Marketing can be very profitable for a company and the success of well-known companies is often accompanied by a successful marketing strategy. Bill Bowerman and Phil Knight founded the largest sportswear firm in the world, Nike, under the name Blue Ribbon Sports in 1964. The company changed its name to Nike in 1971, and as the industry leader, it ascended to become one of the top sports brands in the world, with revenues far exceeding those of its competitors and a market share of 38.23% of the entire industry, and is widely recognised in the sports industry for its strong marketing capabilities. It has positioned itself as an active brand with people's aspirations, comfort and national values in mind and has achieved great success worldwide. In this paper, authors will use Nike as case study and conduct an in-depth analysis of the marketing strategies used by Nike personalised customization, content marketing, social media, and hungry marketing. Combined with a comprehensive analysis of relevant literature and market data, consumer demand for unique, bespoke, personalised experiences can be met by personalisation, increasing customer loyalty and generating more business for the company. Content marketing builds consumer awareness and loyalty through the creation of engaging content and effectively communicates brand image and values. The rise of social media has provided consumers with an avenue to identify product needs and reference the buying behaviour of others, while helping to build brand awareness and positive emotional communication. Hunger marketing creates higher brand value and consumer demand through scarcity and limited sales strategies, further enhancing Nike’s uniqueness and price premium. The author also uses the current emerging sports brands lululemon and lining as references and uses the successful development strategies of these two brands to provide effective marketing suggestions for Nike.

Since the trade friction between China and the US in 2018, the US has maintained a surplus in agricultural trade between China and the US, and China will also impose agricultural tariffs in response to the US sanctions. the list of tariff increases announced by China and the US in 2018 covers all types of agricultural products traded between China and the US, meaning that agricultural trade becomes an important part of the game between the two countries. Based on the list and the analysis of previous literature, this paper focuses on the development of Chinese agriculture and tries to provide countermeasures. The study finds that the US-China tariff war has both positive and negative impacts on China's agricultural development, bringing benefits to some enterprises and pushing back the development of new foreign trade markets. It has caused losses to relevant stakeholders, increased price volatility of agricultural products, and is detrimental to the development of agricultural foreign trade and the restructuring of agriculture. In the future, the development of Chinese agriculture can be promoted through the promotion of market stability, the continuous optimization of the agricultural structure and the improvement of modernization.

Under the trend of economic globalization, the cost of renewable energy development has been decreasing globally, and renewable energy development has become the focus. But since 2018 the trade war between China and the United States has been escalating, and China's renewable energy development has been hindered by the United States because China's core technology is still not fully mastered. How to deal with related issues is crucial for China's development. The article uses literature analysis to analyze the impact of the US-China trade war and China's own overcapacity problem on China's renewable energy development, and to provide countermeasures to address the corresponding problems. This paper argues that the sustainable development of China's renewable energy industry faces three basic problems, which are restricted by the trade barriers of the US-China trade war, the lack of some core technologies, and the potential overcapacity problem in the future. In order to achieve sustainable development of renewable energy in China, it is suggested seizing the opportunity of the Belt and Road policy development with energy cooperation as the key area of development. By analyzing the various factors affecting the development of China's renewable energy industry, and by giving suggestions and countermeasures, this paper hopes to give a comprehensive understanding of the development history and prospects of the renewable energy industry in China and provide reliable suggestions for the development of related industries in China.

Service Trade is one of the key topics of current research. With the impact and changes brought about by the out break of COVID-19 pandemic on China’s service trade, China is facing a series of challenges such as changing consumer demands, insufficient openness, cross-border cooperation, and sustainable development. Based on literature review and analysis, this paper sorts out the traditional field of China’s service trade from its initial stage. It explores the transition from a marginal role to a prominent position in the period from 1978 to 2001. It further invetigates the increasing internationalization of China’s service trade from 2001 to 2012. Since 2013, China’s service trade has entered a new phase of development. China’s trade in services has continued to go up, especially in emerging areas such as digitalization, the Internet, and cross-border e-commerce. To obtain comprehensive information and data, this research examines relevant literature and policy documents on China’s service trade, focusing on strategic adjustments and development in response to the influence of the pandemic. The study indicates that in the context of the pandemic, China’s service trade strategy is characterized by diversification and flexibility, which will help reinforce the competitiveness of China’s service trade and boost the recovery and development of the global economy.

This study explores the relationship between brand image and consumer behavior from the perspective of consumers. It consists of three parts. The first introduces the establishment and maintenance of brand image. The second reviews and analyzes the relationship between brand image and consumers. The third part reviews and analyzes the impact of brand image on consumer purchasing behavior. Brand and marketing are both very important to enterprises. Brand and marketing support each other, complement each other, jointly serve the market.

