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  5. New Perspectives in Forecasting the Cross Section of Returns
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New Perspectives in Forecasting the Cross Section of Returns

Paper Session

Saturday, Jan. 4, 2025 8:00 AM - 10:00 AM (PST)

San Francisco Marriott Marquis, Yerba Buena Salon 14 & 15
Hosted By: American Finance Association
  • Danling Jiang, Stony Brook University

Mosaics of Predictability

Lin William Cong
,
Cornell University
Guanhao Feng
,
City University of Hong Kong
Jingyu He
,
City University of Hong Kong
Yuanzhi Wang
,
City University of Hong Kong

Abstract

Existing studies often regard return predictability as an attribute of predictors or models. This paper argues that return predictability is an unobserved yet inherent asset characteristic linked to expected returns, varying across stocks and over time. We propose a novel tree-based clustering method to measure heterogeneous return predictability by grouping asset-return observations with similar levels of predictability. The resulting clusters are characterized using high-dimensional firm characteristics and aggregate predictors. Our empirical analysis reveals significant patterns of heterogeneous return predictability in individual U.S. stocks. First, asset clusters with low trading volumes, high earnings-to-price ratios, and high unexpected earnings exhibit the highest predictability. Second, predictability declines sharply when the dividend yield is low, while it peaks during periods of high dividend yield and low default yield. Furthermore, we identify a new predictability anomaly: highly predictable long-only portfolios generate unexplained alphas of about 1% across various factor models over the past two decades, while a long-short portfolio based on predictability yields even higher alphas.

Empirical Asset Pricing with Probability Forecasts

Songrun He
,
Washington University-St. Louis
Linying Lyu
,
Zhejiang University
Guofu Zhou
,
Washington University-St. Louis

Abstract

We study probability forecasts in the context of cross-sectional asset pricing with a large number of firm characteristics. Empirically, we find that a simple probability forecast model can surprisingly perform as well as a sophisticated probability forecast model, delivering long-short portfolios whose Sharpe ratios are comparable to those of widely used return forecasts. Moreover, we show that combining probability forecasts with return forecasts yields superior portfolio performance versus using each type of forecast individually. Additionally, probability forecasts augment existing factor models and significantly improve tail risk forecasts. These results underscore the unique and valuable insights probability forecasts offer in understanding the cross-section of stock returns.

Narrative Attention Pricing

Hojoon Lee
,
Boston College
Xiaoxia Lou
,
University of Delaware
Gideon Ozik
,
EDHEC Business School
Ronnie Sadka
,
Boston College

Abstract

This paper demonstrates that economic narratives significantly price the cross-section of stocks. Using a vast dataset of more than 150k digital media sources since 2013, roughly 350 narratives are quantified, and corresponding narrative-mimicking, long-short portfolios are constructed using stock return narrative betas. Narrative-mimicking portfolios of recently trending narratives outperform those of descending attention by about 7% annually, controlling for standard risk factors. The cross-sectional narrative-beta-pricing is independent of past return and is neither significantly impacted by narrative coverage at the stock level nor earnings announcements. The results suggest that while investors respond to short-run narrative shocks as measured by narrative betas, they under-react to long-run narrative trends, manifesting narrative momentum returns.

Moving Targets

Lauren Cohen
,
Harvard University
Quoc Nguyen
,
DePaul University

Abstract

We find that managers strategically shift targets in their communications with investors and markets. Using the complete history of the earnings conference call transcripts by U.S. corporations from 2006 – 2020, we employ natural language processing techniques to analyze conference calls and find that managers choose and re-choose targets to ensure they clear their endogenously chosen hurdle. For instance, if they have seen same-store sales growth for 16 consecutive quarters, they will mention this intensively. However, when they encounter a quarter without same-store sales growth, they shift the conversation to another metric, such as cost savings or strategic R&D. When managers change the target, this predicts significant negative returns and realizations for the firm in question. In particular, in the quarter following a moved target, firms underperform by up to 99 basis points per month (t-stat = 4.38) in value-weighted monthly abnormal return (alpha). Moreover, we find that the effects are significantly stronger with more complex targets, non-financial targets, and the most persistent targets.

Discussant(s)
David Rapach
,
Federal Reserve Bank of Atlanta
Allaudeen Hameed
,
National University of Singapore
Kuntara Pukthuanthong
,
University of Missouri
Lin Peng
,
CUNY-Baruch College
JEL Classifications
  • G1 - General Financial Markets