Traditional portfolio optimization typically relies on a single fitted return distribution, thus ignores non-normality and uncertainty across distributional states. Mean–variance–ambiguity (MVA) models, motivated by the smooth-ambiguity preferences of Klibanoff, Marinacci, and Mukerji (2005) and the KMM-to-MVA Arrow–Pratt reduction of Maccheroni, Marinacci, and Ruffino (2013), extend conventional risk penalties to account for distributional uncertainty, but they missed how state-conditional return distributions affect portfolio optimization stability. We develop Deep Mean–Variance–Ambiguity (Deep MVA) to resolve this gap by learning state-conditional return distributions and embedding the resulting conditional information loss into the ambiguity penalty. The resulting local certainty equivalent combines reduced-law mean–variance risk with a quadratic penalty for second-order disagreement. Conditional-mixture state resolution yields a Deep ambiguity covariance equal to the Traditional ambiguity covariance plus a positive-semidefinite state-feature increment. This increment is the conditional L2 loss from averaging state-specific distributions before optimization. On compact convex feasible sets, we show that the state-resolving program has stronger ambiguity-relevant curvature, lower mean-input sensitivity, and a unique global solution under restricted positive curvature or a common ridge; its projected iterations converge linearly. An LSTM-MDN learns state-conditional distribution densities from 60-minute bars, and Probability and VoV scores enter weekly MVA, with GARCH_Resid as a comparator. Tests on the SSE 50, CSI 300, and CSI 500 universes from 2017–2025 find statistically significant Sharpe out-of-sample improvements over Traditional MVA and MVO across all three universes and ambiguity specifications.

Households form beliefs by recalling past experiences, but recall is context dependent. Variation in contexts alone can therefore generate non-fundamental disagreement. A stylized model à la Wachter and Kahana (2024) illustrates this cognitive source of disagreement. When combined with salience-based frictions in memory encoding, context-dependent recall also generates testable predictions for belief skewness. Using weather as a contextual cue that triggers the recall of past shopping experiences, I construct a measure of memory-based household inflation disagreement in the U.S. and show that it explains survey-based disagreement, with stronger effects in unusual contexts, and predicts greater belief skewness. The memory effect is distinct from experience and mood effects and generalizes across countries and outcome domains. Finally, memory-based disagreement weakens monetary policy transmission by attenuating intended consumption responses.

We investigate how the loss of informative salient signals in financial markets influences stock return volatility, using the 2024 intraday disclosure reform of the mainland China-Hong Kong Stock Connect program as a natural experiment. The reform eliminated the real-time disclosure of northbound capital (NC) flows on trading platforms, rendering NC trading information invisible to Chinese investors during market hours. We find that the removal of NC signals induces increased investor belief dispersion and intensifies informed trading, thereby amplifying intraday volatility in NC-eligible stocks. Moreover, this effect is more pronounced for stocks with higher investor attention, indicating that attentive investors suffer stronger anchor loss when NC signals disappear. In contrast, lottery-type stocks and stocks with alternative NC trading clues exhibit weaker volatility responses, since the presence of strong alternative signals reduces the effect of NC signal loss. These findings highlight the informational role of insightful salient signals in stabilizing stock returns.

Traditional information asymmetry theories suggest that greater corporate transparency should reduce investor disagreement. Using Chinese mutual fund holdings, we document the opposite pattern: transparency amplifies disagreement among institutional investors. Mechanism tests show that transparency discourages herding while intensifying private information acquisition among fund managers. The effect is stronger for growth-oriented and high-skill funds, and during periods of elevated market sentiment, and among firms with lower credibility, excessive disclosure frequency, and greater investor attention. Further analysis indicates that this transparency-induced disagreement stems from informed trading rather than noise, thereby enhancing price informativeness and market efficiency. Overall, the evidence reveals the dual nature of transparency as both an informational input and a behavioral catalyst that increases disagreement in financial markets.

