correlation: Explores the BAC strategy, comparing its performance to BAB and evaluating the impact of volatility and correlation on the strategy's returns.
3.3.1 BAC Leverage Effects: Analyzes the contribution of leverage to the performance of the BAC strategy across different mispricing quintiles.
3.3.2 BAC and Size Effects: Investigates the significant factor loading of the BAC strategy on the size factor and its implications for strategy profitability.
3.3.3 BAC and Sentiment Effects: Examines whether investor sentiment regimes provide predictive power for the performance of the BAC strategy.
3.4 BAB versus BAI: Contrasts the betting-against-beta (BAB) and betting-against-IVOL (BAI) strategies to determine which captures the anomaly more effectively.
4. Conclusions: Synthesizes the findings, supporting the view that the beta anomaly is heavily influenced by mispricing and idiosyncratic factors rather than beta exclusively.
Keywords
Beta Anomaly, Idiosyncratic Volatility, IVOL, Asset Pricing, Betting-Against-Beta, BAB, Betting-Against-Correlation, BAC, Betting-Against-IVOL, BAI, Market Mispricing, Leverage Constraints, Investor Sentiment, Fama and French, Empirical Finance
Frequently Asked Questions
What is the primary focus of this thesis?
The thesis investigates the "beta anomaly"—the empirical observation that low-beta stocks often outperform high-beta stocks—to determine if this phenomenon is truly driven by beta or if it is instead a byproduct of idiosyncratic volatility (IVOL), mispricing, and institutional constraints.
What are the central thematic areas covered?
The core themes include the analysis of the beta anomaly, the influence of idiosyncratic volatility, the role of leverage in investment strategies, the impact of firm size, and the influence of investor sentiment on asset returns.
What is the core research question or objective?
The main objective is to verify recent empirical findings (specifically by Liu et al. and Asness et al.) to clarify whether leverage-based explanations for the beta anomaly are robust or if they are "in disguise" as mispricing and IVOL effects.
Which scientific methods are utilized?
The study utilizes empirical quantitative methods, primarily conducting independent portfolio sorts, time-series analysis, and regression testing using the Fama and French 3-factor and 5-factor models on historical market data.
What is examined in the main part of the work?
The main section performs empirical tests by creating portfolios sorted on mispricing, beta, correlation, and volatility. It decomposes strategy returns (like BAB and BAC) to isolate the impact of leverage from "pure" anomaly spreads.
Which keywords best characterize the work?
Key terms include Beta Anomaly, Idiosyncratic Volatility (IVOL), Betting-Against-Beta (BAB), Betting-Against-Correlation (BAC), Asset Pricing, and Market Mispricing.
Why is the "size effect" considered surprising in this context?
The author notes that finding a very high and significant factor loading on the size factor was unexpected, as it suggests that the strategies designed to exploit low-beta or low-correlation anomalies are partially capitalizing on small-cap stock performance.
How does this thesis reconcile the conflicting views of Liu et al. and Asness et al.?
The thesis identifies that differences in sample filtering (specifically excluding stocks below $5) and methodology (such as sorting on correlation versus beta) largely explain the contradictory conclusions, ultimately suggesting that Liu et al.'s methodology is superior in capturing the underlying drivers.
- Citation du texte
- Mark Matern (Auteur), 2017, An Analysis of the Beta Anomaly from an Idiosyncratic Volatility Perspective, Munich, GRIN Verlag, https://www.grin.com/document/413290
