ISSN 2959-6130 (Print) ISSN 2960-1312 (Online) Editor-in-chief: Rana Muhammad Ammar ZAHID （The Business School, RMIT University） Associate Editor: Hadi Hussain （Xi'an Jiaotong University） Editorial Board Member: Muhammad Waleed Youn as （Xi'an Jiaotong University） Zi Mu （The University of Manchester） The Journal of Finance and Economics (JFE) is an accessible, peer-reviewed academic platform dedicated to the next generation of thinkers. Designed specifically for college students, young researchers and innovators, JFE serves as a bridge between foundational academic study and professional scholarly publishing. Our journal provides a high-quality venue for emerging voices to share original research, case studies, and innovative theories in the fields of Finance, Economics, and Management. We are committed to fostering academic growth by providing a rigorous yet supportive peer-review process that helps early-career scholars refine their work and contribute to the global conversation. Scope of subjects Finance: Exploring corporate finance, fintech, markets, and investment strategies. Economics: Investigating micro and macroeconomic trends, public policy, and behavioral shifts. Management: Analyzing organizational leadership, strategic innovation, and modern business operations. Interdisciplinary Studies: Welcoming work that connects these three pillars with technology, sustainability, and social impact. At JFE, we believe that innovation often begins with fresh perspectives. Whether you are an undergraduate student presenting your first empirical study or a young professional proposing a new management model, the Journal of Finance and Economics is your platform for academic excellence. All articles will be submitted to: Google Scholar, Crossref, U.S. Librarian Database, ProQuest (part of Clarivate’s Web of Science), Ulrich's Periodicals Directory, SEMANTIC SCHOLAR, OpenAlex.
How Executives' Green Awareness Affects Corporate ESG Performance: Empirical Evidence based on Green Investment Intermediation Mechanisms and Environmental Dimensions
Against the backdrop of "dual carbon" goals and highquality development, ESG performance has become an important indicator for measuring a company's sustainable development capabilities. Based on a sample of A-share listed companies in China from 2009 to 2024, this paper constructs an executive green awareness index, adopts a two-way fixed-effects model to empirically examine the impact of executive green awareness on corporate ESG performance, and further conducts an in-depth analysis from the environmental dimension perspective and the mediating mechanism of green investment. The empirical results indicate that executive green awareness significantly enhances corporate ESG performance, with such an effect mainly manifested in the environmental dimension. Furthermore, green investment exerts a partial mediating effect on the relationship between executive green awareness and corporate environmental performance. Robustness tests show that the above conclusions still hold after using lagged variables and replacement variables. This paper enriches the research on the influencing factors of corporate ESG performance from the perspective of executive cognition, and provides micro-level empirical evidence for enterprises’ green transformation and environmental governance practices.
The Impact of AI Application on Corporate ESG Performance: Empirical Evidence from Shanghai and Shenzhen A-share Listed Enterprises
In the context of the digital economy and sustainable development, artificial intelligence (AI) has emerged as an important technological approach for enterprises to enhance ESG governance. Using Shanghai and Shenzhen A-share listed enterprises from 2010 to 2024 as the research sample, this study empirically examines the impact of AI application on corporate ESG performance. The results show that AI application significantly improves corporate ESG performance, with stronger effects observed among state-owned enterprises and large enterprises. These findings remain robust after excluding high-tech enterprises and shortening the sample period. This study extends the literature on the economic consequences of AI by shifting attention from financial outcomes to non-financial ESG performance and provides empirical evidence for enterprises to develop differentiated AI-enabled ESG strategies.
Revisiting the Secretary Problem under Noisy Evaluation: Robustness and Performance Analysis
This paper investigates how the existence of different degrees of noise in the real world affects the final results of the classic secretarial problem by constructing a noise inclusive secretarial strategy model. Rather than assuming that decision-makers know the precise rankings of all candidates, this study has set up a situation where the observed rankings of each candidate by the decisionmaker are subject to noise and, as a result, deviate from the optimal probability-maximizing outcome of the classical secretarial problem. To study the effect of this uncertainty, the noisy secretarial strategy model also uses Monte Carlo simulation. A set of indices for the new model environment also includes the success rate, average selection rank and regret value. Based on the above results, a relatively small amount of noise will still significantly reduce the final decision. With an increase in initial noise, the proportion of the selected best candidate decreases significantly. Based on the above study, the strength of the classic secretarial problem is highly sensitive to the initial observations. Although this strategy has almost the same success rate in a low-noise environment, it is still not feasible for realworld application.
