Exchange Traded Funds (ETF) can largely avoid non-systematic risk, but investors often cannot avoid systemic risk, and the hedging function of stock index futures can do this. Therefore, hedging ETF with stock index futures has become a good investment strategy. Based on the trading data of the China Securities Index (CSI) 300 stock index futures and CSI 300 Exchange-Traded Fund (ETF), this paper analyzes the optimal hedging ratio of CSI 300 stock index futures by Ordinary Least Squares (OLS) and dynamic Error Correction Model - Generalized Autoregressive Conditional Heteroskedasticity (ECM-GARCH) model and compares the hedging performance predicted by the model to avoid the systematic risk of ETF. The results show that the hedging effect of the dynamic hedging model is better than that of the static model, and the ability to avoid systemic risk is also better. The predictions of both models show that stock index futures hedge ETFs very well. The dynamic model is more able to reduce the heteroscedasticity of the transaction data.

In today's urban planning field, Transit-Oriented Development (TOD) has become a crucial research topic. With the acceleration of urbanization and increasing population density, traffic problems have become key factors affecting urban development and living quality of residents. Therefore, discussing how to effectively plan urban traffic and realize the harmonious coexistence between traffic and city has become the focus of urban planning scholars and practitioners. However, there is still a lack of unified understanding regarding the role of railway TOD planning in urban development. Therefore, this paper takes Shenzhen as an example and adopts a SWOT analysis to examine the TOD development mode of Shenzhen's railways, based on the city's structure, railway layout, and development strategy. This paper finds that the polycentric urban development pattern of Shenzhen is closely connected to its railway planning. The foundation for Shenzhen's railway TOD construction is relatively solid, and the prospects for development are optimistic. However, many issues in the future development space and planning design still need to be addressed.

Studying the impact of changes in the savings rate on fluctuations in US Treasuries is significant. This paper conducts a linear regression analysis of the yield of US Treasuries, inflation rate, GDP growth rate, and US savings rate over the past decade, aiming to explore their relationships and influences. Based on economic data from the United States, a model is constructed, which is further applied to data from the European Union to validate its applicability and accuracy across different economic systems and to investigate the impact of disparities in data between different regions on the results. After analyzing the data and obtaining results, various types of economic data from the European Union are used as model variables for testing. Following a correlation analysis of the data, the conclusion is drawn that even different regions or countries exhibit varying positive or negative correlations between their economic data and US Treasury yield fluctuations. This paper delves into the analysis and comparison of the interaction between US Treasury yields and economic indicators of both the United States and the European Union, exploring whether these interactions manifest differently in the two distinct economic systems.

Through the dynamic stochastic general equilibrium model (DSGE model), this article explores the balance between economic development and environmental protection, focusing on the in-depth mutual influence among economic agents such as households, banks, producers, and government in promoting economic growth and achieving environmental protection. The model comprehensively considers factors such as production technology, carbon tax policies, bank loan rates, and government fiscal policies, aiming to analyze the specific impacts of these factors on economic growth, environmental protection, and social welfare. By detailed settings and analysis of consumption, savings, and labor supply decisions of households, the financial intermediary role of the banking sector, and carbon emissions and environmental technology use in the production sector, this study provides theoretical support for an environmentally friendly economic growth path. Through policy analysis, this article reveals the short-term and long-term effects of positive technological shocks, taxation on energy firms' loan rates, carbon tax policies, and government spending on the economy and the environment, providing a theoretical basis and reference for formulating relevant economic and environmental policies. The results indicate that appropriate macroeconomic policies can effectively promote economic growth while reducing carbon emissions and enhancing social welfare.

This study investigates the predictive capabilities of Support Vector Regression and Long Short-Term Memory networks on stock price trends across different market conditions—bear, bumpy, and bull markets. With the ongoing evolution of machine learning technologies, their application in financial forecasting has shown substantial potential for capturing complex patterns in vast datasets, which traditional models often fail to process efficiently. This study particularly focuses on the performance of these models in forecasting stock prices from the S&P 500 index, evaluated through the lens of Modern Portfolio Theory (MPT). The models are assessed based on their ability to forecast trends and their implications when applied to constructing investment portfolios, evaluating key financial metrics such as expected returns, standard deviation, Sharpe ratio, and maximum drawdown. The findings indicate that while both SVR and LSTM exhibit competence in trend prediction, especially in bull markets, their predictions diverge from actual market performance when applied to portfolio construction under MPT. This discrepancy underscores the need for further refinement in modeling approaches to enhance accuracy and reliability in real-world investment scenarios. This research contributes to the empirical literature by demonstrating the practical implications of deploying advanced machine learning and deep learning models in dynamic market environments and suggests directions for future enhancements.

