We review the modelling and pricing of American options both in classical mathematical finance and in reinforcement learning (RL). Some of the main difficulties when pricing an American put option with reinforcement learning is to make sure that the continuation value one step before maturity is...

We consider the problem of being a cross-chain wealth management platform with deposits, redemptions and investment assets across multiple networks. We discuss the need for blockchain bridges to facilitates fund flows across platforms. We point out several issues with existing bridges. We...

In recent years major financial exchanges have introduced Frequent Batch Auctions (FBAs) as a novel automated auction mechanism for matching buyers and sellers of various types of asset, in contrast to the traditional Continuous Double Auction (CDA) that has been the basis of such exchanges...

We propose a class of execution algorithms that consists of a strategic layer and a speculative layer. The strategic layer is an optimal trading schedule that encodes the trader's objective, her tolerance to risk, and the impact of her own trades in the market. The schedule of the strategic...

This paper examines the short-run weak-form efficiency of equities, using a proprietary data set permitting analysis of orderbook characteristics to measure short-term price predictability. The results show that high levels of algorithmic trader activity in a stock lowers the level of short-run...

We ask how machine learning can change a crucial step of the scientific process in economics: the advancement of theories through the discovery of "anomalies." Canonical examples of anomalies include the Allais Paradox and the Kahneman-Tversky choice experiments, which are concrete examples of...

We use machine learning techniques on textual data to identify financial crises. The onset of a crisis and its duration have implications for real economic activity, and as such can be valuable inputs into macroprudential, monetary, and fiscal policy. The academic literature and the policy realm...

We investigate trade-offs when trying to encourage adoption of stock-trading algorithms. We organize an artificial stock market experiment over three weeks where investors experience trading on their own and with the help of a financial algorithm.They then choose whether to adopt it. We vary the...

In respect to racial discrimination in lending, we introduce global Shapley value and Shapley-Lorenz explainable AI methods to attain algorithmic justice. Using 157,269 loan applications during 2017 in New York, we confirm that these methods, consistent with the parameters of a logistic...

In this paper we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will...