We develop a model economy with active financial markets in which a policymaker's adoption of a gradualistic approach constitutes a Bayesian Nash equilibrium. In our model, the ex ante policy proposal influences the supply side of the economy, while the ex post policy action affects the demand side and shapes market equilibrium. When choosing policies, the policymaker internalizes the impact of her decisions on the precision of the firm-value signal. Moreover, financial markets provide a price signal that informs the government. The policymaker learns about the productivity shocks not only from firm-value performance signals but also from financial market prices. Access to information through both channels creates strong incentives for the policymaker to adopt a gradualistic approach in a time-consistent manner. Smaller policy steps yield more precise information about the productivity shock. These results hold robustly for both exogenous and endogenous information models.

We introduce a deep visualization (DV) framework that turns conventional commodity data into images and extracts predictive signals via convolutional feature learning. Specifically, we encode futures price trajectories and the futures surface as images, then derive four deep‑visualization (DV) predictors, carry ($bs_{DV}$), basis momentum ($bm_{DV}$), momentum ($mom_{DV}$), and skewness ($sk_{DV}$), each of which consistently outperforms its traditional formula‑based counterpart in return predictability. By forming long–short portfolios in the top (bottom) quartile of each DV predictor, we build an image‑based four‑factor model that delivers significant alpha and better explains the cross‑section of commodity returns than existing benchmarks. Further evidence shows that the explanatory power of these image‑based factors is strongly linked to macroeconomic uncertainty and geopolitical risk. Our findings reveal that transforming conventional financial data into images and relying solely on image-derived features suffices to construct a sophisticated asset pricing model at least in commodity markets, pioneering the paradigm of image‑based asset pricing.

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.

Using unique account-level data from a leading Chinese fintech platform, we investigate how financial influencers, the key information intermediaries in social finance, attract followers through a process of social transmission bias. We document a robust performance-following pattern wherein retail investors overextrapolate influencers’ past returns rather than rational learning in the social network from their past performance. The transmission bias is amplified by two mechanisms: (1) influencers’ active social engagement and (2) their index fund-heavy portfolios. Evidence further reveals influencers’self-enhancing reporting through selective performance disclosure. Crucially, the dynamics ultimately increase risk exposure and impair returns for follower investors.

We construct a novel risk-based similarity peer network by applying machine learning techniques to extract a comprehensive set of disclosed risk factors from firms' annual reports. We find that a firm's future returns can be significantly predicted by the past returns of its risk-similar peers, even after excluding firms within the same industry. A long-short portfolio, formed based on the returns of these risk-similar peers, generates an alpha of 84 basis points per month. This return predictability is particularly pronounced for negative-return stocks and those with limited investor attention, suggesting that the effect is driven by slow information diffusion across firms with similar risk exposures. Our findings highlight that the risk factors disclosed in 10-K filings contain valuable information that is often overlooked by investors.

To specify the peer effects that affect influencers’ sponsored content strategies, the current research addresses three questions: how influencers respond to peers, what mechanisms drive these effects, and the implications for social media platforms. By using a linear-in-means model and data from a leading Chinese social media platform, the authors address the issues of endogenous peer group formation, correlated unobservables, and simultaneity in decision-making and thereby offer evidence of strong peer effects on the quantity of sponsored content but not its quality. These effects are driven by two mechanisms: a social learning motive, such that following influencers emulate leading influencers, and a competition motive among following influencers within peer groups. No evidence of competition motive among leading influencers or defensive strategies by leading influencers arises. Moreover, peer effects increase influencers’ spending on in-feed advertising services, leading to greater platform revenues, without affecting the pricing of sponsored content. This dynamic may reduce influencers’ profitability, because their rising costs are not offset by higher prices. These findings emphasize the need for balanced strategies that prioritize both platform growth and influencer sustainability. By revealing how peer effects influence competition and revenue generation, this study provides valuable insights for optimizing content volume, quality, and financial outcomes for social media platforms and influencers.

This paper examines how the integration of artificial intelligence (AI) and big data affects banking operations, emphasizing the crucial role of big data in unlocking the full potential of AI. Leveraging a comprehensive dataset of over 4.5 million loans issued by a leading commercial bank in China and exploiting a policy mandate as an exogenous shock, we document significant improvements in credit rating accuracy and loan performance, particularly for SMEs. Specifically, the adoption of AI and big data reduces the rate of unclassified credit ratings by 40.1% and decreases loan default rates by 29.6%. Analyzing the bank's phased implementation, we find that integrating big data analytics substantially enhances the effectiveness of AI models. We further identify significant heterogeneity: improvements are especially pronounced for unsecured and short-term loans, borrowers with incomplete financial records, first-time borrowers, long-distance borrowers, and firms located in economically underdeveloped or linguistically diverse regions. Our findings underscore the powerful synergy between big data and AI, demonstrating their joint capability to alleviate information frictions and enhance credit allocation efficiency.

Portfolio underdiversification is one of the most costly losses accumulated over a household’s life cycle. We provide new evidence on the impact of financial inclusion services on households’ portfolio choice and investment efficiency using 2015, 2017, and 2019 survey data for Chinese households. We hypothesize that higher financial inclusion penetration encourages households to participate in the financial market, leading to better portfolio diversification and investment efficiency. The results of the baseline model are consistent with our proposed hypothesis that higher accessibility to financial inclusion encourages households to invest in risky assets and increases investment efficiency. We further estimate a dynamic double machine learning model to quantitatively investigate the non-linear causal effects and track the dynamic change of those effects over time. We observe that the marginal effect increases over time, and those effects are more pronounced among low-asset, less-educated households and those located in non-rural areas, except for investment efficiency for high-asset households.

This study reconsiders the timing of the equity risk factors by using the flexible neural networks specified for image recognition to determine the timing weights. The performance of each factor is visualized to be standardized price and volatility charts and `learned' by flexible image recognition methods with timing weights as outputs. The performance of all groups of factors can be significantly improved by using these ``deep learning--based'' timing weights. In addition, visualizing the volatility of factors and using deep learning methods to predict volatility can significantly improve the performance of the volatility-managed portfolio for most categories of factors. Our further investigation reveals that the timing success of our method hinges on its ability in identifying ex ante regime switches such as jumps and crashes of the factors and its predictability on future macroeconomic risk.

We develop an information choice model where information costs are sticky and co-move with firm-level intrinsic uncertainty as opposed to temporal variations in uncertainty. Incorporating analysts' forecasts, we predict a negative relationship between information costs and information acquisition, as proxied by the predictability of analysts' forecast biases. Finally, the model shows a contrasting pattern between information acquisition and intrinsic and temporal uncertainty, where intrinsic uncertainty strengthens return predictability of analysts' biases through the information cost channel, while temporal uncertainty weakens it through the information benefit channel. We empirically confirm these opposing relationships that existing theories struggle to explain.