The Spain's Housing Bubble began from 1996 to 2007, during which the house price rose rapidly. During the 2008 global financial crisis, the Spanish housing market experienced a serious bubble burst. This paper aims to explore the reasons for the emergence and collapse of the Spanish housing bubble in 2008. Through the collation and research of relevant literature, it combs the important role and impact of financial innovation in it. The financial innovation before the housing bubble in 2008 was mainly characterized by the securitization of the housing market, deregulation and the massive inflow of foreign capital, which had a significant impact on the economic situation of Spain's housing bubble and its collapse highlighted the dangers of excessive dependence on real estate investment and credit expansion. Spain effectively addressed the crisis with policy adjustments, restoring some economic stability. This experience serves as a valuable reference for other nations facing similar challenges, highlighting the significant warning role of financial innovation in real estate bubbles.

Traditional thinking holds that institutional investors play a dominant role in driving stock price changes. However, the sharp rise in GameStop's stock price in January 2021 challenged this conventional view, drawing academic attention to the possibility that retail investors can also lead stock price fluctuations under the influence of social media. Using the VAR model, the impact of trading on prices can be divided into information transmitted to the market through trading and non-information factors, such as noise and sentiment. This study aims to examine the influence of institutional and retail investment on stock price returns during this event. The empirical results show that after removing the impact of noise on stock prices, the same transaction volume from retail investors has a far greater permanent impact on prices than institutional investors. Unlike the information trading hypothesis, which suggests that informed trading constitutes a larger proportion of investment volume, the findings support the view that advancements in social platforms and emerging stock trading technologies compensate for retail investors' informational disadvantages, thus demonstrating a more significant influence in this event.

This paper examines the impact of various leadership styles on the development of organizational innovation within an enterprise. A structured argument has been presented where the ability of different leadership styles and paradigms to impact and foster innovation within the company has been critically assessed. The analysis reveals that certain leadership styles are more conducive to innovation; however, this does not imply that a single leadership style is the most ideal framework for it. Based on this, it can be argued that ambidextrous leadership theory should be considered and introduced, as it focuses on the combination of different leadership styles. By doing so, it can effectively facilitate and foster innovation.

At present, the global economy is in a slow recovery phase, and there are many unstable factors in the international financial market, such as persistent geopolitical conflicts, volatility in the global stock market, high inflation and foreign exchange market, and the challenge of global debt sustainability, and the market volatility has intensified, which has had many negative impacts on the survival and development of enterprises. This thesis focuses on how the development of international financial markets affects the strategic decisions of Chinese enterprises. This paper employs the case study method. It focuses on the specific case of Huawei, a renowned enterprise. The analysis explores how Huawei has successfully navigated the fluctuations of the international financial market during its development. The aim is to enhance the resilience of China’s enterprises against the fluctuations of the international financial market. Huawei not only focuses on short-term response measures to cope with challenges but also pays more attention to long-term development planning, continues to increase R&D investment and insists on independent innovation, which is also the key to Huawei's continued growth in the turbulent international financial market.

With the rapid advancement of information technology, traditional business management models are facing unprecedented challenges, and companies must promptly adjust to adapt to the fast-changing market environment. Therefore, this paper focuses on the development strategy of enterprise management informatization in Zhejiang Province in the digital economy era. As a leading region in economic development, Zhejiang Province faces multiple pressures, such as optimizing resource allocation and intensified market competition, making it urgent to achieve transformative management practices through informationization. The implementation of development strategies for business management informationization is an essential path for Zhejiang Province's high-quality economic development, with profound practical significance and forward-looking value. Through in-depth reading and analysis of relevant literature, it becomes clear that informationization is not only a key factor in enhancing management efficiency and decision-making capabilities but also the foundation for promoting innovation and competitiveness. By strengthening emphasis on and investment in informationization construction, Zhejiang Province can effectively respond to market changes, seize opportunities presented by the digital economy, and realize the optimization and upgrading of its economic structure.

