New England Realty Associates LP Credit Rating

BOSTON (AI Credit Rating Terminal) Tue Jul 21 2020 21:49:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded New England Realty Associates LP because of derivatives receivables represent more than 5% of total assets for entities reporting under IFRS (or under local GAAP similar to IFRS for the accounting of derivatives) and are domiciled in countries for which our BICRA group is '5' and above.. We use econometric methods for period (n+1) simulate with Exponential Moving Average (EMA) Pearson Correlation. Reference code is: 1469. Beta DRL value REG 30 Rational Demand Factor LD 4581.7548. If we believe a company would use cash trapped at a foreign subsidiary to meet debt maturities or other liquidity uses at that foreign subsidiary, we would include this cash as a source of liquidity up to the amount of the corresponding use. We generally haircut the cash to be included under sources when a material proportion of a group's cash is held in a different part of the structure than where the debt is located, and we believe the cash may not be fully fungible within the group. 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 New England Realty Associates LP as below:

Credit Ratings for New England Realty Associates LP as of 21 Jul 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*Ba2Ba3
Semantic Signals8577
Financial Signals4286
Risk Signals8086
Substantial Risks7431
Speculative Signals6834

*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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