In light of the increasingly vital role of Artificial Intelligence (AI) has played in this era, it is imperative to conduct a comprehensive examination of the exact impact of specific event associated with AI on advanced corporations, economies and even countries. This study focuses on analyzing the recent event involving the connection of ChatGPT to Microsoft. To be more specific, this paper employed 3 machine learning models and one Stata model “event study” to respectively identify the best fitted model and its precise impact. In this work, 3 machine learning have been used, namely Support Vector Regression (SVR), K-Nearest Neighbor categorization algorithm (KNN) and Random Forest, to spot the model that fits the Microsoft stock the best. Initially, data was collected from Yahoo Finance, which is set and indexed in advance. Subsequently, data is individually put to train the 3 models. Ultimately, Mean Square Error (MSE), Root Mean Squared Error (RMSE) and R squared score are calculated with care and compared to obtain the results. Additionally, after collecting the data, a sensible window of event study has been set. Experimental results demonstrated that Random Forest performs the best among the 3 models and the specific event of ChatGPT connecting to Microsoft has a limited effect on the firm’s stock price.

This article investigates the relationship between AI and the labor market, with a specific focus on job replacements and new opportunities. It examines the impact of AI on various sectors such as technology, law, medicine, finance, and creativity. AI has revolutionized work by automating repetitive tasks and, consequently, displacing certain job roles. However, it has also generated new avenues that demand a combination of technical expertise and human skills. To thrive in this evolving landscape, individuals and organizations must adapt to the changing nature of work and harness AI's potential for innovation and productivity. While embracing AI-driven advancements, it is crucial to uphold human values and ethics. The article emphasizes the need for a harmonious association between humans and AI in the labor market. By leveraging the unique strengths of both, individuals and organizations can drive efficiency, creativity, and productivity. This entails cultivating agility and adaptability to navigate the transformative impact of AI. Organizations must proactively integrate AI as a tool to enhance productivity and create new opportunities while ensuring that the human element remains integral to decision-making and problem-solving processes.

This paper utilizes an ARIMA model to predict the future exchange rate and trade balance of China. The primary data is the monthly average exchange rate of USD/RMB, while China's monthly trade balance serves as auxiliary data. The findings indicate a projected downward trend in the USD/RMB exchange rate, continuing from 2023 to 2024 and stabilizing around 6.7 from 2024 to 2025. This implies a depreciation of the Chinese currency against the US dollar. Additionally, China's trade balance is expected to experience modest growth over the next two years, albeit at a significantly reduced rate compared to previous years. These projections highlight the challenges faced by Chinese exporters and suggest evolving global trade dynamics. The paper discusses policy implications for managing exchange rate fluctuations and sustaining balanced trade relations. It emphasizes the usefulness of the ARIMA model for forecasting exchange rates and trade balances while acknowledging the limitations and potential impact of unforeseen events or policy changes on the outcomes. The study concludes by suggesting avenues for future research to improve the accuracy and robustness of such forecasts, encouraging continued exploration in this dynamic field of study.

In recent years, China has been gradually and progressively promoting the opening of its capital markets to the outside world. In this study, a questionnaire survey and SEM (Structural Equation Modeling) model analysis were used to investigate the mechanisms and outcomes of capital market openness on Chinese investor sentiment based on the Land-Hong Kong Link Policy.The study reveals that following the opening of the capital markets, individual investor sentiment tends to become more rational due to factors such as changes in investor structure and shifts in value investment concepts. In the future, it is recommended that China's capital markets promote basic investment education and enhance the development of financial regulations to progress towards a deeper level of openness.

Due to the improvement of information technology, the emergence of 5G network, and the rapid development of global informatization, the Internet economy, as an emerging force, has developed rapidly, and gradually formed a network economy and society with Internet as the center. By April 2023, the number of Internet users worldwide has reached 8.5 billion. In this case, more and more people advocate Internet finance, the real economy has been affected to a certain extent. The real economy is the source and cornerstone of social development, marking the economic development level of a country or region. How to improve the growth of the real economy has been the focus of the municipal government. Therefore, the purpose of this study is to explore the impact of Internet finance on the real economy, find out the reasons for the slow development of the real economy, and explore how to use Internet finance to promote the development of the real economy. By using iterature review and some data from the Internet, this article finds that Internet finance will promote and hinder the development of the real economy.

