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Revolution Analytics Customer Day
American Century Investments

February 26, 2013

Tal Sansani, CFA
Quantitative Analyst
Portfolio Manager
Sampath Thummati
IT Manager/Advisor
American Century Investments: Company Overview


 American Century Investments | Kansas City, MO                                     Notes

  – Founded in 1958
  – $125 billion assets under management*
  – One of the 20 largest mutual fund companies

 Quantitative Equity Group | Mountain View, CA
  – $8.5 billion in assets under management across 22 mutual funds and separate
    accounts
  – This group takes an objective, systematic, and disciplined investment approach
  – Combines quantitative stock-selection models with portfolio optimization
    procedures, to systematically determine which stocks to buy or sell.
  – Fully Transparent Process: Stock-selection models are founded on
    economically sensible ideas and implemented using carefully calibrated
    statistical methods.
 The Team
  – 10 experienced investment professionals with backgrounds in
    finance, economics, accounting, mathematics, and statistics.
  – Supported by a team of 4 IT professionals



                                                                                             2
About Me: Tal Sansani


 Quantitative Research Analyst & Portfolio Manager                                  Notes

 With American Century Investments’ Quantitative Research Team for 7+
  years.

 Research Responsibilities
  – Research and develop stock-selection signals (alpha) that systematically
    inform our funds on which names to buy or sell.


  – Research and develop portfolio construction techniques that help our funds
    mitigate unintended risks and exposures.


  – Monitor the performance dynamics of our models and asset positions with
    proprietary analytics and attribution dashboards


  – Currently putting research projects aside (briefly) to revamp our research and
    production platforms:
      Helping lead the design and development of an end-to-end quantitative
       research platform, built atop an internal/collaborative R-package rACI




                                                                                             3
Revamping our Research and Production Platforms with RevoR

 In 2012, after years of pain and suffering, we initiated a move away from our
  existing infrastructure…                                                        Notes

 Extensive limitations with our pre-existing platform:
  – A disparate blend of CLOSED 3rd party financial software
  – Functionally limited and difficult to customize
  – Restricted to specific data vendors/sources/asset-classes
  – Difficulty streamlining multi-dimensional processes
  – Cumbersome and costly
 In-house Solution: a streamlined, scalable end-to-end quantitative platform
      Data Acquisition, Data Cleaning & Model Building
        – RevoR w/ SQL, populated with variety of data-sources, and proprietary
          feeds
      Portfolio Optimization and Strategy Simulation
        – RevoR w/ powerful 3rd Party Optimization API
      Model Analytics & Performance Attribution
        – RevoR w/ tableau (and existing R graphics/publishing packages)
      Production Processes
        – Controlled environment, deployment

                                                                                          4
rACI: A growing, multi-team, collaborative R-package within
American Century Investments

Data Feeds                                                                             Notes


 Market Data from Thomson      American Century Quant   Additional 3rd Party Data
    Reuters (QA-Direct)           Proprietary Data              Vendors




                rACI Package (w/ RevoR)
                  Data Acquisition Function Library

                   Model Building Function Library                      Portfolio
                                                                    Optimization and
                                                                     Simulation API
                      Analytics Function Library




                                                                      Live Analytics


              PRODUCTION MODEL GENERATION
                 AND TRADING PROCESSES
Immediate Research Benefits Gained By Infrastructure Revamp


 Why Research likes RevoR?                                                          Notes

  – We love R, and all the benefits of the fastest growing open-source statistical
    programming language, but with $8 billion on the line, we sought a trusted
    enterprise solution for research and production processes.
  – Optimized performance: We’ve observed our simulations to be 20x faster than
    with base-R, vastly improving research turnaround

 Immediate Results: New RevoR-driven solution is a huge upgrade on our
 pre-existing platform
  – With improved analytics and streamlined research processes, we can better
    understand the behavior of our models and more quickly adapt to material
    market changes.
  – Decoupling our investment processes from closed 3rd-party vendors has
    allowed us to combine and analyze more types of financial assets (not just
    stocks), leading to new investment products (combining credit
    instruments, options, commodities, etc.)
  – We can now leverage all the rapidly evolving libraries of R in our
    research, leading to more proprietary and cutting-edge quantitative models.




                                                                                             6
Example 1: Streamlined Research Simulations/Diagnostics

A 3-Step Process:                                         Notes
1) Construct a stock-selection
    signal and submit it to the
    database
2) Run customized simulations
    and pre-packaged analytics
3) Visit the Quant Research
    Portal for the results




                                                                  7
Example 2: Opening up Our Research With R’s Rapidly Evolving Open-Source
Library
 By integrating existing financial                                    Notes
                                                                 The Economic
  datasets with new/unique                                       Ecosystem
  information, while leveraging a
  variety of packages available in
  R, our group can explore new
  avenues of research.



 In this example, we use
  revenues between customers
  and suppliers, to explore how
  information travels through an
  economic network.



 Note: R’s igraph package was
  used for much of the internal
  analysis, while Gephi was used
  to construct the chart you see on
  the right.




