This dissertation consists of three research papers on financial system efficiency and stability. The findings of the thesis expand our understanding of equity market liquidity, efficiency, herding behaviour and potential systemic risks. The results also have significant implications for policy makers and various market participants, in particular those concerned with the impact of changes to trading rules on equity markets . Paper 1 examines the impact of the Order Protection Rule (OPR) on market liquidity and price discovery in North America. The OPR is one of the most influential policies that guarantees that trades are executed at the best available price and provides fair protection for investors. Using the difference - in - differences (DiD) and two - stage least squares (2SLS) methodologies, we find that market liquidity increases and transaction cost decreases. We evaluate two possible channels, market fragmentation and active returns, and find that only the former explains the result. Furthermore, we find a more efficient price discovery process, while the in creased liquidity mainly happens to U.S. lit markets where orders are displayed on order book transparently. We also find a significant improvement in national quoted prices and depth in small trading venues in Canada.
Paper 2 examines the impact of the U. S. Securities and Exchange Commission Tick Size Pilot on commonality in liquidity, i.e. the level of co - movement between a security's liquidity and that of the corresponding Tick Size Pilot group. The Tick Size Pilot increases the tick size from 1 cent to 5 cents for a chosen list of small capitalisation stocks. Using a DiD methodology, we find an increase in liquidity commonality due to the higher analyst c overage and institutional ownership across treatment stocks in the pilot period. We further investigate the reactions from stocks not included in the pilot period and find that the tick unconstrained stocks attracted more analysts' attention and institutional investors, resulting in a higher commonality in liquidity. Paper 3 examines herding behaviour among investors in the renewable energy sector in the U.S. Over the last decade, the renewable energy sector demonstrated significant growth in the global economy. However, the sector also has variations in performance, with periods of relatively high active returns and others with substantial underperformance. We examine the relationship between the level of equity return dispersion - measured by the cross - sectional absolute deviation (CSAD) of returns - and the overall market return in the renewable sector. Using data from January 2000 to December 2015, we find significant evidence of excess return dispersion, or dispersion in the renewable energy sector for several sub - periods. We also find evidence of asymmetric return dispersion, indicating a different imp act of positive or negative market returns on return dispersion. These results also hold when considering risk - adjusted returns for the renewable sector based on a CAPM or multifactor model. In addition, we find some evidence of investor herding during periods of low market liquidity. Our results indicate a unique behaviour of returns in the renewable energy sector compared to other equity markets. Overall, investors in renewable energy stocks seem to disagree on their interpretation of large market movements, leading to an even higher return dispersion than predicted by standard asset pricing models.
History
Table of Contents
Chapter 1. Introduction -- Chapter 2. Order protection rule, market liquidity, and price discovery -- Chapter 3. Commonality in liquidity: the impact of the U.S. tick size pilot -- Chapter 4. Investor herding and dispersion in the renewable energy sector -- Chapter 5. Conclusion.
Notes
Bibliography: pages 153-160
Theoretical thesis.
Awarding Institution
Macquarie University
Degree Type
Thesis PhD
Degree
PhD, Macquarie University, Macquarie Business School, Department of Actuarial Studies and Business Analytics
Department, Centre or School
Department of Actuarial Studies and Business Analytics