Publications

This paper evaluates bootstrap simulation techniques for calculating the distribution of the maximum drawdown (MDD), an important risk indicator in stock and cryptocurrency markets. Using stochastic dominance tests, we assess the full distributional properties of MDD under different methods. Our findings reveal that the standard Efron (1979) bootstrap, which assumes independence and identically distributed random variables, systematically underestimates the true MDD. While the moving block bootstrap provides reasonable estimates, it is subject to non-stationarity bias, particularly when large drawdowns occur at the boundaries of a return series. Alternative procedures, such as the block-block bootstrap and the tapered bootstrap, do not lead to better results. Of all the methods studied, the stationary bootstrap of Politis and Romano (1994) produces the most accurate and robust results, particularly with longer block lengths. We recommend this method as the preferred choice for researchers and practitioners modelling drawdown risk.

I examine the value consequences of biodiversity incidents and their association with institutional ownership. Markets react negatively, with average shareholder losses ranging from $76 million to $344 million per incident. Additional stock price declines around subsequent earnings announcements are consistent with market underreaction. Institutional ownership is negatively associated with incident occurrence. Using plausibly exogenous shocks to monitoring intensity, I distinguish monitoring from selection effects. Long-term and domestic institutions, especially insurance firms and public pension funds, reduce incidents through active monitoring, whereas short-term and foreign investors, particularly investment advisors, exhibit selection behavior. Evidence from shareholder proposals supports a governance-via-voice mechanism, with withdrawn biodiversity-related proposals associated with lower incident re-occurrence. Finally, incidents are positively associated with the cost of equity capital, suggesting that investors demand compensation for biodiversity risk exposure.

This paper examines how institutional investors exchange private information through co-shareholding networks and the implications for corporate acquisition outcomes. Using New York City taxi data, we identify face-to-face interactions among investor pairs and document that co-blockholders are more likely to seek on-site meetings, consistent with an increased exchange of information. Given evidence for an information channel, we construct the broader blockholder network between US institutional investors and show that acquirers held by more centrally positioned institutional investors earn higher announcement returns. The valuation effect is strongest when targets are more opaque and private information is more valuable. Consistent with an advisory channel, the effect only exits among investors with a comparative advantage in exploiting information, and facilitates “hidden gem” acquisitions rather than preventing poor deals. Overall, our findings suggest that co-shareholding networks promote private information flows that translate into superior advice and enhance M&A performance.

This study explores the link between investor heterogeneity represented on venture boards and firms' post-seed funding performance. We document a statistically and economically significant negative association of investor heterogeneity on both a firm's likelihood of obtaining new funding and the volume raised in new funding rounds. This suggest that investor heterogeneity decreases venture board efficacy and the quality of venture governance. The marginal impact of investor heterogeneity is non-linear and diminishing across a venture's funding life cycle. Our results remain robust after controlling for endogeneity issues and for alternative measures of investor culture.

The maximum drawdown (MDD), the maximum peak-to-trough loss associated with a series of returns, is a simple but highly important measure for investors with a downside risk budget. This paper compares the performance of three bootstrap simulation methods to estimate the entire distribution of MDDs from various global stock-bond allocations, quantifying the economic costs of biased estimates for three realistic decision-making scenarios. Compared to its benchmarks, the stationary bootstrap of Politis and Romano (1994) leads to the most precise estimates for the MDD which, in turn, helps avoid costly investment errors in portfolio construction and dynamic risk control strategies.

Institutional investors improve the environmental, social, and governance (ESG) performance of small-and medium-sized enterprises (SMEs). Our difference-in-differences framework shows that the backing from private equity and venture capital funds leads to an increase in SMEs' externally validated ESG scores compared to their matched non-investor-backed peers. Consistent with "ESG-as-insurance" theory, the ESG performance of SMEs with a higher probability of failure is more likely to benefit from the backing of institutional investors. This positive effect is heterogeneous; while SMEs with high ex-ante ESG performance tend to further improve their ESG performance following institutional investor backing, SMEs with low ex-ante ESG performance are unlikely to implement any improvements. Entrepreneurial finance seems to help sustainable entrepreneurs develop into "sustainability champions," while neglecting the betterment of non-sustainable SMEs.

Analyzing a large sample of U.S. firms over the 1990–2019 period, we show that firms with more central institutional investors in the equity holdings network engage less in accrual-based earnings management. This finding is robust to controlling for clique and common ownership, using alternative network and earnings properties, and extends to real earnings management. We establish causality using exogenous variation in network centrality and investor attention. The effect is most pronounced for institutions with a comparative advantage in exploiting monitoring information. Overall, our results suggest that central institutional investors gain an information advantage through access to the network’s resources, increasing their monitoring ability.

This paper compares bootstrap simulation approaches in the context of the maximum drawdown (MDD) risk measure for stock market and cryptocurrency returns. Our comparisons are based on the complete distribution of the MDD using stochastic dominance tests. The standard Efron (1979) bootstrap severely underestimates the true MDD. The simulation results of the moving block bootstrap approach are reasonably good as long as the stationarity problem does not become striking. The stationary bootstrap approach of Politis and Romano (1994) provides the best results. Investment practitioners should choose the Politis and Romano (1994) method as their first choice to model MDD risk.

Coordination frictions may prevent the efficient adoption and governance of digital platforms. We document that crypto funds (CFs) create value, inter alia, by smoothing such frictions on blockchain-based decentralized digital platforms (DDPs). CF-backed DDPs obtain higher valuations in the primary market (i.e., in initial coin offerings, ICOs), outperform their peers post ICO, and benefit from token price appreciation around CF investment disclosure in the secondary market. In line with our theory, primary transaction data from the Ethereum ledger shows that the valuations of DDPs with meager adoption and relative centralization benefit more from CF backing. Moreover, the positive valuation and performance effects for CF-backed DDPs are higher for CFs with more central investor networks.

This paper shows evidence of a positive relationship between institutional dual holders, who hold both equity and debt in a firm, and voluntary greenhouse gas (GHG) emission disclosure. Considering dual holders as particularly risk-sensitive institutional investors, we docu-ment that voluntary GHG emission disclosure improvements are motivated by not only cli-mate-conscious but also risk-related considerations. The positive effect of institutional dual ownership is more pronounced when firms face severe environmental risks, where disclosure enables explanations and prevents exaggerated stakeholder reactions. The impact of dual ownership is also stronger in firms with poor information environments, where dual holders exploit their salient monitoring capacity from gathering information from their public equity and private debt holdings. Supporting our risk-based explanation, voluntary GHG emission disclosure reduces the cost of equity and increases firm valuation in firms with higher dual ownership.

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.

We document high economy-wide correlations between the Equity Risk Premium (ERP) and the aggregate volume (rho=-0.69) and value (rho=-0.75) of patenting activity by public firms in the United States over the 1977-2018 period, contradicting Schumpeter's (1939) opportunity-costs hypothesis of countercyclical inventive productivity in a representative sample. We propose a ''hurdle-rate theory of inventive procyclicality'' and offer supportive evidence at the firm level. High-ERP periods 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.

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.

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.