Abstract

"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 resolves investment risk, but future data creates 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.

Download PDF

Citation

Farboodi, Maryam, and Laura Veldkamp. "Long Run Growth of Financial Data Technology." American Economic Review 110, no. 8 (August 2020): 2485-2523.

Each author name for a Columbia Business School faculty member is linked to a faculty research page, which lists additional publications by that faculty member.

Each topic is linked to an index of publications on that topic.