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...

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...

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...

Inspired by the bankruptcy of Lehman Brothers and its consequences on the global financial system, we develop a simple model in which the Lehman default event is quantified as having an almost immediate effect in worsening the credit worthiness of all financial institutions in the economic...

Purpose – We use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017 to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models.Design/methodology/approach – We apply six...

We develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection...

We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps...