My full name is Charles Sumner Crow IV. I was raised in Princeton, New Jersey and moved to New York City for graduate studies in 2003. I currently live in northern Brooklyn and work in midtown Manhattan.
I have worked on the buy-side as an alternative money manager for the majority of my career. My primary interest is the generation of market-neutral, absolute returns for institutional investors. I generate data-driven investment decisions for long/short, multi-asset class portfolios using modern open source software and machine learning.
I am an advocate for empirically-driven decisions at both the investment and operational levels within an investment firm (for more details, see my paper “Frontline Data Science: Human + Machine”). I believe the integration of a high impact Data Science team across an organization is non-trivial, but paramount to success in the modern era.
I prefer models that offer greater explanatory power and incorporate the “human-in-the-loop” approach over opaque “black box” models for high impact decisions. Interactive data visualization and reproducible research (e.g., R Markdown) are also topics of interest.
Discretionary and systematic investing have always relied on the extraction of insight from data (though, historically, the tools have sometimes been crude). The Data Scientist’s toolbox is highly relevant to modern investing, but also needed is effective communication abilities, market experience and an ability to recognize new, forward-looking input(s) outside of a pre-existing model.
Even though I am heavily quantitative, I believe both discretionary and systematic alternative strategies have great strengths and should be a component of any robust institutional portfolio (assuming incentive fees are commiserate with pure alpha received and not paid for commoditized beta exposures). The thoughtful application of two will lead to the highest probability of future success in the modern era of capital markets, dominated by (1) interconnected, fast-moving markets and (2) vast amounts of data, routinely characterized by unstable correlations.
The bulk of operations research can be partitioned into two fields of study: optimization and stochastic processes. Loosely speaking, the former seeks to select the “best” solution for a given problem subject to various constraints. The application of optimization theory is pervasive in society – for a layman’s introduction, I recommend Steve Sashihara’s “The Optimization Edge“. Stochastic Processes, meanwhile, are used to model the dynamics of real-world uncertainty.
Mathematics provides a framework for disciplined research, but software is required to obtain empirical results. I view programming languages as tools and select the most appropriate language for a given task.
While most of my code is not open source, I have created a few projects for the open source community, namely: Rooter and DistSolve: An Open Source Web Service for Solving Sparse Systems of Equations with Alex Pletzer.
I am a former Treasurer and Trustee of The Oliver Scholars Program, a New York City-based 501(c)(3) nonprofit. I was the Chair of the board’s Finance and Compensation Committees, as well as a mentor through the end of 2015. The program’s mission statement is:
The Oliver Scholars Program identifies and engages extraordinary New York City students of African and Latino descent and prepares them for success at leading independent high schools and prestigious colleges.
Please reach out to me if you’d like to contribute to this program.
Outside of the above activities, I enjoy abstract contemporary art through various mediums, in particular: audio, visual, and architecture.
My favorite artists include (but are not limited to): Christian Fennesz, Mountains, Ryoji Ikeda, Tim Hecker, Alva Noto, Mark McGuire, William Basinski, Jefre Cantu-Ledesma, Stephan Mathieu, John Fahey, Barnett Newman, Agnes Martin, and Robert Ryman.