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(Created page with "Machine Learning for Stock Selection A Talk by Keywan Rasekhschaffe Tuesday, December 10, 2019 5:45 PM Registration 6:00 PM Seminar Begins 7:30 PM Reception Ab...")
 
 
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Machine Learning for Stock Selection
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=== Thalesians Seminar (New York) —  Keywan Rasekhschaffe — Machine Learning for Stock Selection ===
  
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[[File:Keywan Rasekhschaffe pic.jpg|frameless|100px|none|left|Keywan Rasekhschaffe]]
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'''Keywan Rasekhschaffe'''
  
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==== Date and Time ====
  
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Tuesday, December 10, 2019 at 6:00 p.m.
  
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==== Venue ====
  
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Fordham University, New York, NY
  
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==== ''Meetup.com'' ====
  
A Talk by 
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You can register for this event and pay online on ''Meetup.com'':
Keywan Rasekhschaffe
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https://www.meetup.com/thalesians/events/266299169/
  
Tuesday, December 10, 2019
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==== Abstract ====
  
 
5:45 PM Registration
 
6:00 PM Seminar Begins
 
7:30 PM Reception
 
 
 
   
 
Abstract
 
 
Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools.
 
Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools.
  
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We describe some of the basic concepts of machine learning and provide a simple example of how investors can use machine learning techniques to forecast the cross-section of stock returns while limiting the risk of overfitting.
 
We describe some of the basic concepts of machine learning and provide a simple example of how investors can use machine learning techniques to forecast the cross-section of stock returns while limiting the risk of overfitting.
  
   
 
  
Biography
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==== Speaker ====
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Keywan Rasekhschaffe, PhD, is a portfolio manager and senior quantitative strategist at Gresham Investment Management LLC where he focuses on Gresham’s systematic absolute return strategies. Prior to joining Gresham he oversaw quantitative research at System Two Advisors L.P. where he developed machine learning strategies for global stock selection. Previously he was a Chazen Visiting Scholar at Columbia Business School. He earned his Ph.D. at the University of Lugano, Swiss Finance Institute, 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 is forthcoming in the Financial Analysts Journal.
 
Keywan Rasekhschaffe, PhD, is a portfolio manager and senior quantitative strategist at Gresham Investment Management LLC where he focuses on Gresham’s systematic absolute return strategies. Prior to joining Gresham he oversaw quantitative research at System Two Advisors L.P. where he developed machine learning strategies for global stock selection. Previously he was a Chazen Visiting Scholar at Columbia Business School. He earned his Ph.D. at the University of Lugano, Swiss Finance Institute, 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 is forthcoming in the Financial Analysts Journal.
  
  
Acknowledgments
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==== Acknowledgments ====
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Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.
 
Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.

Latest revision as of 19:09, 8 November 2019

Contents

Thalesians Seminar (New York) — Keywan Rasekhschaffe — Machine Learning for Stock Selection

Keywan Rasekhschaffe

Keywan Rasekhschaffe

Date and Time

Tuesday, December 10, 2019 at 6:00 p.m.

Venue

Fordham University, New York, NY

Meetup.com

You can register for this event and pay online on Meetup.com: https://www.meetup.com/thalesians/events/266299169/

Abstract

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools.

Although machine learning algorithms can uncover subtle, contextual, and nonlinear relationships, overfitting poses a major challenge when one is trying to extract signals from noisy historical data.

We describe some of the basic concepts of machine learning and provide a simple example of how investors can use machine learning techniques to forecast the cross-section of stock returns while limiting the risk of overfitting.


Speaker

Keywan Rasekhschaffe, PhD, is a portfolio manager and senior quantitative strategist at Gresham Investment Management LLC where he focuses on Gresham’s systematic absolute return strategies. Prior to joining Gresham he oversaw quantitative research at System Two Advisors L.P. where he developed machine learning strategies for global stock selection. Previously he was a Chazen Visiting Scholar at Columbia Business School. He earned his Ph.D. at the University of Lugano, Swiss Finance Institute, 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 is forthcoming in the Financial Analysts Journal.


Acknowledgments

Special thanks to the Fordham University Gabelli School of Business for hosting and sponsoring the seminar.

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