Algorithmic Trading, Price Efficiency and Welfare: An Experimental Approach

We develop a novel experimental paradigm to study the causal impact of two classes of trading algorithms on price efficiency, trading volume, liquidity, and welfare. In our design, public information about the asset value is revealed during trading, which gives algorithms a reaction speed advantage. We distinguish market-order (aggressive) and limit-order (passive) algorithms, which replace human traders from the baseline markets. Relative to human-only markets, limit-order algorithms improve welfare, although human traders do not benefit, as the surplus is captured by the algorithms. Market-order algorithms do not change welfare, though they do lower human traders’ profits. Both types of algorithms improve price efficiency, lower volatility, and increase the share of profits for unsophisticated human traders. Our results offer unique evidence that non-exploitative algorithms can enhance welfare and be beneficial to unsophisticated traders.