Quantitative Investment Strategies Built On
strategies that make sense with a robust foundation in mathematics
Who, What, When, Where and Why?
Sistine Capital is the first and only fully quantitative hedge fund owned and operated by physicists and mathematicians in the state of Kentucky. In this region specifically, the approach to investing is overwhelmingly traditional and monotonous, creating significant value in any approach rooted in a unique perspective such as ours. We believe that every investor can benefit from a portion of their portfolio working in a space that is completely independent of political, economic, and social events. By allocating capital according strictly to mathematical and physical patterns, the investor can capitalize on the emotional and impulsive decisions made by those whose strategies do rely on those socioeconomic and political events.
Strategy and Investment Philosophy
The investment strategies at Sistine Capital are the culmination of years of experience in computational and theoretical physics blended with behavioral psychology and common sense. We hold the belief that markets (for the most part) are not driven by mathematics or logic, but by human behavior. The "edge" of quantitative investing lies in a strategy's ability to capitalize on patterns in that behavior, or recognize themes in the sea of action/reaction scenarios that play out on a cyclical basis. The Python programming language is used to mold our investment strategies into algorithms that can remove the impulse and emotion that often tamper with the decision making process.
Rather than investing in individual companies or trying to hold board positions and assist in the management of those companies, our only goal is to offer strategies that achieve a maximum risk adjusted return for our clients. To do this, our investment process is built around asset class momentum. With a strong background in physics, the concept of momentum translates well into the realm of investing. It is well established that human nature is nearly impossible to alter, and investing based on momentum allows us to capitalize on a fundamental aspect of human nature: the Fear of Missing Out (FOMO). With that in mind, momentum appears to be a core strategy factor that will work regardless of market conditions, so long as human nature does not change. The logic behind asset class momentum is quite simple: if money is flowing out of one asset, it is flowing into another. The mission of our algorithms is to track that flow and follow it just as a surfer watches for waves.
Our flagship strategy is built on Dynamic Leveraged Momentum, a proprietary algorithm designed in-house. It uses mathematically defined parameters to rank the momentum of each asset class. If a particular asset class has momentum, the level of that momentum is then determined. Just like the gears on a bicycle are used depending on the effort needed to peddle, our strategy assigns different levels of leverage like "gears" for each asset class. For the sake of discretion, further information on the strategy is only disclosed to clients.
Born and raised in south-eastern Kentucky I could not have started farther from Wall Street. Luckily a passion for problem solving led me to pursue a degree in Physics with a concentration in theoretical astrophysics. While in college I maintained a paid research position with NASA Goddard working on magnetic field activity in the Sun's atmosphere. In 2017 I was awarded a research fellowship at Harvard University in the Smithsonian Center for Astrophysics, continuing to work on high energy magnetic activity in the solar regime. Through these research projects I developed a knack for characterizing complex systems and building algorithms to better understand their behavior. After I returned home from Harvard I used my last year in school to apply the Python programming language and discrete mathematics to an extensive project on the history of military encryption, specifically the Enigma Machines in World War II. While looking into other applications of mathematics I tried to write a simple algorithm for the stock market and immediately fell in love with the process.
After two years of development on an algorithmic investment process I decided it was time to start a fund and offer this strategy to others. My background in tracking the flow of energy along magnetic field lines was perfectly applicable to tracking the flow of money between asset classes. Outside of the fund I remain involved in the theoretical physics community. I'm currently working on developing and publishing my Multi-Dimensional Information Gradient Theory alongside a collection of thoughts on the philosophical implications of theoretical physics.