CFA Institute

The rise of ensemble learning marks a turning point in quantitative finance. It offers a rare combination of predictive accuracy, scalability, and interpretability, making it well-suited to the challenges investment leaders face today. CIOs, portfolio managers, data science heads, and risk leaders can use ensembles to sharpen forecasts, build more resilient portfolios, and defend decisions in front of the most demanding stakeholders. The chapter suggests that in the future ensembles will grow more relevant as data complexity increases and governance pressures rise. By blending domain expertise with ensemble-driven insights, investment organizations can harness the power of modern machine learning while preserving the transparency and trust that capital markets demand. Generative AI and large language models (LLMs) will accelerate feature discovery, code generation, and documentation; they will also be ensembled. Yet investment use cases will continue to reward methods that combine predictive strength with accountability. The durable edge, according to the chapter, lies in hybrid frameworks that blend domain knowledge, transparent linear components, and nonlinear ensemble learners — governed by rigorous validation and explained in plain language. For teams navigating scarce alpha, fragmented data, and rising oversight, ensembles are not just another tool, they are the operating system for modern investment modeling. This summary is based on the CFA Institute Research Foundation and CFA Institute Research and Policy Center monograph “Ensemble Learning in Investment: An Overview,” by Alireza Yazdani, PhD, which explores how ensemble learning enhances financial forecasting and risk management. source

Industries worldwide are evolving rapidly amid new technologies and policy shifts, while markets are more interconnected than ever. Information travels almost instantaneously across global networks, meaning a shock in one market can ripple quickly through others. The investment industry must continually adapt to changing economic and market environments, yet traditional financial models — built on assumptions of equilibrium and rational actors — often struggle to capture the unpredictable, networked, and nonlinear behaviors observed in financial markets. This report reconsiders how we understand financial markets, framing them as complex systems and offering alternative approaches to traditional financial models. By applying methods from complex systems sciences, it equips financial professionals with new tools for systemic risk analysis, portfolio management, and system-level investing. Techniques such as agent-based modeling and network theory can be used to understand and capture complex market phenomena such as emergent behavior, nonlinearity, feedback loops, and structural resilience. For portfolio managers and risk analysts, adopting a systems perspective means moving beyond normal distributions and equilibrium-based models to capture investment complexity and better inform scenario planning, portfolio optimization, and risk management. For regulators, it means leveraging new models to strengthen systemic risk oversight and macroprudential policies. source

In this upcoming episode of Conversations with Frank Fabozzi, CFA, Mark Anson, CFA, they discuss how institutional investors are positioning portfolios in a less-synchronized global economy.  Key Talking Points:  Private credit’s evolution from shadow banking to mainstream allocation Geographic diversification in a less-synchronized global economy Applying the equity risk premium as a valuation discipline Allocating to artificial intelligence across platforms, data centers, and power   source

Laurens Swinkels, Jan Anton van Zanten, Bruno Rein, and Rikkert Scholten 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

Conventional growth indices suffer from two important shortcomings. First, stocks that are anti-value (very expensive) are not necessarily growth stocks. The decision to include a stock in a growth index should be based on fundamental growth measures, such as growth in sales, profits, or R&D spending, rather than price-based measures. Second, when these indices are weighted by objective measures of growth, rather than by market value, performance markedly improves. Overpaying for growth is unhelpful. We also assert that some stocks with poor growth prospects and unattractive valuations may have no place in either value or growth indices. source

SVMs remain a valuable, underappreciated tool in the age of artificial intelligence (AI) hype. Although neural networks and deep learning dominate headlines, SVMs continue to offer a practical, mathematically sound, and interpretable way to classify, predict, and optimize in complex financial environments. For practitioners, the appeal is clear: SVMs deliver robust results, handle nonlinear data well, and work without the massive infrastructure that more complex AI methods demand. Whether screening stocks, predicting market moves, assessing credit risk, or optimizing portfolios, SVMs can be an efficient and effective ally. This summary is based on the CFA Institute Research Foundation and CFA Institute Research and Policy Center chapter “Support Vector Machines,” by Maxim Golts, PhD, which demonstrates how SVMs effectively classify, predict, and optimize data in financial markets while maintaining accuracy and minimizing overfitting. source

These are approximations, but they tie the exit multiple to the assumptions about long-run growth (g), WACC, ROIC, margins and taxes. Valuers should then cross-check their exit multiple assumption against current medians, long-run sector bands, and transaction evidence. If comps diverge, valuers can explain why; differences in growth durability, capital intensity, or risk. In reality, the selection of the multiple is based on the median or average of current valuations at the time of the analysis, or the average of the median over the last five to 10 years. But is this correct? Well, as always—it depends. It could be. Data teaches us something important that we should incorporate into our thinking when selecting the exit multiple. For exit EBITDA multiples, Michael Mauboussin found that expected EBITDA growth and the spread between ROIC and WACC have a significant impact on valuation for unprofitable companies. However, determining ROIC or exit EBITDA margin is difficult when companies are not yet profitable or in a stable phase. For this reason, revenue growth and gross margin are often used instead. source