Association of Park Renovation With Park Use in New York City.

Importance: Equity-driven citywide park redesign and renovation, such as the Community Parks Initiative (CPI), has the potential to increase park use and opportunities for physical activity in underserved communities. Objective: To evaluate changes in patterns of park use following park redesign and renovation in low-income New York City (NYC) neighborhoods. Design, Setting, and Participants: The Physical Activity and Redesigned Community Spaces study was a prospective quality improvement preintervention-postintervention study design with matched control parks. Thirty-three intervention and 21 control neighborhood parks were selected based on specific criteria related to poverty rates, population growth, and population density in park neighborhoods and not having received more than $250 000 in investment in the past 2 decades. Data were collected at baseline (prerenovation) and 2 follow-up points (3 months and 1 year post renovation) between June 5 and December 4 from 2016 to 2022. Participants were individuals observed as users of study parks. Intervention: The CPI, which involved the redesign and renovation of neighborhood parks by the municipal government of New York City. Main Outcomes and Measures: Main outcomes encompassed park use and physical activity levels assessed using the well-validated System for Observing Play and Recreation in Communities. Park use was quantified by total number of park users, categorized by age group (≤20 years vs ≥21 years), sex, and physical activity level (sitting or standing vs walking or vigorous activity). Changes in outcomes between groups were compared via the generalized estimation equation. Results: A total of 28 322 park users were observed across 1458 scans. At baseline, 6343 of 10 633 users (59.7%) were 20 years or younger, 4927 of 10 632 (46.3%) were female and 5705 (53.7%) were male, and 4641 of 10 605 (43.8%) were sitting or standing. Intervention parks showed more net park users compared with control parks from baseline to the final follow-up (difference-in-difference relative rate ratio, 1.69 [95% CI, 1.22-2.35] users/scan; P = .002). The association was driven by a significant increase in adult users at intervention parks and overall decrease in all users at control parks. Park users engaging in sitting or standing at intervention parks increased (difference, 4.68 [95% CI, 1.71-7.62] users/scan; P = .002) and park users engaging in walking or vigorous physical activity at control parks decreased (difference, -7.30 [95% CI, -10.80 to -4.26] users/scan; P < .001) over time. Conclusions and Relevance: In this quality improvement study, park redesign and renovation were positively associated with park use in low-income neighborhoods. However, park renovations may need to be accompanied by other programmatic strategies to increase physical activity.

What influences the performance of carbon emissions in China?-Research on the inter-provincial carbon emissions' conditional configuration impacts.

The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development.

The peer effects of resident stock market participation: Evidence from 2019 CHFS.

Limited resident's participation in the stock market has become a key constraint to the capital market development. Utilizing the 2019 China Household Financial Survey (CHFS) data, our paper designs probit models to examine the peer effects of residents' stock market participation and explore the intermediary mechanisms with a multiple intermediary model. We find that: (1) Resident involvement in stock market decision-making exhibits significant peer effects. (2) Heterogeneity analysis reveals that males and rural residents display more pronounced peer effects than females and urban residents. Additionally, middle-aged residents demonstrate more potent peer effects than their younger and older counterparts, with the intensity of peer effects correlating with education levels. (3)We observe that the peer effects of market participation operate by altering economic expectations and enhancing residents' financial literacy. (4) Further investigation establishes that individuals engaging in stock market investments manifest peer effects when deciding whether to diversify their stock portfolio. This study holds reference value for analyzing the impact of social interaction on financial behaviors and regulating individuals' financial conduct.

Participatory methods used in the evaluation of medical devices: a comparison of focus groups, interviews, and a survey.

BACKGROUND: Stakeholder engagement in evaluation of medical devices is crucial for aligning devices with stakeholders' views, needs, and values. Methods for these engagements have however not been compared to analyse their relative merits for medical device evaluation. Therefore, we systematically compared these three methods in terms of themes, interaction, and time-investment. METHODS: We compared focus groups, interviews, and an online survey in a case-study on minimally invasive endoscopy-guided surgery for patients with intracerebral haemorrhage. The focus groups and interviews featured two rounds, one explorative focussing on individual perspectives, and one interactive focussing on the exchange of perspectives between participants. The comparison between methods was made in terms of number and content of themes, how participants interact, and hours invested by all researchers. RESULTS: The focus groups generated 34 themes, the interviews 58, and the survey 42. Various improvements for the assessment of the surgical procedure were only discussed in the interviews. In focus groups, participants were inclined to emphasise agreement and support, whereas the interviews consisted of questions and answers. The total time investment for researchers of focus groups was 95 h, of interviews 315 h, and survey 81 h. CONCLUSIONS: Within the context of medical device evaluation, interviews appeared to be the most appropriate method for understanding stakeholder views since they provide a scope and depth of information that is not generated by other methods. Focus groups were useful to rapidly bring views together. Surveys enabled a quick exploration. Researchers should account for these methodological differences and select the method that is suitable for their research aim.

