PANEL - The Disruptors in Investment Management - Alternative Data and Artificial Intelligence
Channel: Beryl Elites
Date: 2019-01-22

BERYL KNOWLEDGE CENTER

The Disruptors in Investment Management: technology solution providers and emerging AI and Alternative data centric hedge funds discuss current trends.

Speakers: ❖ Evan Schnidman Ph.D. - Prattle Analytics  ❖ David Itzkovits - Sanlam Global Investment Solutions ❖ Thársis Souza - Yewno ❖ Barry Star - Wall Street Horizon

- David Itzkovits
"The truth is AI only has value if it can improve the investment outcomes for clients. Whether that's on predictive side or on the risk management side or hopefully a combination of both. AI can enhance significantly the analytical power, research capability, it amplifies it by magnitudes. But the real AI is the one that is continuously learning and adaptive. The challenge is that a lot of algos today are fixed rule-based algorithms. AI is not the silver bullet, far from it. All it can do is maybe give you a little bit of a predictive edge. Any AI capability that you see out there that says we get things right seventy, eighty, ninety percent of the time it is hogwash, it doesn't happen. One of the biggest mistakes that I have seen in other AI capabilities is that they focus on one piece of value chain which is getting the prediction correct but they do not focus on the risk management side. So when markets are going down and if you get that piece right with the AI that could be a big winner." 

- Thársis Souza
"You can extract an infinite amount of dimensions of knowledge from earnings calls by analyzing data in a solid form as well as by analyzing data in conjunction with other data sets. Then you can extract knowledge and this knowledge is what I think is alternative data today.  From technology provider perspective we observe two key pain points. The first one is information overload as large firms have access to more information channels. And second, since most data are structured, how can we transform structures data into actionable scores so you can build them into your models. The AI systems should be able to learn by themselves and then evolve with time as opposed to have a rule-based system. We work a lot in the index space for instance and if you look at desired characteristics of a given product in that space it is a rule-based strategy. So how can you develop the AI driven strategy that is really evolving over time without having rules but at the same time have a rule-based system. So that's the tradeoff and that's the paradox. The point is how to deploy AI systems with transparency is one of the big challenges that we face today."

- Barry Star 
"In my business we do incremental edge on data. Big box terminals distribute really good data and our clients make money arbitrating the difference between pretty good data and really good data. And the same is true, if you have an AI based model that can give you a couple of points, that's all you need. We do not use technology to replace people, we use technology to augment people. We have a whole bunch of analysts to collect data and we still also have a half million lines of proprietary code. The code is there not to replace data analysts but to make their jobs better."

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