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SQA/CFA Series: Modernizing Investment Research
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SQA/CFA Series: Modernizing Investment Research

10/1/2020
When: Thursday, Oct. 1, Thursday, Oct. 15, Tuesday, Oct. 27, and Thursday, Nov. 5
12:00 - 1:00 PM.
Contact: Rebecca Harrington
518-694-3157


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Series: Modernizing Investment Research

 

Modernizing Investment Research Series Overview

 

Active managers face three headwinds – declining fees, outflows, and the increasingly difficult task of delivering consistent alpha in a competitive market. Our industry has the opportunity to address these issues by modernizing investment research.

 

The Society of Quantitative Analysts (SQA) and CFA Society New York are pleased to present a series of timely panel discussions on this opportunity. Each featured panel will focus, respectively, on one of four key steps identified as fundamental to the modernization of investment research.

 

The series kicks off by examining the incorporation of alternative data (Session One), followed by a discussion that will explore how best to leverage machine learning and text-mining techniques (Session Two). The series will then proceed to tackle the requisite ingredients for building the infrastructure (Session Three) and firm culture (Session Four) at the core of any successful transformative modernization endeavor.

 

Fundamental managers interested in embracing quantitative techniques, alternative data, and artificial intelligence techniques will find these sessions invaluable. Quantitative and “quantamental” managers will also glean useful insights across the range of informed perspectives that will be presented throughout the Modernizing Investment Research series. Our slate of expert-speakers and panelists includes a diverse set of practitioners—including investors from different asset classes, large and small firms, long and short-term investment horizons—as well as long-only and long-short mandates.

 

Sign up to be a member now to enjoy the whole series and other upcoming SQA events!

  •  Free to members
  • $25.00 for non-members

 

Data, Data Everywhere

October 1

 

There is an ocean of data available today; how can investors wade through it all? What should they look for?

 

Together, we examine:

 

    • Strategies for prioritizing datasets to investigate and research.
    • How to curate the right information for your investment process.
    • How to balance datasets with longer history against newer datasets which are more timely and accurate.
    • How to determine when a dataset becomes obsolete or loses its informational edge.
    • Uses of data for alpha generation vs. risk management.
When data be purchased vs. collected firsthand.

 

Panel 1: 
      • Ingrid Tierens, Head of Data Strategy, Global Investment Research, Goldman Sachs
      • Sheedsa Ali, Head of Systematic Strategies, PineBridge Investments
      • Chris Chin, Head of Systematic Equity Data Strategy, Tudor Investments
      • Matei Zatreanu, CEO and Founder, System2 (moderator)

 

Using AI to Derive Insights

October 15

 

While using artificial intelligence to derive insights, what techniques should you use?

 

Together, we examine:

 

How to evaluate different algorithms, and their respective advantages and pitfalls.
Sample use cases for machine learning and textual analysis
Resources designed to help you learn more about these techniques, including—most valuably—new research.
The training analysts should seek to get started.

 

Panel 2:
  • Matthew Dixon, Assistant Professor of Applied Math, Illinois Institute of Technology
  • Aric Whitewood, Founding Partner and Head of Machine Learning, xAI

 

The Infrastructure to Make It All Work

October 27

 

The right infrastructure is vital to a firm’s ability make the leap toward a more modern approach to investment research.

 

Together, we examine:

What infrastructure do you need to implement a modern investment research process?
What options are there for platforms and tools? Are they different for large vs. small firms?
When to build versus when to buy.
Whether adoption can be a piecemeal process, or demand an all-in approach.
What tends to be built, e.g., alpha models, niche data acquisition, risk models.
What tends to be bought, e.g., risk models, optimizers, certain datatypes.
 
Panel 3 (Oct 27th):
      • Chris Petrescu, CEO, CP Capital
      • Tom Taylor, Head of Alpha Technology, Man Numeric
      • Andrew Chin, Chief Risk Officer and Head of Quantitative Research, AllianceBernstein

 

Cultural Change and Business Model Implications

November 5

 

A modern investment process requires non-traditional skills.

 

Together, we examine:

Which skillsets are key to making it all work.
Whether teams should be integrated, or if not, effective strategies by which talent can be siloed in order to account for varying skillsets
The cultural changes worth considering in order to be successful.
Obtaining the budget for funding of your modernization endeavor.

 

Panel 4 : 
        • Michael Craig, Head of Asset Allocation and Derivatives, TD Asset Management
        • Gavin Smith, Director of Research, QMA
        • Madison KrausPrincipal AI, Data & Analysis, Heidrick & Struggle

 

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