Navigating Geopolitical Risk: A Multi- Objective Optimization Framework for Dual-Source Procurement of Advanced Lithography Equipment at SMIC
Because the U.S. and its allies are tightening export controls on semiconductor equipment, Chinese chip foundries now face huge challenges in getting lithography tools. This study uses SMIC as a case example to analyze the situation. This paper aims to question a common purchasing strategy used by many foundries, which focuses too much on short-term cost savings but ignores the importance of having a backup plan to protect against supply shocks. In 2024, SMIC’s net profit dropped by half, mostly because the costs of equipment depreciation went up sharply. This financial drop clearly shows the high risk of depending on just one supply channel. To solve this problem, a multi-objective stochastic programming model is built in this study. This model can help SMIC find a better balance between older, imported equipment and new domestic alternatives. Hopefully, this balance can help the company push its monthly output past one million 8-inch equivalent wafers in 2025. The model tries to reach four goals at the same time: making more profit, protecting supply chain security, ensuring new machines work well with current production lines, and helping domestic equipment improve. By finding the Pareto-optimal frontier, the study calculates the best ratio between the two types of equipment. Through this method, a new purchasing system is created to balance financial profits with supply safety, while still keeping costs low and production smooth.
Structural Bottlenecks and Optimization Strategies for Cross-border E-commerce Logistics at the Zhengzhou Airport Economy Zone
Under the new development paradigm of dual circulation, cross-border e-commerce has become a crucial channel for inland regions to integrate into the global value chain. As a national comprehensive airport economic pilot zone, Zhengzhou Airport Economy Zone has maintained steady growth in cross-border e-commerce transaction volume; its logistics system still has obvious structural defects in multimodal transport connection, local service capacity, digital intelligent construction, and professional talent reserve. These problems restrict the zone’s development and prevent it from transforming from a simple transit node into an international logistics hub with value-added functions. Besides, incomplete air cargo network coverage, backward overseas logistics node layout, and low port customs clearance efficiency cause an imbalance between logistics supply and demand; this increases the operation costs of cross-border enterprises and weakens the stability of supporting logistics services. Based on hub coordination, enterprise incubation, digital empowerment, and talent cultivation, this paper analyzes the operation status and core problems of the zone’s logistics system and proposes feasible optimization measures. The research results can provide theoretical references and policy suggestions for inland airport economies to integrate into global value chains under the dual-circulation pattern.
The Economic Benefits of Currency Competition in the Digital Age
The rapid development of cryptocurrencies, stablecoins, and central bank digital currencies (CBDCs) has transformed the global monetary landscape and accelerated the transition toward a cashless society. While critics argue that digital currencies threaten financial stability due to volatility, disintermediation, energy consumption, and regulatory concerns, this paper contends that the increasing competition among digital and fiat currencies can generate significant economic benefits. By examining the evolution of cryptocurrencies, the emergence of stablecoins, the global adoption of CBDCs, and the case of Zimbabwe's hyperinflation, this study argues that currency competition encourages governments to pursue more disciplined fiscal and monetary policies, strengthens policy credibility, and helps anchor inflation expectations. Greater monetary credibility also expands policymakers' ability to respond effectively to future economic downturns. Although digital currencies present important risks, many of these challenges can be mitigated through technological innovation, appropriate regulation, and institutional development. Overall, this paper concludes that a wellmanaged transition toward a cashless society can promote competition, innovation, and long-term economic resilience rather than undermine financial stability.
The Impact of Quantifying Financial News Features on Short-term Stock Return Volatility —— Backtesting analysis based on publicly available financial text information
This study examines whether measurable features of public financial news can help explain short-term stock return volatility. Public announcements, financial news, institutional opinions, social media heat and market data are converted into five variables: sentiment, topic type, popularity, publication timing and market controls. Based on an initial backtesting sample of twenty anonymized events, the paper applies event-window calculation, group comparison and a simplified regression framework to observe T+1, T+3 and T+5 market reactions. The early results show that negative news, high-heat news and core-topic news related to performance, regulation, policy or capital flows are more closely associated with stronger T+3 volatility. However, abnormal returns do not show a stable direction across groups. These findings suggest that text quantification is more useful for identifying information shocks, risk attention and review priority than for producing independent trading signals. The study also provides a basic structure for future expansion to a larger sample with clearer stock codes, information sources and release times.
