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.

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 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.

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.

This paper firstly discusses alignment of SWFs with macroeconomic policy. We believe that SWFs can become an effective tool for fiscal policy; SWF investments should be made in alignment with the monetary authority, and help stabilize the exchange rate. SWFs also contribute to stability of the national balance sheet. Asset allocation of SWFs has significant impacts on the current and capital accounts of both domestic and international balance sheets. Secondly, this paper explores the impacts of SWFs on the global financial market and its stability, including those on asset bubbles, equity risk premium and financial market stability. We argue that the potential negative impact of SWFs on the global financial market is very limited, and that they are important stabilizing forces in the global financial market. We believe that SWFs contribute to the coordination of macroeconomic policy from a domestic point of view and to the stability of global financial market from an international point of view.

Much recent work has documented evidence for predictability of asset returns.We show how such predictability can affect the portfolio choice of long-lived investors who value wealth not for its own sake but for the consumption their wealth can support.We develop an approximate solution method for the optimal consumption and portfolio choice problem of an infinitely-lived investor with Epstein-Zin utility who faces a set of asset returns described by a vector autoregression in returns and state variables.Empirical estimates in long-run annual and postwar quarterly US data suggest that the predictability of stock returns greatly inceases the optimal demand for stocks.The role of nominal bonds in long-term portfolios depends on the importance of real interest rate risk relative to other sources of risk.We extend the analysis to consider long-term inflation-indexed bonds and find that these bonds greatly increase the utility of conservative investors,who should hold large positions when they are available.