We apply the Diebold and Yilmaz (2014) methodology to daily stock prices of the largest 40 U.S. financial institutions to construct a volatility connectedness index. We then estimate the contemporaneous return sensitivity of every non-financial U.S. company to this index. We find that there is a...

We use lasso methods to shrink, select and estimate the network linking the publicly-traded subset of the world's top 150 banks, 2003-2014. We characterize static network connectedness using full-sample estimation and dynamic network connectedness using rolling-window estimation. Statistically,...

I use the financial crisis of 2008 as a natural experiment to identify the value of non-financial firms' stake in the banking system. Unrated firms underperform investment grade rated firms when Lehman Brothers fails, and overperform when the Treasury injects capital into the nine largest US...

If information is not perfect, theories prescribe a negative relation between information availability and expected stock returns. Using two readily available variables, price and volume, I construct a new proxy for information and test its relation to returns in the 1964-2007 period on...

The private pension fund system in Turkey presents a unique institutional structure where bank holding companies can own both private pension companies and asset management firms. More often than not, pension companies delegate their operational mandates to the asset management arm of the same...

We examine the hypothesis that stock prices underreact to corporate news and that trading volume and return variability are proxies for latent news flow. Consistent with this notion, we find that that price continuation and potential momentum profits are larger after elevated levels of volume...

Using a survivorship and selection bias free database of actively managed private Turkish Pension funds we show that most managers are not able to provide performance above and beyond what could be earned by passive indexing. At its simplest, funds with self-declared benchmarks fail to beat them...

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual...

We revisit the classic semiparametric problem of inference on a low di-mensional parameter Ø0 in the presence of high-dimensional nuisance parameters Û0. We depart from the classical setting by allowing for Û0 to be so high-dimensional that the traditional assumptions, such as Donsker...

We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most...