Analysis of the Dynamic Co-movement Mechanism Between Cryptocurrencies and Traditional Assets
This paper selects the data of Bitcoin, Gold, and the S&P 500 index from 2018 to 2025, utilizing GARCH(1,1) and DCC-GARCH models to depict the dynamic conditional correlations among assets. By incorporating the Global Geopolitical Risk Index, the 10-year breakeven inflation rate, and the VIX panic index, it constructs daily and monthly cross-frequency regression models to examine their macro-driving mechanisms. The results show that whether at the high-frequency daily level or the smoothed monthly level, macroeconomic variables exhibit extremely significant driving effects on the co-movement of Bitcoin. Under the liquidity squeeze concerns triggered by intensified global panic or high inflation expectations, Bitcoin fails to act as a haven alongside gold. Instead, it exhibits a stronger synchronous crash with the US stock market. This empirical study rejects the hypothesis of Bitcoin as "digital gold," revealing its essence as a "risk amplifier" highly dependent on traditional liquidity, and provides quantitative support for international investors in asset allocation under extreme macroeconomic scenarios.
Evolution of Statistical Methods in Financial Time Series Analysis: An Empirical Analysis from ARIMA to the GARCH Family of Models
Financial time series analysis is one of the key tools for asset pricing and forecasting, and its statistical methods have been continuously developing according to market demand. In the early days, the ARIMA model had great advantages in macro data analysis through its assumptions of linearity, homoscedasticity, and stationarity. Its stationary linear model was often used to predict shortterm economic indicators. However, as market products gradually emerged, non-linear and highly volatile data gradually dominated the trading market, indicating that the ARIMA model was unable to characterize non-linear characteristics such as volatility clustering, asymmetric leverage effects, spike tails, and long memory, which became increasingly apparent. Therefore, the academic community has successively introduced models based on nonlinear assumptions such as ARCH, GARCH family models, EGARCH, TGARCH, and FIGARCH, which have achieved accurate prediction of financial market risks. This article conducts a literature review and comparative analysis to study the evolution, improvement, and application of ARIMA models from traditional ARIMA models to GARCH family models. It explores the advantages and limitations of ARIMA model in modeling financial time series analysis statistics, and GARCH improves the modeling framework. It summarizes the impact of its development on financial time series analysis and provides a selection of modeling methods for financial analysis.
An Empirical Study of Machine Learning for Filtering Momentum Signals: Daily Frequency Strategy Analysis Based on the A-Share Market
Momentum strategies are commonly used in quantitative investing, but they are susceptible to noise signals, false breakouts, and market reversals in the A-share market, leading to significant performance volatility. This paper selects 80 A-shares from January 2020 to May 2025 as samples to investigate whether machine learning filtering can improve the effectiveness of momentum signals. The study generates buy signals based on 20-day cumulative returns and rolling quantile thresholds, and constructs features such as momentum, volatility, trading volume, price position, and trend. A gradient boosting classifier is used to predict the return direction the day after the signal is issued, and the effectiveness of the strategy before and after filtering is compared through single-asset backtesting considering stop-loss and transaction costs. The results show that after machine learning filtering, the Sharpe ratio and win rate of most stocks are improved, and the improvement in Sharpe ratio is statistically significant. However, there is no stable positive correlation between AUC and strategy improvement, indicating that machine learning is more suitable as a signal evaluation tool.
