We consider a single security market based on a limit order book and two investors, with different speeds of trade execution. If the fast investor can front-run the slower investor, we show that this allows the fast trader to obtain risk free profits, but that these profits cannot be scaled. We...

We present a holistic framework for risk and reward management for decentralised lending protocols, such as AAVE or Compound. This framework highlights tensions and trade-offs in protocol design and the choice of various risks and incentives parameters.Using this, we identify the fundamental...

We investigate whether the fee income from trades on the CFM is sufficient for the liquidity providers to hedge away the exposure to market risk. We first analyse this problem through the lens of continuous-time financial mathematics and derive an upper bound for not-arbitrage fee income that...

A look at regulatory challenges and recommendation in the age of AI. Investigating topics like monopoly formation, machine learning auditability, bias mitigation strategies and automated regulatory monitoring

This is a holistic framework to approach fair prediction outputs at the individual and group level. This framework includes quantitative monotonic measures, residual explanations, benchmark competition, adversarial attacks, disparate error analysis, model agnostic pre-and post-processing,...

DataGene is developed to identify data set similarity between real and synthetic datasets as well as train, test, and validation datasets. For many modelling and software development tasks there is a need for datasets to have share similar characteristics. This has traditionally been achieved...

I document that a simple portfolio strategy, selling stocks with worsening business outlook, provides significant abnormal returns. I construct a portfolio of firms one day after they experience a change in business outlook for all sample trading days. Over the sample of 56 consecutive trading...

This thesis focuses on the use of machine learning in financial event prediction. In the past, finance academics had to be content with mostly linear models that could only ingest a small number of variables of a particular type. Now we can use non-linear models with a larger number of variables...

I study the use of non-linear models and accounting inputs to predict the occurrence of litigated bankruptcies and their associated filing outcomes. The main purpose of this study is to identify the accounting patterns associated with bankruptcies. The filing outcomes include, among others, how...