CFA Institute

CFA Institute responds to the FCA’s Mills Review on AI in retail financial services – advocating tiered governance, AI-tailored Consumer Duty articulation, SM&CR clarity on third-party AI dependencies, and hybrid talent as core UK competitive infrastructure. source

The book covers the key concepts required for a data science course focused on finance, organized into 14 chapters that center on theory and techniques, with clear exercises on applications and use cases. Each chapter presents a unique data science topic, with discussions and interpretations not typically covered in empirical finance texts. Key data science topics include: • principal component analysis, • cluster analysis • advances beyond linear regression • linear and nonlinear classifiers and kernel methods • deep learning with neural networks • advances in portfolio optimization beyond the mean/variance model • financial networks, and • text analytics, which includes large language models (LLM) and natural language processing (NLP). All these topics provide essential tools for any quantitative analyst who wants to stay current without having to work from source material in academic research. Unified notation that is consistent across techniques is of critical value, making learning easier. The authors do an effective job of presenting what could be considered classical techniques as foundations and then show variations that can serve as application enhancements. For regression and optimization, alternative methods are shown to potentially improve solutions and yield better outcomes. The book enables the reader to navigate through the theory behind data science. It does an effective job of unifying the mathematical concepts behind data science techniques and providing insight into how an analyst can apply them to real-world problems. This is not a “cookbook,” but instead emphasizes the math behind its key data science topics, followed by application exercises. The reader who works through the book’s focused exercises and applications should be able to understand how these theories can be used to solve real problems. Nevertheless, from a practitioner’s viewpoint, a greater focus on the applied side of data science beyond the exercises would have been beneficial. This means providing explanations for why a new tool will yield improved predictions and tapping into the authors’ collective wisdom by offering insights into when and how these techniques will be helpful, and when simple methods will suffice. Techniques are tools, and the book does a good job of explaining the different tools. It needs, however, to present more clearly when and why analysts should use specific tools and how to interpret model output. The rationale for using a particular technique and the skill in applying it come from experiential knowledge that is hard to gain from any course or textbook, yet imparting the process engineering for analyzing data is the critical piece that will elevate this book above others for the CFA Institute readership. Too often, beginning quant analysts who have learned new techniques apply them to every problem they encounter without considering which tool is best for a given problem. For example, the explosion of machine learning techniques is changing how quantitative analysis is conducted in finance. Yet there is the nagging question of which complex methods are best suited to a given problem rather than a more straightforward technique. This thinking goes beyond torturing data until it talks and just reporting better prediction metrics. While including new techniques is an advantage for this book, the emphasis on key topics detracts from its value. From a user’s perspective, data science should be a way of thinking about how to process information, not just a set of techniques. The primary challenge is establishing a framework for conducting data analysis. The process of doing data science is what makes it distinct from merely applying techniques from a toolbox to data problems. How should an analyst systematically look at data, regardless of the problem? A step-like process of analyzing data should always be at the forefront and is timeless. There is also limited discussion of time series, which is foundational to financial analysis, as well as cross-sectional analysis. If you are a quant analyst, both are critical to the successful application of data science to complex prediction problems. Similarly, the authors do not address key issues such as p-hacking and model overfitting. With cheap computing, data science requires thoughtful approaches to find better predictions efficiently, without overfitting or hunting for fitted results. Despite some drawbacks, Financial Data Science provides readers with an understanding of and exposure to many new techniques with potential financial value. Financial Data Science is at the current forefront of quantitative knowledge transfer. Some of these techniques will become part of the standard toolbox while others may become less valuable over time. Analysts and managers will both need to understand the good data science tools and those that may be fads; otherwise, they will be at a competitive disadvantage to other firms. Reading the book requires hard work with both a scratch pad for the math and some programming skill, but the payoff is high for anyone who wants to stay current. source

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CFA Institute Research and Policy Center A new SDG scoring method aligns government bond portfolios with sustainable goals, guiding capital to nations with strong SDG policies. It addresses income bias in sovereign ESG ratings and offers a practical framework for impactful investing. source

Infrastructure debt has evolved into a global, institutional asset class central to the energy transition, digitalization, and modernization of essential services. Its appeal lies in the intrinsic characteristics of infrastructure assets — long-duration, tangible, essential, and historically resilient, providing investors with typically predictable cash flows, relatively limited correlation with public markets, and collateral value. Over the past decade, annual private infrastructure investment (debt and equity) has more than doubled to exceed USD1 trillion, driven by three macro forces: large-scale public stimulus (e.g., the EU’s NextGenEU fund and incentives linked to the Inflation Reduction Act in the United States), accelerating decarbonization targets (in Europe and, increasingly, in Asia), and surging electricity demand from the AI boom. The brief is an overview of the different types of infrastructure debt. It outlines how energy, digital, and transportation debt financing has grown increasingly diverse and complex. We highlight how renewable energy now dominates deal flow but introduces new challenges (intermittency, curtailment risk, and transmission bottlenecks). Digital infrastructure has become a mission-critical asset class for the 21st century, with stable contracted revenues but heightened exposure to power supply constraints. We also discuss different types of financing for different stages of development. In the development stage, financing typically flows through grants, seed equity, and early-stage debt. Construction relies mainly on bank-led syndicated loans. In the operational phase, refinancing through long-term loans or project bonds attracts institutional investors seeking inflation-linked, investment-grade cash flows. Finally, our brief highlights a European case study, which shows underwriting adapting to modern infrastructure debt financing. Overall, we conclude that infrastructure debt is a key investment opportunity for allocators looking for long-term, steady cash flows and diversification — though it requires strong due diligence and underwriting skills. For governments, in an era of underinvestment and capital scarcity, it is a crucial addition to public financing. For society at large, it represents a vehicle to help long-term development. source

1. Trading platforms — We support UK legal entity requirements for retail-serving CATPs, non-discretionary execution, and QCDD gating for retail access. We recommend clarification on cross-border insolvency treatment and substantive UK presence thresholds. 2. Conflicts of interest — We do not support permitting principal dealing within the same legal entity as a CATP operator. The conflicts — informational asymmetry, impaired surveillance neutrality, distorted price formation — are structural, not behavioural. We recommend an initial prohibition, with a thematic review once the MAR regime and supervisory capabilities have matured. 3. Intermediaries — We support best execution without carve-outs, the “three sources” pricing guidance, all-in cost transparency, a broad PFOF prohibition, and functional separation between proprietary trading and client execution — with legal separation where functional measures demonstrably fail. 4. Lending, borrowing, and staking — We do not support retail access to cryptoasset lending and borrowing at this stage. Disclosure and consent do not sufficiently mitigate structural risks, including insufficiently mapped stress transmission dynamics. We recommend restricting to professional clients, with future review contingent on market maturity. On staking, we support per-event consent given material variation in risks across protocols. 5. DeFi — We support “same risk, same regulatory outcome” where a controlling person is identifiable. We encourage FCA guidance on indicators and thresholds for determining control, particularly where governance operates through token voting or foundation structures. In conclusion, CFA Institute and CFA UK support the FCA’s ambition to establish a comprehensive cryptoasset regulatory framework. Given rising cryptoasset ownership among UK retail investors and the inherent risks associated with these assets, our positions are grounded in the view that investor protection and market integrity are inseparable: weak governance, poor disclosure, or inadequate redress mechanisms risk undermining confidence across the wider financial system. We encourage continued coordination across the FCA, Bank of England, and HM Treasury to ensure coherence across prudential, conduct, and market surveillance frameworks, and welcome continued dialogue to support seamless implementation. source