The rapid development of digital payment technologies has accelerated the global transition toward cashless societies, raising important questions regarding financial efficiency, market competition, and strategic risk. While cashless payment systems can reduce transaction costs, improve payment efficiency, and promote financial innovation, they may also generate concerns related to market concentration, privacy protection, financial inclusion, and systemic vulnerability. This paper aims to examine whether a cashless society should be regarded as a source of economic progress or a potential threat to social welfare. This study adopts a qualitative analytical approach by integrating theories from industrial organization, digital finance, and welfare economics. It evaluates the economic benefits and potential risks of cashless payment systems through comparative analysis of digital payment platforms, network effects, market structure, and the emerging role of central bank digital currencies (CBDCs). Relevant theoretical literature and international case studies are used to support the discussion. The findings suggest that the welfare consequences of a cashless society depend less on the disappearance of cash itself than on the institutional and competitive environment in which digital payment systems operate. Excessive market concentration resulting from network effects may weaken competition, reduce consumer welfare, and increase strategic risks. Conversely, effective competition policies, data governance, and well-designed CBDCs can mitigate these challenges while preserving the efficiency gains of digital payments. This paper contributes to the literature by providing an integrated framework linking digital finance, market competition, and strategic risk. It further offers policy recommendations aimed at balancing technological innovation, financial inclusion, and market regulation, thereby supporting the sustainable development of future digital payment ecosystems.
Can the Short-Selling System Restrain Corporate Financial Risk?
This paper explores whether the margin trading and shortselling mechanism can suppress corporate financial risk (Li et al., 2024). Using a sample of Chinese A-share listed firms from 2010 to 2023, we adopt the multiperiod difference-in-differences (DID) method to test the influence, moderating mechanisms and transmission paths of the short-selling mechanism on financial risk. The results indicate that: first, the short-selling mechanism notably mitigates corporate financial risk by restraining managerial opportunism and improving financial decisions (Mishra et al., 2024). Such results remain robust after replacing dependent variable, tail winsorization and placebo test. Second, corporate financial traits positively moderate the risk-mitigating impact of short-selling. Third, the inhibition effect shows significant heterogeneity, which is more evident in non-state-owned enterprises and firms with high information transparency (Xu & Lv, 2024). Fourth, the short-selling mechanism functions mainly through reducing agency costs and improving information disclosure quality, both of which act as partial mediators. This study enriches existing literature on capital market institutional governance and provides reliable empirical evidence for policy formulation.
Advertising Investment as a Supplement to Utility Theory: Explaining Chinese Luxury Consumers’ Behaviour 2025
For a long time, China's luxury goods market has been dominated by neoclassical utility theory, but this theory cannot grasp the core symbolic value and social value of market behaviour. This study fills this research gap by constructing an extended utility model with advertising investment as the core endogenous variable and combining the preliminary survey data of Chinese luxury consumers in 2025. The model adds linear social utility terms to the logarithmic Cobb-Douglas utility function ( γ Ax ) to quantify the impact of advertising on payment willingness and budget allocation. The research results show that the traditional utility theory cannot explain China's luxury consumption, because advertising reshapes the utility function through the "Mianzi" social utility. Advertising shapes utility through multiple paths - including social dimensions, experience dimensions and financial dimensions - and presents significant income heterogeneity: the absolute increase in luxury spending of high-income consumers is greater, while the proportion of social utility of low-income consumers is higher. In all groups, advertising reduces price sensitivity and increases willingness to pay by embedding symbolic values. This research provides a quantifiable supplement to the traditional utility theory and lays the methodological foundation for modelling culture-specific consumer behaviour.
Short-Form Video Platforms and the Reactivation of Traditional Brands: A Case Study of Stanley’s Marketing on TikTok
In the last few years, short-form video platform greatly changed digital marketing. Unlike other ads, things like TikTok are using the algorithm to give out their contents along with user interactions so you don’t need huge amounts of promotion for them to be seen. This paper studies the role of short form video marketing in reactivate traditional brand by analyzing the case of stanley cup. With the application of AIDA model as analytical model, it analyzes about which type of content generates attention, interest, desire and Action through short form videos. And the main mechanism is about algorithmic visibility, creator's credibility, scarcity by going viral and users' involvement. These show that marketing messages get into daily stuff and spread because people talk to each other. Based on the research results it is found that the reactivation of traditional brands on short video websites is due to a combination effect from platform algorithm, influencers behavior and taking part in online activities. It gives us some new ideas about digital marketing communication as well as how real brands can talk to young people.
