CFA Institute

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

Other recent work documents systematic biases in LLM-based financial analysis, including foreign bias in cross-border predictions (Cao, Wang, and Xiang, 2025) and sector and size biases in investment recommendations (Choi, Lopez-Lira, and Lee, 2025). Building on this emerging literature, four potential channels are especially relevant for investment practitioners: 1. Size bias: Large firms receive more analyst coverage and media attention, therefore LLMs have more textual information about them, which can translate into more confident and often more optimistic forecasts. Smaller firms, by contrast, may be treated conservatively simply because less information exists in the training data. 2. Sector bias: Technology and financial stocks dominate business news and online discussions. If AI models internalize this optimism, they may systematically assign higher expected returns or more favorable recommendations to these sectors, regardless of valuation or cycle risk. 3. Volume bias: Highly liquid stocks generate more trading commentary, news flow, and price discussion. AI models may implicitly prefer these names because they appear more frequently in training data. 4. Attention bias: Stocks with strong social media presence or high search activity tend to attract disproportionate investor attention. AI models trained on internet content may inherit this hype effect, reinforcing popularity rather than fundamentals. These biases matter because they can distort both idea generation and risk allocation. If AI tools overweight familiar names, investors may unknowingly reduce diversification and overlook under-researched opportunities. source

The voluntary CFA Institute Inclusion Code (UK) provides investment industry organizations with a structured framework for supporting executive leaders, investment professionals, Inclusion specialists, and HR professionals in crafting impactful and measurable strategies to enable better people management. source

Cognitive abilities describe how humans collect, process, and interpret information such as attention, memory, pattern recognition, logical reasoning, and quantitative analysis. Non-cognitive abilities include traits such as motivation, perseverance, communication, ethical judgment, and the capacity to act under uncertainty. The framework below categorizes these capabilities across two dimensions: cognitive versus non-cognitive, and basic versus advanced. Basic cognitive capabilities (QIII: third quadrant), such as memorization, structured record-keeping, and routine calculation, have long been automated. Their automation marked the first wave of technological compression. Advanced cognitive capabilities (QII), including high-dimensional modeling, statistical inference, and complex analytical verification, are increasingly within the reach of AI systems. As these tools scale across firms, analytical differentiation narrows. By contrast, advanced non-cognitive capabilities (QI), such as setting goals under uncertainty, exercising ethical judgment, and creating or obtaining first-order information, remain less amenable to standardization. These capabilities influence how organizations interpret ambiguous signals, coordinate decisions, and allocate capital when data is incomplete. The implication is organizational rather than purely technical. When analytical tools become widely accessible, sustainable advantage depends less on computational sophistication and more on how firms structure teams, cultivate judgment, and design decision processes that integrate technology with human insight. source

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Fangfang Wang, Florina Silaghi, Steven Ongena, and Miguel García-Cestona CDS spreads rise sharply after ESG downgrades — most notably within the social pillar and among financially constrained firms — while upgrades show little effect. Positive ESG sentiment and transparency can mitigate these adverse credit impacts. source

Institutional allocators rely on managed futures strategies for diversification and drawdown control, yet often misunderstand how risk is actually taken inside these allocations. They frequently lack clarity on which trend horizons drive performance, how similar managers truly are to one another and to benchmarks, and how differences in horizon mix shape behavior during periods of market stress. By decomposing CTA managed futures returns into a small set of distinct trend horizons (fast, medium, and slow), this post shows that much of the variation across managers and benchmarks reflects differences in horizon mix rather than fundamentally different strategies. Framing managed futures allocations in this way allows investors to better diagnose overlap, benchmark more precisely, and assess whether their exposure is aligned with its intended role in the portfolio. The analysis that follows is necessarily technical, introducing a horizon-based framework that decomposes CTA returns into a limited set of systematic building blocks. While the mechanics are described in detail, the objective is practical: to provide a clearer, more transparent way to interpret managed futures behavior and to link observed outcomes to explicit, governable risk choices. source