Long-Run Growth of Financial Data Technology

• 成果类型: Article
• 署名作者: Farboodi, Maryam; Veldkamp, Laura
• 署名单位: Massachusetts Institute of Technology (MIT); National Bureau of Economic Research; Centre for Economic Policy Research - UK
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20171349
• 发表日期: 2020
• 页码: 2485-2523
• 关键词: information acquisition; public communication; asset prices; uncertainty; signal; noise

摘要

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.