Abstract: Does data mining always increase price efficiency? Not necessarily. I incorporate data mining into a standard asset pricing model and identify a novel cost of complexity that arises endogenously from data mining. When a data miner explores alternative data, she faces a scarcer training history relative to potential predictors (increasing complexity) and an increasing difficulty in extracting useful signals (decreasing return in data efficacy). The cost of complexity and decreasing return in data efficacy together imply a finite optimal data mining level, such that excess data mining will lead to lower price informativeness. Empirically, I provide evidence of decreasing return in data efficacy in the context of the ``factor zoo'', and I show that the release of satellite data reduces price informativeness in a difference-in-difference setting.
Abstract: We propose that public investors react differently to patent issuance depending on its novelty, and these misreactions exert real impacts on the firms' future innovations. First, using textual analyses of patent documents to measure patent novelty, we find that investors underreact to the issuance of path-breaking innovations while overreact to the trend-following ones. (Non-)novel issuance predicts a return drift (reversal) of around 1% in two years. Novel patent issuance predicts lower risk but positive forecast errors, consistent with a non-risk-based novelty mispricing mechanism. A bounded-rationality model, where investors cannot figure out the true novelty of a patent at issuance due to cognitive limits, explains the empirical patterns well. Second, using exogenous distraction shocks, such as earthquakes, we present causal evidence that following disappointing returns, novel firms shift innovation directions from novelty-seeking to copycatting. The findings highlight that investors' misreactions to patent novelty impact firms' future innovation directions by steering them away from higher-valued, groundbreaking research.
Discussant: Yiming Qian, University of Connecticut
Abstract: I propose a model to examine how ESG investors influence firms' real green investments and greenwashing jointly. Paradoxically, stronger investor ESG preferences may reduce real green investments due to increased greenwashing, which undermines the reliability of ESG information. When this information distortion is severe, firms are disincentivized to make real green investments, as the market-perceived ESG gains are obscured by misinformation, while financial losses are fully reflected in stock prices. This unintended consequence is most likely when the cost of manipulating ESG information is low, the correlation between fundamentals is weak, and financial information quality is high. In addition, brown firms with poor financial performance are particularly prone to greenwashing, benefiting from ESG investors despite their actual impact. These findings raise concerns that ESG investing could backfire without effective disclosure regulations. I discuss policy measures to enhance real impact by curbing greenwashing, such as diversifying green technology options and linking executive compensation to ESG outcomes.
