Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Javier Cifuentes-Faura, University of Murcia
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.
Chinese real estate is an important pillar of the economy, and the risk of it has shown a greater risk potential in recent years due to the impact of COVID-19. This study investigates the risks of Chinese real estate market and analyzes the corresponding risk-solving countermeasures in combination with government policies to achieve the smooth operation of the real estate market. Firstly, the study summarizes and investigates the risks of the real estate market in recent years, and comes up with four risk factors of the real estate market in China. Secondly, the study searches government policies in Chinese real estate industry through qualitative analysis and then analyzes the effects of their implementation. On this basis, the paper proposes a series of targeted countermeasure suggestions. According to the analysis, it is expected to put forward feasible proposals and preventive measures for the risks in Chinese real estate market, in order to promote the sound development of real estate enterprises.
Customer satisfaction plays an increasingly important role as it drives customer loyalty, fosters positive brand image, and provides a competitive advantage. Airlines that prioritize customer satisfaction are better positioned to thrive in a highly competitive market, attract and retain customers, and achieve long-term success. This paper focuses on enhancing customer satisfaction in the airline industry, with a specific case study of Delta Airlines. The study utilizes sentiment analysis techniques to analyze customer reviews from Skytrax and conducts a comprehensive SWOT analysis. Based on the analysis, targeted suggestions are provided to Delta, including leveraging technology to reduce the negative impact of delays, establishing uniform service standards, broadening global networks and partnerships, committing to sustainability and ESG initiatives, and ensuring quick responses to policy changes. However, the study acknowledges limitations such as reliance on a single data platform, the subjective nature of sentiment analysis, and the oversimplification inherent in SWOT analysis.In all, this study helps Delta to improve its customer satisfaction and contributes an research example to explore customer satisfaction in the airline industry.
The outbreak of the COVID-19 pandemic in 2020 has brought various risks and threats to the entire banking industry, from which we can also know the importance of bank risk management. This paper mainly describes, analyzes, and summarizes the five aspects of liquidity risk, credit risk, market risk, and systemic risk. With regard to liquidity risk, the article describes the impact of the Federal Reserve’s continuous interest rate hikes on the banking industry in order to curb the high inflation caused by the COVID-19 pandemic and takes the collapse of Silicon Valley Bank as an example for analysis. Regarding credit risk, this article takes Credit Suisse Bank as an example. Highly leveraged speculative transactions caused huge losses to the company due to the high default rate during the epidemic. For the analysis of market risk, the article synthesizes the deeds of Credit Suisse Bank and Silicon Valley Bank, focuses on the interest rate, and the reasons for the collapse of Credit Suisse and Silicon Valley Bank are different on the surface, but the underlying logic of the two events is traced to the same There is a close relationship between the current interest rate environment and the changes in interest rates in recent years. As for systemic risk, the article mainly writes about the purpose and effect of Basel III and the supervision of the capital adequacy ratio during the epidemic. Overall, commercial banks need to closely monitor these risks and take steps to mitigate them to ensure their stability and continued operations during the pandemic.
With the rapid development and upgrading transformation of Chinese financial industry and increasing investment quality, the investment enthusiasm of the masses has been improved. The prediction of stock price has an effective reference for investors to determine investment strategies. In this paper, ARIMA model is used to compare the model performance of Shanghai Composite Index, Shenzhen Composite Index and Science and Technology Innovation Board. This model is also fitted to the normal period and the shock period of the stock market respectively. The study found that the Shanghai Composite Index is the most mature market, and the fitting performance of the stock market in the normal period is better than that in the shock period. From the middle of 2015 to the beginning of 2016, the Shanghai Composite Index and Shenzhen Composite Index fluctuated significantly. This study not only helps investors to adopt more reasonable investment strategies, but also has important reference value for market regulators to guide the market effectively, avoid violent fluctuations in the stock market and maintain market stability.
The COVID-19 pandemic has had a profound impact on the global macroeconomic landscape, social order, and corporate operations. In light of the prevailing trend of economic globalization, the pandemic has significantly affected this process, but it will not deter the progress of economic globalization. The COVID-19 pandemic has not only had a considerable impact on various countries worldwide but has also affected China. To protect the lives and health of our citizens, China has implemented strict containment policies. The pandemic has also had a significant impression on company operations. Our research focuses on two major Chinese real estate companies, "Poly Developments" and "Vanke A." Both companies have publicly available and transparent financial reports that include information on profit figures, profit margins, cash flow, and debt situations. Despite the impact of the COVID-19 pandemic, Vanke witnessed a downward trend in total revenue and cash flow from 2019 to 2021, but a slight recovery was observed in 2022. Vanke displayed stable revenue growth in its development activities and gradually stabilized its profitability. The company also prioritized effective cash flow management and pursued a diversified development strategy, leading to a significant year-on-year increase in investment cash flow. Overall, Vanke exhibited remarkable performance in sustainable strategic planning, collaboration, and operational activities, maintaining its position as an industry leader.
