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.

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 paper studies a novel channel through which climate risks affect households’ choices of risky asset allocation: a stringent climate change regulation elevates labor income risk for households employed by high-emission industries which in turn discourages households' financial risk-taking. Using staggered adoptions of climate change action plans across states, we find that climate change action plans lead to a reduction in the share of risky assets by 15% for households in high-emission industries. We also find a reduction in risky asset holdings after the stringent EPA regulation. These results are stronger with experiences of climate change-related disasters. Our study implies an unintended consequence of climate regulations for wealth inequality by discouraging low-wealth households' financial risk-taking.

Evidence from the U.S. and Europe suggests that current risk assessment tools used by researchers and financial professionals to determine individuals’ risk tolerance and provide suitable portfolio recommendations may be flawed due to “mis”perceptions of risk. Limited research has examined the reliability of these tools as measures of relative risk tolerance for households in emerging economies like China. This study develops a multidimensional index of risk tolerance specifically tailored for Chinese households using a psychometric approach. The effectiveness of this multidimensional index in predicting individuals’ financial decisions is tested and compared to traditional unidimensional measures of risk tolerance commonly used in developed countries. The findings indicate that multidimensional measures are more consistent and significant predictors of Chinese households’ investment decisions. Additionally, the study uncovers evidence that cultural differences, related to market expectations and social networks, which are often overlooked in U.S. and European models, play a crucial role in shaping individuals' risk perceptions and investment choices in China. Robustness checks were conducted to account for potential endogeneity between risk tolerance and investment decisions. The findings provide valuable insights for researchers and financial professionals seeking to develop more accurate risk assessment tools that capture risk attitudes and perceptions in China and other developing countries. By adopting a multidimensional approach that accounts for cultural and psychosocial factors, these improved tools can enhance the precision of risk evaluation and facilitate more appropriate investment recommendations.

Using a unique FinTech data containing monthly individual-level consumption, investments, and payments, we examine how FinTech can lower investment barriers and improve risk-taking. Seizing on the rapid expansion of offline usages of Alipay in China, we measure individuals’ FinTech adoption by the speed and intensity with which they adopt the new technology. Our hypothesis is that individuals with high FinTech adoption, through repeated usages of the Alipay app, would build familiarity and trust, reducing the psychological barriers against investing in risky assets. Measuring risk-taking by individuals’ mutual-fund investments on the FinTech platform, we find that higher FinTech adoption results in higher participation and more risk-taking. Using the distance to Hangzhou as an instrument variable to capture the exogenous variation in FinTech adoption yields results of similar economic and statistical significance. Focusing on the welfare-improving aspect of FinTech inclusion, we find that individuals with high risk tolerance, hence more risk-taking capacity, and those living in under-banked cities stand to benefit more from the advent of FinTech.

This paper studies the optimal scale and asset allocation of Sovereign Wealth Fund (SWF), taking China’s SWF as an example. We use the AR (1) process to simulate the future foreign exchange earnings of China and generate three patterns of the future earnings. With these three scenarios and based on Deaton’s precautionary saving model, we find that the optimal scale and asset allocation of China’s SWF mainly depend on the expected trend and fluctuation of the future foreign exchange earnings and expected yields that SWF can get. When foreign exchange earnings shows an upward trend, the scale of SWF should not be expanded even the expected investment yield is very high, and ratio of risky assets should be kept stable and high. When foreign exchange earnings is stabilized as its growth rate slows down, the scale of SWF has the positive correlation with the degree of earnings fluctuation and expected yield of investment, and also ratio of risky assets is generally lower. When foreign exchange earnings decrease, the scale of SWF should be expanded even the expected investment yield is not so high, and the ratio of risky assets is dependent on the characteristics of expected investment yields. We also conclude that investment policy of China’s SWF should follow Temasek’s investment model, under the current trend of China’s foreign exchange trend, and strive for high yield investment chances.

Property casualty insurance insurers’ investment in risky assets appears to be cyclical. Insurance investment cycle is correlated with underwriting cycle. Insurers invest less in stocks in hard markets and more in soft markets. Nevertheless, the correlation between investment and underwriting cycles exists only for stock insurers, not for mutual insurers. Furthermore, investments of mutual and stock insurance firms react differently to capital market indicators reflecting investor sentiment, such as equity market indices and aggregate newly issued equity.