Journal of Innovation and Development (JID) is an inter-disciplinary, peer-reviewed journal published by Darcy & Roy Press. The mission of the journal is to provide an academic forum for discussing the issues of innovation, development across the world. All articles published are rigorously and fast reviewed meeting the Journal Quality standards.
A Study of Stock Price Forecasting Using Improved LSTM-Based Models
With the advancement of big data technology, machine learning has found widespread application in financial market forecasting due to its exceptional data processing and predictive capabilities. Upon reviewing recent research on stock prediction based on machine learning, it is found that under different market environments, Long Short-Term Memory significantly reduces prediction errors compared to other single-model methods. Therefore, to further explore the potential of LSTM models, this paper reviews recent research on stock price prediction based on improved LSTM models, focusing on the basic principles and characteristics of five models: BiGRU-LSTM、 VMD-CSSA-LSTM、 DMD-LSTM、 SF-GET-LSTM、 Doc-W-LSTM. Comparative analysis is conducted with other advanced models. Meanwhile, through comparative analysis of the literature, it is found that current research faces issues such as insufficient interpretability, limited research markets, and inadequate verification methods. It is proposed that introducing methods such as SHapley Additive exPlanations, causal inference, and time-series cross-validation may address these shortcomings. Finally, future research may endeavour to integrate the five models, constructing a multimodal adaptive and explainable intelligent financial forecasting system. This would provide theoretical and technical support for intelligent financial decision-making, driving further optimisation and innovation in deep learning within the forecasting domain.
Stock price forecasting; Machine learning; Long Short-Term Memory.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Research on the Construction of Enterprise Ecological Strategy in the Context of Platform Economy
Against the backdrop of in-depth digital economic transformation, platform economy has become a core engine reshaping the global industrial pattern and driving high-quality economic development, while the construction of ecological strategy is a key grasp for platform enterprises to break through the bottleneck of single business and build long-term competitive advantages. Taking the "dynamic balance between openness and control" in the construction of platform ecological strategy as the core entry point, this paper accurately defines the core connotation and dimensional characteristics of openness and control in platform ecology, and constructs a special "Dimension-Mechanism-Performance" analytical framework based on core theories such as two-sided market theory and ecosystem theory. By selecting typical multi-track platform enterprises at home and abroad, including Tencent Video, iQiyi, TikTok, and SHEIN, this paper deeply analyzes the typical manifestations and underlying causes of the imbalance between openness and control. Combined with the background of regulated development of platform economy and case practice experience, it proposes phased and multi-dimensional paths to achieve dynamic balance. The research conclusions not only accurately solve the core governance dilemma of platform enterprises of "chaos with excessive openness and rigidity with excessive control", but also provide operable practical guidance for platform enterprises to optimize ecological strategic layout and improve ecological governance efficiency. Meanwhile, it enriches the special research results in the interdisciplinary field of platform economy and enterprise ecological governance, and provides theoretical support for the standardized and high-quality development of platform economy.
Platform Economy, Enterprise Ecological Strategy, Openness and Control, Dynamic Balance, Core Contradiction.
The Impact Mechanism of ESG Performance on the Financial Performance of Listed New Energy Companies under the Dual-Carbon Goals
This study examines how ESG performance affects the financial performance of listed new energy firms in China under the “dual-carbon” goals. While the sector benefits from policy support and growing demand, it also faces cost volatility and fast technological change, making financial performance a key indicator of sustainability. Using a framework that links market forces, internal governance, and reputation, and drawing on cases such as CATL, BYD, and LONGi, the study shows that stronger ESG performance improves financial outcomes mainly by lowering financing costs, raising operating efficiency, and enhancing brand value. The findings provide practical guidance for firms and decision support for investors and policymakers.
new energy industry, ESG, financial performance.
The Balance Mechanism Between Short-Term and Long-Term Benefits of Large-Scale Sports Events -- A Case Study Of 2022 Qatar World Cup
With the development of the global sports event economy, the relationship between some short-term benefits and long-term benefits has become a key topic in major sports events. The Qatar World Cup is a classic high-cost competition, and it has important research value. This article discusses the rationality and long-term effects of the huge investment of funding in infrastructure, and also focuses on analyzing the positive economic benefits brought by the competition, such as a significant increase in travel consumption, improvement of employment opportunities and expansion of international fame. It also discusses some negative effects such as high cost, resource waste and residents' living pressure. Research shows that the Qatar World Cup drives the economy in a short time and achieves remarkable achievements in improving the country’s image, but some risks still exist, such as facility idleness and social cost problems. Therefore, this article propose resource recycling, and push resource recycling, sports culture recycling, make short-term benefits to long-term benefits.
long-term profits and short-term benefits, facility, cost.
