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SQA/CFA Society NY Joint Conference "Data Science in Finance, The Final Frontier?"
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When: Thursday, January 18, 2018

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Event Recap:

On January 18, 2018, SQA co-hosted along with the CFA Society New York hosted the full day conference, Data Science in Finance: The Final Frontier?  The event addressed recent progress in data science applications in the financial industry by bringing together leading academics and industry practitioners to discuss how they are using Data Science in their work.  Many thanks to the SQA, led by Inna Okounkova, and CFA Society NY’s Fintech Thought Leadership Group, led by Carole Crawford, for organizing the conference and recruiting the speakers.

Read the Full Summary Here


 8:30 – 9:00 a.m.     Registration Breakfast
 9:00 – 10:00 a.m.        Vasant Dhar, New York University "When should we trust autonomous learning systems with decisions?"
 10:00 – 11:00 a.m.             Robert Schapire, Microsoft - “The Contextual Bandits Problem: Techniques for Learning to Make High-Reward Decisions”
 11:00 – 11:15 a.m.   Coffee Break
 11:15 – 12:15 p.m.   Bryan Kelly, University of Chicago - “Text as Data”
 12:15 – 1:15 p.m.   Lunch
 1:15 - 2:15 p.m.   Paul Gao, University of Notre Dame - “Search Data and Finance”
 2:15 - 3:15 p.m.  Ronnie Sadka, Boston College - “Media and Its Interaction with Financial Markets”
 3:15 - 3:30 p.m.  Coffee Break
 3:30 – 4:30 p.m.   Claudia Perlich, Dstillery – "What Machine Learning can and cannot do!"
 4:30 – 5:30 p.m.   Cocktails/Networking


Vasant Dhar, New York University "When should be trust autonomous learning systems with decisions?"



Smarter and more adaptive machines are rapidly becoming as much a part of our lives as the internet, and more of our decisions are being handed over to intelligent algorithms that learn from ever-increasing volumes and varieties of data. As these “robots” become a bigger part of our lives, we don’t have any framework for evaluating which decisions we should be comfortable delegating to algorithms and which ones humans should retain. That’s surprising, given the high stakes involved.

I propose a risk-oriented framework for deciding when and how to allocate decision problems between humans and machine-based decision makers. I’ve developed this framework based on the experiences that my collaborators and I have had implementing prediction systems over the last 25 years in domains like finance, healthcare, education, and sports. I discuss the implications of the framework for managers interested in shaping a data science strategy for their organization.


Vasant Dhar is a professor at the Stern School of Business and the Center for Data Science at New York University. He is Editor-in-Chief of the journal Big Data, and the founder of SCT Capital Management, a machine-learning-based hedge fund in New York City.


Dhar is a data scientist whose research interests are in the design of autonomous data-driven learning and decision making systems where the central question is "when to trust robots with decisions?". His research in predictive analytics addresses this question in a number of areas, including the prediction of returns in financial markets, social media and healthcare, and networks. A central question underlying this research in finance is “when do computers make better decisions than humans?" which is addressed in the article "Should You Trust Your Money to a Robot?"


Robert Schapire, Microsoft - “The Contextual Bandits Problem: Techniques for Learning to Make High-Reward Decisions”


We consider how to learn through experience to make intelligent decisions.  In the generic setting, called the contextual bandits problem, the learner must repeatedly decide which action to take in response to an observed context, and is then permitted to observe the received reward, but only for the chosen action.  The goal is to learn to behave nearly as well as the best policy (or decision rule) in some possibly very large and rich space of candidate policies.  This talk will describe progress on developing general methods for this problem and some of its variants.


Robert Schapire is a Principal Researcher at Microsoft Research in New York City.  He received his PhD from MIT in 1991.  After a short post-doc at Harvard, he joined the technical staff at AT&T Labs (formerly AT&T Bell Laboratories) in 1991.  In 2002, he became a Professor of Computer Science at Princeton University.  He joined Microsoft Research in 2014.  His awards include the 1991 ACM Doctoral Dissertation Award, the 2003 Gödel Prize, and the 2004 Kanelakkis Theory and Practice Award (both of the last two with Yoav Freund).  He is a fellow of the AAAI, and a member of both the National Academy of Engineering and the National Academy of Sciences.  His main research interest is in theoretical and applied machine learning, with particular focus on boosting, online learning, game theory, and maximum entropy.



Bryan Kelly, University of Chicago - “Text as Data”


An ever increasing share of human interaction, communication, and culture is recorded as digital text. We provide an introduction to the use of text as an input to economic research. We discuss the features that make text different from other forms of data, offer a practical overview of relevant statistical methods, and present a variety of applications.


Bryan Kelly is a Professor of Finance at the University of Chicago Booth School of Business, a Research Fellow at the National Bureau of Economic Research, visiting professor at the Yale School of Management, and a consultant at AQR Capital Management. Professor Kelly’s primary research fields are asset pricing and financial econometrics. He is interested in volatility, correlation, and tail risk in financial markets, banking sector systemic risk, financial intermediation, financial networks, and statistical methods for high dimensional systems. His papers have been published in the American Economic Review, the Quarterly Journal of Economics, the Journal of Finance, the Journal of Financial Economics, and the Review of Financial Studies, among others. He is an associate editor at the Journal of Finance, the Journal of Business and Economic Statistics and the Journal of Financial Econometrics. Kelly is a two time finalist and one time winner of the AQR Insight Award, and is a winner of various other awards and research grants including the Fama/DFA Prize for best asset pricing paper in the Journal of Financial Economics, the Jack Treynor Prize, the Roger F. Murray Award, and the JP Morgan Award for Best Paper on Financial Institutions and Markets.


