Stock Coverage

We consider the full spectrum of human trading interaction (varying from data based analysis to market signals, from trend actions to speculative ones and many more) and adapt them to the machine learning model with support of engineers to mimic and future-reflect everyday trading experiences. To do that 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. 


Coverage
of*
As of 2022* As of 2023
NYSE* 1850 2400
NASDAQ 1400 2850
LSE 600 1000
TSX-ASX 1400 1800
Major
Stock Indices
90 170


*Number of Stocks

*AC Investment Research will expand its coverage of U.S. stocks (NYSE & NASDAQ) from 3250 currently to more than 5250 stocks by 2023.

Power Your Investment Decisions

Whether its generating investment ideas, performing financial analysis, or conducting risk assessments on companies and sectors across the globe, our machine learning based robust collection of research can help you gain the insights you need.

We conduct machine learning based financial market analysis. We’re committed to meeting the highest methodological standards — and to exploring the newest frontiers of research. 

Our Research Areas

Explore our research across a wide range of disciplines.

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.

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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.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.






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*See the machine learning based stock market analysis and AC Invest Rank which indicates potential outperformance based on earning estimate revisions and surprises.
*View the current market risk, operational risk and outlook.
*Get daily signal notifications.
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*View prediction confidence score.

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