                                                                                8
About me: Sampath Thummati


 Responsibilities                                                                      Notes

   – Architect and design investment management systems to support quantitative
      research and portfolio management.
   – Production support for quant model generation and other investment
      management processes.
   – Currently leading the implementation of quant roadmap to build efficient cross-
      asset class research platform for alpha generation, back-testing and analytics.

 R Experience
   – R-user for couple of years now
   – Integrating applications interfacing with R code
       Database, Java Components, Batch Scheduling System and Custom
          applications
   – Building configuration functions
       Error handling
       Application logging




FOR INTERNAL USE ONLY
                                                                                                9
Technology’s Role in Innovative Quantitative Investment
Management
 ACCESS TO UNIQUE DATA-SETS                                                           Notes

  – New, innovative investment ideas are the life-blood of our group, and by
      extension, so too is our ability to process new information. It’s absolutely
      critical for us to rapidly adapt to complex data-sets and new technologies.

 COMPUTATIONAL CAPACITY
  – Controlled risk management and modern portfolio construction techniques
      require sophisticated optimization toolsets.

 CUSTOMIZED ANALYTICS
  – Building proprietary models requires proprietary analytics/feedback into the
      model

 ROBUST DATA FORENSICS
  – Proprietary data quality tools ensure inputs into trading processes go through a
      battery of tests

 INDUSTRIAL STRENGTH PRODUCTION PROCESSES




FOR INTERNAL USE ONLY
                                                                                               10
Immediate Production Benefits Gained By Infrastructure Revamp


 Why Production likes RevoR?                                                      Notes

  – Open-source tools generally avoided in large-scale money management
     Revo support model
     Package verification and certification eliminates risks of malicious code
  – Optimized performance
     Enables us to run overnight production processes in time for next business
      day
  – Business and production friendly programming language
     Research and production now share a common language, reducing risk of
      errors in code translation
     Reduced time to production implementation




                                                                                           11
Research-to-Production Transition

                                    Notes




                                            12
What we did on the production side?


 Error handling                             Notes

  – Intensive ‘try-catch’ use
  – Storing images at the point of failure
 Robust logging procedures
  – Easy to use calls to log
  – Rolling logs
 Setup batch jobs
  – Use of Rscript
  – Handling return code




                                                     13
Example

 Try-Catch Example   Notes




                              14
Example

 Batch example   Notes




                          15
What we did on the production side?


 Interface with dependency management system                Notes

 Controlled processes to stabilize production environment
  – Third-party packages
  – Deploying application and modified packages
  – Use of Rprofile for enterprise settings




                                                                     16
Current Status


 We are about 75% complete with our transition to RevoR                             Notes

 There is growing interest from other parts of the company to contribute and
  employ rACI

 So far, we haven’t experienced any setbacks and are very satisfied with what has
  been accomplished with RevoR




                                                                                             17
Q&A

                                Notes

      Sampath Thummati
      st8@americancentury.com

      Tal Sansani
      t4s@americancentury.com




                                        18

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American Century (Revolution Analytics Customer Day)

