ac investment research


Research Reports

1-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.

2-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.

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

4-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. 

5-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.

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

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

8-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.

9-How Does Rating Model Work?
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.

Case Studies

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1-Çetinkaya,Adem (2010).Calculus:For Economics.CSIP.Cambridge

2-Çetinkaya,Adem (2011).Speculative Growth: Extreme Stock Market Valuations.CSIP:London

3-Çetinkaya,Adem (2017).The Price Theory.CSIP.Cambridge

4-Çetinkaya,Adem (2014).Probability Theory.CSIP.Cambridge
AC Investment Research

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.

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