Job Market Paper
- Presented at CMU Mini-Conference Emerging Scholar Session
This paper studies factors that determine efficient information processing. I exploit a unique small business lending setting where the entire codified information set that loan officers use in their decision-making is observable (to the researcher). I decompose the loan officers’ decisions into a part driven by codified hard information and a part driven by uncodified soft information. I show that a machine learning model substantially outperforms loan officers in processing hard information. Using the machine learning model as a benchmark, I find that limited attention and overreaction to salient information largely explain the loan officers’ weakness in processing hard information. However, the loan officers acquire more soft information after seeing salient hard information, suggesting salience has a dual role: it creates bias in hard information processing, but facilitates attention allocation in new information acquisition.
Is Information Production for the U.S. Stock Market Becoming More Concentrated? (sole-authored, draft available upon request)
The US stock market has experienced dramatic shifts in structure in the past two decades. While small firms have disappeared, large ones have increasingly gained market share. I find that market share concentration leads to information production concentration, resulting in a divergence of stock price informativeness between large and small firms. This divergence is best explained by analysts’ incentive to follow large firms and investors’ incentive to trade them.
Information Inequality (with Ethan Rouen, draft available upon request)
Work in Progress
Bank Information Production and Credit Cycles (with Joao Granja)