Against the background of the rapid evolution of the current Web3.0 technology paradigm, blockchain technology is gradually penetrating into the core of the tourism industry, heralding a disruptive industry transformation. This paper focuses on the Trekki NFT project launched by Ctrip as a reference to explore in depth the innovative application of Web3.0 technology in tourism service platforms and the deep-rooted value it implies. This paper firstly elaborates the core concept and key technical architecture of Web3.0, as well as the theoretical connotation and practical boundaries of non-homogenized tokens (NFT). Through a panoramic analysis of the Trekki NFT project, the paper reveals how Web 3.0 technology can stimulate the restructuring of business models of travel platforms, optimize the quality of user experience, and explore new opportunities for industry growth. In particular, the study demonstrates the strategic significance of Web 3.0 technologies in promoting the innovative development and industrial upgrading of the tourism industry. The discussion section of the study has refined a set of strategic frameworks and action recommendations for the deep integration of tourism platforms and Web 3.0 technologies, aiming at guiding and accelerating the technology adoption and progress of the tourism industry in the wave of the digital economy in the future.

In times of economic turbulence and geopolitical uncertainty, the fluctuations in crude oil prices can be particularly pronounced, posing significant challenges to investors by heightening market risks. This study sets out to explore the multifaceted landscape of risk associated with both crude oil and silver assets, with a specific focus on portfolio volatility. Through meticulous analysis guided by the Sharpe ratio, we aim to delineate an in-depth understanding of the efficient frontier, comparing portfolio performance against that of the S&P 500 index, especially during periods characterized by extreme market volatility. Our empirical investigations underscore that while silver displays certain tendencies towards risk aversion, it does not meet the criteria to be deemed a dependable "safe-haven" asset in the context of crude oil. These findings have significant implications, providing a catalyst for driving innovation and fortitude across interconnected domains. By enhancing our comprehension of portfolio dynamics in turbulent market environments, this research contributes to the advancement of strategies aimed at navigating risks effectively.

With the global trend of digitization gaining prominence, the usage of machine learning methods such as Support Vector Machines and Reinforcement Learning for stock price prediction is becoming a hot topic. Over the past 40 years, China's economic market has undergone significant changes since the country's reform and opening up. In this study, the closing price and return of China's CSI 300 stock index are used as the database, and various data processing methods such as wavelet domain denoising, RSI screening and various SVM model optimization methods such as grid search and cross-validation are used to predict the upward or downward trend of stocks on the day after. The results of the study are presented by the model evaluation report and the heat map of the confusion matrix, which shows that the model prediction accuracy is 61% with the default parameters, and the accuracy improves to 67% after optimization. The results indicate that support vector machines are effective in stock price prediction, but there is still room for further improvement. This paper offers a potential approach that can increase return on investment and assist investors and financial institutions in making more informed investment decisions.

This paper focuses on a specific analysis of the corresponding situation of Disney. It introduces the development of Disney as a whole and lists three companies in different industries in the market that compete with Disney in different aspects, namely Netflix, Comcast and Mattel. By collecting and analyzing the corresponding data, the four companies are specifically compared in terms of liquidity, repayment ability and profitability respectively. The result is that Disney's profitability is slightly weaker than that of the other three companies, which may be due to the fact that Disney has a large investment in streaming media, but not resulting in a return that matches it. This paper also analyses Disney's stock market performance in relation to its competitors and shows that although Disney stock is overvalued at this stage, the NTM P/E ratios show that it will improve in the next period. This paper also analyses and summarizes Disney's strategy and risk profile and makes some predictions for the future. This paper believes that in the future Disney will have a better growth and also a better experience for the consumers.