We show that off-exchange (wholesaler) executions provide significant trading cost savings
to retail investors. Despite industry concentration, three findings suggest that wholesalers do …

The integration of algorithmic trading and reinforcement learning, known as AI-powered
trading, has significantly impacted capital markets. This study utilizes a model of imperfect …

Recent academic research has extensively examined algorithmic collusion resulting from
the utilization of artificial intelligence (AI)-based dynamic pricing algorithms. Nevertheless, e …

Research on the possibility of algorithmic collusion has rapidly expanded in recent years
and has come to the attention of competition authorities worldwide. Claims regarding the …

In the dynamic discrete-time trading setting of Kyle (Econometrica 53: 1315–1336, 1985), we
prove that Kyle's equilibrium model is stable when there are one or two trading times. For …

This paper proposes a dynamic model of the limit order book to test if a trading algorithm will
learn to spoof the order book. We derive testable conditions that are simple to implement …

We apply machine learning to estimate the average daily bid-ask spread by combining
classical microstructure models with widely available low-frequency (daily) data, in the US …

We study the effects of quantitative investing, an increasingly popular investment style, on
financial market quality. Within a noisy REE model of strategic speculation with two informed …

As firms' pricing strategies increasingly rely on algorithms, two concerns have received
much attention: algorithmic tacit collusion and price discrimination. This paper investigates …

Two issues of algorithmic collusion are addressed in this paper. First, we show that in a
general class of symmetric games, including Prisoner's Dilemma, Bertrand competition, and …