(Bloomberg News is fond is reporting that some name "is trading at X upfront, which implies N% probability of default". They neglect to mention what recovery assumption they used, and that this is risk-neutral probability, not physical.)

For corporate names, [Ed Altman][1] published the well-known paper on [Z-score][2]. His basic idea is: look at some fundamental ratios, and see what percentage of corporations with similar ratios defaulted historically. It is easy to reproduce **if** you have the data. There are several newer versions by various people that also consider the macroeconomic regime and the industry. Some examples of commercially available databases of physical probabilities of default based on versions of Z-score are from KMV (Moody's bought them) [EDF (Expected Default Frequency)][3] and Citigroup/Yieldbook HPD (Hybrid Probability of Default). This [paper by Sobehart and Keenan][4] has a good overview. Not much has changed since it was published, despite advances in data mining.


  [1]: https://www.stern.nyu.edu/faculty/bio/edward-altman
  [2]: https://en.wikipedia.org/wiki/Altman_Z-score
  [3]: https://www.moodysanalytics.com/-/media/products/edf-expected-default-frequency-overview.pdf
  [4]: https://cms.rmau.org/uploadedFiles/Credit_Risk/Library/RMA_Journal/Credit_Portfolio_Management/A%20Practical%20Review%20and%20Test%20of%20Default%20Prediction%20Models.pdf