About Us

As AC Investment Research, our goal is to do fundamental research, bring forward a totally new, scientific technology and create frameworks for objective forecasting using machine learning and fundamentals of Game Theory. 

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Our Mission

There are many widely used traditional techniques of stock forecasting each with its own limits and biases. Human-oriented approaches focus more on back-dated data while not being able to well define or relate how those back-dated financials or people actions will reflect on future performance or pricing. At its best the number of facts or signals that can be considered and correlated simultaneously in human-oriented approaches is quite limited and is subject to personal biases. As AC Investment Research, our goal is to do fundamental research, bring forward a totally new, scientific technology and create frameworks for objective forecasting using machine learning and fundamentals of Game Theory. 

Our Research 

Modern machine learning models are highly flexible but many lack transparency and/or they are “black-boxes” in terms of architecture. In this project, we are developing methods for explaining the predictions made rather than constraining the models themselves to be interpretable. 

 

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. 

Our Methods

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.

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.

Our People

We are led by Adem Çetinkaya and have a staff of more than 20 people and 4 research teams. Our experts combine the support-vector networks and financial skills  with the analytical rigor of scientists.

Our History

Launched as AC Investment Research in 2017, AC Investment Research is the investment research arm of AC Advisory. AC Investment Research is headquartered in (Saint Helier) Jersey, with offices in San Francisco, Boston, New York, Cambridge and London.

Careers

We believe we can solve problems faster and better together.

That's why we work together to produce solutions suitable for the new needs of the changing world with an inclusive perspective. At AC Investment Research, we are a global community where everyone feels like they belong, develop unique skills, and work on exciting topics, powered by the cutting edge of technology. We invite you to be a part of this community where everyone is seen, heard and cared about.

To build a better future

Join our community of solution creators!


AC Invest mobile app lets you:

*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.
*Get daily market risk notifications.
*View prediction confidence score.

301 Massachusetts Avenue Cambridge, MA 02139 667-253-1000 pr@ademcetinkaya.com


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