Семинар AIM Lab "Market efficiency, informational asymmetry and pseudo-collusion of adaptively learning agents"

The article "Market efficiency, informational asymmetry and pseudo-collusion of adaptively learning agents" will be presented and discussed at the seminar. Abstract: We examine the dynamics of informational efficiency in a market with asymmetrically informed, boundedly rational traders who adaptively learn optimal strategies using simple multiarmed bandit (MAB) algorithms. The strategies available to the traders have two dimensions: on the one hand, the traders must endogenously choose whether to acquire a costly information signal, on the other, they must determine how aggressively they trade by choosing the share of their wealth to be invested in the risky asset. Our study contributes to two strands of literature: the literature comparing the effects of competitive and strategic behavior on asset price efficiency under costly information as well as the actively growing literature on algorithmic pseudo-collusion in financial markets. We find that for certain market environments our results contradict the predictions of Kyle [1989]* in that a market with strategically acting traders can be more efficient than a purely competitive one. Furthermore, we obtain novel results on the ability of independently learning traders to coordinate on a pseudo-collusive behavior, leading to non-competitive pricing. Contrary to some recent contributions (see e.g. [Cartea et al. 2022]**), we find that the pseudo-collusive behavior in our model is robust to a large number of agents, demonstrating that even in the setting of financial markets with a large number of independently learning traders non-competitive pricing and pseudo-collusive behavior can frequently arise. 

* Kyle, Albert S. "Informed speculation with imperfect competition." The Review of Economic Studies 56, no. 3 (1989): 317-355.
** Cartea, Álvaro, Patrick Chang, Mateusz Mroczka, and Roel Oomen. "AI-driven liquidity provision in OTC financial markets." Quantitative Finance 22, no. 12 (2022): 2171-2204.