A Monte Carlo Study of the Gambler’s Ruin Problem
The Gambler's Ruin problem is one of the classical stochastic models of an agent who gains or loses capital repeatedly, till reaching his target level or till his ruin. In this paper, the problem is theoretically analyzed and solved by Monte Carlo simulation. This paper obtains the closed-form expression for the ruin probability and discuss its sensitivity with respect to the initial capital and the probability bias. The simulation results are close to the theoretical predictions and show how the results of the long-term are sensitive to the small changes of the probability. This paper also carries out an analysis of the real-life applications in the finance and economy field in addition to theoretical and numeric analysis. In particular, this paper looks at the applicability of the Gambler's Ruin problem in the case of grid trading strategies, asymmetric investment decisions, and firm survival dynamics. The applications in the paper show the utility of the model in the areas of long-term risk accumulation, capital sustainability, and decision making under uncertainty. The results show how critical probabilistic reasoning is to assess strategies that might seem to be profitable on the short term but have high risks on the long term.
Does AI-based sentiment analysis predict consumer purchasing behavior in subscription services?
This essay investigates the use of AI-written text sentiment analysis as a predictor of consumer churn in the digital streaming economy. The authors employed over 210,000 records of users of Netflix to generate the primary sentiment scores of the raw reviews left by the user by implementing a Natural Language Processing (NLP) algorithm. These were then inputted into a Logistic Regression model that predicted binary cancellation possibilities. Despite the fact that preliminary values of the Weight of Evidence (WOE) factor have allowed establishing a mathematical relationship between the presence of extremely negative mood and subscription termination, the predictive factor has a final Area Under the Curve (AUC) prediction of 0.4944, which is analogous to a shot in the dark. The extremely low Total Information Value (0.0014) demonstrates that the human behaviour in purchasing is highly multidimensional. It is discovered that, as much as sentiment analysis is applied in the establishment of the overall brand health, text sentiment cannot be applied mathematically to engineer a commercially viable churn prediction system without integrating with other behavioral metrics.
Does the use of ESG scoring in FinTech platforms enhance ethical financial decision-making, or does it create new forms of bias?
The paper explores how ESG rating frameworks are embedded into FinTech tools such as AI-driven investment systems, credit-assessment algorithms, and automated portfolio managers. While ESG adoption supports more responsible and sustainable investing by improving transparency, accountability, and long-term risk oversight, several significant obstacles still persist. These involve inconsistencies between ESG ratings among providers, a lack of data quality, algorithmic bias, and increasing risk of greenwashing. Empirical evidence shows that the data divergence in ESG and incomplete disclosures may result in making misinformed financial decisions and reinforcing structural disparities, especially in the case of emerging markets. It finds that despite the revolutionary promise of ESG-based FinTech to financial sustainability, it requires better standardisation, reliability of data, and clear algorithmic regulation to reduce bias and deliver genuinely ethical financial results.
Does Big Data Adoption Enhance Competitive Advantage in Commercial Banking?
The paper explores whether the use of big data contributes to the competitive edge of commercial banks, analyzing a substantial number of secondary sources, which include scholarly articles, industry reports, and empirical case studies. Literature analysis shows that commercial banks are resorting increasingly to massive data, sophisticated analytics, and automated intelligence to enhance customer interaction, credit-risk, fraud detection, and efficiency in operations. Companies that manage to implement analytics in their fundamental operations have in most cases, recorded notable gains in their profitability, service delivery, accuracy of decisions and risk management. Such improvements in performance help in the creation of longterm competitive advantage, especially in industries that are typified by the existence of digital competition and ever-changing customer demands. The results also indicate that big data does not bring an advantage by default. The measures that are strategic alignment, organizational culture, availability of talents, technological infrastructure/underlying governance structures play a significant role in determining the effectiveness of analytics initiatives. Without such capabilities, banks may not be able to achieve the full potential of big data and hence imbalances in digital maturity will emerge in the sector. The secondarydata method, whereas giving a general overview of the said dynamics, does not offer the necessary access to proprietary data that can cause the establishment of causal relations unconditionally. In general, the study takes the view that big data is an effective facilitator of competitive advantage provided it is by well-organized organizational preparedness and integrated analysis abilities.
Does Leadership Diversity Correlate with Innovation Output in Multinational Corporations?
