Publikationen

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.

Crypto funds (CFs) are a growing intermediary in cryptocurrency markets. We evaluate CF performance using metrics based on alphas, value at risk, lower partial moments, and maximum drawdown. The performance of actively managed CFs is heterogeneous: While the average fund in our sample does not outperform the overall cryptocurrency market, there seem to be some few funds with superior skills. Given the non-normal nature of fund returns, the choice of the performance measure affects the rank orders of funds. Compared to the Sharpe ratio, the most commonly applied metric in the asset management practice, performance measures based on alphas and maximum drawdown lead to diverging fund rankings. Depending on their ranking order of preferences, CF investors should consider a bundle of metrics for fund selection and performance measurement.

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.

The authors examine to what extent the market for initial coin offerings (ICOs) is driven by investor sentiment. Their results, based on a comprehensive set of sentiment and coin price data, suggest that the ICO market is driven by crypto-related sentiment, but is almost unrelated to general capital market sentiment. Among the crypto-related sentiment, social media channels, rather than traditional news channels, are the main source of investor sentiment. The authors find that ICO firms exploit “windows of opportunity” and avoid periods of negative sentiment. Coins listed during periods with negative investor sentiment generate negative returns in the short run. Moreover, returns to investors on the first day of trading predict long-run returns up to six months.