The Gradient: Perspectives on AI

Ben Wellington: ML for Finance and Storytelling through Data

Mar 14, 2024

01:07:40

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Ben Wellington from Two Sigma discusses applying ML techniques to quantitative finance, building predictive features, and the balance between human insights and algorithm performance. They explore the challenges of black box models in finance, the importance of accurate timestamp data, and blending small wins in trading models. The podcast also delves into improv comedy techniques for enhancing science communication and storytelling.

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Quick takeaways

Feature forecasting in finance involves creating innovative features for competitive predictions.

Accurate data timestamps are crucial in financial modeling to avoid misinterpretations based on hindsight.

AI in finance faces challenges due to incomplete data, emphasizing the importance of reproducibility for reliable results.

Deep dives

The Evolution of AI Techniques in Quantitative Finance

In the podcast episode, the speaker discusses the evolution of AI techniques in quantitative finance over the years. They emphasize the shift towards more complex approaches like deep learning and nonlinear methods compared to traditional linear models. The speaker highlights the importance of creating novel and creative features that give a competitive edge in predicting market trends. Furthermore, the discussion delves into the significance of rich historical data and the constant need to stay ahead of competition by exploring more sophisticated and clever data sets.

Importance of Accurate Timestamp Data in Finance and Data Analysis

04:25

Innovative Data Analysis and Effective Science Communication

02:45

Introduction

2min

From Academia to Finance: Navigating the Intersection of NLP and Statistics

18min

Balancing Performance and Interpretability in Computational Modeling for Investing

8min

Balancing Short and Long-Term Research Initiatives in Finance

9min

Importance of Accurate Timestamp Data in Finance and Data Analysis

4min

Blending Small Wins in Trading Models

23min

Exploring Communication, Science Personalization, and Storytelling

4min

In episode 115 of The Gradient Podcast, Daniel Bashir speaks to Ben Wellington.

Ben is the Deputy Head of Feature Forecasting at Two Sigma, a financial sciences company. Ben has been at Two Sigma for more than 15 years, and currently leads efforts focused on natural language processing and feature forecasting. He is also the author of data science blog I Quant NY, which has influenced local government policy, including changes in NYC street infrastructure and the design of NYC subway vending machines. Ben is a Visiting Assistant Professor in the Urban and Community Planning program at the Pratt Institute in Brooklyn where he teaches statistics using urban open data. He holds a Ph.D. in Computer Science from New York University.

Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pub

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Outline:

* (00:00) Intro

* (01:30) Ben’s background

* (04:30) Why Ben was interested in NLP

* (05:48) Ben’s work on translational equivalence, dominant techniques

* (10:14) Scaling, large datasets at Two Sigma

* (12:50) Applying ML techniques to quantitative finance, features in financial ML systems

* (17:27) Baselines and time-dependence in constructing features, human knowledge

* (19:23) Black box models in finance

* (24:00) Two Sigma’s presence in the AI research community

* (26:55) Short- and long-term research initiatives at Two Sigma

* (30:42) How ML fits into Two Sigma’s investment strategy

* (34:05) Alpha and competition in investing

* (36:13) Temporality in data

* (40:38) Challenges for finance/AI and beating the market

* (44:36) Reproducibility

* (49:47) I Quant NY and storytelling with data

* (56:43) Descriptive statistics and stories

* (1:01:05) Benefits of simple methods

* (1:07:11) Outro

Links:

* Ben’s work on translational equivalence and scalable discriminative learning

* Two Sigma Insights

* Storytelling with data and I Quant NY

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The Gradient: Perspectives on AI

Ben Wellington: ML for Finance and Storytelling through Data

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

The Evolution of AI Techniques in Quantitative Finance

The Role of Time and Data Recording in Financial Modeling

Navigating Challenges in Investment Strategies

Challenges of AI in Finance

Reproducibility in AI and Data Science

Importance of Accurate Timestamp Data in Finance and Data Analysis

Innovative Data Analysis and Effective Science Communication

Get the Snipd
podcast app

AI-powered
podcast player

Discover
highlights

Save any
moment

Share
& Export

AI-powered
podcast player

Discover
highlights

The Gradient: Perspectives on AI

Ben Wellington: ML for Finance and Storytelling through Data

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

The Evolution of AI Techniques in Quantitative Finance

The Role of Time and Data Recording in Financial Modeling

Navigating Challenges in Investment Strategies

Challenges of AI in Finance

Reproducibility in AI and Data Science

Importance of Accurate Timestamp Data in Finance and Data Analysis

Innovative Data Analysis and Effective Science Communication

Get the Snipdpodcast app

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Discoverhighlights

Save anymoment

Share& Export

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Discoverhighlights

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