CFA Institute

Adaptive learning in markets faces challenges that are less pronounced in other industries. In computer vision, a cat photographed in 2010 looks much the same in 2026. In markets, interest rate relationships from 2008 often do not apply in 2026. The system itself evolves in response to policy, incentives, and behavior. Financial AI therefore cannot simply learn from historical data. It must be trained across multiple market regimes, including crises and structural breaks. Even then, models can only reflect the past. They cannot anticipate unprecedented events such as central bank interventions that rewrite price logic overnight, geopolitical shocks that invalidate correlation structures, or liquidity crises that break long-standing relationships. Human oversight provides what AI lacks: the ability to recognize when the rules of the game have shifted, and when models trained on one regime encounter conditions they have never seen. This is not a temporary limitation that better algorithms will resolve. It is intrinsic to operating in systems where the future does not reliably resemble the past. source

“How did you decide which variables to include in your model, and which did you deliberately exclude?” The value of the question lies in what it reveals. You are not asking for a list of variables. You are asking whether the inclusion and exclusion decisions were grounded in economic reasoning rather than statistical fit alone. In my conversations with both allocators and managers, the responses fall into three distinct categories. A strong answer: The manager explains the economic mechanism behind each variable’s inclusion. Crucially, they discuss variables they excluded and why, showing that specification was a deliberate design choice. They distinguish between variables that drive their target factor and variables that result from it. The strongest managers trace a chain of economic causality: how macro forces project onto stock-level signals, and why the model reflects those causal chains rather than mining for correlations. A standard answer: The manager cites statistical criteria: information ratio, R-squared improvement, significance tests. This is current industry practice. It is not wrong, but it is incomplete. Statistical fit alone cannot distinguish between a variable that belongs in the model and one that introduces distortion while improving fit metrics. This is exactly the trap in the opening story. A concerning answer takes one of two forms: “We use all available variables and let the model select” signals structural vulnerability to factor mirages. On the other hand, “Our variable selection process is proprietary” may reflect legitimate IP protection. But a manager who cannot explain the reasoning behind their specification, even without disclosing specific variables, cannot demonstrate that the reasoning exists. source

Private credit faces a fundamental verification and information problem. Recent market developments have brought these issues into sharper focus. As liquidity tightens, and redemption pressures increase, private markets are undergoing what appears to be a structural test rather than a cyclical slowdown. Years of capital accumulation in semi-liquid structures are now colliding with more constrained liquidity conditions, exposing tensions between asset valuation and the ability to realize those valuations. The misalignment between fund managers and investors is evident in the persistent discounts seen in business development companies (BDCs) relative to reported net asset values (NAVs). These discounts reflect credit risk, liquidity, and market conditions, but they also signal that investors are applying a discount when they cannot fully interpret or validate model-based valuations against market pricing. These discounts reflect credit risk, liquidity, and market conditions, but also highlight the gap between model-based valuations and market pricing—particularly when investors attempt to infer value from non-traded assets. Private credit lacks comparable public market mechanisms—continuous price discovery, mandatory disclosures, and standardized auditing—that provide transparency and external validation. As a result, investors have limited ability to independently verify how valuations are constructed. Verification does not make valuation assumptions correct, but it does make them transparent, reproducible, and open to scrutiny. In a market where key inputs remain judgment-based, improving verifiability does not eliminate uncertainty, but it can reduce ambiguity around how valuations are constructed. This post examines how a combination of approaches, including statistical data screening, cryptographic proof, and stress testing, can improve different aspects of the verification process and strengthen confidence in private credit valuation. source

Default and term structure risk are key drivers of fixed income performance. Ignoring this information when comparing investment strategies can be misleading. This study proposes an algorithm derived from mimicking factor portfolios to neutralize risk differences, thereby distinguishing selection from market timing. For a well-diversified portfolio, this method allows for simultaneous management of multiple risk dimensions, ensuring the final portfolio remains investable. The algorithm can be modified in such a way as to guarantee positive weights, thus offering greater flexibility compared with conventional methods. We apply it to credit sector portfolios to neutralize discrepancies in duration times spread (DTS) and find notable differences between risk-adjusted and unadjusted performance. source

