Green accounting is a new discipline that has emerged in order to deal with environmental governance, ensure that the economic benefits of enterprises are harmonized with environmental development, and implement the implementation of sustainable development strategies. In today's deteriorating ecological environment, the emergence of green accounting is particularly important. This paper describes the concept of green accounting and the necessity of green accounting for the current global ecology, discusses the possible problems of implementing green accounting according to China's national conditions and the development history of green accounting in countries around the world, and then gives the corresponding practicable solutions to the existing problems. Although the implementation of green accounting has not been widely popularized at this stage due to the imperfections of laws and regulations and the lack of consciousness of companies and individuals, it is expected to be vigorously implemented in the future as the economy develops and people's awareness of environmental protection increases.

Within the framework of the digital economy age, artificial intelligence is sweeping across the world today. Artificial intelligence technology has made remarkable progress, and various industries have been affected, resulting in significant and even profound changes. The employment market has also been impacted as a result. This article uses case and problem analysis methods to explore artificial intelligence's influence on the labor market and societal structures in the digital age. AI's effects on the employment sector are mostly focused on in three aspects: the dangers of automation at work, the effects of AI on employment that are balanced, and how AI affects employment structures. It also has short-term and long-term effects on income inequality and requires the transformation of worker skills to high-tech digitization. Based on this, this article also puts forward suggestions for the follow-up development of enterprises, government, society, and education and puts forward thoughts.

The main axis of the US-China game deepens from Trump's trade war to Biden's tech wars. At the same time, the chip industry has gradually become the main battlefield for the United States to suppress and curb China's scientific and technological fields. Whereas chips are an essential part of modern military equipment, communications facilities, nuclear power plants, transport systems and other critical infrastructure. Early mastery of the core chip technology can be early not to be constrained by others, to avoid the risk of being "necked".This paper will analyse the reasons why China makes the US feel that its national security is threatened, the impact of the US-China trade friction on China's science and technology sector, and find out the measures that China can take to deal with the US sanctions related to the science and technology sector, as well as the right way for China to get along with the US.

The objective of this study is to predict the Chevron’s Corporation stock market performance by conducting a comparative analysis of contemporary and conventional machine learning approaches, with a particular focus on the CNN-LSTM and ARIMA models. Given the unpredictable characteristics of the crude oil industry, forecasting stock prices with precision has emerged as a pivotal dilemma for both investors and analysts. This research utilizes ARIMA, which is representative of conventional time series forecasting methods, and CNN-LSTM, which embodies the latest advancements in deep learning techniques, to address the intricacies associated with predicting stock prices in the energy sector. Through a comprehensive data preparation process and the application of sophisticated modeling techniques, this study aims to rigorously assess the predictive capabilities of both models in forecasting Chevron's stock prices. Traditional statistical analysis often relies on the ARIMA model as a benchmark, while the CNN-LSTM model seeks to identify the complex, non-linear patterns prevalent in financial market time series data. This research conducts a comparative evaluation of the two models, focusing on their accuracy, strengths, and limitations. The findings carry important implications for the realm of financial forecasting, shedding light on how modern deep learning techniques stack up against traditional approaches in predicting stock market movements. Beyond contributing to scholarly debates on financial prediction, this study also provides actionable insights for financial analysts.

As an integral component of the financial market, stock prices have attracted the attention of many investors. Due to the frequent fluctuations and sensitivity to market dynamics, predicting stock prices is challenging. The volatility of stock prices and potential significant differences across different periods add to the difficulty of forecasting and reduce its accuracy. The Random Forest model and the LSTM model, as representative models in decision trees and deep learning algorithms respectively, demonstrate high accuracy and adaptability in predicting stock prices. The paper will separately utilize the Random Forest model and the LSTM model to fit the S&P 500 price data from 2013 to 2018 (represented by Apple's stock prices) as training and testing sets, and then compare the fitting results of the two models. The conclusion is as follows: In the absence of white noise in the data, the Random Forest model demonstrates smaller biases in predicting data compared to the LSTM model, and it can also respond more swiftly to price fluctuations.

