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Archives & Presentations

March 24, 2022


Everything that you Wanted to Know About NFT's but Were Afraid to Ask



Professor Tse-Chun Lin, The University of Hong Kong as Assistant Professor at HKU Business School

August 20, 2021


Loom Videos



Jules Healy

May 20, 2021


When Diversification Fails



Sebastien Page, T Rowe Price​

June 24, 2021


SQA Series on ESG Investing (Part 3)



Yesim, Tokat-Acikel, QMA

March 18, 2021


Hiring Trends in Quantitative Investment Management: Everything You Wanted to Know About Recruiting But Were Afraid to Ask


  • Robert Shainheit, Cambio Systematic LLC (Moderator)

  • Madison Kraus, Heidrick & Struggles

  • Tom Hudson, Locke Partners

  • Patricia Muller, Bay Street Advisors, LLC

  • James Bailey, Clifton Global

February 18, 2021


SQA Series on ESG Investing 

  • 12:00 - 12:45

Aggregate Confusion: The Divergence of ESG Ratings - Florian Berg, MIT

  • 12:45-1:30

Imputation of Missing ESG Data Using Deep Latent Variable Models - Achintya Gopal, Bloomberg

November 11 & 12, 2020 Conference


  • November 11, 2020: 12:00 - 12:45

"Portfolio Protection? It's a Long (Term) Story..."

Dan Villalon, Managing Director and Global Co-head of the Portfolio Solutions Group, AQR

  • November 11, 2020: 12:45 - 1:30

"Earnings Expectations in the COVID Crisis"

David Thesmar, Franco Modigliani Professor of Financial Economics at the MIT Sloan School of Management

  • November 12, 2020: 12:00 - 12:45

"Advances in Estimating Covariance Matrices"

Jose Menchero, Head of Portfolio Analytics Research, Bloomberg

  • November 12, 2020: 12:45 - 1:45

Expert panel on Quantitative investing during the COVID crisis: Portfolio Construction and Risk Management 


  • Sanne de Boer, Director of Quantitative Equity Research, Voya Investment Management

  • Ken Hightower, Director of Quantitative Analytics, East Rock Capital

  • Jason MacQueen, Smart Portfolio Strategies

  • Hitendra Varsani, Exectutive Director, MSCI

July 8th, 2020


Panel: "Quant Investing During the Pandemic: The Good, The Bad, and The Ugly"

  • ​Andrew Chin, Chief Risk Officer and Head of Quantitative Research, AllianceBernstein​

  • Christos Koutsoyannis, Chief Investment Officer, Atlas Ridge Capital

  • Lilian Quah, Managing Director, Portfolio Manager, Head of Quantitative Research, Epoch Investment Partners

    • Moderator: Ken D'Souza, Portfolio Manager, QMA​

January 16th, 2020


SQA - CFANY Joint Conference

  • ​Machine Learning for Stock Selection

    • Financial Analysts Journal, vol. 75, no. 3 (Third Quarter 2019)

    • Keywan Rasekhschaffe, Gresham Investment Management, LLC

    • Robert Jones, Arwen Advisors

  • The Intuitive Appeal of Explainable Machines

    • 87 Fordham Law Review 1085 (2018)​

    • Andrew D. Selbst, UCLA School of Law

    • Solon Barocas, Cornell University

  • When Words Sweat: Identifying Signals for Load Default in the Text of Loan Applications

    • Columbia Business School Research Paper No. 16 - 83​

    • Oded Netzer, Columbia Business School - Marketing

    • Alain Lemaire, Columbia University, Columbia Business School, Marketing, Students

    • Michal Herzenstein, University of Delaware

  • Predicting Returns with Text Data

    • Zheng Tracy Ke, Harvard University​

    • Bryan T. Kelly, Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)

    • Dacheng Xiu, University of Chicago - Booth School of Business

  • The Structure of Economic News

    • Leland Bybee, Yale School of Management​

    • Bryan T. Kelly, Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)

