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Generation AI: The New Data-Driven Investor

The RavenPack Research Symposium returns to New York on September 12th - register to receive updates on the agenda.

RavenPack Research Symposium - Generation AI: The New Data-Driven Investor

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.

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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.

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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 WorldQuant
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?

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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:

Last Updated: August 1, 2018

Marcos Lopez de Prado
Dr. Marcos Lopez de Prado
True Positive Technologies
Marcos founded Guggenheim Partners’ Quantitative Investment Strategies (QIS) business, where he developed high-capacity machine learning (ML) strategies that consistently delivered superior risk-adjusted returns. After managing up to $13 billion in assets, Marcos acquired QIS and successfully spun-out that business from Guggenheim in 2018. Among several books, Marcos is the author of "Advances in Financial Machine Learning" (Wiley, 2018).
Rajesh T. Krishnamachari
Rajesh T. Krishnamachari
Head of Data Science, Equities
Bank of America Merrill Lynch
Rajesh expertise is on utilizing big/alternative data and machine learning / A.I. algorithms. He is undergoing research signals for systematic trading across asset classes by coupling data analysis with market intuition. Previously, he was Investment Strategist at JP Morgan.
Bartt Charles Kellermann
Bartt Charles Kellermann
Founder and CEO
Global Capital Acquisition
Bartt Kellermann is the founder and host of Battle of the CRYPTOS and Battle of the Quants. An industry leader in Quantitative Finance, Bartt founded Global Capital Acquisitions (GCA) in 2002, a hedge fund consulting firm focused on bringing clients smart, innovative investment strategies in the alternative investment space.
Matthew Dixon
Matthew Dixon
Assistant Professor of Finance and Statistics
Illinois Institute of Technology
Matthew published over 20 peer reviewed publications on deep learning and financial modeling, has been cited in Bloomberg Markets and the Financial Times as an AI in fintech expert, and is a frequently invited speaker in Silicon Valley and on Wall Street. He has consulted for several prop trading and asset management firms around AI and is the co-founder of the Thalesians.
Nitish Maini
Nitish Maini
General Manager, Virtual Research Center / Vice President, Portfolio Management
Nitish specializes in building the trading and business strategy for Virtual Research Center. He is also involved in setting up the research environment for WorldQuant’s new offices and building collaborations with academia for the firm. Nitish has gained exposure to a variety of areas, including risk, consulting, business development, quantitative research and trading.
Hong Li
Hong Li
Head of U.S. Equity Quantitative Research | Managing Director
Citi Research
Hong began his career in 1997 as an analyst in the Equity Derivatives Research group of Salomon Brothers, one of the predecessors of Citi. He has published numerous papers on academic and industry journals, such as Journal of Portfolio Management, Risk, and Journal of American Statistical Association.
Dr. Ronnie Shah
Dr. Ronnie Shah
Director and Head of U.S. Quantitative Research and Quantitative Investment Solutions
Deutsche Bank
Prior to Deutsche Bank, Dr. Ronnie Shah led quantitative research efforts in various senior roles for Gerstein Fisher Funds, Dimensional Fund Advisors and for the Scientific Active Equity team at BlackRock. His research has been published in various financial journals. He also serves as an adjunct professor of Finance at the University of Texas McCombs School of Business.
Tim Harrington
Tim Harrington
BattleFin Group
Tim Harrington is the CEO of BattleFin Group, the global trading tournaments set to democratically identify the best liquid investment strategies across asset classes and geographies. With over 20 years of experience in the financial services industry, Tim worked his way through the investment world, and is now focused on algorithmic and big data related investing strategies.
Matei Zatreanu
Matei Zatreanu
CEO / Founder
Matei founded System2 on the belief that fundamental investing will be augmented by data-driven technology. He previously played a leading role in the data initiative of a large global hedge fund. There, he sourced alternative data from hundreds of vendors and performed research studies.
George Bonne
George Bonne
Executive Director, Equity Factor Research
George and his team work to create new and innovative factors to be used in MSCI’s analytics and index products. Current projects involve creating new Sentiment factors derived from options, short interest, and estimates data, as well as exploring alternative big data sources and algorithms.
Igor Halperin
Igor Halperin
Research Professor of Financial Machine Learning
NYU Tandon School of Engineering
Igor is Research Professor of Financial Machine Learning at NYU Tandon School of Engineering. His research focuses on using methods of Reinforcement Learning, Information Theory, neuroscience and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents.
Armando Gonzalez
Armando Gonzalez
Armando Gonzalez is President & CEO of RavenPack, the leading provider of big data analytics for financial institutions. Armando is an expert in applied big data and artificial intelligence technologies. He has designed systems that turn unstructured content into structured data, primarily for financial trading applications. Armando is widely regarded as one of the most knowledgeable authorities on automated text and sentiment analysis.
Peter Hafez
Peter Hafez
Chief Data Scientist
Peter is a pioneer in the field of applied news analytics, bringing alternative data to banks and hedge funds. He has more than 15 years of experience in quantitative finance with companies such as Standard & Poor's, Credit Suisse First Boston, and Saxo Bank.

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Wednesday, September 12
9:00 am - 5:00 pm

Convene Center Midtown West
117 W 46th Street, NY 10036

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.

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Chris Petrescu, formerly a Data Strategist at WorldQuant
Tim Harrington, CEO, BattleFin Asset Management
Adam Honore, Executive Director of Product for Global Data Licensing Services, CME Group

Anna Reitman, Journalist at MarketBrain
Jason Malatesta, Global Head of Partners and Alliances and Business Development at Dow Jones
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