Information, Security Selection, Portfolio Construction, and Active Equity Management

This article revisits the roles of information, security selection, and portfolio construction in active equity management within a Markowitz mean–variance framework. Using data from 2004 to 2024 across US and global equity markets, the study evaluates regression-based composite models that integrate valuation metrics, earnings forecasts, revisions, breadth, and momentum. Robust estimation methods, including weighted latent root regression (WLRR) and least angle regression (LAR), show that earnings-related variables, particularly composite forecast measures, consistently generate statistically significant return signals. Long-standing enhanced multifactor models deliver higher information coefficients and forecast efficacy than traditional valuation-only approaches. When implemented within mean–variance optimization, these models produce economically meaningful excess returns in MSCI ACWI and Emerging Markets portfolios, even after accounting for transaction costs and data-mining adjustments. Results further indicate that portfolio construction materially amplifies security selection skill, particularly with higher tracking error and active weights. The article also explores the possibility of improving forecasting results using alternative high-dimensional and machine learning methods and finds that robust regression approaches (WLRR and LAR) deliver the highest out-of-sample information coefficients. These findings, along with related published research, support the notion that disciplined, interpretable methods remained highly competitive tools for equity forecasting during much of the past 30 years. Finally, the analysis shows that expanding the investment universe to include a more comprehensive set of global stocks with earnings forecasts improves Sharpe and information ratios, underscoring the benefits of broad, information-rich opportunity sets and robust statistical modeling in active management.