Practitioner’s Digest • Vol. 23, No. 4

The “Practitioner’s Digest” emphasizes the practical significance of manuscripts featured in the “Insights” and “Articles” sections of the journal. Readers who are interested in extracting the practical value of an article, or who are simply looking for a summary, may look to this section.

A Survey of Managed Fund Ratings and their Predictive Content

by: Russ Wermers

Fund rating systems can include quantitative and qualitative considerations, and can be either backward-looking “grade cards” or forward-performance forecasting systems (or both), with different investors using them for different purposes. This paper provides a detailed description of the most widely used publicly available rating systems today: the various ratings systems of Lipper and Morningstar. The more recently introduced Zacks fund rating system is also discussed in some depth. For each of these ratings systems, the predictive ability of their ratings is discussed. Finally, this paper offers some suggestions for future improvements that may lead to better predictions of future risk-adjusted fund returns.

The Risk Matters Hypothesis

by: Victor Haghani, James White, Elm Wealth, Vlad Ragulin

The investment landscape has undergone a seismic shift with the advent of commission-free trading and low-cost ETFs, empowering retail investors but also introducing new challenges. While fees have significantly decreased, hidden costs related to increased risk have emerged. This article explores Warren Buffett’s critique of “casino-like” investor behavior and introduces the “risk matters hypothesis” (RMH), a corollary to John Bogle’s “cost matters hypothesis” (CMH).The RMH posits that the average risk of active portfolios exceeds that of the market portfolio, leading to a lower return-to-risk ratio (Sharpe ratio) for concentrated stock portfolios than diversified index funds.

Yield Curve Forecasting using Machine Learning and Econometrics: A Comparative Analysis

by: Aman Singh, Tokunbo Ogunfunmi, Sanjiv Das

While machine learning has revolutionized many fields such as natural language processing (NLP) and computer vision, its impact on time-series forecasting is still widely disputed, especially in the finance domain. This paper compares forecasting performance on U.S. Treasury yield curve data across econometrics/time-series analysis, classical machine learning, and deep learning methods, using daily data over 47 years. The Treasury yield curve is important because it is widely used by every participant in the bond markets, which are larger than equity markets. We examine a variety of methods that have not been tested on yield curve forecasting, especially deep learning algorithms. The algorithms include the Autoregressive Integrated Moving Average (ARIMA) model and its extensions, naive benchmarks, ensemble methods, Recurrent Neural Networks (RNNs), and multiple transformers built for forecasting. ARIMA and naive econometric models outperform other models overall, except in one time block. Of the machine learning methods, TimeGPT, LGBM and RNNs perform the best. Furthermore, the paper explores whether stationary or nonstationary data are more appropriate as input to deep learning models.

Why Traditional Risk Models Overstate Factor Risk

by: Jose Menchero, Marty O’Brien, Anthony Lazanas

We show that traditional equity factorrisk modelstend to overstate factorrisk fortwo basic reasons. First, the standard industry practice of using regression weights proportional to the square root of market capitalization leads to excessively noisy estimates of factor returns, which in turn inflatesthe factor variance estimates. Second, traditional techniques fail to remove the idiosyncratic component from the variance estimates of the pure factor portfolios, which further inflates factor risk. In this paper, we describe solutions to both problems. In addition to yielding more accurate risk forecasts and better decomposition of portfolio risk, we show that our approach produces more efficient optimized portfolios and mitigates spurious correlations between factor returns and idiosyncratic returns.

The Rise of Tokenized Crypto ETFs (a.k.a. Tokenized Securitized Tokens)

by: Seoyoung Kim

"Case Studies” presents a case pertinent to contemporary issues and events in investment management. Insightful and provocative questions are posed at the end of each case to challenge the reader. Each case is an invitation to the critical thinking and pragmatic problem solving that are so fundamental to the practice of investment management.

Thinking with Machines the Brave New World of AI

by: Vasant Dhar • reviewed by: Andrew Li

Book Review, Mark Kritzman

Forthcoming 2Q2026: Option Return Anomalies

by: Sanjay K. Nawalkha and Xiaoyang Zhuo

This paper surveys empirical option return anomalies in both unhedged and delta-hedged strategies. We document persistent deviations from classical models, including the sharply negative returns on out-of-the-money puts, the negative performance of zero-beta straddles, and the large losses of delta-hedged portfolios documented in many studies. These patterns remain difficult to reconcile because most option-pricing models provide valuation formulas but not the formulas for finite-horizon expected returns or higher-order return moments. Equivalent Expectation Measures (EEMs) and Multiverse EEMs (MEEMs) supply closed-form expressions of these moments, enabling cleaner comparisons between model-implied and realized returns. We outline how these tools can be used to address longstanding anomalies and highlight future research opportunities for explaining cross-sectional predictability, volatility risk premia, and finite-horizon option returns.