We study how mutual fund managers update beliefs and whether such updating reflects managerial skill. Using regulator-mandated, forward-looking investment outlook disclosures from Chinese mutual funds, we construct a novel measure of belief updating intensity based on year-over-year changes in stated beliefs, and further decompose these updates into macro and micro themes. We show that belief updating is strongly state contingent. During recessions, funds with more intensive updating shift their outlooks toward macroeconomic conditions, reduce procyclical stock exposures, and scale back on-site company visits, while the opposite pattern emerges in expansions. Belief-updating intensity positively predicts future performance, and this payoff is strongest when managers reweight beliefs in ways that align with prevailing economic conditions, indicating that belief updating captures an ex ante dimension of managerial skill. Consistent with a signaling interpretation, investors allocate more capital to funds with stronger belief updating, particularly when past returns are less informative.

Investors and forecasters often extrapolate from past returns, but whether this reffects behavioral bias or efficient information processing remains unclear. We address this questionby inferring Chinese mutual fund managers’ market expectations from textual analysis oftheir commentaries and linking them to portfolio choices and performance. Extrapola-tion is state-dependent: it is stronger when growth is above trend and idiosyncratic riskis relatively more important. It is associated with weaker market timing and strongerstock picking, leaving overall performance unchanged. Our findings support a rational-inattention model of expectation formation, in which managers shift scarce attentionbetween aggregate and stock-speciffc information as the relative importance of differentrisks change.

This paper investigates the high-frequency dynamics of macroeconomic expectations and disagreement among professional forecasters. We propose a novel mixed-frequency estimation approach that integrates daily asset returns with quarterly expectation data from the Survey of Professional Forecasters. Our findings indicate that consensus forecasts are updated efficiently according to Bayes' rule, independent of prior forecasts. By employing "representative forecasters" as proxies for real-world agents, we derive a simple yet intuitive evolution equation for disagreement, revealing that changes in disagreement are primarily driven by different interpretations of new information. Furthermore, we reconstruct daily series of expectations and disagreement concerning macroeconomic growth, achieving impressive R2 values of 93.3% and 84.5% against the true quarterly series.