Brain activity of professional investors signals future stock performance.

A major aspiration of investors is to better forecast stock performance. Interestingly, emerging "neuroforecasting" research suggests that brain activity associated with anticipatory reward relates to market behavior and population-wide preferences, including stock price dynamics. In this study, we extend these findings to professional investors processing comprehensive real-world information on stock investment options while making predictions of long-term stock performance. Using functional MRI, we sampled investors' neural responses to investment cases and assessed whether these responses relate to future performance on the stock market. We found that our sample of investors could not successfully predict future market performance of the investment cases, confirming that stated preferences do not predict the market. Stock metrics of the investment cases were not predictive of future stock performance either. However, as investors processed case information, nucleus accumbens (NAcc) activity was higher for investment cases that ended up overperforming in the market. These findings remained robust, even when controlling for stock metrics and investors' predictions made in the scanner. Cross-validated prediction analysis indicated that NAcc activity could significantly predict future stock performance out-of-sample above chance. Our findings resonate with recent neuroforecasting studies and suggest that brain activity of professional investors may help in forecasting future stock performance.

Private equity investment in health care delivery, Australia, 2008-2022.

OBJECTIVES: To examine the scale of private equity investment in Australian health care delivery assets (clinics, hospitals, imaging facilities, other doctor-led health care services). STUDY DESIGN, SETTING: Extraction of information about private equity acquisitions of hospitals, clinics, imaging centres and in vitro fertilisation facilities in Australia, 2008-2022, from a commercial database (PitchBook), supplemented by information from publicly available online media sources. MAIN OUTCOME MEASURES: Number and value of private equity acquisitions of health care assets, 2008-2022; numbers of clinic parent company and clinic acquisitions, 2017-2022. RESULTS: A total of 75 private equity acquisitions of health care delivery assets in Australia during 2008-2022 were identified; the annual number rose from three acquisitions in 2008 to eighteen in 2022. During 2008-2010, five of seven acquisitions were of in vitro fertilisation providers; during 2020-2022, 22 of 39 acquisitions were of clinics or clinic groups, including eleven of eighteen in 2022. The total value of the 39 acquisitions for which purchase price could be ascertained (52%) was $24.1 billion. During 2017-2022, the clinic specialty with the greatest number of private equity acquisitions was general practice (256 of 446 clinics purchased within acquisitions). Seven companies owning ophthalmology clinics (24 clinics) were acquired by private equity. Four private equity acquisitions during 2017-2022 included 60 oncology clinics, all related to a single clinic group. CONCLUSIONS: The number of private equity acquisitions of Australian health care delivery assets increased during 2008-2022. Doctors should be aware of the motivations and dynamics of private equity companies, as they are increasingly likely to interact with these firms and assets owned by these firms.

Improving new energy subsidy efficiency considering learning effect: A case study on wind power.

Subsidy policies are instrumental in driving the development of new energy. However, the effective allocation of new energy subsidies over time is challenging given fiscal constraints. This study addresses this challenge by considering the learning effect associated with the new energy industry. A two-stage dynamic programming model is proposed to capture the investment decision-making process of companies under new energy subsidy policies and government subsidy setups. Theoretical findings suggest that company investment decisions in new energy are influenced by a guiding principle: The subsidy rate should be negatively correlated with the variation rate of production scale increment (VRPSI). We calibrate this investment decision principle using wind power data from 14 countries. According to this principle, excessive subsidy rates may result in a low VRPSI, thereby diminishing future investment profitability in the new energy industry and leading to subsidy inefficiency. Upon investigating the efficiency of annual subsidy allocation, we find that the subsidy rates were potentially set too high in 2014, 2016, and 2017. Furthermore, the government should exercise caution regarding an inefficient subsidy pattern whereby companies invest in new energy only when the subsidy rate exceeds a certain threshold, neglecting traditional power sources. It is crucial to note that although this study uses wind power industry data for calibration and simulation, the theoretical model can be broadly applied to other new energy industries and emerging industries with increasing marginal net profit.

The cost of inaction on health equity and its social determinants.

