Times Neighborhood Holdings Ltd Credit Rating

BOSTON (AI Credit Rating Terminal) Sat Aug 01 2020 16:19:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Times Neighborhood Holdings Ltd because of normalized loss rates using default and transition studies for corporate, sovereign, and financial institutions exposures and our assessment of long-term average annualized through-the-cycle expected losses informed by historical losses for retail and personal exposures. This normalized, through-the-cycle loss estimate is more conservative than an expected loss calculation based on a shorter time horizon, which might exclude periods of recession. We use econometric methods for period (n+7) simulate with Clapp Oscillators ANOVA. Reference code is: 2554. Beta DRL value REG 41 Rational Demand Factor LD 4359.9024. When determining the cash to be included under sources (A), we use cash that will be available to cover monetary outflows. As a result, we may make haircuts to account for cash trapped overseas (for example, haircut for taxes payable upon repatriation of cash held abroad), apply a discount to lower-quality marketable securities, and exclude restricted cash held for specific purposes. 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 Times Neighborhood Holdings Ltd as below:

Credit Ratings for Times Neighborhood Holdings Ltd as of 01 Aug 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B1Ba3
Semantic Signals3643
Financial Signals4879
Risk Signals8756
Substantial Risks3890
Speculative Signals8164

*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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