Providing authenticity insurance services for consumers on online platforms can effectively solve the problem of fake goods in online shopping and improve consumers' purchase intention. Based on the supply chain structure composed of brand suppliers and online platforms, this paper takes brand suppliers and online platforms as the main bodies respectively under the influence of fake goods, establishes two decision models before and after the platform joins the authenticity guarantee insurance under different channel modes, considers that the cooperation mode between the platform and insurance companies is fixed insurance commission, compares different modes, obtains the optimal pricing, demand and profit, and analyzes the impact of adding authenticity guarantee insurance services on the supply chain and the channel mode preferences of supply chain members under different situations.

In the post-financial crisis era, global trade protectionism rises, and the cost of domestic production factors increases. The traditional operation and management mode of "OEM + agency" of small and medium-sized foreign trade enterprises in China can no longer adapt to the new market environment. These enterprises urgently need to transform and upgrade. This study takes Hangzhou Hexun Industrial Co., Ltd. as the research sample. It uses literature research method, PEST macro environment analysis method, SWOT matrix analysis method and case analysis method. The study sorts out four core problems of the traditional operation and management mode of small and medium-sized foreign trade enterprises in China. It analyzes the macro environment and industrial characteristics of China's foreign trade industry in the new trade environment. Combined with the internal resources and capabilities of Hangzhou Hexun, the study puts forward an innovative operation and management model for small and medium-sized foreign trade enterprises. This model takes "customer demand as the core and six dimensions as support". In addition, the study discusses the implementation path, applicable conditions and industry universality of the model. It provides theoretical reference and practical path for small and medium-sized foreign trade enterprises in China to break through development bottlenecks and improve core competitiveness.

This study examines whether companies can manage market expectations by voluntarily making substantial documents on Artificial Intelligence (AI) governance public. Using Meta’s July 18, 2023 release of Llama 2 and its accompanying governance materials (including the Responsible Use Guide and System Card) as the focal event, the study applies an event-study framework and a GARCH(1,1) model to examine abnormal returns and volatility dynamics., conducted an event study to estimate abnormal returns, using the Generalized Autoregressive Conditional Heteroskedasticity (garch) model (1,1), using the volatility comparison and the pseudo event. Variation in the variance of test conditions. The results show a delay in market assimilation. On launch day, the outstanding return is low, but the following trading day shows a significant outstanding return (+ 1.24%). In addition, post-disclosure specific volatility decreased by approximately 39.7% in one week, with a significantly negative GARCH event coefficient, which reduced perceived causal risk. Overall, proactive and detailed disclosure of AI compliance serves as a reliable signal to stabilize prices and reduce risk premiums. This study provides important insights into how positive disclosure of the AI regulatory framework affects market dynamics, especially in high-tech sectors. Focus on Meta's July 18, 2023 disclosure and review subsequent market responses, including exceptional returns and low volatility.

In the context of the rapid development of the digital era, the development of digital fashion has been widely discussed. Meta universe brand space, virtual clothing, NFT products, and so on are constantly emerging. The development of the fashion industry in the physical aspect has attracted much attention, but whether it can drive the real development of the real economy has not been fully discussed. The analysis shows that the brand reduces the threshold of entity purchase by identifying with consumers in the virtual space. Expand brand awareness and cultivate new consumer groups through social media communication. Brand can help the data in the digital space move forward in production logic, reduce resource waste and feed back the real economy. At the same time, this paper points out that this path largely depends on the brand's own cultural accumulation, and emerging brands may not be able to copy and use it directly. This paper puts forward the following suggestions: brands should pay attention to different consumer groups and optimize the quality of physical products to achieve sustainable development while promoting digital experience.

In terms of the context of China's dual carbon goal,carbon emission trading has become an important kind of market mechanism which promotes green industry governance. In this study we choose listed manufacturing enterprise from 2007-2022 as our main subject and use DID method to find the real effect of such policy. From the results we can see that carbon trading enhances companies' total factor productivity and it comes from two aspects of technological improvement and better refinement. All sorts of robustness checks are done and the main findings hold true. This proves that the Porter Hypothesis is correct, providing good empirical evidence to improve the Carbon Trading System and promote the high-quality development of the Manufacturing Industry.