  • 1. Revolution Analytics Customer Day American Century Investments February 26, 2013 Tal Sansani, CFA Quantitative Analyst Portfolio Manager Sampath Thummati IT Manager/Advisor
  • 2. American Century Investments: Company Overview  American Century Investments | Kansas City, MO Notes – Founded in 1958 – $125 billion assets under management* – One of the 20 largest mutual fund companies  Quantitative Equity Group | Mountain View, CA – $8.5 billion in assets under management across 22 mutual funds and separate accounts – This group takes an objective, systematic, and disciplined investment approach – Combines quantitative stock-selection models with portfolio optimization procedures, to systematically determine which stocks to buy or sell. – Fully Transparent Process: Stock-selection models are founded on economically sensible ideas and implemented using carefully calibrated statistical methods.  The Team – 10 experienced investment professionals with backgrounds in finance, economics, accounting, mathematics, and statistics. – Supported by a team of 4 IT professionals 2
  • 3. About Me: Tal Sansani  Quantitative Research Analyst & Portfolio Manager Notes  With American Century Investments’ Quantitative Research Team for 7+ years.  Research Responsibilities – Research and develop stock-selection signals (alpha) that systematically inform our funds on which names to buy or sell. – Research and develop portfolio construction techniques that help our funds mitigate unintended risks and exposures. – Monitor the performance dynamics of our models and asset positions with proprietary analytics and attribution dashboards – Currently putting research projects aside (briefly) to revamp our research and production platforms:  Helping lead the design and development of an end-to-end quantitative research platform, built atop an internal/collaborative R-package rACI 3
  • 4. Revamping our Research and Production Platforms with RevoR  In 2012, after years of pain and suffering, we initiated a move away from our existing infrastructure… Notes  Extensive limitations with our pre-existing platform: – A disparate blend of CLOSED 3rd party financial software – Functionally limited and difficult to customize – Restricted to specific data vendors/sources/asset-classes – Difficulty streamlining multi-dimensional processes – Cumbersome and costly  In-house Solution: a streamlined, scalable end-to-end quantitative platform  Data Acquisition, Data Cleaning & Model Building – RevoR w/ SQL, populated with variety of data-sources, and proprietary feeds  Portfolio Optimization and Strategy Simulation – RevoR w/ powerful 3rd Party Optimization API  Model Analytics & Performance Attribution – RevoR w/ tableau (and existing R graphics/publishing packages)  Production Processes – Controlled environment, deployment 4
  • 5. rACI: A growing, multi-team, collaborative R-package within American Century Investments Data Feeds Notes Market Data from Thomson American Century Quant Additional 3rd Party Data Reuters (QA-Direct) Proprietary Data Vendors rACI Package (w/ RevoR) Data Acquisition Function Library Model Building Function Library Portfolio Optimization and Simulation API Analytics Function Library Live Analytics PRODUCTION MODEL GENERATION AND TRADING PROCESSES
  • 6. Immediate Research Benefits Gained By Infrastructure Revamp  Why Research likes RevoR? Notes – We love R, and all the benefits of the fastest growing open-source statistical programming language, but with $8 billion on the line, we sought a trusted enterprise solution for research and production processes. – Optimized performance: We’ve observed our simulations to be 20x faster than with base-R, vastly improving research turnaround  Immediate Results: New RevoR-driven solution is a huge upgrade on our pre-existing platform – With improved analytics and streamlined research processes, we can better understand the behavior of our models and more quickly adapt to material market changes. – Decoupling our investment processes from closed 3rd-party vendors has allowed us to combine and analyze more types of financial assets (not just stocks), leading to new investment products (combining credit instruments, options, commodities, etc.) – We can now leverage all the rapidly evolving libraries of R in our research, leading to more proprietary and cutting-edge quantitative models. 6
  • 7. Example 1: Streamlined Research Simulations/Diagnostics A 3-Step Process: Notes 1) Construct a stock-selection signal and submit it to the database 2) Run customized simulations and pre-packaged analytics 3) Visit the Quant Research Portal for the results 7
  • 8. Example 2: Opening up Our Research With R’s Rapidly Evolving Open-Source Library  By integrating existing financial Notes The Economic datasets with new/unique Ecosystem information, while leveraging a variety of packages available in R, our group can explore new avenues of research.  In this example, we use revenues between customers and suppliers, to explore how information travels through an economic network.  Note: R’s igraph package was used for much of the internal analysis, while Gephi was used to construct the chart you see on the right. 8
  • 9. About me: Sampath Thummati  Responsibilities Notes – Architect and design investment management systems to support quantitative research and portfolio management. – Production support for quant model generation and other investment management processes. – Currently leading the implementation of quant roadmap to build efficient cross- asset class research platform for alpha generation, back-testing and analytics.  R Experience – R-user for couple of years now – Integrating applications interfacing with R code  Database, Java Components, Batch Scheduling System and Custom applications – Building configuration functions  Error handling  Application logging FOR INTERNAL USE ONLY 9
  • 10. Technology’s Role in Innovative Quantitative Investment Management  ACCESS TO UNIQUE DATA-SETS Notes – New, innovative investment ideas are the life-blood of our group, and by extension, so too is our ability to process new information. It’s absolutely critical for us to rapidly adapt to complex data-sets and new technologies.  COMPUTATIONAL CAPACITY – Controlled risk management and modern portfolio construction techniques require sophisticated optimization toolsets.  CUSTOMIZED ANALYTICS – Building proprietary models requires proprietary analytics/feedback into the model  ROBUST DATA FORENSICS – Proprietary data quality tools ensure inputs into trading processes go through a battery of tests  INDUSTRIAL STRENGTH PRODUCTION PROCESSES FOR INTERNAL USE ONLY 10
  • 11. Immediate Production Benefits Gained By Infrastructure Revamp  Why Production likes RevoR? Notes – Open-source tools generally avoided in large-scale money management  Revo support model  Package verification and certification eliminates risks of malicious code – Optimized performance  Enables us to run overnight production processes in time for next business day – Business and production friendly programming language  Research and production now share a common language, reducing risk of errors in code translation  Reduced time to production implementation 11
  • 13. What we did on the production side?  Error handling Notes – Intensive ‘try-catch’ use – Storing images at the point of failure  Robust logging procedures – Easy to use calls to log – Rolling logs  Setup batch jobs – Use of Rscript – Handling return code 13
  • 16. What we did on the production side?  Interface with dependency management system Notes  Controlled processes to stabilize production environment – Third-party packages – Deploying application and modified packages – Use of Rprofile for enterprise settings 16
  • 17. Current Status  We are about 75% complete with our transition to RevoR Notes  There is growing interest from other parts of the company to contribute and employ rACI  So far, we haven’t experienced any setbacks and are very satisfied with what has been accomplished with RevoR 17
  • 18. Q&A Notes Sampath Thummati st8@americancentury.com Tal Sansani t4s@americancentury.com 18