Tatjana Xenia Puhan

We show that business cycle dynamics and, in particular, the cyclicality of a firm’s growth opportunities, determine the value of corporate cash holdings. An additional dollar of cash is more valuable for firms with less procyclical expansion opportunities. This valuation effect is strongest for low leverage and high R&D firms, but is independent of their financial status. Corporate cash holdings provide the flexibility to invest for firms that have expansion opportunities during crisis times with business cycle downturns and supply-side financial constraints. Cash holdings in firms with less procyclical growth opportunities are associated with higher investment and better operating performance.

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.

This paper tests the predictive performance of machine learning methods in estimating the illiquidity of U.S. corporate bonds. Machine learning techniques outperform the historical illiquidity-based approach, the most commonly applied benchmark in practice, from both a statistical and an economic perspective. Tree-based models and neural networks outperform linear regressions, which incorporate the same set of covariates. Gradient boosted regression trees perform particularly well. Historical illiquidity is the most important single predictor variable, but several fundametal and return- as well as risk-based covariates also possess predictive power. Capturing interactions and nonlinear effects among these predictors further enhances predictive performance.