Understanding Technology, Big Data and AI
More Technology, Big Data and AI Content
The Automation Ahead Content Series
This series breaks down the reality of GenAI’s current state, guiding you through the innovations, possibilities, and risks of this new wave of automation.
Investment Model Validation: A Guide for Practitioners
As the investment landscape evolves, proactive model validation is critical for the reliability and effectiveness of any investment process. This monograph provides a practical, detailed overview of validation tools available to practitioners.
Creating Value from Big Data in the Investment Management Process: A Workflow Analysis
This report examines the increasing adoption of AI and big data technologies in investment management, highlighting their transformative role in workflows and decision making. It explores risks, challenges, opportunities, and global usage patterns.
Featured Content
ML Models Need Better Training Data: The GenAI Solution
As complex machine learning models become more prevalent in investment management, their tendency to overfit to specific historical conditions poses a growing risk to investment outcomes. Generative AI-based synthetic data is emerging as a potential solution to this challenge, writes Dan Philps, PhD, CFA. But there is no safe fix for excessive complexity, opaque models, or weak investment rationales, he cautions.
Pensions in the Age of Artificial Intelligence
This report highlights how AI technologies, including generative AI and machine learning, are being implemented across the pension value chain to enhance efficiency, personalization, and governance while promoting sustainable retirement security.
How Much Does ChatGPT Know about Finance?
Finance applications of large language models (LLMs) have implications for practitioners and researchers. Analysis shows that some models and methods are associated with higher levels of accuracy, making use of LLMs task specific, not job specific.
Policy in Action
An Investment Perspective on Tokenization — Part I & II
This report examines the legal and regulatory changes needed for tokenization to grow responsibly while ensuring investor protection and market integrity. It analyzes global regulatory regimes, international standards, and the need for legal clarity.
Response to IOSCO Artificial Intelligence in Capital Markets
CFA Institute’s Public Comment re: IOSCO – Artificial Intelligence in Capital Markets: Use Cases, Risks, and Challenges
AI in Investment Management: Ethics Case Study Part II
In this post, Jon Stokes reveals how well you applied the Codes and Standards to analyze the case study presented in Part 1.
Be Informed
Abnormal FX Returns and Liquidity-Based Machine Learning Approaches
Navigating FX market volatility requires more than traditional analysis. Liquidity-aware models and machine learning techniques can provide an edge in detecting and forecasting abnormal returns, explains Sebastian Petric, CFA.
Creating Value from Big Data in the Investment Management Process: A Workflow Analysis
Cheryll-Ann shares insights from her research, which involved a comprehensive survey and interviews with investment professionals worldwide. The conversation explores the use of AI and big data technologies in the finance sector, highlighting the challenges and opportunities that practitioners, regulators, and executives face in adopting these innovations.
Conversations with Frank Fabozzi, CFA
Join us for an engaging panel discussion that brings together three leading experts in asset management and financial modeling to explore the transformative trends shaping the industry.
Driving Digital Transformation: CFA Institute Research and Policy Center Explores Machine Learning and AI Advancements in Investing
CFA Institute Research and Policy Center is addressing how big data, machine learning and advancements in natural language processing, understanding, and generation will accelerate the digital transformation of the investment industry.
The theme Understanding Technology, Big Data and AI tracks the evolving opportunity set for investment firms and professionals stemming from artificial intelligence (AI), big data, and new analytical tools and technologies.
Our research recognizes that the future of the investment industry is one where big data and AI, smart machines and systems, and data analytics will play a more central role in how the world of finance evolves.