With multinational corporations (MNCs) operating in the international sphere, the leadership diversity becomes a crucial source of innovation, and discussions are based on its effect on patent registration due to increased focus on diversity, equity, and inclusion (DEI) efforts. This project explores the hypothesis that leadership diversity leads to more innovation output in the MNCs, with the hypothesis that diverse executive boards correlate with more patent filings, 18 percent of which are investigated using secondary corporate reports and Poisson regression modeling. The project is developed in a systematic way with the emphasis on such metrics of diversity as gender, ethnicity, and nationality and considers such socio-economic factors as access to resources and cultural barriers. It relies on case studies of major MNCs, such as technological and pharmaceutical ones, to evaluate the environmental and economic impacts on innovation. The analysis is informed by secondary data such as reports provided by McKinsey and WIPO databases to reduce carbon footprints in similar sustainability situations and examine costs on the behalf of underrepresented groups. The corporate governance and investor belief is needed to intensify various views. The study indicates a positive correlation between board diversity and patent generation, and indications are that it enhances by 10-25% more collaborative inventions but not across all sectors up to the 18% mark. This implies that it is practical through focused initiatives, including improved DEI policies. Some of the recommendations to make involve the establishment of inclusive teams to enhance innovation and ensuring that the MNCs are competitive at the global level without ignoring the socio-economic differences.
Does ESG Scoring Predict Lower Default Risks for Corporate Bonds in Developed Economies?
The dissertation is an investigation on whether or not increased ESG (Environmental, Social and Governance) scores will be correlated with reduced default risk of corporate bonds in developed economies. As the market of sustainable finance grows fast, and regulatory attention to ESG disclosure rises sharply, the correlation between ESG performance and credit risk is one of the most important issues to investors and policymakers. The study is on the case of corporate bonds in the United States, the United Kingdom, the Eurozone and Japan between 2018 and 2025. The quantitative methodology was chosen in terms of primary data analysis. MSCI and Bloomberg provided ESG ratings on a sample of 1,250 investment grade and highyield corporate bonds. Credit spreads and credit probability of default (PD) were used to measure default risk. The data sets were analysed with Pearson correlation, Ordinary Least Squares (OLS) regression and logistic regression to determine the predictive ability of the overall ESG scores and individual E, S and G pillars. The findings indicate that there is a statistically significant negative correlation between the ESG scores and the default risk. There were reduced credit spreads and reduced risk of default in higher ESG-rated bonds and the ESG scores accounted about 14. 2% of credit risk variation (pooled R 2 = 0.142). The environmental pillar presented the greatest predictive influence especially in Eurozone and Japan. This paper concludes that ESG scoring possesses moderate and significant predictive quality of lower default risk in developed markets of corporate bonds. Such results suggest the inclusion of ESG factors in the credit risk assessment and is applicable in practice to sustainable sustainable fixed-income portfolio construction and regulation.
Does Sentiment Analysis of Financial News Predict Stock Price Movements in Emerging Markets
The study explores how sentiment analysis of financial news can be used to predict the stock price movement in the emerging markets. The study aims to fill a gap in the literature by considering Brazil (Bovespa), India (Nifty 50), and China (Shanghai Composite) between 2020 and 2025 and using available tools to test the theory on volatile and retail-based markets where behavioural bias is high. A quantitative method was employed to use the VADER sentiment algorithm on 512 English language news items in Reuters and Bloomberg to obtain daily polarity scores (-1 to +1). These scores were correlated with next day stock returns and were analysed using Pearson correlation and Ordinary Least Squares (OLS) regression. The processing of primary data was in the form of structured NLP pipeline, which was transparent and reproducible. Findings show that news sentiment and returns have positive statistically significant relationship in all markets. The variance in the returns was attributed to sentiment to the tune of 14.4 percent in India, 11.6 percent in China and 8.4 percent in Brazil (pooled R 2 = 0.109). India was found to be the most predictive and Brazil was found more sensitive to external shocks. In big crises, anomalies were observed, which shows the weakness of sentiment. The research finds that sentiment analysis provides modest yet useful value of prediction in emerging markets. Results confirm the theory of behavioural finance and give the investors a convenient additional decision-making tool in the short term.
