This analysis tracks several metrics related to deal performance and credit profile, putting them into a historical context by comparing the same metrics for recent-vintage deals against those of ‘similar’ cohorts in the time leading up to the 2008 housing crisis. You’ll see how credit metrics are trending today and understand the significance of today’s…
As Hurricane Florence’s projected track becomes increasingly certain, we have assembled the following interactive charts illustrating each CRT (CAS and STACR) deal’s exposure to the MSAs mostly likely to be impacted by the storm. Click on a deal ID along the left-hand side of the plot to view its exposure to each MSA. As we…
Most analysts estimate that for a given project well over half of the time is spent on collecting, transforming, and cleaning data in preparation for analysis. This task is generally regarded as one of the least appetizing portions of the data analysis process and yet it is the most crucial, as trustworthy analyses are borne…
The ability of machine learning models to predict loan performance makes them particularly interesting to lenders and fixed-income investors. This expanded post provides an example of applying the machine learning process to a loan-level dataset in order to predict delinquency. The process includes variable selection, model selection, model evaluation, and model tuning. The data used...ShareTweetShare+1
Analysts and data scientists are constantly seeking new ways to parse increasingly intricate datasets, many of which are deemed “high dimensional”, i.e., contain many (sometimes hundreds or more) individual variables. Machine learning has recently emerged as one such technique due to its exceptional ability to process massive quantities of data. A particularly useful machine learning...ShareTweetShare+1
There are two main challenges when implementing a machine learning solution: building a model that performs well and effectively leveraging the results. Having a good understanding of the machine learning process and model being used is key to tackling both issues. Using a predictive model without appropriately understanding it can substantially increase risk and lead to missed opportunities. If the performance of a model is unclear, misunderstood, or overestimated then subsequent decisions will be biased or outright wrong. Likewise, if the ability of a model is underestimated then its use will not be optimized.