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.

We develop an information choice model where information costs are sticky and co-move with firm-level intrinsic uncertainty as opposed to temporal variations in uncertainty. Incorporating analysts' forecasts, we predict a negative relationship between information costs and information acquisition, as proxied by the predictability of analysts' forecast biases. Finally, the model shows a contrasting pattern between information acquisition and intrinsic and temporal uncertainty, where intrinsic uncertainty strengthens return predictability of analysts' biases through the information cost channel, while temporal uncertainty weakens it through the information benefit channel. We empirically confirm these opposing relationships that existing theories struggle to explain.

Using a unique dataset from China spanning 2005 to 2023, we investigate how superstitious beliefs influence mutual fund managers’ risk-taking behavior and how this influence evolves over their careers. We find a significant 6.82% reduction in risk-taking during managers’ zodiac years, traditionally considered unlucky in Chinese culture. This effect is particularly pronounced among less experienced managers, those without financial education backgrounds, and those with lower management skills. The impact also intensifies during periods of high market volatility. Our findings challenge the traditional dichotomy between retail and professional investors, showing that even professional fund managers can be influenced by irrational beliefs early in their careers. However, the diminishing effect of superstition with experience and expertise suggests a gradual transition towards more rational decision-making. Our results provide insights into the process by which financial professionals evolve from exhibiting behavior akin to retail investors to becoming the rational actors often assumed in financial theory.

This paper develops a network to analyze inter-industry risk spillovers during high and low volatility periods. Our findings indicate that China's Industrials and Consumer Discretionary exhibit the greatest levels of spillovers in both high and low volatility states. Notably, our results demonstrate the "event-driven" character of structural changes to the network during periods of pronounced risk events. At the same time, the economic and financial network exhibits clear "small world" characteristics. Additionally, in the high volatility stage, the inter-industry risk contagion network becomes more complex, featuring greater connectivity and direct contagion paths. Furthermore, concerning the spillover connection between finance and the real sector, the real economy serves as a net exporter of risk. The study's findings can assist government agencies in preventing risk contagion between the financial market and the real economy. The empirical evidence and policy lessons provide valuable insights for effective risk management.

In the face of severe climate change, researchers have looked for assistance from financial instruments. They have examined how to hedge the risks of these instruments created by market fluctuations through various green financial derivatives, including green bonds (i.e., fixed-income financial instruments designed to support an environmental goal). In this study, we designed a green bond index option contract. First, we combined an autoregressive moving-average model (AMRA) with a generalized autoregressive conditional heteroskedasticity model (GARCH) to predict the green bond index. Next, we established a fractional Brownian motion option pricing model with temporally variable volatility. We used this approach to predict the closing price of the China Bond–Green Bond Index from 3 January 2017 to 30 December 2021 as an empirical analysis. The trend of the index predicted by the ARMA–GARCH model was consistent with the actual trend and predictions of actual prices were highly accurate. The modified fractional Brownian motion option pricing model improved the pricing accuracy. Our results provide a policy reference for the development of a green financial derivatives market, and can accelerate the transformation of markets towards a more sustainable economic development model.

Based on institutions' general role and the specialty of motivated investors' relatively larger stake, we examine whether ownership by motivated investors is associated with the focal firm's ESG rating disagreement in China. Our results suggest that ownership by motivated investors can decrease the focal firm's ESG rating disagreement. That relationship is strengthened by a better internal or external information environment. What's more, ownership by motivated investors can increase the quality of ESG disclosure and the level of consensus ESG rating. ESG rating disagreement increases stock return volatility and price synchronicity, while motivated investors can mitigate those negative effects. Our results confirm that motivated investors have greater incentive and capability to discipline managers and influence corporate policies and actions even in an emerging market with weak investor protection and the popularity of exploration by ultimate controllers. That would shed valuable insights into the high-quality development of other emerging markets, especially those in south-east Asian.

The study aims to explain the economic impact of Internet implication in tourism sector by taking sample of mega project listed countries (which provide big pitch to boost tourism business). Our work find the volatility cause of tourism revenue at country i, by examining the inbound tourist expenditures as a factor of technological infrastructure. We deploy data ranging from 1990 to 2017 and uses error correction model as representative of Autoregressive-Distributed Lag (ARDL) model after addressing diagnostic tests (for data reliability concern). We found long- and short-run association between tourism expenditure and information and communication technology (ICT) proxies in case of developed economies, while only short-run association in underdeveloped countries. The startling scenario about underdeveloped economies are also confirmed by one-way causation in our analysis. After sensitive analysis at each slot, the study concludes that tourism revenue is streaming low across those boundaries where tourists a

We examine the relationship between customer concentration and capital structure adjustment speed using a sample of US listed firms from 1977 to 2020. We found that the customer-concentrated firms have a lower speed of leverage adjustment. Customer concentration affects leverage adjustment speed mainly through increased cash flow volatility and asset specificity. The negative association is more pronounced in firms with high relationship-specific investments and low switching costs for their customers. Stock market reacts to leverage deviation strongly for firms with concentrated customers. Our findings highlight the vital role of customers as key stakeholders in capital structure decisions.

We examine a unique phenomenon among exchange traded funds (ETFs) in the Chinese stock market, finding that ETFs pervasively participate in initial public offerings (IPOs) to profit from underpricing. The ETF IPO participation passes primary market benefits to retail investors, providing benefits from hard-to-reach investment opportunities. These active moves showing ETFs are not entirely passive highlight the gains of the active management. However, we observe that this activity leads to increased non-fundamental volatility and short-term return reversals, as well as decreased investment-q sensitivity among ETF member stocks, presenting a negative externality. Using a policy shock as the quasi-natural experiment, we establish the causality of these effects, underscoring the dual nature of ETFs active management.

Prior literature shows that implied volatility spreads between call and put options are positively related to future underlying stock returns. In this paper, however, we demon- strate that the volatility spreads are negatively related to future out-of-the-money call option returns. Using unique data on option volumes, we reconcile the two pieces of evidence by showing that option demand by sophisticated, firm investors drives the posi- tive stock return predictability based on volatility spreads, while demand by less sophis- ticated, customer investors drives the negative call option return predictability. Overall, our evidence suggests that volatility spreads contain information about both firm funda- mentals and option mispricing.