Abstract: As stock market concentration has risen, regulatory limits on fund portfolio concentration have become increasingly binding, especially for large-cap growth funds. When funds approach these limits, they trim their largest holdings and reduce equity exposure. Funds perform worse when constrained. A constraint-based ownership measure predicts stock returns, particularly among the largest firms. These findings suggest that high market concentration can distort stock prices by limiting the ability of optimistic investors to scale their positions. Just like short-sale constraints can produce overpricing by limiting pessimistic investors' views, constraints on long positions can generate underpricing by suppressing optimists' views.
Abstract: The top 10% of carbon-emission-intensive firms (heavy emitters) typically account for over 90% of all Scope 1 emissions from U.S. public companies. Transition risk exposure varies systematically across factor portfolios: heavy emitters comprise up to 35% of the market capitalization of Value portfolios, compared to just 5% for Growth portfolios, a pattern that is robust to alternative definitions. Notably, the remaining stocks in the Value portfolio are, on average, as ‘green’ as the Growth stocks. Focusing on the heavy and light emitters within the Big Value portfolio (stocks with similar fundamentals, but different ‘browness’) we find that from 2006 until the 2015 Paris Agreement, these sub-portfolios exhibited similar average realized returns (both raw and risk-adjusted) and expected return proxies. From 2015 to 2020, heavy emitters underperformed and we find a significant rise in their expected returns proxies. Since the COVID period, however, the expected return proxies for heavy and light emitters have converged, suggesting no persistent incremental premium for transition risk. Furthermore, comparing Big Value and Big Growth portfolios restricted to light emitters (stocks with similar ‘greenness’, but different fundamentals) we find that Big Growth light emitters consistently earned higher risk-adjusted returns (alongside declining implied cost of capital), suggesting that climate concerns alone cannot explain the recentsuperior relative performance of Growth stocks.
Miao (Ben) Zhang, University of Southern California
Abstract: We postulate that our historical record has become adequately long and informative that newly arriving economic states often resemble historical states. Building on this insight, we develop a framework to predict future economic outcomes using the average of the realized outcomes that follow highly similar historical states. Using 210 million newspaper articles from 1815 to 2021, we identify historically similar months for each focal month and construct a predictor of aggregate U.S. stock returns, “SeenItRet”. SeenItRet strongly forecasts future market-wide stock returns up to two years ahead, with an annualized impact of 4–7% for a one standard deviation shift. Our framework is general and also predicts real economic outcomes, including recessions, inflation, and patenting activity. A virtue of our approach is its use of economic principles to reduce the high dimensionality of the underlying state space to an ex-ante measurable and intuitive unidimensional predictor. Our model performs better when historical states are more similar to the focal state, and it offers interpretable economic insights by highlighting the specific themes that drive its predictions.
Discussant: Kuntara Pukthuanthong, University of Missouri
Leonid Kogan, Massachusetts Institute of Technology
Jun Li, University of Texas-Dallas
Xiaotuo Qiao, Harbin Institute of Technology-Shenzhen
Abstract: The recent linear factor models (e.g., Fama and French (2015) and Hou, Xue, and Zhang (2015)) use total asset growth as the measure of investment, largely due to its stronger return predictive power than its components such as the long-term and current asset growths. We offer an explanation of the latter finding by extending the standard q theory of investment into a two-capital setup in which firms use both long-term and current asset as production inputs. We uncover a novel asset imbalance channel which creates negative comovement between current and long-term asset growths that are unrelated to discount rate. This comovement is muted in the total asset growth, giving rise to its stronger return prediction. Once controlling for this comovement, the return predictive power of current and long-term asset growths substantially improves. Furthermore, we document strong evidences for the model's prediction that the asset growth effects are more prominent among firms with low asset imbalance. Our results support the q theory based explanation for the asset growth effect.
