Participants in an experimental market choose to enter private value trades manually and/or algorithmically. Each algorithm or trading robot makes or takes liquidity based on the trader’s current marginal valuation modulo a spread chosen by the trader. We evaluate experimental outcomes against both competitive equilibrium and equilibrium of the strategic game if all participants choose robots. Data from six laboratory experimental sessions support many of the theoretical findings. Most traders deploy an algorithm whenever available (the average trader deploys a robot in 82% of the rounds, and only 4% of subjects never deploy a robot), and learn to use them with experience. Compared to rounds with only manual trading, algorithms improve allocative efficiency. Realized gains from trade increase from 55% to 84%. While the allocative efficiency increases across the board, those who benefit most are the traders who perform poorly in manual trading. Our results highlight how algorithm choice can affect relative outcomes and market observables.

A core responsibility of the Federal Reserve is to ensure financial stability by acting as the “lender of last resort” through its Discount Window (DW). Historically, however, the DW has not been effective because its usage is stigmatized. The experimental analysis of Armantier and Holt (2020) suggest that regular random DW borrowing
can overcome DW stigma. In this paper, we report on experiments showing that random borrowing remains effective even after the financial system is hit by solvency or liquidity shocks. However, random borrowing alone may not be sufficient to remove pre-existing stigma

We examine how professional traders behave in two financial market experiments; we contrast professional traders’ behavior to that of undergraduate students, the typical experimental subject pool. In our first experiment, both sets of participants trade an asset over multiple periods after receiving private information about its value. Second, participants play the Guessing Game.
Finally, they play a novel, individual-level version of the Guessing Game and we collect data on their cognitive abilities, risk preferences, and confidence levels. We find three differences between traders and students: Traders do not generate the price bubbles observed in previous
studies with student subjects; traders aggregate private information better; and traders show higher levels of strategic sophistication in the Guessing Game. Rather than reflecting differences in cognitive abilities or other individual characteristics, these results point to the impact of traders’ on-the-job learning and traders’ beliefs about their peers’ strategic sophistication.

Money is called essential when better outcomes are incentive feasible with money than without it. We study essentiality theoretically and experimentally, using finite-horizon monetary models that suit our purposes well in the lab. Following mechanism design, we also study the effects of strategy recommendations when they are incentive compatible and when they are not. Results show
the use of money and welfare are significantly higher in treatments where it is essential, and recommendations help when incentive compatible but not much otherwise. Sometimes money gets used when it should not, and we investigate why using surveys plus measures of social preferences.