How Artificial Intelligence Shapes the Human Capital Structure: Evidence from The Supply Chain Digitalization Pilots
Against the backdrop of the in-depth advancement of the Digital China initiative and the accelerated restructuring of supply chain systems, digital technologies are profoundly reshaping the internal factor allocation structure of enterprises by remodeling the information flow, capital flow and collaboration modes among them. Based on the data of A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2013 to 2022, this paper takes the pilots of supply chain innovation and application as a quasi-natural experiment and employs the difference-in-differences method to investigate the impact of supply chain digitalization on enterprises' human capital structure and its underlying mechanisms. The study finds that supply chain digitalization drives the optimization of the human capital structure. Mechanism analysis shows that supply chain digitalization enhances enterprises' capacity to absorb high-skilled labor through channels such as raising enterprises' market attention, boosting public trust in brands, alleviating financing constraints and promoting the accumulation of digital intangible assets. Heterogeneity analysis reveals that this facilitating effect is more pronounced in enterprises facing higher external environmental uncertainty, operating in more competitive industries and located in the eastern region of China. From the perspective of supply chain network collaboration and factor reallocation, this paper uncovers the micro-mechanism through which digital policies drive the upgrading of corporate human capital, and provides empirical evidence and practical insights for deepening supply chain digitalization construction and optimizing the talent structure of enterprises.
Supply Chain Digitalization; Human Capital Level; Artificial Intelligence; Difference-in-Differences.
Does Corporate Climate Risk Information Disclosure Increase Audit Fees? ——An Empirical Study Based on Chinese Listed Companies
This paper takes A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2009 to 2024 as the research sample to empirically examine the impact of climate risk information disclosure on audit fees and its underlying mechanism. The results show that the level of corporate climate risk information disclosure is significantly and positively correlated with audit fees. Climate risk information disclosure drives up audit fees by raising media attention. The higher the degree of information asymmetry, the stronger the positive impact of climate risk information disclosure on audit fees. Compared with state-owned enterprises, the positive impact of climate risk information disclosure on audit fees is more pronounced in non-state-owned enterprises. The above relationship is more significant in non-heavy polluting industries and when the auditors are non-Big Four accounting firms. The conclusions of this paper enrich the relevant research on the economic consequences of climate risk information disclosure and the influencing factors of audit fees, and also provide empirical evidence for understanding the transmission path through which climate risk information disclosure affects audit fees.
Climate Risk; Climate Risk Information Disclosure; Audit Fees.
Policy Coordination, Generative Artificial Intelligence, And Overseas Investment Efficiency: An Integrated Framework from The Perspective of Chinese Cities
Against the backdrop of research on how coordinated urban governance influences firms’ overseas investment efficiency, this study incorporates the technological perspective of generative artificial intelligence and develops an integrated analytical framework at the Chinese city level. Using a bidirectional fixed effects model and instrumental variable approaches, the paper systematically examines the internal relationships among policy coordination, generative artificial intelligence, and firms’ overseas investment efficiency. The results show that urban policy coordination significantly enhances firms’ overseas investment efficiency. However, generative artificial intelligence exhibits a pronounced substitution effect rather than a simple reinforcing role. Specifically, a high level of artificial intelligence application weakens the marginal effectiveness of policy coordination. Mechanism analysis indicates that policy coordination improves investment efficiency through multiple channels, including reducing capital costs, compressing risk premia, and curbing agency costs. Further heterogeneity analyses and robustness checks confirm the reliability of the core findings. This study not only enriches theoretical discussions at the intersection of government governance and corporate internationalization in the digital era, but also provides important insights for local governments seeking to optimize coordinated governance and for firms aiming to leverage artificial intelligence technologies to enhance overseas investment performance.
policy coordination; generative artificial intelligence; overseas investment efficiency; city-level perspective.
Current Status and Countermeasures of Sustainable Development in Green Finance
With the global economy accelerating toward a low-carbon and green transformation, the Environmental, Social, and Governance (ESG) framework has gradually become a core standard for measuring sustainable development. Green finance, as a bridge connecting capital and sustainable projects, aims to promote resource allocation toward environmentally friendly projects to achieve balanced development in the economy, environment, and society. This paper analyzes the current status of sustainable development in green finance under the ESG framework, explores trends in the development of the green finance market, the extent of the adoption of ESG investment concepts, policy implementation, and challenges faced in financial product innovation, and proposes relevant countermeasures and recommendations.