    • Asaf Manela, Washington University in St. Louis - John M. Olin Business School

    • Dacheng Xiu, University of Chicago - Booth School of Business

Speaker Bios

  • Keywan Rasekhschaffe

    • Keywan Rasekhschaffe, PhD, is a portfolio manager at Quantbot Technologies, LP. Prior to joining Quantbot he was a senior quantitative strategist at Gresham Investment Management LLC and he oversaw quantitative research at System Two Advisors LP. He was a Chazen Visiting Scholar at Columbia Business School and earned his Ph.D. at the University of Lugano, and received his MBA from the University of Oxford. He holds a joint BSc in Politics and Economics from the University of Bristol. His research is focused on asset pricing anomalies in the macro and equities space and applied machine learning methods. His article Machine Learning for Stock Selection was recently published in the Financial Analysts Journal.

  • Yuriy Bodjov

    • Mr. Bodjov joined TD Asset Management in 2008. He is responsible for the research and development of low volatility, absolute return and derivatives strategies and the quantitative risk modeling. Mr. Bodjov has many years of portfolio management experience. From 2004 through 2008, he was director and portfolio manager at the Caisse de dépôt et placement du Québec where he managed tactical asset allocation strategies. Prior to that, Mr. Bodjov was portfolio manager of U.S. and international equities at Fiera Capital and Elantis Investment Management and fixed income portfolios at Baker Gilmore and Associates. He worked also as a senior consultant at BARRA International – a leader in financial risk management. Mr. Bodjov has Master’s degrees in Economics from the University of National and World Economy in Sofia, Bulgaria and from the Université du Québec à Montréal. Mr. Bodjov is a CFA charterholder.

  • Solon Barocas

    • Solon Barocas is Assistant Professor in the Department of Information Science at Cornell University. His current research explores ethical and policy issues in artificial intelligence, particularly fairness in machine learning, methods for bringing accountability to automated decision-making, and the privacy implications of inference. He is also a Principal Researcher in the New York City lab of Microsoft Research and a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard University.

    • His research explores ethical and policy issues in artificial intelligence, particularly fairness in machine learning, methods for bringing accountability to automated decision-making, and the privacy implications of inference.

    • He co-founded the annual workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) and later established the ACM conference on Fairness, Accountability, and Transparency (FAT*).

    • He was previously a Postdoctoral Researcher at Microsoft Research as well as a Postdoctoral Research Associate at the Center for Information Technology Policy at Princeton University. He completed his doctorate at New York University, where he remains a Visiting Scholar at the Center for Urban Science + Progress.

  • Oded Netzer

    • Professor Netzer's expertise centers on one of the major business challenges of the data-rich environment: developing quantitative methods that leverage data to gain a deeper understanding of customer behavior and guide firms' decisions. He focuses primarily on building statistical and econometric models to measure consumer preferences and understand how customer choices change over time, and across contexts. Most notably, he has developed a framework for managing firms' customer bases through dynamic segmentation. More recently, his research focuses on leveraging text-mining techniques for business applications.

    • Professor Netzer published numerous papers in the leading scholarly journals. His research was nominated for and won multiple awards including, ISMS Long-term Contribution Award, the John Little Best Paper Award, the Frank Bass Outstanding Dissertation Award, the Paul E. Green Best Paper Award, the William O’Dell Best Paper Award, the Gary L. Lilian ISMS/MSI Practice Prize Award, the Society for Consumer Psychology (SCP) Best Paper Award, and the George S. Eccles Research Fund Award. He serves on the editorial board of several leading journals including: Marketing Science, Management Science, Journal of Marketing Research, Journal of Marketing, Quantitative Marketing and Economic, and International Journal of Research in Marketing.

    • Oded teaches several courses including the Core Marketing course, a course on Marketing Research, a course on Developing Quantitative Intuition (QI), a masters and doctoral course on Empirical Models in Marketing, as well as several executive education programs. Professor Netzer has won the Columbia Business School Dean’s Award for Teaching Excellence, and the Columbia University GSAC Faculty Mentoring Award to commemorate excellence in the mentoring of Ph.D. students.

