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How Does Rating Model Work?


Decision making

Neural networks

game theory

support-vector machınes

solving risk problems

fuRTher reading



DECISION MAKING


Credit ratings are forward-looking opinions about the ability and willingness of debt issuers, like corporations or governments, to meet their financial obligations on time and in full. They provide a common and transparent global language for investors and other market participants, corporations and governments, and are one of many inputs they can consider as part of their decision-making processes. That is, investors act like players in a game; they cooperate to achieve a set of overall goals. 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. AI based risk heat map is a tool used to present the results of a invest-risk assessment process visually and in a meaningful and concise way.

Our AI based credit ratings are designed to provide relative rankings of creditworthiness. They are assigned based on transparent methodologies available free of charge on our website. These methodologies are calibrated using stress scenarios.

AC Invest 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. As part of ratings surveillance, Neural network continuously analyze real-time and historical data. If network see events taking place that impact our view on an issuer’s relative creditworthiness, we adjust our ratings accordingly to communicate our views so the market has the correct perception of how we view relative creditworthiness

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.

*In our 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. 


*Neural networks are made up of collections of information-processing units that work as a team, passing information between them similar to the way neurons do inside the brain. Together, these networks are able to take on greater challenges with more complexity and detail than traditional programming can handle.AI design teams can assign each piece of a network to recognizing one of many characteristics. The sections of the network then work as one to build an understanding of the relationships and correlations between those elements — working out how they typically fit together and influence each other. 


*In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.The Support Vector Machine (SVM) algorithm is a popular machine learning tool that offers solutions for both classification and regression problems.

SOLVING RISK PROBLEMS


In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account risk, i.e., increased awareness of events of small probability and high consequences. Accordingly, the objective of this project is to present efficient reinforcement learning algorithms for risk-constrained Markov decision processes (MDPs), where risk is represented via a chance constraint or a constraint on the conditional value-at-risk (CVaR) of the cumulative cost. We collectively refer to such problems as percentile risk-constrained MDPs. Specifically, we first derive a formula for computing the gradient of the Lagrangian function for percentile riskconstrained MDPs. Then, we devise policy gradient and actor-critic algorithms that estimate such gradient, update the policy in the descent direction, and update the Lagrange multiplier in the ascent direction. For these algorithms we prove convergence to locally optimal policies. Finally, we demonstrate the effectiveness of our algorithms in an optimal stopping problem and an online forecast application.

FURTHER READING


Deep Reinforcement Learning in Large Discrete Action Spaces
Applying reasoning in an environment with a large number of discrete actions to bring reinforcement learning to a wider class of problems.


Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
Introducing slate Markov Decision Processes (MDPs), a formulation that allows reinforcement learning to be applied to recommender system problems.


Massively Parallel Methods for Deep Reinforcement Learning
Presenting the first massively distributed architecture for deep reinforcement learning.


Adaptive Lambda Least-Squares Temporal Difference Learning
Learning to select the best value of λ (which controls the timescale of updates) for TD(λ) to ensure the best result when trading off bias against variance. 


Learning from Demonstrations for Real World Reinforcement Learning
Presenting Deep Q-learning from Demonstrations (DQfD), an algorithm that leverages data from previous control of a system to accelerate learning.


Value-Decomposition Networks For Cooperative Multi-Agent Learning
Studying the problem of cooperative multi-agent reinforcement learning with a single joint reward signal.


Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
Demonstrating an alternative view of the training of GANs.


Risk-Constrained Reinforcement Learning with Percentile Risk Criteria
Presenting efficient reinforcement learning algorithms for risk-constrained Markov decision processes (MDPs) and demonstrating their effectiveness in an optimal stopping problem and an online marketing application.



TAKE A LOOK AT*

Retrophin, Inc. Credit Rating & Financial Statements Analysis

BOSTON (AC Invest Credit Rating Terminal) Sun Jan 16 2022 11:33:57 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below: Credit Rating Rationales & Model We do not include potential future debt issuances as a source of liquidity because of the uncertainty of a company's ability to access debt markets in times of financial stress, even for investment-grade issuers. For instance, in the case of a proposed financing, with the intended use of proceeds to repay existing debt, we will assess a company's liquidity excluding the proposed financing until it's obtained or fully underwritten. Rating Model for Retrophin, Inc.: We estimate the credit risk parameters by Money Flow Index (MFI) and ElasticNet Regression Credit Ratings for Retrophin, Inc. as of 16 Jan 2022 Credit Rating Short-Term Long-Term Senior AI Rating Class* Baa2 B3 Semantic Signals 86 48 Financial Signals 66 46 Risk Signals 73 39 Substantial Risks

FIRST SECURITY BANK Credit Rating & Financial Statements Analysis

BOSTON (AC Invest Credit Rating Terminal) Sun Jan 16 2022 11:59:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below: Credit Rating Rationales & Model Larger, investment-grade issuers that have access to both public and private debt markets have greater flexibility than companies that depend solely on private bank loans. In addition, we consider whether a company can borrow on an unsecured basis, has access to the commercial paper markets, and issues debt in multiple geographies. It is more costly to raise debt in the public bond markets and often requires a company to establish a track record among investors. These costs and information asymmetry issues sometimes make it impractical for smaller, speculative-grade issuers to raise small amounts of debt in public markets. Rating Model for FIRST SECURITY BANK: We estimate the credit risk parameters by Rating and Spearman Correlation Credit Ratings for FIRST SECURITY BANK as of 16 Jan 202

Cactus Wellhead, LLC Credit Rating & Financial Statements Analysis

BOSTON (AC Invest Credit Rating Terminal) Sun Jan 16 2022 11:17:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below: Credit Rating Rationales & Model Other factors we consider include a company's frequency of debt issuance and market access, especially during times of company-specific stress or credit market turbulence. Rating Model for Cactus Wellhead, LLC: We estimate the credit risk parameters by KDJ and Multiple Regression Credit Ratings for Cactus Wellhead, LLC as of 16 Jan 2022 Credit Rating Short-Term Long-Term Senior AI Rating Class* B1 B1 Semantic Signals 39 59 Financial Signals 53 88 Risk Signals 70 67 Substantial Risks 53 51 Speculative Signals 89 35 *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.

