“Big data” financial technology raises concerns about market inefficiency. A common concern is that the technology might induce traders to extract others’ information, rather than to produce information themselves. We allow agents to choose how much they learn about future asset values or about others’ demands, and we explore how improvements in data processing shape these information choices, trading strategies and market outcomes. Our main insight is that unbiased technological change can explain a market-wide shift in data collection and trading strategies. However, in the long run, as data processing technology becomes increasingly advanced, both types of data continue to be processed. Two competing forces keep the data economy in balance: data resolve investment risk, but future data create risk. The efficiency results that follow from these competing forces upend two pieces of common wisdom: our results offer a new take on what makes prices informative and whether trades typically deemed liquidity-providing actually make markets more resilient.

The Great Recession was a deep downturn with long-lasting effects on credit, employment, and output. While narratives about its causes abound, the persistence of gross domestic product below precrisis trends remains puzzling. We propose a simple persistence mechanism that can be quantified and combined with existing models. Our key premise is that agents do not know the true distribution of shocks but use data to estimate it nonparametrically. Then, transitory events, especially extreme ones, generate persistent changes in beliefs and macro outcomes. Embedding this mechanism in a neoclassical model, we find that it endogenously generates persistent drops in economic activity after tail events.