High Frequency Quoting and Price Discovery in the Foreign Exchange Market

This thesis studies the process of price discovery in the FX market via three empirical chapters. In the presence of high frequency trading and its expansion in the FX market, the first empirical chapter contributes to the literature by analysing how high frequency quoting affects price discovery in the foreign exchange market. It finds that while an increase in dealers’ quotation speed is positively associated with short-term (within 1 minute) price discovery, this is not the case for longer-term (1-day) price discovery. These results cast doubt on the overall benefit of high frequency activities for long term price discovery in the foreign exchange market. The second empirical chapter studies the impact of economist affiliation, quoting speed, and the geographical proximity on dealers’ contribution to price discovery around macroeconomic news announcements. The findings show that dealers with affiliated economists have higher contribution to price discovery and their contribution increases by increases in the research scope of their affiliated economists. The locality of dealers and economists to news sources is also found to create an information advantage for dealers. In the presence of the manipulation of the World Markets/Reuters benchmark in the foreign exchange market, regulators need a robust and timely methodology that identifies potential manipulation in order to better direct their limited resources towards more targeted in-depth investigation. The third empirical chapter of this thesis develops a manipulation index (ManIx) which captures the potential manipulation intention of dealers during the fixing period through a unique algorithm and simulation. The application of this model is able to identify banks that are prone to potential manipulative behaviour. The results concerning the identified banks are supported by verification of these bank with disclosure of regulatory investigations. Overall, ManIx offers a decision support tool to both regulators and banks to monitor market participants for manipulative behaviour

Bitcoin Under the Microscope

This paper explores and describes historical on-chain transaction data recorded on the Bitcoin blockchain, constructs a panel of all individual Bitcoin users, and computes their balances in the cross-section and over time. We run clustering algorithms to combine addresses that belong to the same user into wallets and we find that using wallets over addresses as the unit of analysis allows for more economically meaningful interpretations of user behavior. We identify and divide wallets into user categories - miners, exchanges, services, retail wallets and receiving-only addresses - and observe varying activity levels and balances in the cross-section and over time, corresponding to their intended role in the Bitcoin network. Our findings also suggest heterogeneity in financial performance across user categories with miners exhibiting higher realized returns relative to exchanges and retail users

Seeking sigma: Time-of-the-day effects on the Bitcoin network

This research investigates and tests for the presence of time-of-the-day effects on the Bitcoin network. Results indicate that NYSE trading sessions lead Bitcoin trading activity, both on the blockchain and centralised exchanges. Effects are found to have strengthened over time, however, simultaneously diminished at the weekend indicating significant exchange interactions, and that Bitcoin has developed somewhat outside its intended design parameters and is influenced by other forces such as those originating from NYSE trading. While proponents consider Bitcoin trading to be ‘24/7’, our findings suggest that both transaction and on-chain network activity are best described to be, at best, ‘12/5’, presenting significant implications for traders, with regards to centralised exchange liquidity and the speed of their transaction inclusion on the blockchain. Finally, the role and influence of both algorithm and volatility traders cannot be eliminated

Bitcoin under the microscope

This paper explores and describes historical on-chain transaction data recorded on the Bitcoin blockchain, constructs a panel of all individual Bitcoin users, and computes their balances in the cross-section and over time. We run clustering algorithms to combine addresses that belong to the same user into wallets and we find that using wallets over addresses as the unit of analysis allows for more economically meaningful interpretations of user behavior. We identify and divide wallets into user categories -- miners, exchanges, services, retail wallets and receiving-only addresses -- and observe varying activity levels and balances in the cross-section and over time, corresponding to their intended role in the Bitcoin network. Our findings also suggest heterogeneity in financial performance across user categories with miners exhibiting higher realized returns relative to exchanges and retail users

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Time Weighted Price Contribution

In the era of high frequency trading and the pervasiveness of irregularly spaced trading, we control for the time element in the Modified Weighted Price Contribution (MWPC) model by Jahanshahloo and Spokeviciute (2018). We empirically show that our new modification controls for reaction time (Speed) of market participants to arrival of new information

Approximation of Value Efficiency in DEA with negative data 1

This paper considers the problem of negative data in Data Envelopment Analysis (DEA) models. Our focus is on how to calculate value efficiency in DEA models with negative data. In this paper, we will introduce an Multi Objectives Linear Programming (MOLP) model which its objective functions are input/output variables subject to the defining constraints of Production Possibility Set (PPS) of DEA models. So, we propose an effective method to find the best solution, such that by that we obtain the Most Preferred solution (MPS). Finally, value efficiency scores are calculated related to the inefficient units having the same value related to the MPS

Bitcoin under the microscope

This paper explores and describes historical on-chain transaction data recorded on the Bitcoin blockchain, constructs a panel of all individual Bitcoin users, and computes their balances in the cross-section and over time. We run clustering algorithms to combine addresses that belong to the same user into wallets and we find that using wallets over addresses as the unit of analysis allows for more economically meaningful interpretations of user behavior. We identify and divide wallets into user categories – miners, exchanges, services, retail wallets and receiving-only addresses – and observe varying activity levels and balances in the cross-section and over time, corresponding to their intended role in the Bitcoin network. Our findings also suggest heterogeneity in financial performance across user categories with miners exhibiting higher realized returns relative to exchanges and retail users

Trading patterns in the Bitcoin market

Despite the growing literature on Bitcoin and other cryptocurrencies, we know relatively little about who are involved in trading, transacting and using these assets and how they behave. Examining millions of Bitcoin transaction records, we show that less than 1% of Bitcoin users contribute to more than 95% of the market volumes. These ‘whales’ are often associated with strategic trading/transaction volumes, market reactions and timing patterns. Using K-means clustering on a comprehensive transaction dataset, we establish a typology of traders by learning their trading exchange patterns, strategies and impact risk and market microstructure. Our approach ‘learns’ and identifies five distinct groups or types of Bitcoin users, which are somewhat, though not entirely, comparable to popular categorisations used in conventional market such as fundamental, technical, retail and institutional traders as well as market makers. Four of these groups present distinguishable trading patterns with a strong impact on liquidity provision and trading signals