Rising levels of inflation, debt and macrofiscal tightening are putting expenditures on the social sectors including health under immense scrutiny. Already, there are worrying signs of reductions in social sector investments. However, even before the pandemic, evidence showed the significant returns on investments in health equity and its social determinants. Emerging data and trends show that these potential returns have increased during the COVID-19 pandemic - investments in social determinants can mitigate widespread reductions in human capital and the increasing likelihood of costly syndemics, while promoting access to healthcare innovations that have thus far been inequitably distributed. Therefore, we argue that, despite immediate fiscal pressures, this is exactly the time to invest in health equity and its broader social determinants, as the returns on such investments have never been greater.

Does working capital management improve financial performance in China's agri-food sector during COVID-19? A comparison with the 2008 financial crisis.

The objective of this study is to explore the impact of working capital management on firms' financial performance in China's agri-food sector from 2006 to 2021. In addition, we analyze whether this impact is the same during the 2008 financial crisis and the 2020 COVID-19 crisis. Working capital management is measured by working capital investment policy (measured by current assets to total assets ratio), working capital financing policy (measured by current liabilities to total assets ratio), cash conversion cycle, and net working capital ratio. The results reveal that current assets to total assets ratio and net working capital ratio positively influence financial performance measured through return on assets (ROA), while current liabilities to total assets ratio and cash conversion cycle negatively influence ROA. We also find that the relationship between working capital management and financial performance is more affected during COVID-19 than in the 2008 financial crisis. The findings might provide important implications for company managers to make optimal working capital management practices, depending on the economic environment.

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study.

Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it.

Práticas de enfermagem na atenção primária em saúde durante a pandemia de covid-19, Maranhão, Brasil / Nursing practices in primary health care during the covid-19 pandemic, Maranhão, Brazil / Prácticas de enfermería en la atención primaria de salud durante la pandemia de covid-19, Maranhão, Brasil

Objetivo: Este estudo analisou vivências e práticas de enfermagem, de enfermeiros da APS em seis municípios do Maranhão, durante a pandemia da covid-19. Métodos: Trata-se de estudo qualitativo, do qual participaram 23 profissionais vinculados à estratégia Saúde da Família, entrevistados de novembro a agosto de 2021. Os dados foram exportados para o software IRaMuTeQ, extraindo-se padrões de associação, e a partir do dendograma das classes obtido, as categorias foram nomeadas e exploradas por análise temática. Foram identificadas quatro categorias: Dificuldades vivenciadas para o atendimento; Interrupções e adaptações na oferta regular das ações; Enfrentamentos para atendimento da covid-19 e O retorno das atividades e os impactos deixados pela pandemia. Resultados: Observou-se desafiante cenário para as práticas de enfermagem nos atendimentos de pacientes com e sem covid-19, fragilidade institucional e falta de organização da estrutura e dos processos de trabalho, com impactos para a vida e assistência dos entrevistados. Conclusões: Os resultados demonstram a necessidade de diferentes investimentos na APS, que embora tenham sido apontados durante a pandemia, extrapolam este cenário, precisam ser inseridos no cotidiano do trabalho destes profissionais. (AU)

Objective: This study analyzed the experiences and nursing practices of PHC nurses in 6 municipalities in Maranhão, during the covid-19 pandemic. Methods: This is a qualitative study, in which 23 professionals linked to the Family Health Strategy participated, interviewed from November to August 2021. The data were exported to the IRaMuTeQ software, extracting association patterns, and from the dendrogram of the classes obtained, the categories were named and explored by thematic analysis. Four categories were identified: Difficulties experienced in providing care; Interruptions and adaptations in the regular offer of shares; Coping with covid-19 and The return of activities and the impacts left by the pandemic. Results: A challenging scenario was observed for nursing practices in the care of patients with and without covid-19, institutional weakness and lack of organization of structure and work processes, with impacts on the life and care of the interviewees. Conclusion: The results demonstrate the need for different investments in PHC, which, although they were pointed out during the pandemic, go beyond this scenario, it need be inserted in the daily work of these professionals. (AU)

