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 assesses the impact of China’s Green Credit Guidelines (GCG) 2012 on the quality of firms’ green innovation and their financial asset allocations. While examining patent applications and grants, our findings reveal that, although the GCG 2012 led to a significant increase in green patent applications, its influence on granted patents, especially in the invention category, was minimal. This highlights a discrepancy between innovation intent and quality, suggesting that highpolluting enterprises (HPEs) prioritize rapid policy compliance rather than substantial environmental improvements. However, HPEs seem to prioritize liquidity over long-term financialization, potentially indicating enhanced credit allocation efficiency.

This paper represents the first attempt to show how heterogeneous shock experiences help explain the enduring scars on household future behaviors. Using a large-scale household survey with 15,652 observations combined with geospatial transportation big data, we identify a novel belief-updating mechanism through which crises may exert prolonged impacts on household asset allocation and consumption patterns. An increase in the duration of previous lockdown experience is associated with a 10.52% escalation in enhanced anxiety for future precautionary saving motivations. This experience-based learning perspective supports the resolution of long-lasting overreactions to negative shocks via belief revisions and extends to households’ consumption behaviors. The lingering effects continue to skew households' beliefs even when conditions improve. Additionally, households with different individual-based shock experiences may exhibit varying perceptions of external shocks, resulting in disparate belief revision processes.

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.

This paper explores the determinants of cryptocurrency failure and the pricing of crypto failure risk. We document different significant market- and characteristic-based predictors for coin and token failures. The introduction of Bitcoin futures and the outbreak of COVID19 affect the importance of many predictors. Investors require extra return for bearing high failure risk of crypto assets. The return difference across high and low failure risk crypto assets is not explained by the market, size and momentum factors in the cryptocurrency market. Finally, investors benefit from diversifying into high failure risk crypto assets that is little correlated with the stock market.

Deep learning technology is rapidly adopted in financial market settings. Using a large data set from the Chinese stock market, we propose a return-risk trade-off strategy via a new transformer model. The empirical findings show that these updates, such as the self-attention mechanism in technology, can improve the use of time-series information related to returns and volatility, increase predictability, and capture more economic gains than other nonlinear models, such as LSTM. Our model employs Shapley additive explanations (SHAP) to measure the “economic feature importance” and tabulates the different important features in the prediction process. Finally, we document several economic explanations for the TF model. This paper sheds light on the burgeoning field on asset allocation in the age of big data.

As “sin” stocks and “brown” stocks generally earn higher returns than “green” stocks, fund managers face a trade-off between profitability and sustainability preferences when investing in environmental, social and governance (ESG). We explore the investment styles of ESG funds in the Chinese A-share market and analyze the behavior of ESG funds in terms of asset allocation and portfolio adjustment. We find that ESG funds prefer stocks with high return performance over stocks with high ESG performance. Textual analyses of prospectuses reveal a degree of “greenwashing” behavior by ESG funds. Overall, we show that ESG funds not purely ESG-driven.

This paper investigates the consequences of liquidation and reorganization on the allocation and subsequent utilization of assets in bankruptcy. Using the random assignment of judges to bankruptcy cases as a natural experiment that forces some firms into liquidation, we find that the long-run utilization of assets of liquidated firms is lower relative to assets of reorganized firms. These effects are concentrated in thin markets with few potential users, and in areas with low access to finance. The results highlight the importance of local search frictions and financial frictions in affecting the allocation of assets in bankruptcy.

This paper examines the predictive power of trading volume for Chinese equity premium. High (low) trading volume significantly predicts subsequent high (low) equity premium in Chinese stock market in- and out-of-sample. The predictability of trading volume remains significant after controlling for a large number of China economic variables. The predictive power of trading volume is economically important from an asset allocation perspective. Overall, our study suggests that trading volume should be used in conjunction with economic variables to further enhance the Chinese equity premium predictability.

Based on a compound random process including geometric Brownian motion and Poisson process, we established a model which can describe the environmental uncertainty more flexible. And then, we use a stochastic optimal control model to address the issue of resource allocation. Our study conclusions indicate the following: (1) if rare events can be described using a Poisson process, then the fixed-point theorem can be used to solve resource allocation scheme; and (2) if a certain asset or a certain department’s facing a rare event leads to a reduction in value, then the rare event will not only affect investment in this asset or department but will also have ramifications for investment in related assets or departments. After that, we briefly discuss the resource allocation issues of financial institutions and manufacturing enterprises. The results show that the uncertain, flexible environmental of financial institutions can improve the efficiency of asset allocation. Manufacturing companies can respond effectively and positively to such uncertainty through a flexible asset allocation strategy. The contribution of our paper lies mainly in its use of new methods to describe uncertainty. When we re-define the environment of uncertainty, the flexible resource allocation scheme can effectively mitigate the impact of random adverse effects of the environment. In addition, if the description methods are closer to the facts themselves, then the scheme for flexibility in resource allocation may also bring about an excess return.