Publikationen

Using a large sample of institutionally managed portfolios, we study the role of social trust in the equity allocations of 8,088 investors from 33 countries over the 2000-2017 period. The negative relation between social trust and foreign bias suggests that institutional investors from high-social trust countries are less prone to underinvesting in foreign equity. Our results provide credence to an information-based explanation, indicating that social trust reduces foreign bias by compensating the lack of information about foreign markets. The negative relation between social trust and foreign bias does not hold unconditionally, but only relates to host countries with weak formal institutional frameworks. The informal institution of social trust can offset the lack of formal country-level institutions in international portfolio decisions. Social trust helps investors accomplish greater cross-country portfolio diversification.

Crypto Funds (CFs) represent a novel investor type in entrepreneurial finance. CFs intermediate Decentralized Finance (DeFi) markets by pooling contributions from crowd-investors and investing in tokenized startups, combining sophisticated venture- and hedge-style investment strategies. We compile a unique dataset combining token-based crowdfunding (or Initial Coin Offerings, ICOs) data with proprietary performance data of CFs. CF-backed startup ventures obtain higher ICO valuations, outperform their peers in the long run, and benefit from token price appreciation around CF investment disclosure in the secondary market. Moreover, CFs beat the market by roughly 2.5% per month. Their outperformance is persistent, suggesting that CFs deliver abnormal returns because of skill, rather than luck. These performance effects for CFs and CF-backed startups are driven by a fund’s investor network centrality. Overall, our study paves the way for research on what some refer to as the “crypto fund revolution” in entrepreneurial finance.

This paper uses a comprehensive set of variables from the five largest Eurozone countries to compare the performance of univariate and machine learning-based multivariate models in predicting stock market crashes. In terms of statistical predictive performance, a support vector machine-based prediction model outperforms a random classifier and is superior to the average univariate benchmark as well as a multivariate logistic regression. The ability to forecast stock market crashes out-of-sample translates into large value-added for active investors. Incorporating nonlinear and interactive effects is both imperative and foundation for the predictive performance of support vector machines compared to their linear benchmark. It enables identify and explain the complex relationships in the underlying economic conditions that precede large stock market declines. Our findings can help forecast financial fragility and deteriorating macroeconomic outcomes.

Prior research suggests that the disclosure of greenhouse gas (GHG) emissions—a primary cause of climate change—affects firm valuation. In this paper, we provide new insights into the determinants of the voluntary disclosure of GHG emissions. We show that board ancestral diversity has a positive and statistically significant effect on a firm’s scope and quality of voluntary GHG emission disclosure. This effect is robust to controlling for several other dimensions of board diversity as well as to addressing endogeneity and sample selection. Additional analysis suggests that board ancestral diversity has a higher impact on GHG emission disclosure in firms with low institutional ownership and high corporate complexity. We interpret these findings as consistent with the view that board diversity enhances monitoring and advising.

The disclosure of corporate environmental performance is an increasingly important element of a firm’s ethical behavior. We analyze how the legal origin of foreign institutional investors affects a firm’s voluntary carbon disclosure. Using a large sample of firms from 36 countries, we show that foreign institutional ownership from civil law countries improves the scope and quality of a firm’s greenhouse gas emissions reporting. This relation is robust to addressing endogeneity and selection biases. The effect is more pronounced in firms from non-climate-sensitized countries, for which the gap between firms’ environmental standards and investors’ environmental targets is potentially larger, and in less international firms. Firms with a higher level of voluntary carbon disclosure also exhibit higher valuations.

This paper evaluates the predictive performance of machine learning techniques in estimating time-varying betas of US stocks. Compared to established estimators, tree-based models and neural networks outperform from both a statistical and an economic perspective. Random forests perform the best overall. Machine learning-based estimators provide the lowest forecast errors. Moreover, unlike traditional approaches, they lead to truly ex-post market-neutral portfolios. The inherent model complexity is strongly time-varying. The most important predictors are various historical betas as well as fundamental turnover and size signals. Compared to linear regressions, interactions and nonlinear effects enhance the predictive performance substantially.

We examine the impact of foreign institutional shareholders on the prevalence of restrictive bond covenants using a sample of 959 Yankee bonds from 29 countries over the period 2001–2019. We find a significantly negative relation between foreign institutional ownership and debt covenants. This inverse relation is strongest for U.S. institutional ownership of foreign-issued Yankee bonds, and for covenants designed to mitigate such opportunistic behavior as claims dilution and wealth transfers. We also show that the inverse relation between U.S. institutional ownership and restrictive debt covenants is moderated by country- and firm-level variables related to corporate governance, information asymmetry, and agency costs of debt. Additional analyses show that U.S. institutional ownership has a significant pricing effect on Yankee bond investors by lowering the issuer’s cost of borrowing.

Using a comprehensive set of firms from 57 countries over the 2000–2016 period, we examine the relation between institutional investor horizons and firm-level credit ratings. Controlling for firm- and country-specific factors, as well as for firm fixed effects, we find that larger long-term (short-term) institutional ownership is associated with higher (lower) credit ratings. This finding is robust to sample composition, alternative estimation methods, and endogeneity concerns. Long-term institutional ownership affects ratings more during times of higher expropriation risk, for firms with weaker internal governance, and for those in countries with lower-quality institutional environments. Additional analysis shows that long-term investors can facilitate access to debt markets for firms facing severe agency problems. These findings suggest that, unlike their short-term counterparts, long-term investors can improve a firm’s credit risk profile through effective monitoring.