MIXUE Ice Cream & Tea is a leading beverage chain brand in China, offering tea drinks, ice cream, fruit drinks and other products. By the end of 2022, MIXUE has more than 25,000 stores in most Chinese cities and several overseas markets. Because of the huge market size MIXUE possesses and the CSR campaign it promotes, the authors chose MIXUE for their case study. This paper focuses on the CSR strategy of Honey Snow, showing how a company can integrate CSR into its corporate strategy to achieve long-term growth. The authors believe that effective CSR behavior can beneficially address social problems while helping to enhance brand image, gain corporate competitiveness, and contribute to sustainable development.

The application of mathematical models, such as ARIMA and the exponential smoothing model, has covered a wide range of financial analyses to meet the increasing demand for understanding financial assets. In spite of their efficiency, current works do face some cons due to poor fitness in dealing with a large amount of dataset pf high dimensionality. Also, accompanying the US presidential election, Tesla company has always become a hot spot. This paper collects the past 14 years historical data and proposes adoption of appropriate machine learning( Lasso, Random Forest and XgBoost) and deep learning models (RNN and LSTM) in the field of forecasting Tesla stock price and its volatility; adapt the parameters to achieve the best goodness of fit and incorporate the alternative factors, such as political indicators to better capture the complex shocks and jumps in realized volatility; and present how these models outperform traditional mathematical models. The evaluation of the model is done using the root mean squared error (RMSE) , Mean square error (MSE) and R square metrics.

The correlation between risk and expected returns is increasingly critical for both investors and corporations operating within financial markets. The Capital Asset Pricing Model (CAPM) offers a comprehensive framework for elucidating this connection by quantifying anticipated returns in accordance with systematic risks. However, there is little evidence to prove the practicability of CAPM on a particular company and its real-world meaning to that company’s investors and decision makers. For this reason, this essay explores the practical application of CAPM to Apple Inc., a prominent technology company with significant market influence. The study employs CAPM to analyze Apple’s stock performance, using data including the risk-free rate, Apple’s investment beta, and market return rates sourced from financial databases and government publications. By constructing and analyzing a CAPM model on Apple Inc., this research aims to offer insights into how well Apple’s stock compensates for market risk and how CAPM can aid investors in making informed decisions. Furthermore, the results offer crucial insights for Apple’s internal strategic development by evaluating the potential effects of market volatility on the company's share price.

The advent of Artificial Intelligence (AI) in marketing has sparked a transformative change. This paper delves into the burgeoning application of AI in marketing, examining its potential to enhance efficiency and sales while addressing the associated ethical concerns and customer privacy issues. Despite the potential drawbacks, such as data privacy violations and the erosion of consumer autonomy, the study explains the benefits of AI in personalizing customer experiences and streamlining decision-making processes, which can bolster customer satisfaction and drive sales performance. The research reveals that while AI may initially lead to dissatisfactory customer experiences and demotivation due to discriminatory classification, strict regulations and company controls can mitigate these issues, fostering trust and enhancing the benefits of AI in marketing. The paper also discusses the impact of AI on consumer autonomy suggesting that AI can facilitate more efficient decision-making without compromising consumer choice. The paper concludes that the integration of AI in marketing, when managed responsibly, can lead to a more personalized and efficient customer experience, resulting in increased purchase intentions and improved sales outcomes. It calls for future research to investigate the impact of customer characteristics on AI-assisted experiences and decision-making, the alignment of AI intelligibility with customer preferences, and the implementation of ethical programs in AI marketing.

With the rapid development of global economies, loans have become one of the key driving forces for economic growth. However, the outbreak of the subprime mortgage crisis served as a wake-up call for governments worldwide. To prevent malignant financial crises triggered by large-scale debt defaults, financial institutions in various countries should strengthen their regulatory mechanisms for financial loans. Based on this premise, this paper employs four probabilistic statistical models—Logistic Regression, Naive Bayes, Cox Proportional Hazards Model, and Chi-Square Test—to analyze the Credit Risk Dataset from the Kaggle platform. The study delves into the impact of various variables on loan defaults, the origins of credit risk, and its potential effects on the financial system. The results indicate that variables such as the loan-to-income ratio and loan interest rates significantly influence the risk of loan defaults. By comparing different models, this paper provides practical and effective evidence for financial risk regulation, enhancing the accuracy and reliability of credit risk assessments. It offers a new perspective for ensuring the long-term stable development of the economy.