With the development of the industry, brands have experienced a lack of innovation and Homogeneity while the Internet has placed higher demands on them. Therefore, brand co-branding IP as a powerful marketing strategy has become a hot topic of discussion among Chinese and foreign scholars in recent years. This paper presents a review of representative literature on different areas of brand co-branding IP at home and abroad, focusing on the concept of IP and its marketing effects, so as to further sort out the positive and negative effects of brand co-branding IP on consumer behaviour from the perspective of consumers, and then analyse the relevant internal mechanisms of consumer purchasing behaviour, providing reference and ideas for future scholars to study the concept of brand co-branding IP. According to the results of this review, brand co-branding IP is a double-edged sword for brands. A good co-branding can increase customer loyalty and have a positive impact on the brand. At the same time, however, it can also lead to irrational shopping behaviour or negative emotional connections. Therefore, this paper suggests that future scholars could more clearly unify the concept of IP and investigate more long-term and effective forms of brand co-branding IP.

The paper focuses on evaluate the effectiveness of the combined method of LSTM models and minimal variation optimized portfolio in achieving promising return. Daily adjusted closed prices of 21 stocks in American market are collected. Two distinct portfolios are then created and optimized based on their according optimal portfolio weights obtained from forecasting results of LSTM models and minimal variation optimization. The 21-day portfolio return can then be calculated based on the real-world returns of these stocks. Portfolio 1 achieves a 21-day return of 4.3%, and portfolio 2 achieves 0.8%. Returns of both portfolios are significantly higher than the S&P500 index return of the same time period, which is around -4.8%. It is safe to conclude that LSTM enhanced minimal variation optimized portfolios are effective in reaching promising returns even when the market is not optimistic. By adopting and modifying the method, investors can expect to gain considerable returns in the stock market even in the time period when the general market is not optimistic. The research also serves as a replicable example of steps to optimized investing portfolios.

As the financial industry undergoes continuous evolution, efficient asset allocation has become increasingly crucial. However, traditional methods employed for portfolio optimization are often deemed inefficient and in need of improvement. To address this, recent advancements in deep learning techniques provide a promising perspective to tackle portfolio optimization, offering new possibilities for maximizing returns or minimizing risk based on specific objectives and constraints. This study delves into the analysis of stock data from six distinct industries. By utilizing the LSTM model and employing the Monte Carlo method, efficient frontier, and other advanced techniques, a training set is constructed to generate predictions using the first 80% of the data. For testing purposes, the remaining 20% of the data is utilized to assess how well the created portfolio performed. Various performance metrics such as portfolio returns, volatility, Sharpe Ratio, and maximum reduction are calculated to assess the effectiveness of the LSTM-based portfolio. Additionally, a comparison is made against other benchmark portfolios or strategies. The results for evaluation show that the LSTM-based portfolio outperform the commonly used benchmark model. This study illuminates the potential of deep learning in the financial industry, presenting groundbreaking applications that offer novel portfolio allocation strategies.

The study provides an in-depth analysis of the Swiss watchmaker Jaquet Droz's marketing strategy and suggests improvements. The study begins by describing the history and reputation of Jaquet Droz, noting its excellence in mechanical precision and artistic creation. However, Jaquet Droz faces stiff competition from smartwatches and competing brands and the challenge of raising brand awareness. The study suggests that Jaquet Droz highlight the uniqueness of its craftsmanship and materials, positioning the watch as a luxury accessory rather than just a time tool, while emphasizing the brand's historical heritage and unique values. In addition, the study mentions the radical strategic shift adopted by the new CEO to focus on unique artistic and personalized products, abandoning retailer partnerships and selling directly to target customer groups through commercial ambassadors. The study concludes by mentioning that other brands such as Vacheron Constantin market themselves through a combination of online and offline approaches, and suggests that Jaquet Droz learn from this model.

The housing market in China has been booming for two recent decades, and housing issues gradually become one of the most important concerns of the residents in China as the housing price was rising dramatically. This paper would analyze the influences between the housing price and the benchmark interest rate in two major first-tier cities of China, Beijing, and Shanghai from 2011 to 2015 before the interest rate reform in China was completed, by conducting data analysis in the regression model. It is found that the benchmark interest rate for loans has an important role in influencing housing prices. Finally, this paper would help make a clear and better understanding of the mechanism of the Chinese benchmark interest rate for loans, and how Chinese governments used it to sort out residential accommodation issues by comparing it to the recent Benchmark interest rate (LPR).