ESG framework, green finance, sustainable development, low-carbon economy.
Coca Cola's Brand Building and Marketing Localization Strategy from A Global Perspective
Against the backdrop of increasingly fierce competition in the global fast-moving consumer goods industry and the strengthening of healthy consumption and sustainable development concepts, how multinational brands can achieve localized operations while maintaining brand consistency has become an important research topic. This article adopts a combination of literature review and case analysis, and takes Coca Cola as the research object and conducts a systematic analysis around its brand building, business model, market strategy, and sustainable development practices, aiming to provide reference for the global operation of beverage and snack fast-moving consumer goods enterprises. The research results show that Coca Cola has successfully built a highly recognized global brand image and maintained strong competitiveness in different markets through long-term stable emotional value output, perfect global channel layout and the marketing strategy of "global core + local innovation". However, the upgrading trend of health consumption and the pressure of environmental transformation have also posed new challenges to its product structure and brand image. Based on research analysis, this article proposes optimization suggestions from the aspects of product innovation, digital marketing upgrading, strengthening brand social responsibility, and differentiated competition response. Research suggests that Coca Cola's successful experience demonstrates that global operations are not simply about market expansion, but rather a deep integration of brand core values and local cultural needs. This conclusion has important implications for fast-moving consumer goods enterprises to achieve long-term sustainable development.
Coca Cola; brand building; business model; market strategy.
Challenges And Integration of Digital Crowdfunding into The Traditional Charity Economic Structure
The rise of digital crowdfunding stems from the structural limitations of traditional philanthropy. By comparing the operation logic of the two, this study analyzes the breakthrough of digital crowdfunding to the traditional structure in resource mobilization, trust building and response speed. The study found that digital crowdfunding turned personal credit into fundraising power through social networks, activated long tail donation, and promoted the trust base from "institutional endorsement" to "technical confirmation". But it also brings moral hazard, resource mismatch and crowding out effect on systematic public welfare projects, posing challenges to the traditional ethical review and accountability mechanism. Traditional institutions are embedded in digital platforms to expand fundraising channels, and digital platforms introduce traditional audit standards to enhance credibility. The two sides have formed a collaboration at the levels of donation incentive, data sharing, and tracking audit to build a double trust closed loop of "technical transparency and professional evaluation". The conclusion shows that digital crowdfunding does not replace traditional charity, but promotes it to enter the stage of structural remodeling. Technology promotes efficiency and participation, and the system provides norms and sustainability. The two are complementary and symbiotic. This study provides theoretical support for the digital transformation of public welfare, and also provides policy reference for the construction of a modern charity system that takes into account efficiency, fairness and trust.
Digital crowdfunding, traditional philanthropy, economic structure.
Building Brand Value Through the Metaverse: A Case Study of Alo Yoga’s Metaverse Marketing Practices
With the rapid development of immersive technologies, the metaverse has gradually become an emerging space for brand communication that differs from conventional digital platforms. For fashion brands, the metaverse offers an interactive environment. And this environment provides new ways to express brand value beyond static information from the traditional digital market. And Alo Yoga could be an example of explore the metaverse during the early stages of the development of the metaverse. This study uses a case study approach to analyze Alo Yoga’s metaverse initiatives. Moreover, it applies the SPICE framework to explore the reasons and intentions behind these practices. And this analysis indicates that Alo’s metaverse marketing merge it healthy lifestyle brand value into the visual world. Using the immersion and interactive experience of the metaverse raise brand awareness and spread the wellness value. However, there are challenges too. The uncertain returns on the investment, incomplete legislation framework, and ongoing problems with the digital market now are the existing issue for the metaverse market. As the result, suggestions like using gamification designs, virtual influencers, and BNFTs are make.
Metaverse marketing, Alo Yoga, brand value, immersive experience.
Building Fan Stickiness in A Small-Market NBA Franchise: Evidence from The New Orleans Pelicans
Fan participation is the core determinant of the sustainable development of professional sports clubs, which is mainly reflected in its impact on the occupancy rate, commodity demand and media value. However, small market teams like the New Orleans Pelicans, despite the popularity of the NBA as a whole, still face ongoing difficulties in developing stable fan loyalty. Given the team's relatively short history, unstable performance cycle and limited heritage, this problem is particularly prominent. This paper explores the structural reasons for the stickiness and vulnerability of fans by combining a targeted review of the literature related to sports brands and fan identity, as well as case studies on the brand image and fan experience of the Pelicans. Analysis shows that the low loyalty of fans is mainly due to the lack of institutionalized identity paths, limited cultural integration in the game day environment, and over-reliance on star-centered interaction methods, which exacerbates the volatility of fan participation. Therefore, the dependence of fans is transactional and very sensitive to fluctuations in short-term performance. Results show that success on the court alone cannot achieve sustainable fan loyalty. On the contrary, long-term loyalty depends on the institutionalization of the fan identity level, the integration of local cultural narratives into the team's brand, and the repositioning of star players within a continuous, team-centered narrative framework. These conclusions emphasize the importance of identity building and experience design as a strategic basis for improving the loyalty of fans in small market sports clubs.
