Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal nonanswers in earnings call questions and answers (Q&A). We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. As both our method and glossary are free of financial context, we believe that the measure is applicable to other fields with a Q&A setup outside the contextual domain of financial earnings conference calls.

Modigliani and Miller showed the market value of the company is independent of its capital structure, and suggested that dividend policy makes no difference to this law of one price. We experimentally test the Modigliani-Miller theorem in a complete market with two simultaneously traded assets, employing two experimental treatment variations. The first variation involves the dividend stream. According to this variation the dividend payment order is either identical or independent. The second variation involves the market participation, or not, of an algorithmic arbitrageur. We find that Modigliani-Miller’s law of one price can be supported on average with or without an arbitrageur when dividends are identical. The law of one price breaks down when dividend payment order is independent unless there is arbitrageur participation.

This paper provides new evidence of herding due to non- and fundamental information in global equity markets. Using quantile regressions applied to daily data for 33 countries, we investigate herding during the Eurozone crisis, China’s market crash in 2015–2016, in the aftermath of the Brexit vote and during the Covid-19 Pandemic. We find significant evidence of herding driven by non-fundamental information in case of negative tail market conditions for most countries. This study also investigates the relationship between herding and systemic risk, suggesting that herding due to fundamentals increases when systemic risk increases more than when driven by non-fundamentals. Granger causality tests and Johansen’s vector error-correction model provide solid empirical evidence of a strong interrelationship between herding and systemic risk, entailing that herding behavior may be an ex-ante aspect of systemic risk, with a more relevant role played by herding based on fundamental information in increasing systemic risk.