Abstract: We use high-performance Continuous Double Auction trading software and algorithms to study the effects of algorithmic trading on pricing, market stability, and allocative efficiency in a laboratory environment with mean-variance incentives for diversification (CAPM). We design algorithms that bid marginal valuations modulo a spread and a market-making (maker) or liquidity-taking (taker) parameter. The spread and maker/taker parameters are controlled by investors who also decide whether to deploy an algorithm or not. Thus, in our setting, algorithms and their effects are endogenous. Using simulations, we show that allocations and prices always come close to efficiency, but are affected by the choice of spread. Data from four laboratory experimental sessions support and extend findings from simulations. Most traders deploy an algorithm whenever available (82% average across all round), traders are likelier to switch from makers to takers than vice-versa and traders who deploy makers significantly increase the chosen spread with experience. While these algorithm choices—consistent with simulations—are efficiency reducing (high spread depletes gains from trade and taker algorithms deplete liquidity), efficiency is increased with respect to rounds without algorithms. Realized gains from trade increase from 58% to 91% and, while the allocative efficiency increases across the board, those who most benefit are the traders that perform the worst in manual trading.

Co-authors: Elena Asparouhova, Jan Nielsen, Christine Parlour, and Wenhao Yan

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