Due to the COVID-19 epidemic, many countries have taken a number of epidemic prevention and control measures, which have had a huge impact on the global economy. This paper studies the major issues facing the global economy after the relaxation of epidemic control policies, such as economic recession, inflation, debt crisis and energy crisis, and countermeasures. In this paper, corresponding countermeasures are proposed to address economic recession, including increasing government spending, reducing taxes, lowering interest rates, and increasing the money supply and to address the inflation, including reducing government spending, increasing taxes, raising interest rates, and reducing the money supply. In response to the debt crisis, corresponding countermeasures have been proposed from the aspects of strengthening government supervision, adjusting government expenditures, tax rates, interest rates, and money supply. In the light of the energy crisis, this paper puts forward corresponding countermeasures from aspects of improving energy utilization efficiency, reducing energy consumption, developing green energy that can replace traditional energy, and strengthening the supervision of the energy market.

Streaming platforms are popular in China, with many streamers competing with each other; one of the famous streaming services in China is iQiYi. 2020 saw the launch of iQiYi’s own production of theatre: the Light On Theatre. As the first suspense drama theatre, analysing its marketing strategy can help better understand the main marketing objectives for future suspense dramas in China. This paper takes Light On Theatre as a case study and uses the 4c model to analyse the reasons why it has competitiveness in the Chinese streaming market. This paper analyses the Light On Theatre using each element of the 4c model (consumers, cost, convenience, and communication) and provides some recommendations on the results of these analyses. It concludes that the success of Light On Theatre stems from a focus on consumer experience and gives some suggestions to help maintain Light On Theatre’s competitiveness. A positive online environment can help to enhance the consumer’s viewing experience, thereby increasing consumer loyalty.

Since people like to post photos on social platforms as a way to share them in their daily lives, restaurants have discovered that this can be utilized as a free promotional advertisement. The restaurant began to pay attention to the decoration of the store but also forgot the importance of the product itself. The purpose of this research was to discover how to improve the Internet-famous restaurant's current situation when the satisfaction of consumers shows a downward trend. Taking TASTIEN as an example, the marketing mix theory of the 4Ps and data from other scholars' research were utilized for relevant analysis. The reason for the decline in TASTIEN's product quality was the excessive number of franchised stores. Therefore, TASTIEN should improve the detection of food ingredients and continue to innovate. What’s more, the problem of too many complaints from TATINE customers is due to poor quality of service. In this regard, TASTIEN should invest more time in training employees and improving basic behavioral qualities. Similarly, TASTIEN is not promoted well because it is overly reliant on one platform. It should try to follow the social trend, enter the market as a sponsor, and participate in more social platforms.

Pang donglai Business Group is a regional retail enterprise. The main purpose of this article is to analyze the situation of the Pang donglai Business Group in the local market, find the main reasons why the group cannot expand, analyze the group's disadvantages and provide solutions. Propose specific strategic management strategies for the development of the group to help the group achieve better development, and also provide solutions and reference examples for local enterprises that encounter similar situations or have corresponding problems. By researching and comparing the strategies of other retail companies, and analyzing Pang donglai's corporate values, the authors point out that regional retail enterprises need to have clear goals and positioning to target corresponding customer groups in the current market environment to compete with foreign and large domestic chain retail groups. Combined with the local business environment, strengthen trade relations with farmers, abandon customer groups in first-tier cities, and develop markets in rural areas.

The prediction of stock performance is a crucial component in formulating investment portfolios and optimizing portfolios within the realm of quantitative trading. However, the inherent unpredictability and volatility of the stock market pose significant obstacles for investors in accurately predicting stock performance. To build an optimal portfolio, the LSTM model is selected as a forecasting technique. Subsequently, data sourced from Yahoo Finance is acquired for training and testing purposes. Based on the prediction data, the paper applies the maximum Sharpe ratio model and the minimum variance model to reach portfolio optimization. Finally, the paper uses the S&P 500 index as a standard to evaluate the constructed portfolio. The results indicate that the LSTM prediction model has effective functionality and exhibits superior performance in the domain of data forecasting. In addition, the minimum-variance optimization and the maximum Sharpe ratio models explore optimized return and minimized risk in portfolio construction. The constructed portfolio outperforms the S&P 500 in terms of risks and returns. Therefore, the results in the paper are good for investors to reduce risk and increase return in portfolio construction.

