Efficient Prices at Any Cost: Does Algorithmic Trading Deter Information Acquisition?

I demonstrate a powerful tension between acquiring information and incorporating it into asset prices. As a salient case, I analyze the transformative rise of algorithmic trading (AT), which is typically associated with improved price efficiency. Using a new measure of the relative information content of prices and a comprehensive panel of 31,872 stock-quarters of SEC market data, I establish instead that AT strongly decreases the amount of information in prices. Information losses are concentrated in stocks with high shares of algorithmic liquidity takers, suggesting that aggressive AT powerfully deters information acquisition despite its importance for translating available information into prices.