Abstract: We propose that public investors react differently to patent issuance news depending on its novelty, and this misreaction exerts real impact on the firms' future innovation. Using textual analyses of patent documents to measure patent novelty, we find that investors under-react to the issuance of path-breaking innovations while overreact to the trend-following ones. We rationalize the empirical patterns with a bounded-rationality model where investors cannot figure out the true novelty of a patent at issuance due to cognitive limits. We verify the key model mechanism by showing that firms which receive noisier signals (firms with more retail traders) exhibit stronger misreaction. This misreaction is economically significant because novel patents bring higher economic value to the firm and have higher social value than non-novel patents. We also find that firms, on average, follow up less on their novel technology and issue fewer future novel patents, after an issuance of novel innovation. Using price pressure from mutual fund redemptions as an instrument, we present causal evidence that novel firms change innovation directions from novelty-seeking to copycat innovations following disappointing returns. The findings highlight that investor misreaction to patent novelty has a real impact on future innovation directions by steering firms away from higher-valued, groundbreaking research.
Discussant: Aakash Kalyani, Federal Reserve Bank of St. Louis
Abstract: Tweet-level data from a social media platform reveals high dispersion and systematic bias in the. quality of advice by financial influencers, or “finfluencers”: 28% of finfluencers are skilled, generating 2.6% monthly abnormal returns, 16% are unskilled, and 56% have negative skill (“antiskill”)
generating −2.3% monthly abnormal returns. Antiskilled finfluencers have more followers and more
influence on retail trading than skilled finfluencers. The advice by antiskilled finfluencers creates overly optimistic beliefs most times and persistent swings in followers’ beliefs. Consequently, finfluencers cause excessive trading and inefficient prices such that a contrarian strategy yields 1.2%
monthly out-of-sample performance.
Cameron Peng, London School of Economics and Political Science
Abstract: We build a model of the law of small numbers (LSN)---the incorrect belief that even small samples represent the properties of the underlying population---to study its implications for trading behavior and asset prices. In the model, a belief in the LSN induces investors to expect short-term price trends to revert and long-term price trends to continue. As a result, asset prices exhibit excess volatility, short-term momentum, and long-term reversals. The model makes additional predictions about investor behavior, including the coexistence of the disposition effect and return extrapolation, a weakened disposition effect for long-term holdings, "doubling down" in buying, consistency between doubling down and the disposition effect, and heterogeneous trading propensities to past returns. By testing these predictions using account-level transaction data, we show that the LSN provides a parsimonious way for understanding a variety of puzzles about investor behavior and asset prices.
Discussant: Eben Lazarus, University of California-Berkeley
