We develop a model of investment, payout, and financing policies in which firms face uncertainty regarding their ability to raise funds and have to search for investors when in need of capital. We show that capital supply uncertainty leads firms to value financial slack and to adjust their...

This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

Firms with credit-default swaps (CDS) traded on their debt may face "empty creditors'' as hedged creditors have less incentive to participate in firm restructuring. We test for the existence of empty creditors by employing an exogenous change to the bankruptcy code in Germany, that effectively...

The optimal investment-dividend policy of a financially constrained firm whose earnings are subject to additive shocks is shown to exhibit several stylized economic and financial features of the firm life cycle which usually require considerably more complex models. This parsimonious model...

A common method of valuing the equity in highly leveraged transactions is the flows-to-equity method. When applying this method various formulas can be used to calculate the time-varying cost of equity. In this paper we show that some commonly used formulas are inconsistent with the assumptions...

We introduce the concept of “negative bubbles” as the mirror image of standard financial bubbles, in which positive feedback mechanisms may lead to transient accelerating price falls. To model these negative bubbles, we adapt the Johansen-Ledoit-Sornette (JLS) model of rational expectation...

Predictive power has always been the main research focus of learning algorithms with the goal of minimizing the test error for supervised classification and regression problems. While the general approach for these algorithms is to consider all possible attributes in a dataset to best predict...