Researchers in empirical corporate finance often use bounded ratios (e.g., debt ratios) as dependent variables in their regressions. Using the example of estimating the speed of adjustment toward target leverage, we show by Monte Carlo and resampling experiments that commonly applied estimators yield severely biased estimates, as they ignore that debt ratios are fractional (i.e., bounded between 0 and 1). We propose a new unbiased estimator for adjustment speed in the presence of fractional dependent variables that also controls for unobserved heterogeneity and unbalanced panel data. This new estimator is suitable for corporate finance applications beyond capital structure research.

White papers are likely the primary source of information provided to potential Initial Coin Offering (ICO) investors in platform-based ventures that may reduce information asymmetry between ICO issuers and investors. We use textual analysis to measure the information content of white paper documents. We examine how an informative white paper content signal and a concurrent potentially biased expert rating signal correlate with measures of ICO funding success, ICO underpricing, and post-ICO performance. Our empirical results suggest that high-quality ICO issuers signal their type by providing more informative white paper content, i.e., excess or new textual information not contained in recent and peer white papers. However, investors rely on the expert ratings signal in their decision to buy tokens that “jams” the white paper informative content signal. Once listed tokens receive a market valuation, white paper informative content is positively associated with our measures for underpricing, returns, and liquidity, while the expert ratings signal is rendered irrelevant.