Less informative strategic advice can produce more informative prices. We study an informed adviser who both trades on his own account and receives compensation linked to the post-report price. Holding private information fixed, we show how promotion can restrain manipulation motivated by trading profits. At sufficiently high reputation, raising compensation enough to eliminate false bad news used to buy cheaply improves market price informativeness in every equilibrium, even though the strategic adviser’s report becomes uninformative. Under further sufficient conditions, always-favourable strategic reporting yields the most informative current prices among equilibria at every interior reputation. We also derive analytical sufficient conditions under which higher reputation lowers the adviser’s expected payoff and worsens prices within the same equilibrium. Financial influencers motivate the joint incentives: a reward for higher prices can counteract the incentive to depress prices before buying, while greater credibility need not benefit its holder.

I distinguish renminbi movements against the dollar from renminbi-relative repricing around U.S. tariff announcements. Across 26 policy groups, offshore renminbi (CNH) minus a fixed four-currency basket averages 35.077 aligned log basis points. Official reference rates corroborate the relative pattern; pre-event factor adjustment leaves a positive pooled own-CNH residual. Earlier disclosures change the later-period own-CNH sign, while early-to-late attenuation persists. Own-currency event-minus-lead contrasts remain imprecise, and macro-news and dependence screens weaken precision. The evidence supports an RMB-relative association, not a uniformly timed causal depreciation response or verified executable offshore prices.

We construct a text-based measure of war-related diplomatic sentiment from 154,185 foreignministry communications across the 15 largest world economies. The daily index tracks military escalations and ceasefires, varies across countries, and predicts newspaper-based geopolitical risk more than the reverse. Adverse Chinese rhetoric foreshadows stronger southbound reallocation into Hong Kong equities and weaker Stock Connect flows; a one-unit decline shifts daily flows by $42.4 million towards outflows, operating through a relative-price channel widening the AH premium rather than onshore declines. In monthly cross-country analyses, only the U.S. shows safe-haven behavior; adverse rhetoric raises Chinese and U.S. trading volume and U.S. volatility.

We investigate whether overnight news sentiment adds predictive value for overnight returns. We focus on the CSI300 Index, whose ETFs are widely held by Chinese retail investors. Sentiment indi-cators are constructed from minute-level overnight news using a fine-tuned RoBERTa model. These indicators are combined with market-based variables to predict overnight returns via regression and machine learning. Results show that while the sentiment alone has predictive value, its incremental contribution disappears once the A50 overnight return is included.

We document “foreign bias” in AI financial predictions, reversing the classic home bias. U.S.-based ChatGPT is systematically more optimistic than China-based DeepSeek about Chinese firms—in price predictions and directional forecasts—yet significantly less accurate. Evidence supports an information-availability mechanism: bias is strongest when U.S. media coverage of Chinese firms is limited and attenuates for cross-listed firms. Crucially, injecting Chinese news eliminates the prediction gap. Both models produce similar forecasts for U.S. firms, consistent with broader worldwide coverage. LLMs trained in different information environments can create divergent signals, with implications for investors and policymakers as AI increasingly intermediates global markets.

Can fully agentic AI nowcast stock returns? We deploy a state-of-the-art Large Language Model to evaluate the attractiveness of each Russell 1000 stock each trading day, starting in April 2025 when AI web interfaces enabled real-time search. Our data contribution is unique along three dimensions. First, the nowcasting framework is completely out-of-sample and free of look-ahead bias by construction: predictions are collected at the current edge of time, ensuring the AI has no knowledge of future outcomes. Second, this temporal design is irreproducible once the information environment passes. Third, our framework is fully agentic: we do not feed the model curated news or disclosures; it autonomously searches the web, filters sources, and synthesises information into quantitative predictions. We find that AI possesses genuine stock-selection ability, but that its predictive power is concentrated in identifying future winners. A daily value-weighted portfolio of the 20 highestranked stocks earns a Fama-French five-factor plus momentum alpha of 19.4 basis points and an annualised Sharpe ratio of 2.68 over April 2025–March 2026. The same portfolio accumulates roughly 49.0% cumulative return, versus 21.2% for the Russell 1000 benchmark. The strategy is economically implementable: the average bid-ask spread of the daily Top-20 portfolio is 1.79 basis points, less than 10% of gross daily alpha. However, the signal remains asymmetric. Bottom-ranked portfolios generally exhibit alphas close to zero, while the strongest predictive content sits in the extreme top ranks. Delayed-entry tests further show that predictability does not vanish after a single day; rather, the signal remains positive over a broad window of subsequent entry dates, consistent with slow information diffusion rather than a fleeting overnight anomaly.