Fan stickiness, Fan loyalty, Team identification, Sports branding, Small-market franchise.
Analysis Of the Correlation Between Game Price, Distribution Company, And Rating
This study investigates the correlation between game’s launch price, Metascore and user score using a set of data including the top 100 rated games from 2015 to 2025. Prices were recorded as the release price to ensure consistency in all comparison inside the analysis. The writer examines whether price can be seen as an important signal of game quality and explore differences between critics and player scores and identifies some platform-based pricing strategy. Correlation analysis showed a weak positive relation between price and Metascore. Further analysis showed there is a significant difference between user scores and Metascore. On platform base data, there is a difference between game prices on each platform when released. By using Locally Estimated Scatterplot Smoothing (LOESS), the writer identified that there is a price drop around 2020 likely caused by the COVID pandemic. Overall, the finding suggests that price is influenced by many other aspects, including economy, publishing, strategy, and market positioning. It is also important to notice that user scores are another important aspect game companies need to consider.
Game launch price; Metascore and user score; COVID shock; Platform strategy.
Directional Predictability of the CSI 300 Index: A Walk-forward Evaluation of a BILSTM-attention Model
This study looks at short-term predictability in China’s stock market, using the CSI 300 index as the main example. A Bidirectional Long Short-Term Memory (BiLSTM) model with an Attention mechanism is built that uses recent price- and volatility-based technical indicators and is tested with an expanding-window, walk-forward method for both direction classification and return-size regression. The model choice follows earlier work showing that deep sequence models and volatility-aware hybrids can capture nonlinear patterns and changing volatility, even though the economic value of daily predictions is usually small. In the results, the BiLSTM-Attention model gives a stable but modest edge in one-day-ahead direction prediction—AUC, PR-AUC, F1, and hit ratio are all above random—while predicted return sizes are still very noisy, which is consistent with weak-form market efficiency. The paper also discusses a tradability-oriented framework that links calibrated probabilities to position sizing under transaction costs and regime shifts, and outlines possible extensions using volatility-aware hybrids and multi-scale or global attention to improve robustness across different regimes and assets. Overall, the findings show that short-horizon predictability is fragile, and that careful implementation, probability calibration, and risk control are crucial if weak statistical signals are to be turned into investable strategies.
CSI 300 index; BiLSTM Attention; Stock index prediction; Deep sequence models; Volatility aware hybrids.
Research On Value Creation of Digital Economy Platforms: A Case Study of Meituan And JD. Com
The development of the platform economy has profoundly reshaped the way enterprises create value, promoting the construction of an open value network with multi-party participation and resource aggregation. This article, based on the theory of value creation and using China's leading digital economy companies Meituan and JD.com as case studies, finds that the value creation of digital platform enterprises shows differentiated paths under different business models: in terms of value objectives, both types of platforms go beyond a single economic profit orientation and establish a triple value objective system covering economic efficiency, social welfare, and ecological sustainability;In terms of creating environments, Meituan fosters a flexible co-creation environment with diverse collaboration among the government, enterprises, and society through high-frequency consumption scenarios and an open ecosystem, whereas JD.com relies on its self-built logistics and supply chain infrastructure to create a closed, highly efficient operational environment with strong controllability and coordination. In terms of creating capabilities, data governance and intelligent applications become core supports. Meituan emphasizes the social embedding of data and cross-domain integration capabilities to promote the integration of public services and commercial value, while JD.com focuses on backend data-driven operational efficiency improvement and industry empowerment capabilities.This study reveals the differences in value creation among different types of digital platform enterprises, providing empirical evidence for improving the theory of value creation in digital contexts and offering practical insights for platform enterprises to achieve sustainable value creation.
Digital platform enterprises; value creation theory; value of digital transaction platforms; case study.