Objetivo: Este estudio analizó las experiencias y prácticas de enfermeros de la APS en 6 municipios de Maranhão, durante la pandemia de COVID-19. Métodos: Se trata de un estudio cualitativo, con 23 profesionales vinculados a la Estrategia Salud de la Familia, entrevistados de noviembre a agosto de 2021. Los datos fueron exportados al software IRaMuTeQ, extrayendo del dendrograma de las clases obtenidas, las categorías fueron nombrados y explorados por el análisis temático, en cuatro categorías: Dificultades experimentadas en la atención; Interrupciones y adaptaciones en la oferta regular de acciones; Enfrentando el COVID-19 y El retorno de las actividades y los impactos que dejó la pandemia. Resultados: Se observó un escenario desafiante para las prácticas de enfermería en el cuidado de pacientes con y sin COVID-19, debilidad institucional y falta de organización de la estructura y procesos de trabajo, con impactos en la vida y cuidado de los entrevistados. Conclusión: Los resultados demuestran la necesidad de diferentes inversiones en la APS, que, aunque fueron señaladas durante la pandemia, van más allá de ese escenario y se insertan en el cotidiano de su trabajo. (AU)

Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model.

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index's opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model's proficiency in linear trend analysis and the deep learning models' capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index's opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

The walking dead: Are Zombie firms environmentally and socially responsible? A global perspective.

This study examines the association between zombie firms and their environmental and social performance. Using a global dataset of listed firms from 49 countries between 2002 and 2019, we find that zombie firms perform poorly on environmental and social responsibility fronts. This finding supports the argument that zombie firms are characterized by consistent losses and that their existence is risky without external support. Zombie firms, while struggling for survival, may not be able to undertake environmental and social activities that require huge investments, thus falling behind other firms. Further analysis highlights that eco-innovation, the presence of a sustainability committee, and industry nature (i.e., heavily polluting industries) mitigate the negative impact of firms' zombie status on their environmental and social performance. Moreover, a zombie firm's engagement in environmental and social activities improves its financial performance. Our main findings are robust to a battery of estimation techniques, alternative proxies, selection bias, and endogeneity issues.

Cross-asset momentum and the hybrid fund transmission mechanism in China's stock and bond markets.

The allocation of assets across different markets is a crucial element of investment strategy. In this regard, stocks and bonds are two significant assets that form the backbone of multi-asset allocation. Among publicly offered funds (The publicly offered funds in China correspond to the mutual funds in the United States, with different names and details in terms of legal form and sales channels), the stock-bond hybrid fund gives investors a return while minimizing the risk through capital flow between the stock and bond markets. Our research on China's financial market data from 2006 to 2022 reveals a cross-asset momentum between the stock and bond markets. We find that the momentum in the stock market negatively influences the bond market's return, while the momentum in the bond market positively influences the stock market's return. Portfolios that exploit cross-asset momentum have excess returns that other asset pricing factors cannot explain. Our analysis reveals that hybrid funds play an intermediary role in the transmission mechanism of cross-asset momentum. We observe that the more flexible the asset allocation ratio of the fund, the more crucial the intermediary role played by the fund. Hence, encouraging the development of hybrid funds and relaxing restrictions on asset allocation ratios could improve liquidity and pricing efficiency. These findings have significant implications for investors seeking to optimize their asset allocation across different markets and for policymakers seeking to enhance the efficiency of China's financial market.

Evolution analysis of low-carbon cooperation of service providers based on Moran process in cloud manufacturing.

Low-carbon cooperation among cloud manufacturing service providers is one way to achieve carbon peak and neutrality. Such cooperation is related to the benefits to service providers adopting low-carbon strategies and stochastic factors such as government low-carbon policies, providers' environmental awareness, and demanders' low-carbon preferences. Focusing on the evolutionary process of service providers' low-carbon strategy selection under uncertain factors, a stochastic evolutionary game model is constructed based on the Moran process, and the equilibrium conditions for low-carbon cooperation among providers are analyzed under benefit-dominated and stochastic factor-dominated situations. Through numerical simulation, the effects of the cloud platform's cost-sharing coefficient for low-carbon investment, matching growth rate, carbon trading price, and group size on providers' low-carbon strategy evolution are analyzed. The research results show that increasing the cloud platform's low-carbon cost-sharing, carbon trading price, and group size can promote low-carbon cooperation among service providers. With greater low-carbon investment costs and greater stochastic factor interference, the providers' enthusiasm for low-carbon cooperation decreases. This study fills the research gap in the low-carbon cooperation evolution of cloud manufacturing providers based on the stochastic evolutionary game and provides decision-making suggestions for governments and cloud platforms to encourage provider participation in low-carbon cooperation and for providers to adopt low-carbon strategies.

A comparison of human and GPT-4 use of probabilistic phrases in a coordination game.