Accurate forecasting of stock prices and the construction of optimized portfolios are essential for investors in the dynamic technology sector. This paper proposes a comprehensive approach that combines Long Short-Term Memory (LSTM) models with portfolio construction techniques specifically tailored to the stocks of Apple Inc. (AAPL), Meta Platforms Inc. (META), Amazon.com Inc. (AMZN), Microsoft Corporation (MSFT), and NVIDIA Corporation (NVDA) from January 1st ,2018 to May 31st, 2023. By leveraging the sequential nature of historical stock price data(80% of data), LSTM models capture complex patterns and dependencies, enabling more precise predictions of Adjusted Close (Adj Close) prices. Subsequently, the forecasted prices (20% of data)are utilized to construct optimized portfolios that maximize returns and minimize risks within the technology sector using Monte Carlo simulations, efficient frontier analysis, and key risk-return metrics. The overall result of the prediction data is similar to the actual data which implies that the integration of LSTM-based forecasting and portfolio construction provides a robust framework for informed investment decision-making and risk management. And the application of Monte Carlo simulations, efficient frontier analysis, and key risk-return metrics gave us two portfolio allocation options : Minimum Variance model (40% of AAPL,60% of MSFT) and Maximum Sharpe Ratio model ( 47% of META, 53% of NVDA). The evaluation of the two portfolios show that the strategy can significantly beat the SP500 index.

With the continuous development of the internet and its related industries, the internet is constantly popularized in residents' daily life. Therefore, the development and progressive prospects of internet technology companies are significant for investors. However, little research is concentrating on the strategy of investing in the technology sector and its corresponding development. Therefore, the research theme of this paper is to take Apple Inc. as an example to analyze the development trend and corresponding investment strategies of internet technology companies. This paper uses the stock data of the top 30 companies ranked in market value in the technology sector in the past eight years and analyzes the data with a momentum strategy model. This paper finds that the internet technology company and its corresponding technology field will continue to develop and grow. Apple Inc. has gained a leading position in this field through its innovative products and technologies. Additionally, the momentum strategy has higher returns after improvement and application in science and technology. In the future, with continuous development in science and technology, the corresponding investment in this field will be further profitable.

This study aims to forecast U.S. inflation by using crude oil prices as a key variable. WTI crude oil price and Consumer Price Index Year-over-Year (CPI YoY) data from 2000 to 2022 are extracted to construct a time series model. The empirical analysis relies on Autoregressive Integrated Moving Average (ARIMA) model and regression to formulate forecasts based on historical patterns and error dynamics. The study finds out that compared to the automatically generated ARIMA model, using fitted values of the WTI time series model to predict CPI YoY through a multi-variable model is better. According to the prediction, the CPI YoY forecast for 2023 reveals a decreasing tendency in 2023, implying a slower rate of increase in U.S. inflation. The result provides insights into economic conditions, helps decision-making, and mitigates the potential risks associated with price fluctuations. Overall, the research contributes to a deeper understanding of the dynamics between energy markets and inflationary pressures in the United States.

With the improvement of living standards, people have higher and higher requirements for food quality and health. However, the global problem of surplus food is becoming increasingly prominent, especially in China, where a staggering amount of foodstuffs are wasted every year. As a new consumption mode, the surplus food blind box can not only reduce waste, save costs, but also meet the needs of consumers, but how to open new sales ideas is still a problem that needs to be discussed. This paper aims to explore a new idea of how to open the surplus food blind box through diversified product portfolios, social media marketing, online and offline combination and other ways. With the increasing concern of people on environmental protection, surplus food and healthy living, the surplus food blind box has a broad market prospect as a consumption way to use leftover ingredients. In this context, this paper puts forward some innovative sales ideas, including the combination of different flavors of surplus food blind boxes, the combination of online and offline sales models, social media marketing strategies, etc., to meet the needs of different consumers and improve sales and brand influence.

The landscape of the Chinese service business has been drastically altered by the combination of Dianping and Meituan. This study investigates the research background, substance, goal, importance, and policy suggestions while exploring the key results and fundamental concepts associated with this merger. The background research focuses on the dynamics of competition and the reasons for the merger in the Chinese service sector. The research looks at how the merger will affect consumer satisfaction, service diversity, and market share. It also looks at how combining resources and knowledge may improve both operational effectiveness and competitiveness. The goal of the research is to make legislative suggestions to maximize the benefits of the merger while also delivering insights for investors and business executives. These suggestions place a strong emphasis on the value of innovation, customer-focused tactics, and fair competition. The research also recommends that investors keep a careful eye on market movements and adjust their strategy as necessary.