This paper introduces the quantile regression- based Distance-to-Default to Probability of Default (DD-PD) mapping, which links individual firms' DD to their real world PD. Since changes in the DD depend on a handful of parameters, the mapping easily accommodates shocks arising from quantitative...

We develop a sentiment metric to analyze the tone and information amount in financial corporate announcements. We improve existing text processing methods by developing a different word selection approach that allows quantifying the sentiment of financial announcements in an intuitive, but...

This paper introduces the quantile regression- based Distance-to-Default to Probability of Default (DD-PD) mapping, which links individual firms’ DD to their real world PD. Since changes in the DD depend on a handful of parameters, the mapping easily accommodates shocks arising from...

We use a dataset of sell-side analysts' scenario-based equity valuation estimates to examine whether analysts can assess the state-contingent risk surrounding a firm's fundamental value. We find that the spread in analysts' scenario-based valuations captures the riskiness of operations and...

This paper introduces the quantile regression- based Distance-to-Default to Probability of Default (DD-PD) mapping, which links individual firms’ DD to their real world PD. Since changes in the DD depend on a handful of parameters, the mapping easily accommodates shocks arising from...