Skip to main content

Main menu

  • Home
  • Current Issue
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About JPM
    • Awards
    • Editorial Board
    • Published Ahead of Print (PAP)
  • IPR Logo
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter

User menu

  • Sample our Content
  • Request a Demo
  • Log in

Search

  • ADVANCED SEARCH: Discover more content by journal, author or time frame
The Journal of Portfolio Management
  • IPR Logo
  • About Us
  • Journals
  • Publish
  • Advertise
  • Videos
  • Webinars
  • More
    • Awards
    • Article Licensing
    • Academic Use
  • Sample our Content
  • Request a Demo
  • Log in
The Journal of Portfolio Management

The Journal of Portfolio Management

ADVANCED SEARCH: Discover more content by journal, author or time frame

  • Home
  • Current Issue
  • Past Issues
  • Videos
  • Submit an article
  • More
    • About JPM
    • Awards
    • Editorial Board
    • Published Ahead of Print (PAP)
  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter

Improving Investment Operations through Data Science: A Case Study of Innovation in Valuation

Arthur Guimarães, Ashby Monk and Sidney Porter
The Journal of Portfolio Management Fall 2018, 45 (1) 125-140; DOI: https://doi.org/10.3905/jpm.2018.1.083
Arthur Guimarães
is the chief operating officer and head of defined contribution at UC Investments in Oakland, CA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ashby Monk
is the executive and research director for the Global Projects Center at Stanford University in Palo Alto, CA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sidney Porter
is the chief data scientist at FEV Analytics Corp in Kirkland, WA.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Supplemental
  • Info & Metrics
  • PDF (Subscribers Only)
Loading

Click to login and read the full article.

Don’t have access? Click here to request a demo 
Alternatively, Call a member of the team to discuss membership options
US and Overseas: +1 646-931-9045
EMEA: +44 0207 139 1600

Abstract

New technologies in data science are allowing long-term investors to bring much more rigor to their operations. In this article the authors provide empirical examples in support of these data-driven advances, demonstrating their practical applications. They use the UC Investments office as their case study and discuss how adoption of advanced data science techniques can move organizations past the current unsatisfactory state of the art and toward an unprecedented level of operational finesse. Specifically, the authors focus on a methodological innovation in fair valuation of illiquid assets that is supported by an automated, rigorous process. They test this process in a real-world setting and find, at least in this case, that these advances can enhance roll forward outputs in terms of timeliness, accuracy, and granularity. This finding has several potential impacts, not only for reporting, but also for investment, risk management, actuarial purposes, and even personal compensation of teams.

TOPICS: Big data/machine learning, performance measurement

  • © 2018 Pageant Media Ltd
View Full Text

Don’t have access? Click here to request a demo

Alternatively, Call a member of the team to discuss membership options

US and Overseas: +1 646-931-9045

UK: 0207 139 1600

Log in using your username and password

Forgot your user name or password?
PreviousNext
Back to top

Explore our content to discover more relevant research

  • By topic
  • Across journals
  • From the experts
  • Monthly highlights
  • Special collections

In this issue

The Journal of Portfolio Management: 45 (1)
The Journal of Portfolio Management
Vol. 45, Issue 1
Fall 2018
  • Table of Contents
  • Index by author
  • Complete Issue (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on The Journal of Portfolio Management.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Improving Investment Operations through Data Science: A Case Study of Innovation in Valuation
(Your Name) has sent you a message from The Journal of Portfolio Management
(Your Name) thought you would like to see the The Journal of Portfolio Management web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Improving Investment Operations through Data Science: A Case Study of Innovation in Valuation
Arthur Guimarães, Ashby Monk, Sidney Porter
The Journal of Portfolio Management Oct 2018, 45 (1) 125-140; DOI: 10.3905/jpm.2018.1.083

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Save To My Folders
Share
Improving Investment Operations through Data Science: A Case Study of Innovation in Valuation
Arthur Guimarães, Ashby Monk, Sidney Porter
The Journal of Portfolio Management Oct 2018, 45 (1) 125-140; DOI: 10.3905/jpm.2018.1.083
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Tweet Widget Facebook Like LinkedIn logo

Jump to section

  • Article
    • Abstract
    • CASE STUDY: THE FAIR VALUE CONUNDRUM
    • THE INCUMBENT ROLL FORWARD PROCEDURE
    • DATA REQUIREMENTS
    • STUDY 1: FEASIBILITY OF ROLL FORWARD AUTOMATION (RESTRICTED TO CLASS 1 DATA INPUTS)
    • STUDY 2: WHAT PERFORMANCE IMPROVEMENTS CAN AUTOMATION UNLOCK?
    • LIMITATIONS OF THE ROLL FORWARD PROCEDURE
    • A REAL-LIFE EXAMPLE: LARGE EXPOSURES AND UNANTICIPATED VALUATIONS
    • CONCLUSION
    • ACKNOWLEDGMENTS
    • APPENDIX
    • ENDNOTES
    • REFERENCES
  • Supplemental
  • Info & Metrics
  • PDF (Subscribers Only)
  • PDF (Subscribers Only)

Similar Articles

Cited By...

  • No citing articles found.
  • Google Scholar
LONDON
One London Wall, London, EC2Y 5EA
United Kingdom
+44 207 139 1600
 
NEW YORK
41 Madison Avenue, New York, NY 10010
USA
+1 646 931 9045
reply@pm.research.com
 

Stay Connected

  • Follow IIJ on LinkedIn
  • Follow IIJ on Twitter

MORE FROM PMR

  • News
  • Awards
  • Investment Guides
  • Videos
  • About PMR

INFORMATION FOR

  • Academics
  • Agents
  • Authors
  • Content Usage Terms

GET INVOLVED

  • Advertise
  • Publish
  • Article Licensing
  • Contact Us
  • Subscribe Now
  • Sign In
  • Update your profile
  • Give us your feedback

© 2023 With Intelligence Ltd | All Rights Reserved | ISSN: 0095-4918 | E-ISSN: 2168-8656

  • Site Map
  • Terms & Conditions
  • Privacy Policy
  • Cookies