NanoString Technologies Inc Credit Rating & Financial Statements Analysis

BOSTON (AC Invest Credit Rating Terminal) Thu Jan 13 2022 09:07:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below: Credit Rating Rationales & Model Given the earnings volatility companies experience, we have specified for these issuers a more stringent decline in EBITDA percentage for each liquidity category to the extent our cash flow forecasts are not already assuming a downside scenario. Rating Model for NanoString Technologies Inc: We estimate the credit risk parameters by Average True Range (ATR) and Ridge Regression Credit Ratings for NanoString Technologies Inc as of 13 Jan 2022 Credit Rating Short-Term Long-Term Senior AI Rating Class* B1 Baa2 Semantic Signals 60 66 Financial Signals 69 74 Risk Signals 84 72 Substantial Risks 33 76 Speculative Signals 57 90 *Machine Learning utilizes multiple learning algorithms to obtain better predictive powers. In our research, we utilize machine learning to com

Guangzhou Shangpin Home Collection CoLtd Credit Rating & Financial Statements Analysis

BOSTON (AC Invest Credit Rating Terminal) Sat Jan 15 2022 16:26:18 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below: Credit Rating Rationales & Model 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. Rating Model for Guangzhou Shangpin Home Collection CoLtd: We estimate the credit risk parameters by Tuned Collector Oscillator and Pearson Correlation Credit Ratings for Guangzho

LONE STAR STATE BANK OF WEST TEXAS Credit Rating & Financial Statements Analysis

BOSTON (AC Invest Credit Rating Terminal) Sat Jan 15 2022 16:17:33 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below: Credit Rating Rationales & Model 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. Rating Model for LONE STAR STATE BANK OF WEST TEXAS: We estimate the credit risk parameters by Bollinger Bands Width and Stepwise Regression Credit Ratings for LONE STAR STATE BAN

Adtalem Global Education Inc Credit Rating & Financial Statements Analysis

BOSTON (AC Invest Credit Rating Terminal) Mon Jan 17 2022 05:06:02 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below: Credit Rating Rationales & Model 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. Rating Model for Adtalem Global Education Inc: We estimate the credit risk parameters by Ichimoku Cloud (IKH) and Sign Test Credit Ratings for Adtalem Global Education Inc as of 17

Terrabank, National Association Credit Rating & Financial Statements Analysis

BOSTON (AC Invest Credit Rating Terminal) Sat Jan 15 2022 16:17:00 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below: Credit Rating Rationales & Model If, for example, a facility matured in 18 months, we could include the borrowing availability as a source of liquidity in year one, but exclude the amount in year two under the exceptional and strong descriptors (as well as include any drawn portions as debt maturities under uses of liquidity). This is because we do not assume an extension of bank lines--regardless of the company's perceived credit strength or issuer credit rating. For instance, whether the issuer credit rating on the company is speculative grade or investment grade, we do not assume bank lines will be extended beyond the current stated maturity. Rating Model for Terrabank, National Association: We estimate the credit risk parameters by Price and Paired T-Test Credit Ratings for Terrabank, National Association as o

NSE:GENUSPAPER Stock Forecast, Price Targets (Buy or Sell) | GENUSPAPER Genus Paper & Boards Limited Analyst Ratings

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 with Rate of Change (ROC) and Lasso Regression. Machine Learning based stock forecast (n+30) for GENUSPAPER Genus Paper & Boards Limited as below: GENUSPAPER Genus Paper & Boards Limited Stock Forecast (Buy or Sell) as of 13 Jan 2022 for (n+30) How Does Forecast Model Work? x axis:Likelihood % y axis:Potential Impact % z axis:Color (yellow to green) Technical Analysis % NSE:GENUSPAPER Stock Forecast Rationales & Analyst Ratings Time series to forecast n: 13 Jan 2022 for (n+30) In these cases, the level of capital expenditures will be lower than estimates in our base-case forecast to determine an issuer's financial risk profile, particularly for companies that are pursuing discrete growth projects that have not been committed or can be easily curtailed in case of a need to pre

LON:RIII Stock Forecast, Price Targets (Buy or Sell) | RIII RIGHTS & ISSUES INV TST PLC Analyst Ratings

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 with Average True Range (ATR) and Paired T-Test. Machine Learning based stock forecast (n+1y) for RIII RIGHTS & ISSUES INV TST PLC as below: RIII RIGHTS & ISSUES INV TST PLC Stock Forecast (Buy or Sell) as of 15 Jan 2022 for (n+1y) How Does Forecast Model Work? x axis:Likelihood % y axis:Potential Impact % z axis:Color (yellow to green) Technical Analysis % LON:RIII Stock Forecast Rationales & Analyst Ratings Time series to forecast n: 15 Jan 2022 for (n+1y) In determining how prudent a company's risk management is, we look for evidence that management has historically anticipated potential company-specific or market-related setbacks and has taken necessary actions to ensure sufficient liquidity. We estimate RIII RIGHTS & ISSUES INV TST PLC stock forecast parameters by: A

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