BOSTON (AI Credit Rating Terminal) Fri Jul 31 2020 22:49:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview

We downgraded Public Storage DEPOSITARY SHS REPSTG % CUMULATIVE PFD SHS BEN INT (SER P) because of adding the amount of unrecognized gains, after tax, when calculating ACE and TAC. Nevertheless, the adjustment for unrecognized gains would be reduced by the amount of the surplus that we view as unrealizable. We use econometric methods for period (n+1) simulate with Ring Oscillators ANOVA. Reference code is: 1263. Beta DRL value REG 33 Rational Demand Factor LD 4359.9024. We believe that when a company is viewed as being on the cusp between two liquidity descriptors and has higher-than-average cash plus inventory/unadjusted debt compared with similarly constituted peers, that helps support the better liquidity assessment. However, in the case of a nonresidential developer, given that its inventory is typically less liquid (and the greater potential for inventory to suffer value erosion in a downturn), we do not consider this measure as pertinent. Credit Rating AI Process rely on primary sources of information: Sec Filings, Financial Statements, Credit Ratings, Semantic Signals. Take a look at Machine Learning section for Financial Deep Reinforcement Learning.Oscillators are used for generating credit risk signals by using the semantic and financial signals. The value of the oscillators indicate the strength of trend. Using the correlation matrices, the credit rating risk map for Public Storage DEPOSITARY SHS REPSTG % CUMULATIVE PFD SHS BEN INT (SER P) as below:

Credit Ratings for Public Storage DEPOSITARY SHS REPSTG % CUMULATIVE PFD SHS BEN INT (SER P) as of 31 Jul 2020

Credit Rating Short-Term Long-Term Senior
AI Rating Class*Ba3B1
Semantic Signals8732
Financial Signals4483
Risk Signals7281
Substantial Risks3848
Speculative Signals7940

*Machine Learning utilizes multiple learning algorithms to obtain better predictive powers. In our research, we utilize machine learning to combine the results from the Neural Network and Support Vector Machines.
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