A basic tenet of financial economics is that asset prices change in response to unexpected fundamental information. Since Roll’s (1988) provocative presidential address that showed little relation between stock prices and news, however, the finance literature has had limited success reversing this finding. This paper revisits this topic in a novel way. Using advancements in the area of textual analysis, we are better able to identify relevant news, both by type and by tone. Once news is correctly identified in this manner, there is considerably more evidence of a strong relationship between stock price changes and information. For example, market model R2s are no longer the same on news versus no news days (i.e., Roll’s (1988) infamous result), but now are 16% versus 33%; variance ratios of returns on identified news versus no news days are 120% higher versus only 20% for unidentified news versus no news; and, conditional on extreme moves, stock price reversals occur on no news days, while identified news days show an opposite effect, namely a strong degree of continuation. A number of these results are strengthened further when the tone of the news is taken into account by measuring the positive/negative sentiment of the news story.
with Jacob Boudoukh, Ronen Feldman, and Matthew Richardson.
A lot. Using data on startup loan applicants from a U.S. lender that employed an automated algorithm in its application review, we implement a regression discontinuity design assessing the causal impact of receiving a loan on entrepreneurial success. Obtaining a loan has a strong effect on the future financial position of startups. Startups receiving funding are dramatically more likely to survive, enjoy higher revenues and create more jobs. Loans are more consequential for survival among entrepreneurs with more education and less managerial experience. Access to credit creates a skewed firm size distribution by enabling quite small firms to succeed.
with Cesare Fracassi, Mark Garmaise, and Gabriel Natividad.