The frequency of "brilliant" and "genius" in teaching evaluations predicts the representation of women and African Americans across academia

Because of the negative stereotypes against women’s and African Americans’ intellectual abilities, academic fields that prize brilliance and genius might be unwelcoming to members of these stigmatized groups. A recent nationwide survey of academics provided initial support for this possibility, insofar as the fields whose practitioners believed that natural talent is crucial for success in their field were also the fields where women and African Americans were least likely to obtain Ph.D.’s. The present study seeks to replicate this initial finding with a different, and arguably more naturalistic, measure of the extent to which brilliance and genius are prized within a field. Specifically, we measured field-by-field variability in the emphasis on these intellectual qualities by tallying college students’ use of the words “brilliant” and “genius” in over 14 million reviews on RateMyProfessors.com. Consistent with prior work, this simple word count predicted both women’s and African Americans’ representation at the Ph.D. level across the academic spectrum: Fields where the words “brilliant” and “genius” were frequent in undergraduates’ evaluations also had fewer female and African American Ph.D.’s. This relationship held even when accounting for a field’s intellectual rigor (as indexed by students’ average scores on the Quantitative Graduate Record Examination [GRE]), as well as several other explanations concerning group differences in representation. The fact that such a simple, naturalistic measure of a field’s focus on brilliance predicted the magnitude of its gender and race gaps speaks to the tight link between ability beliefs and diversity

Implied Market Loss Given Default in the Czech Republic: Structural-Model Approach

This paper focuses on the key credit risk parameter – Loss Given Default (LGD). We describe its general properties and determinants with respect to seniority of debt, characteristics of debtors and macroeconomic conditions. Furthermore, we illustrate how the LGD can be extracted from market observable information with help of the adjusted Mertonian structural approach. We present a derivation of the formula for the expected LGD and show its sensitivity with respect to other structural company parameters. Finally, we estimate the 5-year expected LGDs for companies listed on the Prague Stock Exchange and find that the average LGD for this analyzed sample is in the range of 20–45 %. To the authors’ knowledge, these are the first implied market estimates of LGD in the Czech Republic.loss given default, credit risk, structural models

Monetary Policy Rules with Financial Instability

To provide a rigorous analysis of monetary policy in the face of financial instability, the authors extend the standard dynamic stochastic general equilibrium model to include a financial system. Their simulations suggest that if financial stability affects output and inflation with a lag, and if the central bank has privileged information about financial stability, then monetary policy responding instantly to deteriorating financial stability can trade off more output and inflation instability today for a faster return to the trend than a policy that follows the traditional Taylor rule. This augmented rule leads in some parameterizations to improved outcomes in terms of long-term welfare, but the welfare impacts of such a rule are small.DSGE models, financial instability, monetary policy rule

Volatility Regimes in Macroeconomic Time Series: The Case of the Visegrad Group

The authors analyze several monthly and quarterly macroeconomic time series for the Czech Republic, Poland, Hungary, and Slovakia. These countries embarked on an economic transition in the early 1990s which ultimately led to their membership in the European Union, with Slovakia joining the euro area in 2009. It is natural to assume that changes of such a magnitude should also influence the major macroeconomic indicators. The authors explore the characteristics of these series by endogenously identifying their volatility regimes. In the course of their analysis, they show the difficulties in the handling of unit roots as a necessary step preceding volatility modeling. The final set of breaks identified shows very few changes near the beginning of the series, which corresponds to the transition period.macroeconomic fluctuations, economic transition, structural breaks, volatility regimes, cumulative sum of squares, unit root testing

Detecting Information-Driven Trading in a Dealers Market

We focus on the extent of information-driven trading originating from order flows to capture the behavior of the market makers on an emerging market. We modified the classical Easley et al. (1996) model for the probability of informed trading using a jackknife approach in which trades of one particular market maker at a time are left out from the sum of all buys and sells. Using the estimates from the jackknife approach, for each market maker we test whether the order flows associated with the particular market maker behaved significantly differently from the others. Data from the Prague Stock Exchange SPAD trading platform are used to demonstrate our methodology. Finding significant differences in the probability of informed trading computed from order flows, we conclude that order flows could reveal the extent of information-driven trading and could potentially be used by regulatory authorities to identify suspicious behavior by market participants.dealers’ market, emerging markets, informed trading, trading systems

Testing Multi-Factor Asset Pricing Models in the Visegrad Countries

There is no consensus in the literature as to which model should be used to estimate stock returns and the cost of capital in the emerging markets. The Capital Asset Pricing Model (CAPM), which is most often used for this purpose in the developed markets, has a poor empirical record and is likely not to hold in the less developed and less liquid emerging markets. Various factor models have been proposed to overcome the shortcomings of the CAPM. This paper examines both the CAPM and macroeconomic factor models in terms of their ability to explain average stock returns using data from the Visegrad countries. We find, as expected, that the CAPM is not able to do this task. However, factor models, including factors such as the excess market return, industrial production, inflation, money, the exchange rate, exports, the commodity index, and the term structure, can in fact explain part of the variance in the Visegrad countries’ stock returns.CAPM, macroeconomic factor models, asset pricing, cost of capital, Poland

Global and Local Sources of Risk in Eastern European Emerging Stock Markets

We study a pricing model for global and local sources of risk in six Eastern European emerging stock markets. Utilizing GMM estimation and an unconditional asset-pricing framework with and without time-varying betas, we perform estimations based on monthly data from 1996 to 2007 for Poland, the Czech Republic, Hungary, Bulgaria, Slovenia, and Russia. Most of these markets display considerable segmentation; the aggregate emerging market risk, as opposed to global market risk, is the significant driver for their stock market returns. It also appears that currency risk is priced into stock prices. The difference between local and global interest rates can be used to model the time-variation in the betas for both sources of risk.market integration, segmentation, asset pricing, emerging markets, Eastern Europe country risk