Valuation sits at the heart of strategic decision-making. At its core, it is the trade-off between today’s capital and uncertain future cash flows. Traditionally, companies forecast cash flows and discount them using the weighted average cost of capital (WACC), derived from the Capital Asset Pricing Model (CAPM). While widely accepted, this framework often fails to reflect the return investors are actually pricing into a company’s shares. Enter the market implied discount rate (MIDR) — the discount rate that equates expected future cash flows, based on consensus forecasts, to the current stock price. Unlike WACC, MIDR reflects the return investors are implicitly demanding, embedding their assessment of risk, credibility, and future performance. Deploying MIDR at scale requires solving practical challenges such as filling gaps in analyst models, validating assumptions, extending forecasts, and automating large volumes of inputs. Once addressed, however, MIDR becomes a reliable valuation metric that can be applied consistently across companies and timeframes. We examine where MIDR and WACC diverge, why intra-sector dispersion is substantial, and how management can use these insights to create value. Using S&P Capital IQ data, we analyzed every company in the S&P 500 over the last three years. The results show meaningful divergence between MIDR and WACC across sectors. source

All of this has slowed strategic M&A. In 2023, global M&A recorded its lowest level in a decade, underscoring the post-pandemic slowdown in dealmaking. Global PE exit count declined to 3,796 from the 2021 peak of 4,383. While off its highs, global PE dry powder is still around $2.5 trillion as of mid-2025, and the pressure to deploy capital remains high even as exit channels tighten. Several forces underpin the recent proliferation. Among them: a lack of traditional exit paths, a looming maturity wall, and a need for LPs to free up cash. First, rising financing costs have constrained leveraged buyouts and widened the bid-ask gap in M&A deals. Continuation funds allow managers to retain high-conviction assets and provide investors with liquidity options. The impending maturity wall is another factor. More than 50% of PE funds are now six years or older, with 1,607 funds set to wind down in 2025 or 2026. Continuation funds allow firms to extend value creation without forced sales. Finally, these funds align with investor demand for flexibility. LPs can exit for immediate liquidity or roll over to chase future upside. New investors gain exposure to proven assets with lower blind-pool risk. Continuation funds boast a 9% loss ratio compared to 19% for buyouts, offering better risk-adjusted returns. source

The headline monthly payroll estimates are produced by the BLS through the Establishment Survey, part of the Current Employment Statistics (CES) program. The survey collects responses from roughly 119,000 businesses and government agencies, covering about 622,000 individual worksites. Because these figures are derived from a sample rather than a full count of employment, they are subject to statistical estimation and revision. The closest approximation to a comprehensive count of jobs is the Quarterly Census of Employment and Wages (QCEW), which compiles administrative records from unemployment insurance filings and covers roughly 97% of US employment. For this reason, the QCEW serves as the benchmark to which nonfarm payrolls are periodically revised. Each year, the BLS benchmarks the CES data to the QCEW. In this process, the payroll level for the previous March is compared with the QCEW estimate, and the difference is distributed evenly across the prior 12 months, rather than applying it all at once (a linear “wedging” adjustment). The result is that the level of nonfarm payrolls is brought into alignment with QCEW data for March of the given benchmark year. In recent years, these benchmark revisions have been relatively large and persistently negative. Over the past three years alone, adjustments have reduced previously reported payroll employment by a combined 1.75 million jobs. source

The model produced several observable patterns in both market behavior and language structure. These findings illustrate how text-based signals align with subsequent yield curve movements. Market Structure and Curve Dynamics First, short-term volatility in the Brazilian fixed income market is higher than long-term volatility. This contrasts with traditional theory and suggests that, in emerging markets, investors react more strongly to short-term news and policy signals. Long-term instruments appear to trade with comparatively lower volatility, reflecting the dominance of institutional investors at longer maturities. In addition, 84% of daily yield curve movements fall into four of the eleven standard configurations identified in the literature, with parallel upward and parallel downward shifts among the most frequent (also confirming this short term volatility flavor). This concentration highlights the importance of correctly classifying a small set of dominant curve dynamics. Extracting Signal from Language To prepare the text data, common words such as “committee,” “scenario,” “billions,” and “prices” were removed as stop words, as they do not contribute to classification. Word frequencies were then mapped for each yield curve movement category, allowing comparison of language patterns across different curve configurations. Seasonality in Curve Movements When examining the language associated with specific movements, a seasonal pattern emerged. For example, bear flattening movements were frequently associated with references to August, September, and October, while bull flattening movements were more often linked to January, February, and March. A chi-squared test provided statistical evidence of seasonality across several yield curve movements. source