We document a novel "news extrapolation" behavior among investors, which distorts the market reaction to corporate news. Specifically, investors tend to extrapolate the value of past news in the immediate reaction to the newly arrived news. News extrapolation generates a biased price reaction to news, which is completely reversed afterwards. Furthermore, the tendency of news extrapolation is related to the recency, consistency, and value uncertainty of news. Investors extrapolate not only from news of the same category but also from news of different categories. By analyzing the trading behavior and sentiment of different investor groups, we find that retail investors tend to be news extrapolators, while institutional investors trade against the news extrapolators.

Periods of market crisis are often accompanied by heightened fear and information overload, which can induce information avoidance behaviors such as the ostrich effect. While prior research has documented investors’ tendency to avoid unfavorable information, little is known about how different information sources—and trust in those sources—jointly shape such behavior under extreme uncertainty. Drawing on Granular Interaction Thinking Theory (GITT) and employing Bayesian Mindsponge Framework (BMF) analytics, this study examines how investors’ regular securities-related information sources is associated with the ostrich effect during the 2022 market downturn in China, and how these associations are conditioned by trust. Using survey data from 1,451 Chinese individual stock investors, we model investors’ recalled frequency of temporarily disengaging from stock investing as an indicator of information avoidance. The results show that regularly consulting professional sources, financial newspapers, and online forums is associated with information avoidance, whereas reliance on personal relationships and company disclosures is not. Importantly, trust moderates these relationships in distinct ways. Higher trust in professional sources is associated with reduced information avoidance, while higher trust in financial newspapers and online forums amplifies avoidance behavior. Among all sources, the interaction between trust and information referral is strongest for financial newspapers. These findings suggest that trust does not uniformly mitigate fear-driven avoidance. Instead, when combined with high-entropy information sources, trust can exacerbate cognitive and emotional strain, increasing investors’ propensity to disengage. By highlighting the joint roles of informational entropy and trust, this study advances behavioral finance research and offers practical insights for investors, policymakers, and regulators seeking to improve decision-making resilience during periods of market crisis.

We examine how social networks shaped by high-speed rail connections influence investor attention and market reactions to earnings announcements in China. Firms in high-centrality cities exhibit stronger immediate and subsequent responses in investor attention, stock price, and trading volume to earnings news. Further analysis shows that earnings-induced local attention predicts future attention spillovers to intercity investors, amplifying both price and volume reactions after announcements. Overall, these findings indicate that high-speed rail networks foster investor social networks that facilitate the dissemination of firm news and help explain predictable patterns in investor behavior and market pricing.

The growing emphasis on Environmental, Social, and Governance (ESG) principles, particularly in corporate sectors, shapes investment trends and operational strategies, whose shift is supported by the increasing role of media in monitoring and influencing corporate ESG performance, thereby driving the energy innovation. Therefore, based on reported events from Baidu News and patent text information of Chinese A-share listed companies from 2012 to 2022, this study innovatively applied machine learning and text analysis to measure ESG news sentiment and corporate energy innovation indicators. Combing with reputation, stakeholder, and agency theories, we find that a good reputation conveyed by positive ESG textual sentiments in the media significantly promotes corporate energy innovation, and the effect is mainly realized through alleviating financing constraints and agency problems and promoting green investment. Further analysis shows that ESG news sentiment promotes corporate energy innovation mainly among private firms, non-growth-stage firms, high-energy-consuming firms, and regions with better green finance development and higher ESG governance intensity. From the perspective of ESG news content and information content, greater ESG news attention can also exert an energy innovation incentive effect, in which the incentive effect exerted by positive media sentiment in the environmental (E) and social (S) dimensions, as well as excellent attention, is more robust. This study provides new insights for promoting green and low-carbon development and understanding the external governance role of media in corporate ESG development.