Hedge fund managers are characterised as either market timers or asset pickers . Their superior performance can be attributed to either timing skill, selection ability or a combination of both. In the existing literature, average hedge fund performance across the entire time span under investigation is usually investigated and measured, and hence, potentially certain subtle but important features exhibited in different time periods can be averaged out in the analysis. This thesis investigates the structural breaks in the selection ability and timing skill of hedge fund managers. This research issue is of particular importance when the hedge fund performance before, during and after the recent financial crisis is compared and contrasted. This thesis conducts a structural break analysis of hedge fund managers performance in relation to market-wide liquidity and liquidity commonality in the foreign exchange (FX) market. Liquidity commonality captures the co-movement of individual asset liquidities. The measure adopted in the existing literature has several limitations. This thesis proposes a new measure, termed the Beta Index, which is derived from the time-varying exposure of individual liquidities to market liquidity movements. It is shown that the developed Beta Index is more able to identify the level of liquidity commonality in the FX market. It is also more flexible in measuring commonality with different data sampling frequency. The obtained empirical results have some practical implications. They show that the selection skill and timing ability of hedge fund managers are subject to regime switches. Under severe market conditions, most hedge fund managers possess the skill to time FX market-wide liquidity and are able to reduce losses from the FX market by reducing their funds FX exposure prior to the FX market-wide liquidity deteriorations. In the meantime, most hedge fund managers are able to deliver excess returns from time to time due to their selection ability. However, when sudden shocks of crisis occur, they fail to forecast the unexpected behaviour in the price of individual assets underlying the funds and display unsuccessful selection ability. In addition, the results suggest that many hedge funds are exposed to the FX liquidity commonality risk which impairs hedging strategies and diversification performance.

Emerging ESG studies have established a negative equilibrium correlation between ESG factors and stock returns in an economy predominately influenced by investors with nonpecuniary preference over high ESG credentials. However, little research has delved into a potential systematic liquidity risk phenomenon associated with aggregate trading activities of ESG-motivated investors who share a common nonzero ESG preference component in their utility function. Focusing on the carbon footprint metric of ESG factors, this thesis aims to investigate the potential existence of an ESG-specific component in liquidity commonality among equities listed on Nasdaq Stockholm, with a key assumption being that the average investor active on the Swedish equity market is cognizant of emission data and willing to forgo financial returns for positive externalities. Using a calibrated portfolio sorting technique and a set of time series regression models, the thesis uncovers novel evidence of liquidity synchronicity among ESG-unfavorable stocks. Additionally, the results indicate that liquidity dynamics of ESG frontrunners tend to be reflective of firm level characteristics. These findings remain robust even after controlling for market-wide driving forces, industry effects, and nonsynchronous liquidity co-movements etc. Investors prioritizing climate efforts may have tilted their capital away from emission laggards which give rise to a “flight to climate” effect on stock liquidity synchronicity among brown equities. Their resultant constrained investor base may lead to simultaneous liquidity oscillation as observed. Notwithstanding, the thesis does not measure explicit mechanisms through which ESG factors impact stock liquidity commonality, leaving this as a topic for future research.

Financial markets perform two major functions. The first is the provision of liquidity in order to facilitate direct investment, hedging and diversification; the second is to ensure the efficient price discovery required in order to direct resources to where they can be best utilised within an economy. How well financial markets perform these functions is critical to the financial welfare of every individual in modern economies. As an example, retirement savings across the world are mostly invested in capital markets. Hence, the functioning of financial markets is linked to the standard of living of individuals. Technological advancements and new market regulations have in recent times significantly impacted how financial markets function, with no period in history having witnessed a more rapid pace of change than the last decade. Financial markets have become very complex, with most of the order execution now done by computer algorithms. New high-tech trading venues, such as dark pools, also now play outsized roles in financial markets. A lot of the impacts of these developments are poorly understood. In the EU particularly, the introduction of the Markets in Financial Instruments Directive (MiFID) and advancements in technology have combined to unleash a dramatic transformation of European capital markets. In order to better understand the role of high-tech trading venues in the modern financial markets' trading environment generally and in the UK in particular, I conduct three studies investigating questions linked to the three major developments in financial markets over the past decade; these are algorithmic/high-frequency trading, market fragmentation and dark trading. In the first study, I examine the changing relationship between the price impact of block trades and informed trading, by considering this phenomenon within a high-frequency trading environment on intraday and inter-day bases. I find that the price impact of block trades is stronger during the first hour of trading; this is consistent with the hypothesis that information accumulates overnight during non-trading hours. Furthermore, private information is gradually incorporated into prices despite heightened trading frequency. Evidence suggests that informed traders exploit superior information across trading days, and stocks with lower transparency exhibit stronger information diffusion effects when traded in blocks, thus informed block trading facilitates price discovery. The second study exploits the regulatory differences between the US and the EU to examine the impact of market fragmentation on dimensions of market quality. Unlike the US's Regulation National Market System, the EU's MiFID does not impose a formal exchange trading linkage or guarantee a best execution price. This has raised concerns about consolidated market quality in increasingly fragmented European markets. The second study therefore investigates the impact of visible trading fragmentation on the quality of the London equity market and find a quadratic relationship between fragmentation and adverse selection costs. At low levels of fragmentation, order flow competition reduces adverse selection costs, improves market transparency and enhances market efficiency by reducing arbitrage opportunities. However, high levels of fragmentation increase adverse selection costs. The final study compares the impact of lit and dark venues' liquidity on market liquidity. I find that compared with lit venues, dark venues proportionally contribute more liquidity to the aggregate market. This is because dark pools facilitate trades that otherwise might not easily have occurred in lit venues when the spread widens and the limit order queue builds up. I also find that informed and algorithmic trading hinder liquidity creation in lit and dark venues, while evidence also suggests that stocks exhibiting low levels of informed trading across the aggregate market drive dark venues' liquidity contribution.