Gold is referred to as a critical safe-haven asset, which is significant to the international financial markets and risk management. As such, it is significant to the investors and financial institutions to predict the prices of gold and the stocks concerned with gold. The paper aims at comparing the gold price forecasting of the data at various data frequencies (daily, weekly, and monthly) using Autoregressive Integrated Moving Average (ARIMA) model. This paper will initially preprocess the data based on historical gold prices data in a representative international market between the years February 1, 2006 and January 1, 2026, by using stationarity tests and differencing operations. This paper, therefore, builds ARIMA models and optimizes them at each frequency to determine how well they predict using measures like MAE and RMSE. The empirical findings indicate that the long-term forecasts provided by lower-frequency data such as monthly yield are more stable and accurate and the short-term forecasts by higher-frequency data are more accurate and volatile in forecasting errors. This paper is not only adding to the existing empirical body of knowledge on the forecasting of gold prices at various data frequencies, but also offers a practical decision support to the market participants to develop specific trading and risk management strategies according to the various forecasting horizons.

It looks at diabetes death data in the U.S. for certain age groups from 1999 to 2020. The goal of this article is to identify the patterns of mortality rate over time and whether past patterns can be applied to predict what will happen in the future. Looking first at the data showed that the time series was not stationary, which meant that it was unable to be modeled directly. Data was differenced before the model was built to fix this problem. This paper chose the ARIMA(0,1,0) model after looking at a number of other options. Although this model was pretty simple, it fit the dataset well. It accurately showed the main trends in the data without making things more complicated than they needed to be. One interesting finding about the data is that the number of deaths went up a lot in 2020. It doesn't fit with how matters have changed in the past, which makes it stand out as a major anomaly.

The reform process within China's financial markets has led to rapid expansion and structural changes in China's futures markets. As globalization and market volatility have developed, futures markets have garnered heightened attention concerning their ability to manage risk and discover prices. The purpose of this paper is to evaluate the structural and institutional challenges to the development of China's futures markets. In China's futures markets, I will consider the market structure, the structure of participation, and the performance of the market, to evaluate conjunctively the market structure, information asymmetry, and the shortcomings of regulations. The principal conclusion is the substantial development China's futures markets have made in both their volume and overall market performance, and the chief deficits emerging market participation, the diversity of available products, and the institutional deficits of China's futures market regulations. The reform of regulations should be the principal focus for the improvement of efficiency and the stability of China's futures markets. This paper is a step toward a qualitative and sustainable growth of the futures markets in China, in a market system that demands a more functional performance relative to the volume of the markets.

The energy sector has experienced significant changes in recent years, with natural gas becoming an important transitional energy source in the shift toward renewable energy. As natural gas plays a key role in electricity generation and industrial production, accurately forecasting its price has become increasingly important. This study applies time series analysis methods to examine and predict Henry Hub natural gas prices using historical data. Specifically, naïve, drift, exponential smoothing (ETS), and ARIMA models are implemented following standard procedures, including stationarity testing, differencing, and model identification using ACF and PACF. The results show that the ARIMA model outperforms other models in terms of forecasting accuracy, indicating that natural gas prices exhibit certain predictable patterns rather than purely random behavior. These findings suggest that time series models can effectively capture the dynamics of energy prices and provide useful insights for short-term forecasting. The study contributes to a better understanding of energy market behavior and offers practical implications for investors and policymakers.

As market conditions become increasingly volatile, stock price predictions are becoming more challenging for all investors and researchers. Therefore, using a suitable model structure for stock price forecasting is crucial. The ARIMA model is a classic stock price prediction model in time series analysis, capturing patterns in past price changes and the impact of past shocks. This article analyzes and predicts Zijin Mining's stock price based on this model, selecting the closing prices of trading days from 2021 to 2025. The model ARIMA(1,1,1), which showed the best fit and had all significant parameters, was selected to predict the next five trading days. The results indicate that the model's short-term predictive effect on Zijin Mining's stock price is only average, possibly because it did not take into account market sentiment and macroeconomic adjustments. In future research, a mixture model or the inclusion of some macroeconomic variables could be considered.