Revolutionizing Investments: AIBig Data Applications in Modern Asset Management

Handbook of and Big Data Applications in Investments

Theme: Technology

Date Published: March 27, 2023

Editor: Larry Cao, CFA

Abstract

This comprehensive guide provides an insider's journey into the modern asset management industry through the lens of and big data. The book, crafted by leading practitioners, illuminates how cutting-edge data science techniques are being deployed to uncover insights and opportunities that were once hidden.

The Handbook serves as a beacon for professionals looking to deepen their understanding or embark on new projects involvingand big data in investments. It's an essential companion, encapsulating the latest developments through diverse author affiliations and backgrounds.

This publication is part of CFA Institute Research Foundation's mission to provide tools and insights that elevate professional standards and empower decision-makers across the global investment community. Building upon previous works like T-Shaped Teams: Organizing to Adoptand Big Data at Investment Firms andPioneers in Investment Management, we're now offering a range of learning opportunities through our professional development programs.

Supporting Excellence in Practice

We invite you to contribute to this mission by donating to the CFA Institute Research Foundation. Your support helps fund high-quality research that not only rses industry standards but also disseminates knowledge widely and freely, benefiting professionals worldwide.

Foreword

Innovations inand big data are revolutionizing every aspect of financial services, including investments management. Aaron Low, PhD, CFA, sets the stage with his insightful foreword on how these technologies are shaping our future and enabling new strategies to optimize returns and mitigate risks.

Introduction: Larry Cao, CFA

Larry Cao introduces this Handbook by guiding readers through a chronological exploration ofapplications in investmentsbeginning with and data science techniques that have been integrated into firms' workflows. He emphasizes the diverse contributions from experts across the industry, showcasing how these advancements are being applied to solve complex problems.

Part I: and Data Science Applications in Investments

Chapter 1: Mike Chen, PhD, and Weili Zhou, CFA Robeco

This chapter dives deep into practical applications within investments. Mike Chen and Weili Zhou share Robeco's experiences with implementing solutions to enhance portfolio management and risk analysis.

Chapter 2: Ingrid Tierens, PhD, CFA, and Dan Duggan, PhD Goldman Sachs Global Investment Research

In this chapter, Ingrid Tierens and Dan Duggan explore the integration of alternative data andin investment research. They outline how big datasets are being utilized to uncover unique insights that traditional methods might miss.

Chapter 3: K Cui, PhD, and Jonathan Shahrabani Neuberger Berman

K Cui and Jonathan Shahrabani offer an in-depth look at using data science for active and long-term fundamental investing. They highlight strategies for leveragingto improve decision-making processes and enhance returns.

Part II: Understanding, Processing, and Generation: Investment Applications

Chapter 4: Andrew Chin, Yuyu Fan, and Che Guan AllianceBernstein

The authors present an overview of how are being employed by AllianceBernstein to extract valuable insights from textual data. This chapter showcases the practical use of NLP in asset management.

Chapter 5: Stefan Jansen, CFA Applied

Stefan Jansen discusses the advancements in understanding for investment management, discussing tools and methods that d analysts in making more informed decisions based on unstructured text.

Chapter 6: Tal Sansani, CFA Off-Script Systems Mikhl Samonov, CFA Two Centuries Investments

Tal Sansani and Mikhl Samonov take a hands-on approach to expln how extracting ESG insights from textual data can be achieved usingtechnologies. They provide for integrating these techniques into investment analysis.

Part III: Trading with and Big Data

Chapter 7: Erin Stanton Virtu Financial

Erin Stanton discusses the role of in trading, focusing on execution support systems that leverage big data to optimize orders and improve trade outcomes. This chapter emphasizes the real-world application ofin market operations.

Chapter 8: Peer Nagy, James Powrie, PhD, and Stefan Zohren, PhD Man Group

This chapter presents an analysis of how Man Group is utilizing for trading strategies, including algorithmic modeling and predictive analytics that inform decision-making processes.

Part IV: The Future Outlook

As we conclude the Handbook, readers are encouraged to anticipate future trs inand big data applications. This section explores potential advancements and their implications on investment management practices.

This Handbook of and Big Data Applications in Investments is a testament to the ongoing digital transformation within the financial sector. It serves as a comprehensive resource for professionals looking to understand, adopt, and benefit from innovations shaping the future of investments.

By delving into this publication, readers gn insights into how leading firms are leveraging algorithms and big data analytics to enhance portfolio management, risk assessment, trading strategies, and beyond.

The journey through these pages not only highlights the current landscape but also paves the way for innovation in the field by showcasing real-world applications and future possibilities.

Join us as we continue to explore new horizons and empower the global investment community with cutting-edge knowledge and onand big data technologies.

of Book

This publication has been meticulously crafted with contributions from industry experts, ming to serve as a resource for professionals seeking to stay at the forefront ofapplications in investments. We invite you to delve into this comprehensive guide and embrace the future opportunities presented by innovations.

: The content herein is solely for informational purposes and does not constitute legal or professional advice. CFA Institute Research and Policy Center cannot guarantee the accuracy, completeness, or applicability of the information provided within this publication.

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