A New Decision Making Strategy: Combining Markov Chain Market Prediction with Kelly Criterion
In some cases, solely relying on the traditional Kelly Criterion, which assumes fixed win and lose rates, is insufficient to cope with the constantly changing market environment. Some present attempts to develop an improved decision-making model combine the Kelly Criterion with a Markov chain, but they are either too complex or have limitations. To figure out a better method, this paper carries out research on dynamic position decision-making. A Markov chain model divides the market into two states by analyzing a state transition probability matrix based on historical price movements. These two states represent increasing and declining market conditions, respectively. Then, the Kelly Criterion is used to calculate the optimal position corresponding to each state, enabling state-dependent position sizing. Each step is easy to apply and does not require much calculation, making the method accessible for practical investment decisions. This novel and simple strategy forms a twostep decision-making system including state identification and position optimization. Empirical results from an A-share stock demonstrate that this dynamic strategy outperforms the static Kelly approach, offering improved cumulative returns while maintaining simplicity and ease of implementation.
Markowitz Portfolio Theory for Household Asset Allocation
Nowadays, there are numerous families that do not manage their finances by scientific methods but rather by their personal feelings. Most common households do not have any professional information about financial and commonly take the extreme measures, such as saving all the money in cash or investing in stocks at any cost. These techniques are not suitable to balance returns and risks. The paper focuses on the application of Markowitz Portfolio Theory that can be used to assist ordinary families to allocate their savings into three basic types of assets: cash, stocks and bonds. The research focuses on three typical types of families-young families, middle-class families and retired families-and offers simple and practical plans for asset allocation for each of these family types. The results indicate that various families ought to select various stock proportions based on how much risk they are able to take. Young families should put 50% in stocks, middle class families should put 30% in stocks and retired families should only put 10% in stocks. The rest of the assets are split between bonds and cash. The structured allocation can effectively balance the expected return and investment risk, making family asset management more scientific, stable and secure. This book makes it easy to understand complicated financial theories, and offers straightforward, actionable tips for everyday families, even those that aren't financial experts.
Duolingo’s Digital Marketing Strategy Based on Gamified Consumer Psychology and Its Effectiveness
This paper goes through Duolingo’s digital marketing strategy from the perspective of gamification. This track is chosen because user retention and engagement have become key challenges for many online learning apps. Therefore, this study aims to analyze how Duolingo has become a leading case under this background. The research is based on a case study approach and secondary data analysis. Through the above method, the paper goes through Duolingo’s core strategies, including gamified design, brand identity, and social media marketing. Moreover, this study especially focuses on Duolingo’s gamification system and users’ needs. Lastly, this research points out several existing limitations of Duolingo’s current strategy. Based on these findings, the paper proposes optimization strategies such as deepening practical learning materials and introducing official test preparation content. Overall, this research demonstrates how integrating product design with consumer psychology and digital marketing can create a sustainable cycle of user attraction and retention, offering valuable insights for other mobile learning platforms.
Comparing IP-Driven business and Traditional Consumer goods business- Evaluating the Investment Potential – Using a case study of POP MART
This study evaluates the investment potential of IP-driven businesses in comparison with traditional consumer goods business with a case study of Pop Mart used. For methodology, a mixed-methods approach is used by combining a questionnaire data from consumers with secondary financial data and literature. Results show consumers rated IP-driven businesses significantly more attractive, blind box mechanism importance acts as the only significant predictor of willingness to pay a premium, and a strong correlation between premium willingness and brand loyalty. These findings support that IP-driven businesses generate more durable pricing power and higher profitability than traditional peers, with Pop Mart’s premium valuation defensible under both P/E and income-based IP valuation frameworks, contingent on IP diversification.
A Study on the Application of InsurTech in Auto Insurance Risk Control
With the development of new energy vehicles (NEVs) and intelligent connected vehicles (ICVs), traditional auto insurance faces a dilemma where “vehicle owners pay too much, and insurers lose money.” InsurTech, through technologies like cloud computing, big data, IoT, and AI, constructs a four-dimensional data system of “;human-vehicle-road-environment” to enable precise risk assessment, intelligent claims processing, and proactive risk intervention. Blockchain and privacy-preserving computation technologies address data credibility and privacy issues. The UBI model achieves a precise match between risk and premiums, while the Internet of Vehicles (IoV) and AI are driving the transformation of auto insurance from “post-event compensation” to “realtime risk intervention.” In the future, the deep integration of 5G+IoV and blockchain technology will promote the development of auto insurance risk control towards multidimensional and dynamic models, forming an ecosystem of shared responsibility among “automakers-insurers-tech companies” to achieve a win-win situation for all parties.