The real estate industry has always been a significant factor in the Chinese economy which can be easily affected by many sectors such as demand and supply imbalance, aging, and information asymmetric between producers and consumers. Under the new economic situation in China, the development and innovation of the real estate industry play a crucial role in the Chinese economy. The paper explores the future trend of real estate by analysing the current situation of the Chinese real estate industry through a method of literature review and data analysis. The result shows that the first, the house vacancy rate is a main factor that the government should control in order to balance the demand and supply. The price of the house would decrease caused by the unbalanced supply and demand. Secondly, the high unemployment rate would also affect the demand, which could lead to decrease in house price. Lastly, the uneven development between different areas would lead to negative impact for the real house industry as well. This paper find that government intervention plays a significant role in the control of demand and supply of the real house industry and The real estate industry ushered in a new stage and began to explore new ways of development that fit publics needs.

In recent years, with the continuous development and innovation of fintech, it also has a series of impacts on network security. This paper uses the literature review method to study the influence of fintech on financial industry network security and regulatory countermeasures. In the aspect of impact analysis, this paper first discusses the data security and algorithm risks brought by the extensive application of artificial intelligence. Secondly, the use of big data technology may lead to the risk of data leakage and abuse. Finally, lagging regulatory policies may increase security risks in the financial sector. In response to these impacts, this paper proposes four regulatory policies, such as strengthening data security and privacy protection, strengthening management efforts, balancing the relationship between innovation and risk, and introducing relevant policies. The study believes that effective regulatory measures can deal with the security challenges in the development of fintech, and ensure the stability of the financial system and the rights and interests of users.

In financial analysis, stock price prediction is a difficult and important problem that has received a lot of attention from researchers and practitioners in recent years. The application of machine learning and artificial intelligence algorithms to stock price forecasting has demonstrated significant potential for increasing forecasting accuracy. Long momentary memory (LSTM) and transient convolutional networks (TCN) are two famous profound learning calculations that have been generally utilized for stock cost expectation. The most recent approaches to stock price prediction using LSTM and TCN methods are reviewed in this paper. We highlight the most recent research trends in this field and talk about these methods' benefits and drawbacks. Additionally, we discuss potential future research directions in this field. The survey is expected to give a knowledge into the present status of exploration on stock value forecast and guide specialists and experts in working on the exactness of expectation.

Real estate price prediction is one of the key research topics contemporarily. Based on the rapid development of Big Data, machine learning has gradually become the mainstream tool for housing price prediction. The XGboost and LightGBM models, as new advanced mod-els in recent years, have received widespread attention in the application in housing price prediction. Therefore, this study identifies the house price prediction based on XGboost model and LightGBM model and compares them with other models in order to obtain an analysis of the advantages and disadvantages of these two models in housing price predic-tion. According to the analysis, both models have ad-vantages such as high accuracy, high efficiency, and fast training speed. However, although XGboost has the smallest error pre-diction, it requires more computational time, thereby increasing computational costs. In ad-dition, LightGBM has disadvantages such as high overfitting risk in small sample sizes and increased sensitivity in noisy datasets. Therefore, besides the model studied in this article, feature selection methods such as Filter and Wrapper can also be introduced in subsequent studies to further improve the prediction accuracy.

With the outbreak of the 2019 epidemic, many industries have been affected to a greater or lesser extent. The real estate market is huge and involves many industries, so it is particularly important to analyze how it will be affected by the epidemic This study analyses the profitability and financial position of three large real estate companies, Vanke A, Jindi Group and China Merchants Shekou, by using a case-based analysis of their revenue and profit growth rates, return on net assets, gross sales margin, net profit margin and debt servicing capacity. companies. Based on the comparison of the three red lines and the impact of the epidemic on the development of the three companies before and after the epidemic, corresponding recommendations and decisions are given. This paper will help to analyze and predict how the property market will fluctuate in the aftermath of the epidemic and what measures should be taken in aftermath.