Developing countries are taking advantage of FinTech tools to provide more people with convenient access to financial market investment through digital wealth management. Using COVID-19 as an uncertainty shock, we examine whether and how digital wealth management affects the resilience of consumption to shocks based on a unique micro dataset provided by a leading Big Tech platform, Alipay in China. We find that digital wealth management mitigates the response of consumption to uncertainty shocks: residents who participate in digital wealth management, especially in risky asset investments, have a lower reduction in consumption. Importantly, digital wealth management helps improve financial inclusion, with a more pronounced mitigation effect among residents with lower-level wealth, living in less developed areas, and those with lower-level conventional finance accessibility. The mitigation effect works through the wealth channel: those who allocate a larger proportion of risky assets in their portfolio and obtain a higher realized return show more resilience of consumption to negative shocks. We also find that digital wealth management substitutes for conventional bank credit but serves as a complement to FinTech credit in smoothing consumption during uncertainty shocks. Digital wealth management provides a crucial way to improve financial inclusion and the resilience of consumption to shocks.

With fintech growth and smartphone adoption, e-wallets, which enable instant transactions while offering cash management products with financial returns, have become increasingly prevalent. Using a unique dataset from Alipay, the world’s largest e-wallet provider, we find that holdings in Yu’EBao—an investment product usable for payments—are less affected by interest rate changes than similar assets without payment functions. This effect is stronger for users who depend on Yu’EBao for daily spending, during peak payment periods, or among less experienced investors. Our findings show that Yu’EBao reduces retail fund flow to riskier assets by 7.7% for every one-percentage-point interest rate cut, dampening monetary policy transmission through the portfolio rebalancing channel.

We study the influence of investor attention on mutual fund investors' fund selection and fund managers' portfolio choice. Using the Google Search Volume Index to measure investor attention on individual stocks, we find fund investors tend to direct more capital to mutual funds holding more high-attention stocks; fund managers tend to perform window-dressing trading to increase the portfolio holdings of high-attention stocks displayed to investors. Our results suggest that funds, particularly those with strong incentives, strategically trade on stock attention to attract investor flows. This strategic trading behaviour is also associated with fund underperformance and leads to larger non-fundamental volatility of holding stocks.

I conduct an exploratory study about the feasibility of factor timing in the Chinese stock market, covering 24 representative and well-identiffed risk factors in ten categories from the literature. The long-short portfolio of short-term reversal exhibits strong and statistically signiffcant out-of-sample predictability, which is robust across various models and all types of predictors. However, such results are not evident in the prediction of all other factors’ long-short portfolios, as well as all factors’ long-wing and short-wing portfolios. The high exposure to the market beta, together with the unpredictability of the market return, explains these failures to some degree. On the other hand, a simple investment strategy based on predicted returns of the reversal factor’s long-short portfolio obtains a signiffcant return three times higher than the simple buy-and-hold strategy in the sample period, with a signiffcant annualized 20.4% CH-3 alpha.

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 investigates the impact of analysts' information source diversity on forecast bias and investment returns. We combine the GPT-4o model and text similarity, to extract the names of information sources from the text of analyst in-depth reports. Using 349,200 sources, we calculate information diversity scores based on the variety of data sources to measure analysts’ ability of selecting relevant information. The findings reveal that higher information diversity significantly reduces forecast bias and enhances portfolio returns. The effect is particularly pronounced for large companies, state-owned enterprises, those with low analyst coverage, low firm-specific experience, and reports with positive forecast revisions. Institutional investors recognize the value of this skill, while retail investors remain largely unaware, which contributes to financial inequality. This study highlights the critical role of information diversity in analyst performance.

This paper employs a proprietary data set on commodity producers’ profit margins (PPMG) and establishes a robust positive relationship between commodity producers’ profitability growth and future returns of commodity futures. The spread portfolio that longs top-PPMG futures contracts and shorts bottom-PPMG futures contracts delivers a statistically significant average weekly return of 36 basis points. We further demonstrate that profitability is a strong SDF factor in commodity futures market. We theoretically justify our empirical findings by developing an investment-based pricing model, in which producers optimally adjust their production process by maximizing profits subject to aggregate profitability shocks. The model reproduces key empirical results through calibration and simulation.

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.

This paper proposes the concepts of ESG information flows and a predictable framework of ESG flows based on AR process, and studies how ESG information flows are incorporated into and affect a dynamic portfolio with transaction costs. Two methods, called the ESG factor model and the ESG preference model, are considered to embed ESG information flows into a dynamic mean-variance model. The dynamic optimal portfolio can be expressed as a traditional optimal portfolio without ESG information and a dynamic ESG preference portfolio, and the impact of ESG information on optimal trading is explicitly analyzed. The rich numerical results show that ESG information can improve the out-of-sample performance, and ESG preference portfolio has the best out-of-sample performance including the net returns, Sharpe ratio and cumulative return of portfolios, and contribute to reducing risk and transaction costs. Our dynamic trading strategy provides valuable insights for sustainable investment both in theory and practice.

Individual institutions marginal contributions to the systemic risk contain predictive power for its potential future exposure and provide early warning signals to regulators and the public. We use higher-order co-skewness and co-kurtosis to construct systemic risk contribution measures, which allow us to identify and characterize the co-movement driving the asymmetry and tail behavior of the joint distribution of asset returns. We illustrate the usefulness of higher-order moment augmented approach by using 4868 stocks living in the Chinese market from June 2002 to March 2022. The empirical results show that these higher-order moment measures convey useful information for systemic risk contribution measurement and portfolio selection, complementary to the information extracted from a standard principal components analysis.