Before joining the University of Chicago, Kelly earned a bachelor’s degree in economics from the University of Chicago, a master’s degree in economics from University of California San Diego, and a PhD in finance from the New York University’s Stern School of Business. Kelly worked in investment banking at Morgan Stanley prior to pursuing his PhD.



Paul Gao, University of Notre Dame - “Search Data and Finance”


I will discuss recent academic research that makes use of different search-based data in predicting firm-specific returns, index returns and accounting fundamentals.


Paul Gao first joined the University of Notre Dame in 2007, where he is currently the Viola D. Hank Associate Professor of Finance. He was an associate professor of finance at the Hong Kong University of Science and Technology (HKUST) Business School, and a visiting senior economist at the Shanghai Stock Exchange (SHSE). He has published in the Journal of Finance, the Review of Financial Studies, and the Journal of Financial Economics. He is the past winner of the prestigious Fama-DFA Prize, Cowen Memorial Prize, CQA Research Award, and Q-Group Research Award. He serves as the associate editors for the Pacific Basin Finance Journal, and the Financial Management. He earned his doctoral degree in Financial Economics from Kellogg School of Management, Northwestern University in 2007.



Ronnie Sadka, Boston college - “Media and It's Interaction with Financial Markets”


We introduce the possibility of a “reinforcement effect” between past returns and media-measured sentiment. When returns and sentiment point in the same direction (either up or down), prices are in the midst of overreacting. Such evidence of overreaction should disappear when returns and sentiment disagree. We find results supporting these views from parallel tests -- across liquid individual stocks, international equity markets, and currencies -- using weekly media scores for each asset culled from extensive data on cross-asset media coverage. Interestingly, the effect is consistently stronger in relatively more liquid assets, assets for which media coverage is relatively broad, and in subsets of media coverage generated by relatively more “local” news outlets. We find that for each of these asset groups, a simple “reinforcement” strategy of buying past losers with low sentiment and selling past winners with high sentiment earns spreads of several hundred basis points annually.



Professor Ronnie Sadka is the Senior Associate Dean and chairperson of the finance department at the Carroll School of Management, Boston College. His research focuses on the liquidity in financial markets. More recently, he has been developing big-data driven investment applications. Sadka is a frequent speaker at academic and practitioner conferences; his work has appeared in various outlets including Journal of Finance, Journal of Financial Economics, Journal of Accounting Research, Journal of Accounting and Economics, Journal of Financial and Quantitative Analysis, and Financial Analysts Journal, and has been covered by New York Times, Wall Street Journal, and CNBC.


Prior academic experience includes teaching at the University of Chicago (Booth), New York University (Stern), Northwestern University (Kellogg), and the University of Washington (Foster). Industry experience includes Goldman Sachs Asset Management and Lehman Brothers (quantitative strategies). Sadka recently served on the economic advisory board of NASDAQ OMX. Professor Sadka earned a B.Sc. (Magna Cum Laude) in industrial engineering and a M.Sc. (Summa Cum Laude) in operations research, both from Tel-Aviv University. He received a Ph.D. in finance from Northwestern University (Kellogg).



Claudia Perlich, Dstillery  - "What Machine Learning can and cannot do!"


Predictive Modeling and Supervised Learning are staple techniques in the Data Science arsenal of algorithms. The origins of some of those solutions trace back more than 50 years, but with the recent wide adoption of data technologies they are receiving a new level of attention. This talk takes on some of the more broadly asked question around predictive modeling and machine learning: Where do we stand today? What can AI really do well? What are the key success factors of making prediction work? Which algorithm is best? When does it even make sense to try predictive modeling? When is a predictive model good enough? And when do predictive models fail?


Claudia Perlich leads the machine learning efforts that power Dstillery’s digital intelligence for marketers and media companies.  With more than 50 published scientific articles, she is a widely acclaimed expert on big data and machine learning applications, and an active speaker at data science and marketing conferences around the world.
Claudia is the past winner of the Advertising Research Foundation’s (ARF) Grand Innovation Award and has been selected for Crain’s New York’s 40 Under 40 list, Wired Magazine’s Smart List, and Fast Company’s 100 Most Creative People.
Claudia holds multiple patents in machine learning.  She has won many data mining competitions and awards at Knowledge Discovery and Data Mining (KDD) conferences, and served as the organization’s General Chair in 2014.
Prior to joining Dstillery in 2010, Claudia worked at IBM’s Watson Research Center, focusing on data analytics and machine learning.  She holds a PhD in Information Systems from New York University (where she continues to teach at the Stern School of Business), and an MA in Computer Science from the University of Colorado.


About SQA

The Society of Quantitative Analysts (SQA) is a not-for-profit organization that focuses on education and communication to support members of the quantitative investment community. SQA has hosted educational events in NYC since 1965. The principal mission of SQA is to encourage the dissemination and discussion of leading-edge ideas and innovations, including analytical techniques and technologies for investment research and management. There is more information about SQA and its history on our website:


About CFA Society New York

CFA Society New York (formerly NYSSA) has been a leading forum for the investment community since 1937. Its mission is to serve the needs of investment professionals and to educate the investing public. With over 10,000 members, CFA Society New York is the largest of more than 142 societies worldwide that comprise CFA Institute. You can find more information about the organization at:



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