In this study, cutting-edge methods of applying deep learning techniques to stock market predictions were explored, specifically focusing on the stock data of Tesla Inc. Long Short-Term Memory networks (LSTMs), an advanced form of Recurrent Neural Networks (RNNs) capable of effectively addressing the issues of vanishing and exploding gradients that traditional RNNs face, were employed. This enhances the model's learning capability and predictive accuracy for time series data. The innovation of this research lies in the integration of the LSTM model with the Random Forest algorithm, forming a hybrid model aimed at leveraging the complementary strengths of both models to improve the accuracy of stock price predictions. Through empirical analysis of Tesla's stock data, it was found that the hybrid model outperformed the individual LSTM model. This result not only proved the effectiveness of LSTMs in handling complex time series prediction problems but also demonstrated the potential of enhancing predictive performance by integrating different types of models. The findings offer a new perspective for financial market analysis and prediction, especially in the use of deep learning technologies for stock price forecasting. They provide valuable references for future research and practice in this field. Further investigations could explore the applicability of this hybrid approach to other financial instruments and markets.

To provide an objective analysis, this study examines three quantitative trading strategies: Momentum, Moving Average Crossover, and Machine Learning individually but in a common methodological setting. In order to achieve higher returns at lower levels of risk due to the advent of algorithmic trading, such strategies must be explored. The two strategies that we analyze include the Momentum strategy that capitalizes on the persistence in price trends and the Moving Average Crossover strategy that relies on average price movements as trading signals. In addition, in this study, Machine Learning methods are applied which implement predictive algorithms to predict the price movements in the future based on their historical patterns. In order to assess the performance of each strategy, this investigation relies on one data set and uses a series of financial metrics to see how well each strategy performs with the objective of identifying both strengths and weaknesses that these strategies exhibit within different market situations.

The science of examining how corporate authority is distributed is known as corporate governance, when used broadly. Viewed narrowly, it is a branch of science that sits at the level of company ownership, investigates the process of appointing professional managers, and performs regulatory activities regarding the discharge of professional managers' obligations. The "management right level" of enterprise management is based on science and involves the enterprise owner and management right authorization, or management right in the case of authorization, in order to accomplish company goals and utilize all available methods of operation behavior. On the other hand, corporate governance is built at the "ownership level" of the business based on science and deals with professional managers' scientific approval and oversight. This article investigates the role of corporate governance in the financial crisis and why stock prices did not anticipate bad corporate governance, setting the scene for the global financial crisis of 2008. On the basis of existing research, analytical studies were conducted and summarized into conclusions. As shown in this paper, inappropriate corporate governance ultimately leads to an increased risk of economic crisis. Therefore, it is important to adopt the necessary tools to improve corporate governance. Management should formulate appropriate corporate strategies and ensure that they are effectively implemented, ensure that internal controls are effective and develop a good corporate culture, etc. The government should also improve the relevant regulations and ensure their implementation.

Since the creation of Bitcoin in 2008, these digital currencies have not only attracted widespread attention from the public and economists, but have also triggered a rethinking of the nature of money, the store of value, and the modes of exchange. This paper explores the transformative impact of Bitcoin and digital currencies on global finance, emphasizing their emergence as a challenge to the traditional concept of money and a paradigm shift. Furthermore, the paper delves into the birth of Bitcoin, its decentralized nature and its pioneering role in the field of digital currencies, discusses the historical background, technological underpinnings, and monetary functions of digital currencies, and highlights the potential and challenges of their integration into the financial system. It aims to examine the characteristics and functions of bitcoin and digital currencies in the contemporary financial landscape, focusing on how they can challenge traditional monetary policy as an emerging financial asset, as well as their potential impact and integration challenges in the global economic system.

With the popularity of blind boxes, they have changed from the original toy products to today’s trend goods, and even more and more products appear in the market in the form of blind boxes. Therefore, this paper would like to analyze the reasons for the popularity and success of blind boxes, as well as the psychology of people who like to buy blind boxes. In this paper, the marketing strategies and consumer psychology of the blind box are analyzed. The first is about the history of the blind box and its development. The second is to analyze the marketing strategies used by blind box companies, including the ordinary style and secret style of blind box, and the strategies of blind box companies to launch products through cooperation with various intellectual properties. At the same time, this paper believes that the marketing strategies of blind box companies are formulated to grasp the psychology of consumers to a certain extent, so this paper also analyzes the psychology of consumers who purchase blind boxes. This paper finds that the ordinary and secret style marketing strategy adopted by blind box enterprises can stimulate the purchasing behavior of consumers, and the strategy of cooperating with popular IP or independently developing new Intellectual property (IP) can promote innovation and the development of the blind box industry. With the expansion of the blind box market, it promotes economic growth to a certain extent.