Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multidimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To tackle this challenge, we develop automated straddle option trading based on reinforcement learning and attention mechanisms to handle unpredictability in high-volatility markets. Firstly, we leverage the attention mechanisms in Transformer DDQN through both self-attention with time series data and channel attention with multi-cycle information. Secondly, a novel reward function considering excess earnings is designed to focus on long-term profits and neglect short-term losses over a stop line. Thirdly, we identify the resistance levels to provide reference information when great uncertainty in price movements occurs with intensified battle between the buyers and sellers. Through extensive experiments on the Chinese stock, Brent crude oil, and Bitcoin markets, our attention-based Transformer-DDQN model exhibits the lowest maximum drawdown across all markets, and outperforms other models by 92.5% in terms of the average return excluding the crude oil market due to relatively low fluctuation.

Cross-firm momentum effects via shared analyst coverage are well-documented in de-veloped markets, but their robustness remains unclear in emerging markets, where information diffusion is asymmetric and analyst coverage is highly concentrated. Our work revisits this effect in an environment of extreme informational frictions — the Chinese market. We reconstruct the information transmission channel within the an-alyst coverage network by introducing a novel weighting scheme based on strength centrality (SC). This measure identiffes inffuential leader firms that command dis-proportionate attention from both analysts and the market. Our results demonstrate that SC-weighted connected-firm returns robustly predict cross-sectional stock returns, yielding significant and persistent profits even under a rigorous stock filter. This per-formance cannot be subsumed by strategies based on alternative weighting schemes or by explanations such as intra-industry cross-firm momentum and information discreteness. Further analysis reveals that the superiority of the SC-based approach stems from its ability to effectively identify firms with stronger cross-period fundamental linkages. In addition, high-SC stocks are characterized by higher investor attention, more efficient information processing, lower arbitrage costs, and greater internationa exposures. With this evidence, we further confirm a directional spillover: cross-firm momentum effects flow exclusively from these high-SC leaders to low-SC laggards, and there is no reverse spillover. Our findings suggest that cross-firm momentum may be systematically underestimated in many international markets due to methodological limitations rather than economic irrelevance. The SC-based framework therefore of-fers a portable tool for global investors and researchers operating in environments with asymmetric information.

We examine price discovery after irregularly scheduled macroeconomic announce-ments. Exploiting time variation in Chinese macro announcements released outside regular trading hours, this paper isolates the role of elapsed non-trading time in facilitating investor learning and price discovery upon market reopening. We show that longer non-trading intervals generate more efficient post-announcement price discovery, reduce information asymmetry, and diminish subsequent intraday return reversals. The mechanism operates through enhanced retail investor learning: during non-trading hours, retail investors actively acquire information, subsequently trade more aggressively, earn higher profits, and face reduced informational disadvantages at market opening. Our findings highlight that retail investor learning during non-trading hours levels the informational playing field among heterogeneous investors and improves price quality around irregularly timed macroeconomic announcements. These results have broader implications for emerging markets, which similarly feature irregular announcement timing and large populations of uninformed retail investors.

With the increasing penetration of renewable energy (RE) in power systems, the electricity spot market has become increasingly uncertain, presenting significant challenges for generation companies (GenCos) in formulating effective bidding strategies. Most existing studies assume that GenCos act as perfectly rational decision makers, overlooking the impact of irrational bidding behaviors in uncertain market environments. To address this limitation, we incorporate prospect theory to model the decision-making process of bounded rational GenCos operating under risk. A bilevel stochastic model is developed to simulate strategic bidding in the spot market. In addition, a distributional re-inforcement learning algorithm is proposed to tackle the decision-making challenges faced by bounded rational GenCos with risk considerations. The proposed model and algorithm are validated through simulations using a 27-bus system from a region in eastern China. The results demonstrate that the algorithm effectively captures market uncertainties and learns the distribution of GenCo’s profits. Furthermore, simulated bidding strategies for various types of GenCos highlight the applicability of prospect theory to describe bounded rational decision-making behavior in electricity markets.

This study employs an imbalanced panel dataset of 524 Chinese commercial banks from 2009 to 2020 to investigate the influence of Basel III on banks' loan loss provisions. Our findings reveal no significant change in the relationship between loan loss provisions and capital adequacy, although it indicates a heightened impetus for Tier 1 capital management. Furthermore, the study finds that earnings management motivations, particularly related to pre-provision profits, influence banks' loan loss provisions. Basel III's enactment reduces the ability of high-earning banks to manipulate earnings using loan loss provisions. This research provides empirical evidence from China for the global assessment of Basel III's impact on commercial banks.

This paper examines how profits from mutual funds’ participation in initial public offerings (IPOs) shape fund performance, investor flows, and market stability in China. Using comprehensive fund–IPO matched data from 2016 to 2023, we decompose fund returns into an IPO-lottery component and residual performance. At the aggregate level, IPO allocations add 2.05% to annualized excess returns; net of IPOs, excess return is −0.35% per year. At the individual level, the contribution of IPO profits varies substantially across funds and is most pronounced among mid-sized funds, inflating perceived managerial skill. Funds with higher IPO-driven gains attract greater inflows despite the absence of performance persistence, leading to capital misallocation. At the market level, IPO-profit-induced trading (PIT) predicts short horizon price run-ups that dissipate and reverse over subsequent months, while raising both total and idiosyncratic volatility. Overall, IPO profits temporarily enhance reported performance but erode market stability by propagating non-fundamental shocks through secondary markets.

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 explores the proclivity of institutional investors in China towards highESG stocks amidst the growth of ESG investment funds. Using A-share data from 2015-2022 and a Tobit model analysis, it is found that these investors indeed favor such stocks, particularly under extensive analyst coverage and in non-state-owned firms. However, rating discrepancies can impact this preference. The attraction lies in reduced operational risks and improved net profits. Notably, independent investors show a stronger ESG preference, especially within high-pollution industries. Thus, fostering ESG investment among institutional investors can improve resource allocation in China's capital market, favoring eco-friendly companies.

Empowered by comprehensive data on smartphone fund investors’ trading and browsing histories from a Chinese financial company, we explore the role of investors’ financial attention in influencing the relationship between unrealized profits and investors’ selling decisions. Against a backdrop in which retail investors are not attentive to their portfolio information, we find supportive evidence suggesting that investors exhibit realization preference when we condition on days when investors pay financial attention. Further, we show that failing to account for investors’ financial inattention may induce observers to reject the realization-preference hypothesis. This paper also offers insights into the determinants of financial attention and the influence of financial attention on investor disposition effect.

This work explored the changes in production decision-making trends of Chinese steel enterprises under the influence of the carbon border adjustment mechanism. First, using evolutionary game theory, the interactive mechanism of complex production strategies among steel enterprises considering the carbon border adjustment mechanism was studied, including the impact of government subsidy coefficients, additional profits and carbon tax prices on enterprise decisionmaking.Second, the influence of key parameters on the dynamic evolutionary process was analysed. On this basis, the empirical simulation method was used to verify the game model and the main conclusions. Finally, the sensitivity analysis of the selected parameters was determined using Matlab software. The results showed that additional profits from green investment, government subsidy coefficients, input-output values and carbon tax prices had a higher impact on the evolution of enterprise production strategies. The results of this study provide a decision-making basis for the selection of future production methods for steel enterprises.