The role of institutional investors in the development of Environmental, Social, and Governance (ESG) criteria lacks consensus in the academic community. This study utilizes a quasi-natural experiment involving Chinese mutual funds that have signed the United Nations Principles for Responsible Investment (UNPRI) to investigate whether institutional Investors’ ESG investment commitments can significantly reduce ESG rating disagreement among the companies in their portfolios. We first find that companies held by ESG commitment institutional Investors exhibit less disagreement in ESG rating compared to those held by Non-ESG commitment institutional Investors. we then show that institutional Investor’ ESG investment commitment influence ESG rating disagreement by enhancing the quality of ESG disclosure and attracting external ESG attention. We further discover that institutional investors’ ESG investment commitments significantly mitigates the ESG rating disagreement among domestic ESG rating agencies and firms with a higher level of corporate governance.

In this paper, we present a rational expectation equilibrium model in which fundamental and ESG traders hold heterogeneous valuations towards the risky asset. Trading occurs based on private information and price signal which is determined by a weighted combination of these diverse valuations. Our findings indicate that higher level of ESG rating disagreement increases ESG information uncertainty, thereby reducing trading intensity among ESG traders and attenuating the price informativeness about ESG. We further discover that allowing fundamental traders access to ESG information increases the coordination possibilities in the financial market, leading to multiple equilibria exhibiting characteristics of strategic substitutability and complementarity. Additionally, through measuring the ESG rating disparities among four prominent agencies in China, we deduce that ESG rating disagreement negatively impacts price informativeness by decreasing stock illiquidity.

We leverage machine learning to introduce belief dispersion measures to distinguish different opinion (DO) and information asymmetry (IA). Our measures align with the human-based measure and relate to economic outcomes in a manner consistent with theoretical prediction: DO positively relates to trading volume and negatively linked to bid-ask spread, whereas IA shows the opposite effects. Moreover, IA negatively predicts the cross-section of stock returns, while DO positively predicts returns for underpriced stocks and negatively for overpriced ones. Our findings reconcile conflicting disagree-return relations in the literature and are consistent with Atmaz and Basak (2018)’s model. We also show that the return predictability of DO and IA stems from their unique economic rationales, underscoring that components of disagreement can influence market equilibrium via distinct mechanisms.

This study explores how Chinese mutual fund managers’ degrees of disagreement (DOD) on stock market returns affect investor capital allocation decisions using a novel text-based measure of expectations in fund disclosures. In the time series, the DOD neg-atively predicts market returns. Cross-sectional results show that investors correctly perceive the DOD as an overpricing signal and discount fund performance accordingly. Flow-performance sensitivity (FPS) is diminished during high dispersion periods. The ef-fect is stronger for outperforming funds and funds with substantial investments in bubble and high-beta stocks, but weaker for skilled funds. We also discuss ffnancial sophisti-cation of investors and provide evidence that our results are not contingent upon such sophistication.

Based on institutions' general role and the specialty of motivated investors' relatively larger stake, we examine whether ownership by motivated investors is associated with the focal firm's ESG rating disagreement in China. Our results suggest that ownership by motivated investors can decrease the focal firm's ESG rating disagreement. That relationship is strengthened by a better internal or external information environment. What's more, ownership by motivated investors can increase the quality of ESG disclosure and the level of consensus ESG rating. ESG rating disagreement increases stock return volatility and price synchronicity, while motivated investors can mitigate those negative effects. Our results confirm that motivated investors have greater incentive and capability to discipline managers and influence corporate policies and actions even in an emerging market with weak investor protection and the popularity of exploration by ultimate controllers. That would shed valuable insights into the high-quality development of other emerging markets, especially those in south-east Asian.

We propose a novel measure, DOT, to capture belief divergence on extreme tail events in stock returns. Defined as the standard deviation of expected probability forecasts generated by distinct information processing functions and neural network models, DOT exhibits significant predictive power for future stock returns. A value-weighted (equal-weighted) long-short portfolio based on DOT yields an average return of -1.07% (-0.98%) per month. Furthermore, we document novel evidence supporting a risk-sharing channel underlying the negative relation between DOT and the equity premium following extreme negative shocks. Finally, our findings are also in line with a mispricing channel in normal periods.