    • Professor Netzer frequently consult to Fortune 500 companies and entrepreneurial organization on strategy, data-driven decision making, marketing research and extracting useful information from rich and thin data.

  • Zheng Tracy Ke

    • Zheng Tracy Ke is Assistant Professor of Statistics at Harvard University. She received her Ph.D. in Operations Research and Financial Engineering from Princeton University in 2014. She was a tenure-track Assistant Professor of Statistics at University of Chicago from 2014 to 2018. Her research interest is in high-dimensional statistics, sparse inference, machine learning, network data analysis, text mining, and biostatistics and bioinformatics. Some of her recent projects include studying the optimal statistical inference when the signals are very rare and weak, finding latent community structures from large social networks, and developing statistical methods for natural language processing.

    • She was the recipient of Gordon Y.S. Wu Fellowship from Princeton University in 2009, LAHA Award from Institute of Mathematical Statistics in 2013, and NSF CAREER Award in 2020.

  • Bryan Kelly

    • Bryan Kelly is Professor of Finance at the Yale School of Management, a Research Fellow at the National Bureau of Economic Research, Associate Director of SOM’s International Center for Finance, and is the head of machine learning at AQR Capital Management, LLC. Professor Kelly’s primary research fields are asset pricing and financial econometrics. He is interested in issues related to financial machine learning; volatility, tail risk, and correlation modeling in financial markets; banking sector systemic risk; financial intermediation; and financial networks. His papers in these areas 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 co-editor of the Journal of Financial Econometrics and associate editor of the Journal of Finance and the Journal of Financial Economics. Before joining Yale, Kelly was a tenured professor of finance at the University of Chicago Booth School of Business.  He 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 New York University’s Stern School of Business. Kelly worked in investment banking at Morgan Stanley prior to pursuing his PhD.​

December, 2019


Alessio de Longis, "Time-Series Variation in Factor Premia: The Influence of the Business Cycle."​​​

April, 2019


Harry Maymayski, "What's In the News? Using Textual Data To Forecast Financial Returns."


March, 2019


Fuzzy Day Conference

  • Ron Alquist, AQR - "Fact, Fiction, and the Size Effect. Examine the multitude of size effect and aim to clarify some of the misunderstanding surrounding it by performing simple tests using publicly available data."

  • Lira Mota, PhD Candidate at Columbia Business School - "Hedging out unpriced risk in factor portfolios"

  • Igor Halperin, NYU Tandon School of Engineering - "Inverse Reinforcement Learning and Reinforcement learning models. How they differ from traditional financial models and how to incorporate them with portfolio optimization techniques."

  • Jennifer Bender, State Street Global Advisors - "Asset Allocation vs. Factor Allocation - Can we build a unified Method? (Published in the December 2018 Journal of Portfolio Management)"

  • Stefano Giglio, Yale SOM - "Hedging macroeconomic and financial uncertainty and volatility. Pricing of shocks to uncertainty and realized volatility using options contracts directly related to the state of the macroeconomy and of financial markets"

  • Huseyin Gulen, Krannert Graduate School of Management at Purdue University - "Extrapolation Bias and the Predictability of Stock Returns by Price-Scaled Variables"​​​

January, 2019


SQA / CFA Society Joint Conference - Data Science in Finance

  • Sanjeev Arora, Princeton University - "What is Machine Learning and Deep Learning?"

  • Slawek Smyl, Uber - "Hierarchical and Hybrid Neural Networks Models for Time Series Forecasting"

  • Bryan Kelly, Yale University and AQR - "Can Machines Learn Finance?"

  • Peter Cotton, JP Morgan and Stanford University - "Open Source Random Variables: Building a Prediction Web."

  • Ingrid Daubechies, Duke University - "Mathematicians helping art conservators and art historians."

