Our Mission

Cutting through the investment noise: data-driven investing powered by machine learning and finance theory

While making financial decisions, investors are prone to cognitive overload and biases. Too much information and news are constantly coming in. “Hot” stock tips abound. It is difficult to cut through the noise, to see if the information is not already reflected in the expectations. Another challenge is to judge the merit of one investment signal compared to other signals and other investments.

For our investment decisions, we combine our profound experience, insights from latest academic research and state-of-the-art technology to quickly and efficiently process a wide variety of data. We continuously analyze hundreds of established and proprietary investment signals for more than tens of thousands of stocks to understand which stocks are most attractive over the next month and cast them into a well-balanced equity portfolio.

Our Values


We work hard on building long-term relationships with our clients through high quality solutions and services.


We offer cutting-edge systematic investment solutions, always keep on improving and rely and strict research standards.


We rely on a modular and scalable infrastructure to deliver bespoke solutions on short notice.


We only offer reasonably priced solutions that we are convinced of and in which we co-invest.

Our Team

Anchored in research and driven by markets

Together, we have over 20 years of experience in developing rules-based investment strategies. Educated at top-tier universities in Europe and the US, our team has the passion and the experience to design investment products based on robust research insights.

Ulrich Carl, PhD, CAIA

Quantitative researcher, UBS Asset Management
Head of Research, Finreon
PhD in St. Gallen (CH) on equity portfolio management
Studies: St. Gallen (CH), SMU (Singapore), USC (US)

Marcial Messmer, PhD

Quantitative researcher, UBS Asset Management
Quant equity portfolio manager, Kraus Partner
PhD in St. Gallen (CH) on equity selection using AI
Studies: Columbia, Boston/BU, UCLA (all US), Mannheim (DE), St. Gallen (CH)

Our Skillset


We gained long experience managing and researching multi-billion institutional quantitative equity solutions.


We have deep know-how in risk-modeling, optimization and portfolio construction.


We built a strong track-record in building state-of-the art software packages, analytics and investment engines.

Tailored Advice

We offer investment advice for institutional clients and HNWIs

Scalable Systems

We rely on modern open-source & commercial software solutions that are powered by cloud computing to achieve scalability and maximum flexibility.

Our Offering

We combine modern finance theory with machine learning

We aim to create value for our investors by combining modern finance theory with machine learning. We believe there is great potential in leveraging human judgement with the computational power of machines, thus, combining the best of both worlds. A scalable and modular technology platform serves as a backbone in this effort.

Quantitative Equities

Our Alpha Proposition

We create value using a thoroughly designed system that exploits a large universe with low-capacity constraints.

There are more alpha opportunities for smaller stocks and international markets. Given our efficient scalable infrastructure, we can focus on lower capacity strategies, that are unattractive for larger players with significant overhead.

Leveraging the latest technology and our lean setup, our investment technology is ahead most of the other players in the industry.

Due to our long experience in designing investment strategies and our attention to details, we can avoid many little design and implementation inefficiencies that detract from performance in the long run.

Our promise is that products will be closed once capacity constraints start to bite. This ensures the protection of our alpha and safeguards early investors.

Our Research Process

A consistent and transparent research process is crucial for successful quantitative research
We are committed to strict research protocols.
To avoid overfitting, we store every single research run (apart from errored outputs), which allows for statistical corrections of the number of trials.
We accept that most research ends in failure. We focus on the quality of the research process, not on seemingly good backtesting results.
Even the most carefully designed backtest does not reflect the future. We use backtests with caution and only use them as one tool among many others.
We have an almost fully automated and standardized research environment that is powered by our scalable analytics engine. This introduces objectivity and comparability.
We set up conceptual frameworks based on economics and finance and let machines figure out the details.

Our stock selection model

We harvest alpha by exploiting market inefficiencies by combining finance theory and machine learning
Our stock selection model has three distinct layers of value creation that build on top of each other, supported by our state-of the art data & analytics infrastructure.