English speakers use probabilistic phrases such as likely to communicate information about the probability or likelihood of events. Communication is successful to the extent that the listener grasps what the speaker means to convey and, if communication is successful, individuals can potentially coordinate their actions based on shared knowledge about uncertainty. We first assessed human ability to estimate the probability and the ambiguity (imprecision) of twenty-three probabilistic phrases in a coordination game in two different contexts, investment advice and medical advice. We then had GPT-4 (OpenAI), a Large Language Model, complete the same tasks as the human participants. We found that GPT-4's estimates of probability both in the Investment and Medical Contexts were as close or closer to that of the human participants as the human participants' estimates were to one another. However, further analyses of residuals disclosed small but significant differences between human and GPT-4 performance. Human probability estimates were compressed relative to those of GPT-4. Estimates of probability for both the human participants and GPT-4 were little affected by context. We propose that evaluation methods based on coordination games provide a systematic way to assess what GPT-4 and similar programs can and cannot do.

An NLP-based novel approach for assessing national influence in clause dissemination across bilateral investment treaties.

International investment agreements (IIAs) promote foreign investment. However, they can undermine crucial health programs, creating a dilemma for governments between corporate and public health interests. For this reason, including clauses that safeguard health has become an essential practice in IIAs. According to the current literature, some countries have played a pivotal role in leading this inclusion, while others follow the former ones. However, the existing literature needs a unique approach that can quantify the influence strength of a country in disseminating clauses that explicitly mention health provisions to others. Following an NLP (Natural Language Processing)-based text similarity analysis of Bilateral Investment Treaties (BITs), this study proposes a metric, 'Influence' (INF), which provides insights into the role of different countries or regions in the propagation of IIA texts among BITs. We demonstrate a comprehensive application of this metric using a large agreement dataset. Our findings from this application corroborate the evidence in the current literature, supporting the validity of the proposed metric. According to the INF, Germany, Canada, and Brazil emerged as the most influential players in defensive, neutral, and offensive health mentions, respectively. These countries wield substantial bargaining power in international investment law and policy, and their innovative approaches to BITs set a path for others to follow. These countries provide crucial insights into the direction and sources of influence of international investment regulations to safeguard health. The proposed metric holds substantial usage for policymakers and investors. This can help them identify vital global countries in IIA text dissemination and create new policy guidelines to safeguard health while balancing economic development and public health protection. A software tool based on the proposed INF measure can be found at https://inftool.com/.

Enterprise innovation and audit pricing: An evidence study from China's A-share listed companies.

Driven by innovation strategy, Chinese enterprises' innovation investment, and research and development capability have been continuously improved, and the audit risk caused by this has attracted widespread attention from the academic community. This study takes China's A-share listed companies from 2013 to 2021 as samples to empirically test the relationship between innovation input and audit pricing of Chinese enterprises. Research shows that the higher the innovation investment, the higher the audit cost. High-quality corporate governance, sufficient research and development personnel, research and development subsidies, and operating cash flow can all play a negative moderating role. A good innovation environment will weaken the positive influence between innovation input and audit fees. This study theoretically confirms the risk-oriented audit pricing mechanism, which is of great significance for optimizing enterprise innovation risk management and improving audit service levels.

The price continuity, return and volatility spillover effects of regular and after-hours trading.

This study employs a bivariate EGARCH model to examine the Taiwan Futures Exchange's regular and after-hours trading, focusing on the critical aspects of spillover and expiration effects, as well as volatility clustering and asymmetry. The objective of this study is to observe the impact on the trading sessions in Taiwan by the influences of the European and American markets, focusing on the essential roles of the price discovery function and risk disclosure effectiveness of the regular hours trading. This research is imperative considering the increasing interconnectedness of global financial markets and the need for comprehensive risk assessment for investment strategies. It also examines the hedging behavior of after-hours traders, thereby aiming to contribute to pre-investment analysis by future investors. This examination is vital for understanding the dynamics of after-hours trading and its influence on market stability. Results indicate price continuity between both trading sessions, with regular trading often determining after-hours price ranges. Consequently, after-hours price changes can inform regular trading decisions. This finding highlights the importance of after-hours trading for shaping market expectations. Significant profit potential exists in after-hours trading open interest, which serves speculative and hedging purposes. While regular trading volatility influences after-hours trading, the reverse is not true. This suggests Taiwan market information poses a higher risk impact than European and American market data, emphasizing the unique position of the Taiwan market in the global financial ecosystem. After-hours trading volatility reflects the absorption of international market information and plays a crucial role in advance revelation of risks. This underscores the importance of after-hours trading in global risk management and strategy formulation.