Tweet-level data from a social media platform reveals low average accuracy and high dispersion 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...

Mergers and acquisitions are often motivated by the intention of creating value from intangible assets. We develop a novel word list of intangibles and apply it to takeover announcements. Deals presented with more “intangibles talk” complete more quickly. However, the value of these deals to...

Donald Trump's election and his nomination of Scott Pruitt, a climate skeptic, to lead the Environmental Protection Agency drastically downshifted expectations on US climate-change policy. We study firms' stock-price reactions and institutional investors' portfolio adjustments after these...

We use the database leak of Mt. Gox exchange to analyze the dynamics of the price of bitcoin from June 2011 to November 2013. This gives us a rare opportunity to study an emerging retail-focused, highly speculative and unregulated market with trader identifiers at a tick transaction level. Jumps...

We propose a technique to avoid spurious detections of jumps in high-frequency data via an explicit thresholding on available test statistics. We prove that it eliminates asymptotically all spurious detections. Monte Carlo results show that it performs also well in finite samples. In Dow Jones...

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes...

We investigate the performance of non-linear return prediction models in the high complexity regime, i.e., when the number of model parameters exceeds the number of observations. We document a "virtue of complexity" in all asset classes that we study (US equities, international equities, bonds,...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance—in terms of SDF Sharpe ratio and average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best...

We show that financial risk-taking originates in preindustrial ancestral population diversity. We use data on immigrants residing in the United States and show that controlling for all known determinants of portfolio decisions, diversity positively affects stock market participation and asset...

We study the impact of global financial sanctions on the Russian banks and economy. Financial sanctions were consecutively imposed between 2014 and 2019, allowing potentially-targeted (but not yet sanctioned) banks to adjust their international and domestic exposures. Compared to similar other...