Alphathon 2024
Attend the SQA Conference
Wednesday, October 9th, 2024
Location: Eagle Alpha, 360 Madison Ave, New York, NY 10017
Time: 12:00 – 1:30 pm
77 teams (148 Participants) were accepted to compete! But at the end, there can only be 1 winner per category!
Attend the SQA conference to hear the winners and panelists on the current state of the art on dealing with large and asynchronous data, AI and LLMs in investment, fund flows, crowding and portfolio construction!
Agenda
12:00 - 12:45
Alphathon 2024 Winner Presentations
Attend the final to see presentations by the winning entries, across the 4 Alphathon 2024 questions:
Q1: A Real-Time Streaming Application, by Point72/Cubist
Q2: Using Large Language Models (LLMs) in Investment, by AllianceBernstein (AB)
Q3: Fund Flows, Crowding and Subsequent Returns, by Principal
Q4: A Long-Short Portfolio Strategy that Accurately Reflects Stock Selection Skill, by Optimal Portfolio Strategies
(See below for details of the questions.)
12:45 - 1:30
Panel: The State of the Quant Industry
The panel will discuss the Winning Entries and the State of the Quant Industry
Speakers include:
Ingrid Tierens
Head of Data Strategy for Global Investment Research
Goldman Sachs
Rong Zhao
Center Director
Center of Excellence in Wireless and Information Technology (CEWIT),
Stony Brook University
Christina Qi
CEO, Databento
Board of Trustees, MIT
Gene Ekster
Data Specialist, Maiden Century
Adjunct Professor, NYU Courant
Pawel Polak
Assistant Professor, Department of Applied Mathematics and Statistics and
Affiliated Faculty, Institute for Advanced Computational Science,
Stony Brook University
Moderator:
Christos Koutsoyannis
CEO, Atlas Ridge Capital
Adjunct Professor, NYU Courant
Executive Advisory Board, Columbia Business School, Program for Financial Studies
The panel will discuss the winning entries of Alphathon 2024, in the context of the broader industry and cutting-edge research.
Conference registration is open!
Become an SQA Member today:
Regular Membership: $200
Academic Membership: $100
Student Membership: $25
and:
Register to attend the Conference:
SQA Members: Free
Non-Members: $50
Individuals taking part in Alphathon 2024 as competitors do not need to register for the Conference.
The SQA conference is part of #DataWeekNYC. Attendees registered for the SQA conference are also invited to optionally attend the rest of the Eagle Alpha conference for free. (Separate registration directly with Eagle Alpha needed.)
Alphathon 2024
A Quantitative Finance Competition
The SQA is thrilled to announce #Alphathon2024, a quantitative finance competition, organized jointly with the Center of Excellence in Wireless and Information Technology (CEWIT) at Stony Brook University!
This competition aims to develop and evaluate cutting-edge forecasting solutions for financial industry professionals, utilizing the latest mathematical, statistical and machine learning techniques and alternative datasets.
Participate in Alphathon 2024
For further details and to take part in Alphathon 2024, please reach out to the SQA at
[email protected] including your LinkedIn profile. If you're interested in recruiting opportunities, you may also attach a PDF version of your Resume. For team registrations, please ensure all participants are included in the email and provide their LinkedIn profiles and any additional relevant details.
Alphathon 2024 is a month-long quantitative finance online competition with an in-person finals in New York City. It is open to graduate students, academics and industry professionals alike, able to attend the final in person in Manhattan if successful. Alphathon 2024 is designed for individuals eager to enhance their skills in quantitative finance and to engage with the latest advancements in technology and data analysis in the financial sector. Winners will have substantial visibility in the industry, including SQA member investment firms and the broader Data Week.
Please see below for the Alphathon 2024 questions.
Key Dates for Participants:
Participant Registration Now Closed
Start Coding: Monday, September 9th
Submissions Deadline: Monday September 30th
Finalists Announcement: Friday, October 4th
Final and Awards: Wednesday, October 9th
More details to come soon!
Alphathon 2024 Partners
The lead partner of Alphathon 2024 is Eagle Alpha, provider of alternative data. DataBento, QuantConnect and Northfield have agreed to provide data, infrastructure, risk models and/or compute time.
