It is back to school season, which is a good occasion to examine the market of muni obligors offering bonds in the Education Sector.
The Education Sector is one of the top two sectors in terms of number of obligors offering bonds and also holdings of CUSIPs by institutional investors. The other is General Obligation bonds for towns, cities and counties. Given its prominence, it is a constant challenge to grasp the health of the entire market. Different regions experience different challenges, a fact that has been especially apparent in recent weeks with successive catastrophic weather events.
But it is possible with technology to visualize the fluctuating risk landscape.
In this visualization we quantify where the obligors are located in the U.S. and how many Primary and Secondary vs. Higher Education obligors are on the market. We also visualize the velocity of negative material changes happening in each obligor. The category ‘Other’ tends to be composed by entities like a Board of Education.
This visualization was produced by Bitvore’s proprietary artificial intelligence that surveils every obligor active in the municipal bond market. By analyzing the frequency in which negative material changes are detected about obligors we can summarize the obligors that are most frequently generating negative warning signs in the first half of 2017. These warning signs represent counts of unique reports of activity that could indicate increasing risk in the asset. Together these counts over time provide a new measure of the velocity and volatility in the material changes of an obligor.
The diagrams in blue portray the overall obligors in the Education sector by the number of unique obligors in a geographic location. The diagrams in red summarize the velocity of early warning indicators during the same time period. The size of the red circles on the heatmap represent the number of early warnings for a particular obligor in the first half of 2017. The stacked bar charts show the individual obligors and the number of unique early warnings along with the month in which they occurred. Similar to seismic activity, the charts visually represent the obligors in the Education sector that are generating the most warnings, and may indicate particularly high volatility and risk in that time period.
Bitvore’s AI identifies unique early warning issues such as budget issues, accreditation loss, layoffs, tax-base changes, or significant financial management issues such accusations of fraud or embezzlement. Bitvore’s AI monitors over 300 different material event types to report issues proactively and create these market visualizations.