Showing papers by "Wei Jiang published in 2020"
TL;DR: In this article, the authors analyzed the drivers, nature, and consequences of such settlement agreements and found no evidence to support concerns that settlements enable activists to extract rents at the expense of other investors.
45 citations
TL;DR: The authors conducted a comprehensive empirical analysis of bank contingent convertible capital securities (CoCo) issues and found that the propensity to issue a CoCo is higher for larger and better capitalized banks, indicating that they generate risk-reduction benefits and lower costs of debt.
38 citations
TL;DR: These findings indicate that increasing machine and AI readership, proxied by machine downloads, motivates firms to prepare filings that are more friendly to machine parsing and processing.
Abstract: This paper analyzes how corporate disclosure has been reshaped by machine processors, employed by algorithmic traders, robot investment advisors, and quantitative analysts. Our findings indicate that increasing machine and AI readership, proxied by machine downloads, motivates firms to prepare filings that are more friendly to machine parsing and processing. Moreover, firms with high expected machine downloads manage textual sentiment and audio emotion in ways catered to machine and AI readers, such as by differentially avoiding words that are perceived as negative by computational algorithms as compared to those by human readers, and by exhibiting speech emotion favored by machine learning software processors. The publication of Loughran and McDonald (2011) is instrumental in attributing the change in the measured sentiment to machine and AI readership. While existing research has explored how investors and researchers apply machine learning and computational tools to quantify qualitative information from disclosure and news, this study is the first to identify and analyze the feedback effect on corporate disclosure decisions, i.e., how companies adjust the way they talk knowing that machines are listening.
37 citations
TL;DR: In this paper, the authors exploit this setting, which is unique because the delay until public disclosure was exogenous and the private information window was well defined, to study informed trading with a random stopping time.
Abstract: For years, the Securities and Exchange Commission (SEC) accidentally distributed securities disclosures to some investors before the public. We exploit this setting, which is unique because the delay until public disclosure was exogenous and the private information window was well defined, to study informed trading with a random stopping time. Trading intensity and the pace at which prices incorporate information decrease with the expected delay until public release, but the relation between trading intensity and time elapsed varies with traders' learning process. Noise trading and relative information advantage play similar roles as in standard microstructure theories assuming a fixed time window.
15 citations
TL;DR: In this article, the authors developed measures for technology decoupling and dependence between the U.S. and China based on combined patent data and found that decoupledness is associated with more patent outputs in China, but lower firm productivity and valuation.
Abstract: We develop measures for technology decoupling and dependence between the U.S. and China based on combined patent data. The first two decades of the century witnessed a steady increase in technology integration (or less decoupling), but China’s dependence on the U.S. increased (decreased) during the first (second) decade. Decoupling in a technology field predicts China’s growing dependence on U.S. technology, which, in turn, predicts less decoupling further down the road. Decoupling is associated with more patent outputs in China, but lower firm productivity and valuation. China’s innovation-oriented industrial policies trade off the inherent conflict between indigenous innovation and firm competitiveness.
2 citations
Posted Content•
TL;DR: This paper analyzed how corporate disclosure has been reshaped by machine processors, employed by algorithmic traders, robot investment advisors, and quantitative analysts, and found that increasing machine and AI readership, proxied by machine downloads, motivates firms to prepare filings that are more friendly to machine parsing and processing.
Abstract: This paper analyzes how corporate disclosure has been reshaped by machine processors, employed by algorithmic traders, robot investment advisors, and quantitative analysts. Our findings indicate that increasing machine and AI readership, proxied by machine downloads, motivates firms to prepare filings that are more friendly to machine parsing and processing. Moreover, firms with high expected machine downloads manage textual sentiment and audio emotion in ways catered to machine and AI readers, such as by differentially avoiding words that are perceived as negative by computational algorithms as compared to those by human readers, and by exhibiting speech emotion favored by machine learning software processors. The publication of Loughran and McDonald (2011) is instrumental in attributing the change in the measured sentiment to machine and AI readership. While existing research has explored how investors and researchers apply machine learning and computational tools to quantify qualitative information from disclosure and news, this study is the first to identify and analyze the feedback effect on corporate disclosure decisions, i.e., how companies adjust the way they talk knowing that machines are listening.
1 citations
TL;DR: Appraisal is a legislatively created right for shareholders to seek a judicial determination of the fair value of their stock in certain transactions as mentioned in this paper, and it has been widely used in finance.
Abstract: Appraisal is a legislatively created right for shareholders to seek a judicial determination of the fair value of their stock in certain transactions. For many decades, appraisal was a little used, frequently maligned, corporate law remedy. Beginning at the turn of the 21st century, this all changed when a group of financial investors, including several hedge funds, began filing appraisal cases. Appraisal arbitrage, as it became known, grew rapidly in popularity.