  • Ken Perry, Consultant in Risk and Quantamental Investing, CRO, formerly of Och Ziff - "Challenge for Finance: Al Interpretability."​​​

September, 2018


Kelly Shue, Yale, "Can the Market Multiply and Divide? Non-Proportional Thinking in Financial Markets"​​

March, 2018


Fuzzy Day

  • Sarah Jiang, AQR, "Craftsmanship Alpha: An Application to Style Investing"

  • Jason MacQueen, Northfield, "Converting Smart Equity Portfolios into Smart Corporate Bond Portfolios"

  • Marcos Lopez de Prado, Berkeley Lab, "The 7 Reasons Most Machine Learning Funds Fail"

  • Robert Stamicar, Axioma, "A CVaR Scenario-Based Framework for Minimizing Downside Risk in Multi-Asset Class Portfolios"

  • Charles Tapiero, NYU, "Financial Data Science: An intelligence challenge"​​

January, 2018


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

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

  • Bryan Kelly, University of Chicago - "Text as Data"

  • Paul Gao, University or Notre Dame - "Search Data and Finance"

  • Ronnie Sadka, Boston College - "Media and its Interaction with Financial Markets"

  • Claudia Perlich, Dstillery - "What Machine Learning can and cannnot do!"​​​​

December, 2017

  • Andrew Ang, "What's in Your Benchmark? A Factor Analysis of Major Market Indexes"​​​

November, 2017


Half Day Conference, "Quantitative Approaches to Retirement Planning: Time to Retire Old Thinking"

  • Dan DiBartolomeo, Northfield Information Services, "Defined Construction Retirement within Lifetime Investing Planning"

  • Rodney Sullivan, AQR, "Defined Contribution Retirement Plans Should Look and Feel More Like Defined Benefit Plans"

  • Martin Tarlie, QMA, "Investment Horizon and Portfolio Selection"

  • Deborah J. Lucas, MIT Sloan School of Management, "Hacking Reverse Mortgages"​​

September, 2017

  • Ben Fernholz, INTECH, "Zipf's Law"​​​

June, 2017

  • Hui Xiong, Rutgers, "Talent Analytics: Prospects and Opportunities"

May, 2017

  • Kent Daniel, Columbia University, "Overpriced Winners"​​​

April, 2017

  • Duncan Brown, Syracuse University, "Detection of Gravitational Waves"

March, 2017

Fuzzy Day Conference, "Big Data? Big Deal!"

  • Dave Donoho,  Stanford University and Renaissance Technologies, "50 years of Data Science"

  • Shane Conway, Kepos Capital, "Probably Approximately Correct: A Brief Tour or All Machine Learning"

  • Serena Ng, Columbia University, "What Do Terabytes of Weekly Scanner Data Say About Economic Condistions?"

  • Dan diBartolomeo, Northfield Information Services, "Big Data in Investment Finance: A Cautionary Comment"

  • Paul Glasserman, Columbia Universtiy, "Does Unusual News Forecast Market Stress?"

  • Claudia Perlich, Dstillery and NYU Stern, "Tales from the Data Trenches of Display Advertising."

Lightning Presentations

  • Patrick Wood, Kensho, "Interacting with Data to Make Better Decisions"

  • Afshin Goodarzi, 1010 data

  • Manish Aurora, Rational Investing, "Value Investing Globally (& Data Preparation)"

  • Sylvain Raynes, CreditSpectrum, "Asset Backed Securities (& Credit Ratings)"​​

February, 2017

  • Martin Leibowitz, Morgan Stanley

"Risk Tolerance and Peak Funding Ratios: A Meta Framework for Strategic Allocation"

"U.S. Corporat DB Pension Plans - Today's Challenges"

January, 2017

  • Mikhail Samonov, Forefront Analytics and GKFO, "Two Centuries of Price Return Momentum"​​​

We are continuing to migrate content from past events. Please stay tuned!

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