Learn more about the event, who attended, what were the topics and the main takeaways in less than 2 minutes.
Welcoming Remarks:Is Big Data a Big Opportunity - or a Big Problem?
Armando Gonzalez, CEO, RavenPack
Armando discussed 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.
Keynote:The World of Alphas
Nitish Maini, General Manager, Virtual Research Center / Vice President, Portfolio Management, WorldQuant LLC
In this presentation, Nitish provided an overview of the quantitative research process and shared how the use of AI, ML & data creates value in this process. He also discussed how the space of alphas are defined in a quantitative world and what could be a systematic approach towards building a diversified quantitative portfolio.
For compliance reason, slides are not available.
News Patterns Matter When Constructing Sentiment Trading Strategies
Peter Hafez, Chief Data Scientist, RavenPack
The Big Data revolution has enabled new ways of constructing orthogonal alpha signals around alternative data. In contrast with market data, most alternative data, and especially news, is available in an uninterrupted fashion, 24 hours a day, 7 days a week. In this presentation, Peter showcased RavenPack’s latest research on how intraday news patterns dictate the construction and implementation of sentiment strategies across thousands of news sources. He showed how to detect these intraday news patterns and how to take advantage of them for profit when constructing trading strategies.
Machine learning for Future Fundamentals Estimation
Dr. Ronnie Shah, Director and Head of U.S. Quantitative Research and Quantitative Investment Solutions, Deutsche Bank
Ronnie developed 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 he showed, adding fundamental acceleration to typically constructed value or “1/P” strategies improves risk-adjusted performance by 80%.
For compliance reason, slides and video are not available.
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 clarified the difference in the role played by new data vis-a-vis the role played by new analysis techniques. Rajesh also classified data science techniques based on their academic provenance. He argued for certain areas/regimes possessing the most and the least potential for application of big data analysis techniques.
For compliance reason, slides are not available.
Panel:Big Data, Big Impact: How Data is Reshaping the Modern Investor
Moderator: Tim Harrington, CEO, BattleFin Group
- Matei Zatreanu, CEO / Founder, System2
- Milind Sharma, CEO, QuantZ Machine Intelligence Technologies
- Rajesh Krishnamachari, Head of Data Science - Equities, Bank of America Merrill Lynch
- 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
Multi-Dimensional Analysis of News Sentiment Factors
Dr. George Bonne, Executive Director, Equity Factor Research, MSCI
George presented his latest research on news sentiment signals in the framework of the Barra equity factor models. He evaluated 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 also demonstrated 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 explained neural networks as a non-parametric econometrics technique. Matthew also provided various examples illustrating the tradeoffs between using Deep Q-learning versus supervised deep learning for predictive modeling with signals such as news sentiment.
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.
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
- Sara Castellanos, Emerging Technologies Reporter, The Wall Street Journal
- 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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