RavenPack’s events have become global, with attendance exceeding 250 buy-side professionals at the London Big Data and Machine Learning Revolution in April 2018. RavenPack Research Symposium returns to New York on September 12th. Industry buzz is that RavenPack’s Research Symposium is the “must attend event” for quantitative investors and financial professionals that are serious about Big Data.
Expect a full day of thought provoking presentations and panel discussions focusing on the impact of Artificial Intelligence (AI) and Big Data in the modern investment process. Speakers will present their research and views on the latest alternative data sets, machine learning techniques, and big data technologies reshaping the way we invest and trade globally.
With content very much research-driven and examples of real world use cases of alternative data and AI in the investment process. See below the session titles and abstracts from some of our confirmed speakers. More to be announced soon. Register to receive updates.
Last Updated: August 14, 2018
10 Financial Applications of Machine Learning
Dr. Marcos Lopez de Prado, CEO, True Positive Technologies
Financial ML offers the opportunity to gain insights from data. At the same time, Finance is not a plug-and-play subject as it relates to machine learning. Modelling financial series is harder than driving cars or recognizing faces. In this presentation, we will review a few important financial ML applications.
Machine learning for Future Fundamentals Estimation
Dr. Ronnie Shah, Director and Head of U.S. Quantitative Research and Quantitative Investment Solutions, Deutsche Bank
We develop a new technique to estimate “fundamental acceleration” using a machine learning lasso technique to forecast fundamental values. Estimating future fundamentals helps resolve the lack of timeliness of past fundamental data when constructing value metrics. The dynamic nature of fundamental forecasting improves capital allocation across sources of expected return. As we show, adding fundamental acceleration to typically constructed value or “1/P” strategies improves risk-adjusted performance by 80%.
Profiting from CAPEX Announcements
Hong Li, Head of U.S. Equity Quantitative Research, Managing Director, Citi Research
In this presentation, we study CAPEX as an stock selection (alpha) factor. We have found that buying stocks with recent CAPEX announcements outperform the market over the long run while high CAPEX stocks based on accounting reports tend to underperform.
Quant Trading 101
Nitish Maini, General Manager, Virtual Research Center / Vice President, Portfolio Management
In this presentation, Nitish will provide an overview of the quantitative research process and share how the use of AI, ML & data creates value in this process. He will also discuss, how is the space of alphas defined in quantitative world and what could be a systematic approach towards building a diversified quantitative portfolio.
Big Data and Machine Learning in Investing: Current Misconceptions and the Path Ahead
Rajesh T. Krishnamachari, Head of Data Science - Equities, Bank of America Merrill Lynch
The talk will clarify the difference in the role played by new data vis-a-vis the role played by new analysis techniques. We will also classify data science techniques based on their academic provenance. We argue for certain areas/regimes possessing the most and the least potential for application of big data analysis techniques.
Multi-Dimensional Analysis of News Sentiment Factors
George Bonne, Executive Director, Equity Factor Research, MSCI
George will present his latest research on news sentiment signals in the framework of the Barra equity factor models. He will evaluate factors constructed from the latest generation of RavenPack data, whereby highlighting improvements over previous versions in coverage, cross-sectional explanatory power and factor returns. George will also demonstrate how the results are robust to factor formulation, geography, and time period.
The Neural Networks Survival Kit for Quants
Matthew Dixon, Assistant Professor of Finance and Statistics, Illinois Institute of Technology
Using examples ranging from portfolio construction to algorithmic trading, this talk will explain neural networks as a non-parametric econometrics technique. Matthew will also provide various examples illustrating the tradeoffs between using Deep Q-learning versus supervised deep learning for predictive modeling with signals such as news sentiment.
Is Big Data a Big Opportunity - or a Big Problem?
Armando Gonzalez, CEO, RavenPack
Armando will discuss how the big data revolution is changing the way decisions are made in finance as we rely more on data and analysis, and less on intuition and experience, and why this is disrupting the very nature of human thinking.
News Sentiment Everywhere!
Peter Hafez, Chief Data Scientist, RavenPack
In order to maintain an edge in the marketplace, asset managers are to a larger extend turning to unstructured content for alpha creation, using NLP and text analysis techniques. In addition, more managers are expanding their mandate, trading global portfolios, to ensure scalable strategies. As part of his presentation, Peter will showcase how news sentiment is valuable across global markets allowing managers to achieve their goal of increased scalability.
Panel: Big Data, Big Impact: How Data is Reshaping the Modern Investor
Moderator: Tim Harrington, CEO, BattleFin Group
- Matei Zatreanu, CEO / Founder, System2
- Peter Hafez, Chief Data Scientist, RavenPack
Today, most financial institutions are working hard to adopt a data-driven approach. Although many asset management firms, banks and hedge funds are beginning to disrupt their analytics landscapes by gathering immense volumes of data assets, these companies are at varying levels of Big Data maturity. Firms able to access huge amounts of data possess a valuable asset that when combined with the ability to analyze it, are outpacing those living in oblivion. In this panel, we discuss how financial institutions can ensure that the potential of Big Data is actually realized by: 1) leveraging the breadth, volume and timeliness of available data; 2) developing machine intelligence that is continuously learning and improving; and 3) understanding the economics that make a data strategy work.
Panel: Will Artificial Intelligence Create a ‘Useless Class’ of Financial Professionals?
Moderator: Bartt Charles Kellermann, Founder and CEO, Global Capital Acquisition
- Matthew Dixon, Assistant Professor of Finance and Statistics, Illinois Institute of Technology
- Rajesh Krishnamachari, Head of Data Science - Equities, Bank of America Merrill Lynch
- Igor Halperin, Research Professor of Financial Machine Learning, NYU Tandon School of Engineering
Are machines likely to become smarter than humans? Is Artificial Intelligence (AI) creating a “useless class” of investors and traders? It isn’t hard to miss the warnings. In the race to make computers more intelligent than us, we are bringing forth the end of days of the traditional investor. In this panel, we debate whether finance professionals will be pushed out of employment by intelligent machines. What should we do with all the superfluous brokers, bankers, and traders once we have highly intelligent algorithms that can do almost everything better than they can? Consequently, will new professions emerge and what will they look like? Will these new jobs be completely reliant on AI and will people lack the basic ability to make their own decisions? What skills will people need to reinvent themselves quickly enough to survive in the industry?
Register to receive updates.
The speaker line-up is almost final. We have invited several top buy and sell-side professionals, as well as academics. Below are confirmed speakers:
A cocktail reception will be held at the conference venue from 5:00 pm.
The event is free to attend for financial professionals with an invitation.