This paper uses provincial-level gasoline retail price data in Canada to study the effect of tax reform on gasoline retail prices. It uses a dynamic difference-in-difference strategy to estimate the dynamic treatment effect of tax reform to see the dynamic changes of treatment effect in post-reform periods. We find that on average, the tax cut tends to be close to or around the full passthrough rate to the gasoline retail price. The treatment effect does not diminish over time and it is immediate after the tax reform. This means that the gasoline tax cut goes directly to consumers, it will work as a great macroeconomic tool in fighting the current inflation. The implications of effective gasoline taxation policy allow governments to adjust the gasoline taxation when needed to fight off inflation knowing that almost full taxation changes would pass down to the retail level. We conducted an additional robustness check to the robustness of our results.
In recent years, the view of economists and issuers of cryptocurrencies has changed a lot, from the original price of cryptocurrencies is unpredictable, to the view that cryptocurrencies are affected by several key factors. Confidence is one of the key factors, and it is thought to be controllable. However, many economists and cryptocurrency issuers have acknowledged that the support for confidence in the cryptocurrency market is complex and volatile. The goal of this paper is to propose ways to stabilize confidence in cryptocurrencies based on these studies. Explicit and implicit policies are one of the important factors affecting cryptocurrencies. The purpose of this article is to illustrate the close connection between cryptocurrencies and information, and to analyze how explicit and implicit policies affect cryptocurrency information and markets. The impact of explicit and implicit policies on the price of cryptocurrencies is unpredictable, but it still has regularity in the impact on the price of cryptocurrencies. This article analyzes several notable cryptocurrency explicit and implicit policy events to illustrate the differences and characteristics between the two types of policies. The influence of explicit policy is not positively correlated with its propaganda intensity in the short term but will be maintained and stable in the long term. Stealth policies tend to show dramatic changes in a short period of time and are highly correlated with popularity. While hidden policies can cause sudden fluctuations, issuers of cryptocurrencies should pay more attention to the effects of explicit policies because they are more durable.
With the increasing debt accumulated by local governments, land finance has become an important issue worth paying attention to in the China’s economy. Based on the political economy literature, this paper selects general public budget expenditure, regional GDP, per Capita GDP, urbanization rate of permanent people, and the development of the secondary industry as independent variables and the dependence on land finance of the local government as dependent variable. Panel data from 22 provincial local governments is collected for empirical research. The empirical results indicate that accelerating urbanization, general public budget expenditure have led to local governments obtaining extra-budgetary land fiscal revenue. The growth of regional GDP and the expansion of the secondary industry will bring revenue to the region, which will help reduce the dependence of local governments on land finance. The political economics research on land finance helps to understand the economic behavior of local governments in China and also contributes to understanding the issue of soft budget constraint.
This paper aims to use an empirical analysis to study and forecast the bilateral trade between the United States of America and the People’s Republic of China in the post-pandemic era. This study was conducted from three perspectives: aggregate demand (import), supply (export), and trading potentials. In sections of import and export analysis, the paper focuses on analyzing the aggregate behavior by households with Schmitt-Groh´e, Uribe, Woodford’s risk-averse demand model in an open economy and the firm production optimization model by Giovanni, Kalemli-Özcan, Silva, and Yildirim. In the analysis of the bilateral trade potentials, a gravity model of trade with one least square regression was applied and subsidized with the test method from Liu. From the study, it was found that the bilateral import and export between the U.S. and China are facing challenges from stringent policies, fragmentation of global value chains, and the residual effects of the pandemic on consumption. Thereby, the trading potential between the U.S. and China is restrained to an intermediate level, which is not beneficial for the robust growth of bilateral trade in the future.
This study delves into the profound impact of behavioral biases on investment decisions and provides valuable insights into methods for counteracting or surmounting these biases. Specifically, it focuses on four prevalent behavioral biases: loss aversion, endowment bias, framing bias, and overconfidence bias, examining how these biases can lead to potential investment mistakes or risks. To mitigate the influence of behavioral biases, the study proposes several strategies. Firstly, it emphasizes the importance of sourcing information from multiple perspectives and cross-referencing data to avoid reliance on biased or limited sources. Objective evaluation of one's skills and knowledge is also highlighted as a crucial step in reducing biases. Leveraging professional advice or feedback can provide an external perspective and help investors make more rational decisions. Furthermore, the study suggests that regular portfolio review and adjustments are essential to address biases and adapt to changing market conditions. Additionally, proactive visualization of potential outcomes can aid in mitigating biases by promoting a more realistic assessment of risk and reward. The study concludes that behavioral biases play a pivotal role in investment decisions. To bolster investment performance and satisfaction, investors are encouraged to comprehend and rectify these biases.