Trip.com Group Ltd Credit Rating & Financial Statements Analysis

BOSTON (AI Credit Rating Terminal) Mon Jun 07 2021 05:00:03 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Financial Statements Overview & Credit Rating Rationales


We rerated Trip.com Group Ltd because of capital metrics would not be eroded by any of the following: repayment of government-contributed equity, recognition of any currently unrecognized economic losses, reduction from capital the amount necessary to appropriately capitalize any materially undercapitalized unconsolidated subsidiaries, and reversal of any property valuation adjustment. (We use econometric methods for period (n+1) simulate with Voltage Controlled Oscillator Simple Regression). Our liquidity uses include dividends and share repurchases that we expect under a stress scenario. Unlike other potential uses of liquidity, such as debt maturities or maintenance capital spending, we view dividends and share repurchases as more discretionary, although more so for the latter. For this reason, when evaluating a company's liquidity position, we may use a lower estimate of dividends and shareholder repurchases than in our base-case forecast based on our views of management and the company's track record in terms of shareholder returns and maintaining a certain minimum level of liquidity. 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.

Risk Heat Map for Trip.com Group Ltd as of 07 Jun 2021


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 Trip.com Group Ltd as below:

Credit Ratings for Trip.com Group Ltd as of 07 Jun 2021


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B2B2
Semantic Signals3661
Financial Signals3370
Risk Signals9050
Substantial Risks4942
Speculative Signals8051

*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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Disclaimers: AC Investment Inc. currently does not act as an equities executing broker, credit rating agency or route orders containing equities securities. In our Machine Learning experiment, we focus on an approach known as Decision making using game theory. We apply principles from game theory to model the relationships between rating actions, news, market signals and decision making.The rating information provided is for informational, non-commercial purposes only, does not constitute investment advice and is subject to conditions available in our Legal Disclaimer. Usage as a credit rating or as a benchmark is not permitted.

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