SoFiE Financial Econometrics Summer School
"Financial Machine Learning"
August 19 – August 23,2024
Note*: The application of this year Summer School has been closed,
if you have questions or concerns, please reach out to
the vins@nyu.edu supporting this event.
Note: This year, the Summer School will be delivered in a hybrid teaching format. However, we highly recommend participants to attend the school in person at the Volatility Institute of NYU Shanghai, Shanghai, China.
The SoFiE Financial Econometrics Schools are annual week-long research-based courses for Ph.D. students and new faculty in financial econometrics. For the first two years, the Summer School was held at Oxford University’s Oxford-Man Institute and in 2014 it moved to Harvard University. In 2015 and 2016, it was held in Brussels. Since 2017, The SoFiE Financial Econometrics Summer School took place in North America, Asia and Europe. Over the past six years, the Summer School in Asia has been hosted at the Volatility Institute of NYU Shanghai. Continuing this successful tradition, the upcoming 2024 Summer School will once again convene at the same location, fostering a rich academic environment for participants to explore and advance the field of financial econometrics.
The editorial board for these annual series is made up of professors as follows: Torben G. Andersen (Northwestern University)
Francis X. Diebold (University of Pennsylvania, past President of SoFiE)
Eric Ghysels (University of North Carolina, Chapel Hill, Secretary and Founding Co-President of SoFiE)
Ravi Jagannathan (Northwestern and past President of SoFiE)
Per Mykland (University of Chicago and past President of SoFiE)
Eric Renault (University of Warwick and past President of SoFiE)
Neil Shephard (Harvard University)
Viktor Todorov (Northwestern University)
Course Description:
The course is intended for Ph.D. students and researchers in statistics, econometrics and finance. It covers machine learning and artificial intelligence methods and their application to asset pricing research. The course will discuss the critical role that ML/AI already plays in improving our understanding of finance and economics and discuss the various research growth areas where ML/AI will play a pivotal role in years to come. It will cover theoretical and empirical aspects of high-dimensional models, including the "virtue of complexity," "double descent," and "benign overfit." Next, we will use the problem of return prediction to introduce modeling tools ranging from penalized regression to deep neural networks, followed by a discussion on integrating ML/AI into models of the risk-return tradeoff including applications to factor pricing, stochastic discount factors, and efficient portfolios. Lastly, it will discuss NLP in financial applications using both traditional models (e.g., topic models/LDA) and state-of-the-art large language models.
Lecturers:
Professor Bryan Kelly (Yale University)
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. Professor Kelly’s primary research fields are asset pricing, machine learning, and financial econometrics. He is interested in issues related to expected return, volatility, tail risk, and correlation modeling in financial markets; financial sector systemic risk; financial intermediation; and financial networks. He has served as co-editor of the Journal of Financial Econometrics and associate editor of Journal of Finance and 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 an AB in economics from University of Chicago, MA in economics from University of California San Diego, and a PhD and MPhil in finance from New York University’s Stern School of Business. Kelly worked in investment banking at Morgan Stanley prior to his PhD.
Professor Dacheng Xiu (The University of Chicago Booth School of Business)
Dacheng Xiu is Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His current research focuses on developing machine learning solutions to big-data problems in empirical finance. Xiu’s work has appeared in the Journal of Finance, Review of Financial Studies, Econometrica, Journal of Political Economy, the Journal of the American Statistical Association, and the Annals of Statistics. He has served as Co-Editor for the Journal of Financial Econometrics and has been on the editorial board as an Associate Editor for many prestigious journals, including the Review of Financial Studies, Journal of the American Statistical Association, Journal of Econometrics, and Management Science. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, AQR Insight Award, EFA Best Paper Prize, and Swiss Finance Institute Outstanding Paper Award. He has been recognized as one of Poets & Quants’ Best 40-under-40 Business School Professors of 2023. Xiu earned his PhD and MA in applied mathematics from Princeton University.
Professor Semyon Malamud (Swiss Finance Institute at EPFL)
Semyon Malamud is an Associate Professor of Finance at the Swiss Federal Institute of Technology in Lausanne and the Director of the Financial Engineering Section. He holds a Senior chair at the Swiss Finance Institute, is a Lamfalussy fellow of the European Central Bank, and a research fellow of the Centre of Economic Policy Research (CEPR) and the Bank for International Settlements.
Semyon's research has been published in top economics and finance outlets, including Econometrica, American Economic Review, Journal of Finance, Review of Financial Studies, and the Journal of Financial Economics. His research has also been recognized with several awards, including the joint INQUIRE Europe-INQUIRE UK prize, the Dauphine-Amundi Chair in Asset Management award, the Europlace Institute of Finance award, and the ETF Academy Award.
Course Outline:
The tentative outline for the course:
1 Introduction: The Case for Financial Machine Learning
2 The Virtues of Complex Models
3 ML/AI Return Prediction Models
4 ML/AI Factor Pricing Models
5 ML/AI SDFs and Optimal Portfolios
6 Financial NLP
Paper Presentations:
All students and researchers are invited to apply. The course will offer a limited number of course participants an opportunity to present their current research and receive feedback from the instructors and other course participants. Students interested in making a presentation (which is optional) should indicate so on their application and submit a draft of their research paper that they wish to present. Students who are selected to make a presentation will be informed at the same time as they receive their admission decisions.
Applications:
Applicants should register and submit electronical materials through the following registration website: https://research.shanghai.nyu.edu/vins/sofie_summer_school_registration. The applications should include a full CV and motivation letter (half-page length) explaining why attending this course would be helpful to the applicant’s research work. All materials should be in pdf version. The application deadline is 14 June, 2024. Decisions will be emailed by 1 July, 2024.
Fees for attending the school:
$400 for academics
$800 for non-academics
Confirmed admission of a selected applicants will be conditional on the fee payment in due time (details will be provided in the admission email). Fees cover the inscription costs, lunches and coffee breaks foreseen in the program.
Travel and accommodation costs: Attendees are responsible for their own travel and accommodation costs. A list of suitable local hotels will be provided. A free welcome reception and social event will be organized during the week where students and faculty can meet informally.
Accommodation:
1) Shangri-La Qiantan Shanghai
Website: https://www.shangri-la.com/shanghai/qiantanshangrila/
Distance to campus: 500m
2) Artyzen Habitat Qiantan Shanghai
Website: https://www.artyzen.com/en/hotels/artyzen-habitat-qiantan-shanghai/
Distance to campus: 60m
Useful Links:
To receive an invoice, please fill the information in this link
All accepted participants will be expected to be members of the Society for Financial Econometrics or join before their place is confirmed.
See https://www.stern.nyu.edu/