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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


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 forecast 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.



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