Sponsor Alphathon 2024
A number of sponsorship tiers and opportunities are available. Please reach out to the SQA at
[email protected] for more information.
Sponsor a Question at Alphathon 2024
Open to select investment firms (buy-side, sell-side or asset owners/allocators), subject to SQA approval. Please reach out to the SQA at
[email protected].
Alphathon 2024 Questions
Point72 / Cubist will provide the following question and join the SQA and Stony Brook in judging the submissions:
Title:
Real-Time Streaming Application
Problem:
CSP is an open-source reactive stream processing library. Using CSP, create an innovative real-time streaming application that leverages multiple asynchronous data sources.
Expected Outcome:
A prototype of a real-time data processing application that can generate actionable insights using a variety of techniques in quantitative finance. The application should demonstrate how it handles streaming data from multiple sources as well as the usefulness and accuracy of the insights it generates.
Data:
Participants will have access to data and infrastructure provided by Eagle Alpha, Quant Connect, Data Bento and/or potentially other providers, including lagged order book data and lagged news feeds.
Evaluation:
The submissions will be evaluated based on the creativity demonstrated in leveraging CSP’s high performance features, the rigorous use of quantitative finance skills, as well as the usefulness of the outputs generated by the application.
AB (AllianceBernstein) will provide the following question and join the SQA and Stony Brook in judging the submissions:
Title:
Using Large Language Models (LLMs) in Investment
Problem:
Can we use LLMs and alternative data to outperform the S&P 500?
Expected Outcome:
Using LLMs with and without alternative data, your study should demonstrate if and how to use LLMs in innovative ways to extract insights and forecast future returns in ways that are incrementally and demonstrably better to simpler techniques with the same data inputs, and additive to known investment styles.
Data:
Participants will have access to data and infrastructure provided by Eagle Alpha, Quant Connect, Data Bento and/or potentially other providers, including market and/or textual data.
Evaluation:
The submissions will be evaluated based on the creativity demonstrated in proving or disproving the value of LLMs in time series forecasting, the rigorousness of the methodological comparison including risk management of known investment styles and realistic market impact assumptions, as well as the practical usefulness of the output for investment management.
Principal Financial Group will provide the following question and join the SQA and Stony Brook in judging the submissions:
Title:
Fund Flows, Crowding and Subsequent Returns
Problem:
Can ETF fund flows prove useful in forecasting future fund flows, as well as subsequent ETF, Country and Sector returns?
Expected Outcome:
Participants will be evaluated on the most rigorous, usable and interesting use of ETF flow data in forecasting.
Data:
Participants will have access to data and infrastructure provided by Eagle Alpha, Quant Connect, Data Bento and/or potentially other providers, including ETF flows, constituents, descriptions and returns by ETF Global.
Evaluation:
The submissions will be evaluated on the usability of the forecasts based on realistic market assumptions, proof of value-added in excess of known styles including momentum in returns and flows themselves, as well as the practical usefulness of the forecasts.
Optimal Portfolio Strategies will provide the following question and join the SQA and Stony Brook in judging the submissions:
Title:
Design a Long-Short Portfolio Strategy that Accurately Reflects Stock Selection Skill
Problem:
Outperformance depends on two skills: Stock Selection and Portfolio Construction. Different portfolios holding the same stocks will have different performance: Some portfolios will do a much better job of reflecting stock selection skill than others. Can you construct a portfolio that best reflects stock selection?
Expected Outcome:
A portfolio construction methodology to maximize the effect of the expected returns (alphas) on the portfolio’s performance. All portfolios should be 100% long, 100% short for 100% cash at all times (fully invested.) The portfolio should be rebalanced as you see fit, and you will be provided with a methodology to include estimated transaction and shorting costs.
Data:
Northfield Information Services will provide the required data. The initial seven year data set will include Northfield risk models at 4-week intervals, a defined universe of around 400 Mid-Cap investable assets, daily returns, and expected alphas at 4-week intervals.
Evaluation:
Submissions will initially be evaluated on creativity and innovation in building portfolios that accurately reflect stock selection skill. Participants will then be given a new data set. Contestants will be judged on the exposure of their portfolio to the alphas, as determined by their contributions to return and risk over this new data set.
Thank you
The Organizing Committee
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