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We utilize technological advancements in computer programming, data science, algorithms and machine learning to develop unique and differentiated investment strategies. Long before the term "big data" became ubiquitious, our founders were exploring ways in which we could capture, transform, and apply new and large datasets to enhance our understanding macroeconomic intricacies and to provide a source of informational advantage with respect to our stock selection process.
With the rise in availability of unstructured, semi-structured, and open-source structured data growing at an astounding pace, analysts and portfolio managers with the requisite coding, programming (R, Python), applied mathematics and advanced statistical skills can develop ways to answer some of the most controversial macroeconomic topics - the subject of debate for years - and more importantly, they can build simulation and predictive models with a significantly-greater ability to predict earnings performance and long-term fundamental results of potential long or short equity investments. This is referred to (loosely) as a "Quantamental" Investment Approach (utilizing botom-up, fundamentals combined with quantitative approaches to distilling masses of data into quantifiable conclusions).
"Quantamental" strategies apply the traditional skills of equity analysts and portfolio managers, those of humans that will always be needed to execute successful long-term investment strategies, while harnessing the value of big data and enhancements in data science and machine learning to dramatically enhance the predictive prowess of investment analysts.
With today's data munging, exploration, and manipulation techniques, our team of data scientists, analysts, and portfolio managers have a unique informational advantage derived from our ability to access and make sense of data that most cannot.
One of our key competitive differentiators comes from our ability to make sense of unstructured data, such as satellite imagery, geolocational data, social media, and data from sensory equipment measuring anything from the weight of containerships leaving China to the relative change in transportation and logistics activity during a given holiday season. After capturing and converting these data into usable datasets, we apply machine learning algorithms supported by rigorous statistical analyses to make and test research hypotheses that can be used to our advantage in the investment decision-making process.
We may also use advanced algorithms and neural networks to mine large datasets that may be able to identify patterns or relationships unobservable to the human eye.
"No man is better than a machine ... and no machine is better than a man with a machine."
- Paul Tudor Jones
Though our ability to harness the big data and internet of things to enhance the investment process through better data insights and analysis, the human element is critical to long-term, consistent investment results. Good portfolio managers have experience; they understand the irrational underbelly of capital markets, as well as their own (and their analysts) behavioral biases and inherent limitations. With this knowledge, we can now teach machines with self-learning algorithms how to deal with Mr.Market's irrational, emotionally-charged schizophrenia. Ultimately, those who don't keep pace with innovations in data science and combine them with traditional analysis will be left in the dust.
Seeking data scientists or experienced fundamental, long/short equity analysts with Python coding skills and a strong statistical background to join the investment team. If you feel you may be qualified, please send your resume to firstname.lastname@example.org.
600 Montgomery St, San Francisco, California 94111, United States
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