Publikationen

Using a large set of firms from 35 countries over the 2010–2017 period, we find that the presence of a sustainability committee is positively associated with higher-quality environmental disclosure. This finding is robust to endogeneity and sample selection bias concerns. The sustainability committee effect is more pronounced when external environmental institutions are too weak to properly monitor corporate environmental disclosure. We also find that raising the quality of environmental disclosure leads to a lower cost of equity capital only for firms with sustainability committees in place. Our findings suggest that sustainability committees play an important role in facilitating and certifying corporate environmental disclosure.

Machine learning-based market beta estimators outperform established benchmark models both statistically and economically. Analyzing the predictability of time-varying market betas of U.S. stocks, we show that machine learning-based estimators produce the lowest forecasting and hedging errors. They also help create better market-neutral anomaly strategies and minimum variance portfolios. Among the various techniques, random forests perform best overall. Model complexity is highly time-varying. Historical betas, turnover, and size are the most important predictors. Compared to linear regressions, allowing for nonlinearity and interactions significantly improves the predictive performance.

This paper examines the predictive performance of machine learning methods in estimating the illiquidity of U.S. corporate bonds. We compare the predictive performance of machine learning-based estimators (linear regressions, tree-based models, and neural networks) to that of the most commonly used benchmark model based on historical illiquidity. Machine learning techniques outperform the historical illiquidity-based approach from both a statistical and an economic perspective. Moreover, tree-based models and neural networks outperform linear regressions, which incorporate the exact same set of covariates. Gradient boosted regression trees perform particularly well. While historical illiquidity, due to its high persistence, is the most important single predictor variable, several fundamental, risk-, and return-based covariates also possess notable predictive power. Capturing interactions and nonlinear effects among these predictors further enhances predictive performance.

Evidence shows that social network structures drive important economic outcomes. Building on social network theory, this study is the first to analyse the impact of team networks on venture success. Using information about team affiliations for a sample of token-financed startups, we model networks based on team interlocks across firms. Ventures with well-connected teams exhibit higher market valuations and higher token market liquidity. These effects seem to be driven by network-induced information and communication advantages. Specifically, we show that networks matter most when publicly available information is limited. The findings remain robust after controlling for non-team networks and endogeneity.

Schumpeterian arguments of “creative destruction” predict that innovation is countercyclical. However, empirical findings demonstrate the contrary. We apply corporate finance principles to innovation economics and propose a “hurdle-rate theory of inventive procyclicality.” Macroeconomic episodes of high equity risk premia (ERP) stifle innovation because many R&D projects do not pass corporate budgeting decisions when discount rates are high. Consistent evidence suggests that the hurdle-rate effect is less pronounced in firms with financial slack, institutional ownership with long-term orientation, and weak product-market competition. In an attempt to reconcile the procyclical evidence with Schumpeter’s countercyclical theory, we show that firms engaging in exploratory search suffer less during high-ERP episodes than those focusing on exploitative search, and patents developed during high-ERP periods have a higher technological impact and receive significantly more forward citations. Finally, we exploit the staggered variation in state-level R&D tax credits in difference-in-differences analyses to establish a causal link between the ERP and patent value.

What role does the selection of an investor and the timing of financing play in initial coin offerings (ICOs)? We investigate the operating and financial performance of ventures conducting ICOs with different types of investors at different points in the ventures’ life cycle. We find that, relative to purely crowdfunded ICO ventures, institutional investor-backed ICO ventures exhibit poorer operating performance and fail earlier. However, conditional on their survival, these ventures financially outperform those that do not receive institutional investor support. The diverging effects of investor backing on financial and operating performance are consistent with our theory of certification arbitrage; i.e., institutional investors use their reputation to drive up valuations and quickly exit the venture post-ICO. Our findings further indicate that there is an inverted U-shaped relationship for fundraising success of ICO ventures over their life cycle. Another inverted Ushaped relationship exists for the short-term financial performance of ICO ventures over their life cycle. Both the fundraising success and the financial performance of an ICO venture initially increase over the life cycle and eventually decrease after the product piloting stage.

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 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.

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.

Token offerings or initial coin offerings (ICOs) are smart contracts based on blockchain technology designed to raise external finance without an intermediary. The new technology might herald a revolution in entrepreneurial and corporate finance, with soaring market growth rates over the last two years. This paper surveys the market evolution, offering mechanisms, and token types. Stylized facts on the pricing and long-term performance of ICOs are presented, and practical implications for this young market to thrive are discussed.