The Big Data & Machine Learning Revolution: Event take-aways, slides & videos

RavenPack | October 27, 2017

Recently, RavenPack hosted its 5th Annual Research Symposium in New York titled “The Big Data & Machine Learning Revolution”. Here are my personal take-aways.

Following the success of last year’s event, this year we had over 700 people register to participate in our annual research symposium. We had to turn many of those away, since our capacity in the room was limited to 250 attendees. Access the presentation slides and video recordings from the event.

Event Highlights

First, I suggest you watch this 3 minutes event highlights video for a brief overview of the presentations.

My Take-Aways

The event started off with our own Armando Gonzalez, RavenPack CEO , going through the concepts of Big Data and Machine Learning in finance, and arguing why we are experiencing a Big Data and Machine Learning Revolution. Armando highlighted four areas that are being disrupted by artificial intelligence (AI) and machine learning, including access to unstructured content, the data modeling/cleaning process, the generation of alpha signals (predictive analytics), as well as portfolio optimization.

5 Signs Showing We Experience a Big data and Machine Learning Revolution

Following Armando’s opening address, we continued with our keynote presentation by Marko Kolanovic and Rajesh T. Krishnamachari, the Global Head and Vice President of the Quantitative and Derivatives Strategy at J.P. Morgan , respectively. They provided an overview of their highly celebrated tour de force titled “Big Data and AI Strategies”, in which they put a host of alternative data providers, including RavenPack, under the microscope. They also presented their team’s latest research which used sentiment data from RavenPack to enhance valuation strategies.

Miquel Noguer Alonso, Executive Director, UBS and Adjunct Assistant Professor at Columbia University then went on to explore the benefits and challenges of different modeling choices in the signal construction process, covering anything from fast machines, to supervised, unsupervised, and deep learning models. He addressed the challenge which arises when discerning how to use the various predictions to create the final portfolio weights. You can learn more about this aspect from Miquel’s presentation.

Next, on behalf of Ravenpack, I had a chance to present some the internal research done by the RavenPack Data Science team, looking at strategies around energy and metals futures using machine learning and event detection. In particular, I presented how, using ensemble methods, we were able to achieve attractive Information Ratios across both commodity baskets with holding periods of several days.

Results - Machine Learning and Event Detection for Trading Energy and Metal Futures

After this, we then had the first exciting panel of the day, focusing on how to prepare your organization for the big data revolution. The moderator, Adam Honore from the CME Group , started off the session on a high by challenging the audience to get the panelists “off their game”. The audience was quick to respond with many thought-provoking and challenging questions, although they didn’t succeed. Instead, Thani Sokka from Dow Jones, Stuart Kozola from Mathworks, and our own RavenPack CTO, Jason Cornez had a lively discussion about how the “cloud” has revolutionized their own businesses and how it will revolutionize finance. The question that caught my attention the most was one asking if the panelists had seen any “resistance” to this revolution.

Adam Honore, Thani Sokka, Stuart Kozola, Jason Cornez on panel

Ichihan Tai, Head of Data Science at Tokio Marine Asset Management spoke about the concept of lean research, using news analytics to replace more expensive human-driven processes related to information gathering, thereby reducing overall R&D costs. In particular, he walked through one example, using RavenPack data to identify boring vs. popular stocks (“the boring stocks premium”) and showed how this approach provided alpha beyond analyst data from IBES as well as traditional factors. That was the presentation I personally liked the most.

Ichihan Tai from Tokio Marine Asset Management presenting his concept of Lean Research

Other interesting talks were given by:

Hedi Benamar, a Senior Economist for the Board of Governors of the Federal Reserve System , who talked about the greater impact of macroeconomic news on treasury bond prices when demand for information around these events are high.

James Hodson, from the Artificial Intelligence Laboratory at the Jozef Stefan Institute , discussed his recent work leveraging large-scale employment data to better understand firm performance, evolution, and labor markets. He found that firms with lower employee turnover systematically outperform those with higher turnover rates.

Jason Cornez, RavenPack CTO , went on to introduce RavenPack’s new custom indicators platform that allows users to easily apply their own filters and aggregators on top of the granular RavenPack data, thereby consuming a single daily snapshot of sentiment or media buzz, delivered via API or through the RavenPack Visual Platform.

Donald Putnam, Managing Partner, Grail Partners gave his opinions on artificial intelligence, focusing on where we have been, and where we might be going. Rather than focussing on the immediate threats and opportunities, Donald related long-standing themes and extended them in a seven to ten-year view. Take a look at his very engaging presentation.

Donald Putnam from Grail Partners speaking about AI, threats and opportunities on the long run

Alternative Data Panel

We finished off an exciting day with a highly entertaining panel on alternative data and why we should care, moderated by Marko Kolanovic from J.P. Morgan . Panelists included Matthew Rothman, Managing Director and Head of Quantitative Equity Research at Credit Suisse, Yin Luo, Vice Chairman at Wolfe Research, Donald Putnam, Managing Partner at Grail Partners , and myself. We all shared our opinions on datasets that we had found either particularly useful or disappointing as part of our own research. In particular, Matthew Rothman shared a very entertaining analysis using Google searches to predict unemployment levels, which produced rather dubious, but amusing correlations. The final comment of the panel concluded with: “I’d rather have a drink than an opinion”.

Marko Kolanovic, Matthew Rothman, Peter Hafez, Yin Luo, Don Putnam on alternative data panel

Slides & Videos

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