Working Papers

“From Numbers to Words: Breaking Down Institutional Beliefs”with Andrea Andolfatto

Conferences: FIRS 2026, WFA 2026 (Scheduled), NBER SI Asset Pricing 2026 (Scheduled), EFA 2026 (Scheduled)

Abstract: We examine how large asset managers form and justify long-horizon beliefs using their Capital Market Assumptions (CMAs), combining the numerical building blocks of return expectations with disclosed modeling assumptions and causal narratives. Valuation-change and growth components explain 77% of cross-sectional dispersion and are most strongly linked to equity allocations; valuation-change expectations are countercyclical and growth expectations procyclical on average, generating countercyclical return expectations overall but with substantial heterogeneity across managers. Disclosed modeling assumptions matter too: mean-reversion and historical calibration predict systematic deviations from peer consensus. We develop a new large-language-model methodology to extract directed, signed causal networks from CMA narratives: managers with structurally more complex networks underreact to positive earnings-news sentiment, and topic attention has opposing effects---attention to valuation-change mechanisms also predicts underreaction, while attention to dividend-yield and downturn topics predicts overreaction. Comparisons with N-CSR shareholder letters show that CMA narratives reflect persistent institution-specific investment views also visible in affiliated fund communications. Finally, while first moments display forward-looking reasoning, volatility and correlation forecasts exhibit substantially less variation and disagreement, and remain closely linked to past realizations.

Award: The Brattle Group Ph.D. Candidate Awards For Outstanding Research, 2026 WFA.

“Bias and Predictability in Analysts’ Beliefs”

Conferences: SFS Cavalcade NA 2026

Abstract: I study the relationship between sell-side analysts’ forecast bias and stock returns by comparing three forecast families—price targets (PTG), earnings per share (EPS), and long-term growth (LTG)—to ex-ante machine-learning (ML) benchmarks. Bias in return expectations co-moves positively with bias in cash-flow expectations, suggesting a common  source of belief distortions. Analysts’ deviations from ML benchmarks  predict returns in a systematic and nonlinear manner. Across horizons, forecast bias predicts returns positively in the short term but negatively in the long term, with LTG-based return-predictability regressions exhibiting similar patterns.  Across the distribution of bias, both extreme optimism and extreme pessimism are followed by higher subsequent returns relative to moderately biased forecasts, generating a robust U-shaped relationship between bias and returns. I further document pervasive conformism to the consensus forecast and show that anchoring provides a powerful reduced-form model of analysts’ belief formation. An asset-pricing model with asymmetric information and positively skewed fundamental shocks accounts for these dynamics: analysts anchor on consensus and misperceive the informativeness of signals, producing the observed bias-return relation.

“The Term Structure of Return Expectations”with Cameron Peng

Conferences: EFA 2025

Abstract: Using a long-running survey of U.S. investors, we examine subjective return expectations for the aggregate stock market across horizons from one month to ten years. Short-term expectations are extrapolative, whereas forward return expectations are contrarian. The term structure of return expectations is upward-sloping in bad times and relatively flat in good times. We introduce perceived mispricing as a complementary belief measure and show that it helps explain the dynamics of the term structure: it is negatively correlated with short-term return expectations but positively correlated with forward return expectations. Examining the information content of these belief measures, and how they are reflected in free-text investment rationales and related to both stated and actual trading decisions, we show that both long-term expectations and perceived mispricing are central to investment behavior.

“Time-Series and Cross-Section of Risk Premia Expectations: Evidence from Financial Analysts”

2nd Round R&R, Journal of Financial Economics

Abstract: I show that—despite potential bias—sell-side analysts’ return expectations are the closest to rational, most informative, and relevant among survey forecasts. They efficiently incorporate available information, exhibit less predictable forecast errors, and align with a multi-factor model in the cross-section. Out-of-sample tests highlight their predictive ability, and a machine learning benchmark trained to predict single-stock outcomes assigns significant importance to their beliefs. Analysts’ expectations are strongly positively correlated with mutual fund institutional flows and allocations, and intraday returns, underscoring their relevance for institutional investment decisions. A multi-asset, heterogeneous-agent, two-period model identifies transmission mechanisms that account for these empirical findings.

Award: John A. Doukas Doctoral Best Paper Award, 2023 EFMA.

“Global Fund Managers' Beliefs, Perceived Mispricing, and Asset Allocation”with Cameron Peng

Abstract: We investigate global fund managers' subjective beliefs about stock market mispricing using a newly compiled dataset from a 30-year survey. Fund managers perceive the market as underpriced when retail investor sentiment is pessimistic, markets have recently declined, risks to market stability are elevated, and expectations for fundamental growth are low. Perceptions of underpricing correlate weakly but positively with equity risk premium expectations from asset managers' long-term Capital Market Assumptions. Perceived mispricing significantly predicts realized excess returns in the aggregate stock market over a three-year horizon, even after controlling for established predictive factors. Perceived mispricing also strongly correlates with fund managers' future investment plans, as well as with future inflows into equity mutual funds.

“On the Dynamics of Subjective Forecasts”

Abstract: Many macroeconomic and financial survey forecasts are reported in levels rather than in growth rates. I analyze forecasts in levels to better understand belief dynamics. I find widespread cointegration relationships between the macro-financial forecasts in levels and the most recent realized historical observation of the same variable. In the long run this implies a heuristic rule which economists rely on when forming their forecasts. In the short run the corresponding conditional error-correction models (ECM) indicate that forecasters’ beliefs display both extrapolative behavior and reversal towards the long-run equilibrium heuristic. I further show how cointegration relationships can be used in predictability tests and Coibion & Gorodnichenko (2015) tests of information rigidity to avoid potential distortions induced by normalizations.

In Preparation

“Trading in Prediction Markets: Evidence from Micro-Level Data” with Cameron Cohen and Marco Sammon