Please tell us a little about the topic you will be speaking on at the upcoming event?
The title of our panel “Finding alpha by acquiring and using alternative data” by itself emphasizes process over access. I think it accurately reflects how the center of the discussion has shifted from whether or not alternative data is a real deal to “how to make use of alternative data?”.
I don’t think we need to start panel discussions with the definition anymore, but we will address some of the common misconceptions about the role of alternative data in finding alpha. We’ll also discuss different use cases across strategies, research approaches and investment time horizons.
Perhaps the most interesting and practical part of the conversation is the approach to building the internal buy-side process. We will discuss common issues investment firms face when implementing alternative data within their existing research framework; what is missing from the infrastructure standpoint; and how tech companies can leverage on the opportunity to fill the niche of pre-processing data for investment research.
Can you tell us a little bit about your background, and how you ended up in your current role?
I’m a Managing Director at Synthesis, a quantitative investment company, focusing on statistical arbitrage in equity markets. Our team apples machine learning methods to extract insights from a wide range of traditional and alternative data sources. In my role, among other things I oversee alternative data acquisition process. That involves discovering new data sources, working with data vendors and building an efficient internal process of vetting data sets.
Before joining a quant trading team about five years ago, I spent over ten years working with traditional hedge fund managers and discretionary traders. Having observed changes in the alternative investments industry, I saw the biggest opportunity in data driven strategies powered by emerging technology. And for such strategies the role of data acquisition team is critically important because the quality of data ultimately defines the outcome of the research effort.
Even though in my data sourcing capacity I’m currently focusing on pure quant use case, my background and formal training in finance gave me a perspective on the fundamental hypothesis driven approach to using data insights. Applications of big data analytics in investment research go far beyond short term quant trading and it is always interesting to discuss a wide range of use cases with industry peers.
What is the biggest challenge you face within your role today, and how are you looking to tackle it?
At the beginning alternative data was an access game, data scout’s value was mainly in their list of data vendors’ contacts. Now the goal of data acquisition teams on the buyside is to keep up with the constantly growing supply of new data while maintaining an efficient internal evaluation process. And this is quite a challenge because even at larger companies with internal data teams and considerable budgets, the resources are not unlimited. At Synthesis we are building a data procurement funnel that allows us to collect information on as many potentially valuable data sources as possible while carefully selecting datasets that get to the comprehensive testing stage.
What are your Key Objectives for attending the upcoming event?
What are you currently most inspired about in regards to AI & Emerging Tech for Finance?
It has been fascinating to see the shift in the industry towards quant, big data and emerging tech not only from the business model perspective, but also in the mindset of the investment community. Working in the industry during the period of “technological revolution” is challenging, it requires adaptation and continuous learning to stay relevant. But it is also very inspiring to work with the early adopters of emerging technology who shape the future of the investment management.