Herding Behavior and Systemic Risk in Global Stock Markets
Journal of Empirical Finance,
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.
Micro Data on Robots from the IAB Establishment Panel
Jahrbücher für Nationalökonomie und Statistik,
Micro-data on robots have been very sparse in Germany so far. Consequently, a dedicated section has been introduced in the IAB Establishment Panel 2019 that includes questions on the number and type of robots used. This article describes the background and development of the survey questions, provides information on the quality of the data, possible checks and steps of data preparation. The resulting data is aggregated on industry level and compared with the frequently used robot data by the International Federation of Robotics (IFR) which contains robot supplier information on aggregate robot stocks and deliveries.
IWH FDI Micro Database
IWH FDI Micro Database The IWH FDI Micro Database (FDI = Foreign Direct...
IWH Alumni The IWH would like to stay in contact with its former employees. We...
Mission, Motivation, and the Active Decision to Work for a Social Cause
Nonprofit and Voluntary Sector Quarterly,
The mission of a job affects the type of worker attracted to an organization but may also provide incentives to an existing workforce. We conducted a natural field experiment with 246 short-term workers. We randomly allocated some of these workers to either a prosocial or a commercial job. Our data suggest that the mission of a job has a performance-enhancing motivational impact on particular individuals only, those with a prosocial attitude. However, the mission is very important if it has been actively selected. Those workers who have chosen to contribute to a social cause outperform the ones randomly assigned to the same job by about half a standard deviation. This effect seems to be a universal phenomenon that is not driven by information about the alternative job, the choice itself, or a particular subgroup.
Real Estate Transaction Taxes and Credit Supply
Deutsche Bundesbank Discussion Paper,
We exploit staggered real estate transaction tax (RETT) hikes across German states to identify the eff ect of house price changes on mortgage credit supply. Based on approximately 33 million real estate online listings, we construct a quarterly hedonic house price index (HPI) between 2008:q1 and 2017:q4, which we instrument with state-speci c RETT changes to isolate the e ffect on mortgage credit supply by all local German banks. First, a RETT hike by one percentage point reduces HPI by 1.2%. This e ffect is driven by listings in rural regions. Second, a 1% contraction of HPI induced by an increase in the RETT leads to a 1.4% decline in mortgage lending. This transmission of fiscal policy to mortgage credit supply is eff ective across almost the entire bank capitalization distribution.
Why are some Chinese Firms Failing in the US Capital Markets? A Machine Learning Approach
Pacific-Basin Finance Journal,
We study the market performance of Chinese companies listed in the U.S. stock exchanges using machine learning methods. Predicting the market performance of U.S. listed Chinese firms is a challenging task due to the scarcity of data and the large set of unknown predictors involved in the process. We examine the market performance from three different angles: the underpricing (or short-term market phenomena), the post-issuance stock underperformance (or long-term market phenomena), and the regulatory delistings (IPO failure risk). Using machine learning techniques that can better handle various data problems, we improve on the predictive power of traditional estimations, such as OLS and logit. Our predictive model highlights some novel findings: failed Chinese companies have chosen unreliable U.S. intermediaries when going public, and they tend to suffer from more severe owners-related agency problems.
Wage Delegation in the Field
Journal of Economics and Management Strategy,
By conducting a natural field experiment, we analyze the managerial policy of delegating the wage choice to employees. We find that this policy enhances performance significantly, which is remarkable since allocated wage premiums of the same size have no effect at all. Observed self‐imposed wage restraints and absence of negative peer effects speak in favor of wage delegation, although the chosen wage premium levels severely dampen its net value. Additional experimental and survey data provide important insights into employees' underlying motivations.