This paper examines whether mandatory ownership disclosure aligns incentives or distorts in-vestor beliefs. Using a sample of 1,436 Chinese equity-oriented mutual funds from 2012 to 2023,we find that higher managerial and senior ownership are significantly associated with larger in-flows, suggesting that investors treat ownership as a quality signal. However, we find no evidencethat ownership forecasts superior future returns or risk-adjusted alphas. Mechanism tests showthat the ownership-flow effect is much stronger in low-marketing funds and that managers increaseownership after weak flows, a countercyclical pattern inconsistent with overconfidence and consis-tent with strategic remedial signaling. Overall, ownership disclosure appears to operate primarilythrough investor perception rather than information about managerial ability, weakening the linkbetween capital allocation and true skill in the mutual fund industry.

This paper develops an autonomous framework for systematic factor investing via agentic AI. Rather than relying on sequential manual prompts, our approach operationalizes the model as a self-directed engine that endogenously formulates interpretable trading signals. To mitigate data snooping biases, this closed-loop system imposes strict empirical discipline through out-of-sample validation and economic rationale requirements. Applying this methodology to the U.S. equity market, we document that long-short portfolios formed on the simple linear combination of signals deliver an annualized Sharpe ratio of 2.75 and a return of 54.81%. Finally, our empirics demonstrate that self-evolving AI offers a scalable and interpretable paradigm.

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 develop a deep-visualization framework for timing the factor zoo. Historical factor return trajectories are converted to two complementary image representations, which are then learned by convolutional neural networks (CNNs) to generate factor-specific timing signals. Using 206 equity factors, our CNN-based forecasts deliver significant economic gains: timed factors earn an average annualized alpha of about 6\%, and a high-minus-low strategy yields an annualized Sharpe ratio of 1.22. The outperformance is robust to transaction costs, post-publication decay, and factor category-level analysis. Interpretability analyses reveal that CNNs extract predictive signals from path boundaries and regime shifts, capturing patterns orthogonal to investor attention.

We propose a novel dual-screening classification framework for fund selection designed to align statistical objectives with investor goals. Testing on the Chinese mutual funds market, a Gradient Boosting model implementing our framework generates a statistically and economically significant 14.65% annual risk-adjusted alpha, substantially outperforming identical models trained under a standard regression framework. Feature importance analysis confirms that fund-level momentum and flows are the most significant predictors of performance in this market. Our findings provide a robust and practical framework for active management, demonstrating that modelling both upside potential and downside risk is critical for superior performance.

本文以某大型销售平台推出旨在引导个人投资者科学投资的优选基金服务为场景，探讨数字时代的投顾服务对基金价值创造的影响。研究发现，优选服务推出后，优选基金的资金净流入显著增加，平均每季度较配对基金高11.3%。然而，优选基金的价值创造却显著下降，以Jensen Alpha度量的超额收益平均每季度较配对基金低1.2%，考虑管理规模后的价值增量低4339万。而且，服务推出半年内，资金净流入越多的优选基金，业绩下降幅度越大。进一步地，本文从基金经理调仓能力、管理积极性、交易冲击成本三个维度探讨了优选基金业绩下降的机制表现。本文的研究表明，在数字时代的海量用户情境下，投顾服务易产生网络负外部性，导致其投资策略出现非预期的收益衰减。这一发现对探索投顾服务数字化的规律具有重要启示。

While many studies document cross-stock predictability where returns of some stocks predict returns of other similar stocks, most evidence comes from US markets. Following Chen et al. (2019), we identify peer firms based on historical return similarity and construct a Peer Return Gap (PRG) measure, defined as the difference between a stock’s lagged return and its peers’ returns. Our empirical evidence from Chinese markets shows that past-return-linked peers strongly predict focal firm returns. A long-short portfolio sorted on PRG generates an equal-weighted monthly return of 1.26% (t = 3.81) and a Fama-French five-factor alpha of 1.10% (t = 2.86). These abnormal returns remain unexplained by several alternative factor 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 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.

It is diffffcult to understand the risk-return trade-off in option market with observable factormodels. In this paper, we employ a latent factor model for delta-hedge option returns over a varietyof important exchange traded options in China, based on the instrumented principal componentanalysis (IPCA). This model incorporates conditional betas instrumented by option characteristics,to tackle the diffffculty caused by short lifespans and rapidly migrating characteristics of options. Ourresults show that a three-factor IPCA model can explain 19.30% variance in returns of individualoptions and 99.23% for managed portfolios. An asset pricing test with bootstrap shows that there isno unexplained alpha term with such a model. Comparison with observable factor model indicatesthe necessity of including characteristics. We also provide subsample analysis and characteristicimportance.