Research on Impulsive Buying Behavior of Consumers in Short Video and Live Streaming Marketing
With the rapid growth of short video and live streaming marketing, consumers’ impulsive buying behavior draws increasing attention due to its high impulsiveness. Existing studies often explore triggering mechanisms from streamer characteristics or platform context separately, lacking an integrated comparison and cross-effect analysis. To explain the formation mechanism from two dimensions—streamer characteristics & emotional mechanism, and platform technology & contextual atmosphere—this study uses a literature review and proposes an analytical framework based on the S-O-R model. Practical suggestions are as follows: Firstly, brands and marketers should improve streamers’ expression and interaction skills, and consumers should avoid over-stimulation. Secondly, platform regulators should ban false scarcity claims, establish early warning systems for impulsive consumption, and strengthen ethical supervision. Thirdly, consumers should enhance media literacy, use delayed-purchase tools, and stay vigilant about vulnerable emotional states. This study offers theoretical and practical guidance for impulsive buying research in digital consumption contexts.
The Impact of Digital Social Platforms on Purchase Intention and Brand Loyalty
In the contemporary business world, online networking has become a central element in determining the promotional tactics and the mental mechanisms that influence the selection of buyers. This paper explores how these virtual spaces determine the readiness of people to purchase products and their future loyalty to particular companies. This manuscript is based on a comprehensive review of the extant scholarly literature to the extent that the likelihood of a customer purchasing an item is largely influenced by the rate of data exposure and electronic word of mouth recommendations. On the other hand, the long-lasting brand loyalty is built mostly through the active interactive engagement, participation in online user communities, and creation of deep psychological connections. Later analyses show that these online platforms do not just initiate immediate transactional responses; they play a key role in developing long-term relationship networks between the buyer and the organization. The findings reached in this paper, therefore, suggest that effective digital promotional campaigns should go beyond superficial visibility. Rather, business organizations should emphasize on substantive two-way communications, developing consumer trust and gradually intensifying long-time relationships.
Research on the Driving Effect of Jiangsu Football Super League on the County Economy of Jiangsu Province
As a hallmark local sports IP in Jiangsu, the Jiangsu Football Super League links professional and grassroots football. Its multi-dimensional economic driving values in boosting county consumption, promoting industrial integration and enhancing regional brand awareness have initially emerged. Based on the background of building a strong sports nation and the high-quality development of county economy in Jiangsu, this study systematically discusses the strategic positioning, driving mechanism and practical mode of Jiangsu Football Super League in promoting the county economy of the province. The study finds that Jiangsu Football Super League should be positioned as a new growth pole of county economy in the province, a catalyst for sports industry integration and an adhesive for coordinated regional development. Its economic driving value is realized through three core paths: direct consumption stimulation, industrial integration linkage and brand value empowerment. In addition, three differentiated practical modes have been formed in southern, central and northern Jiangsu, namely industrydriven mode, culture and tourism integration mode and traffic diversion mode.
Enterprise Service Transformation: Customer Solutions Integrated: A Case Study in the Enterprise Service Transformation
Many of the old technology firms in the digital economy are shifting their business models as customers’ needs are changing and the industry competition is intensifying. Increasing numbers of firms are shifting away from a product-based model comprising of the sale of individual products to a solution-based model comprising of technology, consulting and long term service offerings. The paper examines the use of integrated customer solution strategies by companies when transforming a service based on IBM as a case study. The research will examine the background of transformation at IBM, the shifts in customer requirements, strategic positioning and outcomes of implementation. The findings indicate that IBM has transformed over the years as its strategic changes and restructuring of its business through gradual changes in its strategies have transformed it to become a global leader in the technology and consulting services market. IBM offers individualized end-to-end solutions to enterprise clients worldwide by integrating hybrid cloud infrastructure, cutting-edge artificial intelligence (AI), and business consulting services. The solutions are useful in enhancing customer experience, developing long-term partnerships, and adding value to the business. Nevertheless, there are a few practical difficulties that IBM is still facing in promoting integrated customer solutions, including high implementation cost, challenges on integration of systems and high global competition. To address these issues, this paper indicates that IBM must further enhance technological innovation, create more customer-focused operations, and promote more open and cooperative digital ecosystems.