Arc’teryx's Marketing Strategies: Inappropriate Positioning of The High-End Outdoor Brand and Imbalance in Its Global Market Strategy
As the high-end outdoor sports market grows, building a strong brand and smart marketing have become very essential. This article notices Arc’teryx faced some developing issues, so it focuses on three cases, using the methods of literature investigation and case analysis, to help it do better in marketing. The article shows that marketing activities that deviate from the brand’s spirits will damage brand trust; the improper declaration of high-end position neglects consumers’ perception and causes market dissatisfaction; reliance on the domestic market leads to a global marketing imbalance. Based on these three issues, the conclusion points out that for the brand sustainable development, the company must stay true to its core spirit in all marketing activities. It needs to actively build a good reputation and stronger influence through real quality and customer care. Also, it should find smart local business strategies which suit different markets instead of using the same approach everywhere.
Government's Influence on The Growth of China's New Energy Vehicle Industry and Directions for Future Policy Enhancement
Under the background of the global "carbon neutrality" goal and the transformation of energy structure, the New Energy Vehicle (NEV) industry has become the core direction of the global automobile industry reform. As the largest market for automobile production and sales globally, China's industry has entered the mature period from the cultivation period, and the development logic has undergone a deep transformation. This paper focuses on the guiding role of the Chinese government in the NEV industry. It systematically analyzes the evolution of subsidy policies, charging infrastructure construction support policies, and power battery recycling policies, examining how these measures can effectively promote industrial development and highlighting the various problems faced during their implementation. Based on the evaluation, this paper presents policy optimization suggestions, offering practical guidance to address the challenges of industrial maturity and promote the industry's transformation towards product value-driven development, which will contribute to the global "carbon neutrality" goal and strengthen the competitive edge of China's automobile industry.
New Energy Vehicle, Government Policies, China.
Analysis of the Synergistic Mechanism of Products and Marketing under the Trend of Personalized Consumption: A Case Study of Pop Mart
The current consumer market is undergoing a structural transformation from "mass supply" to "niche customization", and personalized and emotional consumption has become the mainstream trend. This article takes Pop Mart, a representative enterprise in the collectible toy industry, as a case study to explore how companies can achieve effective synergy between product management and marketing under the trend of personalized consumer demands. Research and analysis show that personalized demands pose systematic challenges to enterprises on both the product and marketing fronts: product management has to deal with issues such as unpredictable demands, high costs of small-batch production, and scattered resources. Marketing needs to address challenges such as identifying user groups, the complexity of content customization, and the integration of all channels. To address these challenges, Pop Mart has established a data sharing platform, created a cross-departmental process loop, and implemented project-based resource integration, thus forming an operation mechanism that deeply coordinates products with the market. Based on this, this article proposes suggestions for collaborative optimization from three dimensions: organizational structure, technological empowerment, and talent cultivation. This research not only provides a reference for the continuous development of Pop Mart but also offers a feasible path for other enterprises.
Personalized Consumption, Product Management, Marketing, Collaborative Strategy, Pop Mart.
Based on Machine Learning to Predict if the Client Will Subscribe the Term Deposit
This paper uses several approaches of machine learning to predict if the customers will buy the term deposit by telemarketing of the bank. The data is collected from the phone calls of a Portuguese banking institution from 2008 to 2012. The paper compared five models: logistic regression, k-nearest-neighbors, support vector machine, decision tree and random forest. The analysis revealed several key insights. The predictive significance of variables like duration, p-days, and previous was confirmed by their important effect on the desired outcome. The result of this study is focused on the accuracy, precision, recall, the F1-score, and the Area Under the Receiver Operating Characteristic (ROC) Curve. The logistic regression method has the best result among all the five models, because it has the highest accuracy and the largest Area Under ROC Curve (AUC). This shows a high level of ability to distinguish if the clients will subscribe to the term deposit. This study will give a valuable reference to the bank for precision marketing to their future customers.
Machine learning; Logistic regression; Telemarketing.
The Impact of NBA-Related Signals and Market Factors on the Short-Term Price Movement of DKNG Stock
This study investigates whether NBA-related signals and basic market factors can help predict the short-term behavior of DraftKings (DKNG), a major U.S. sports betting company whose business activity is closely tied to NBA games. Using daily price data, market benchmarks, technical indicators, and selected game-intensity variables, the study builds predictive models for two tasks: next-day return direction classification and next-day volatility regression. Three machine learning models—Ridge, Random Forest, and XGBoost—were trained and evaluated using time-ordered rolling validation. Results show that non-linear models significantly outperform linear baselines. Direction prediction improves only modestly, but calibration scores become more reliable. Volatility prediction shows stronger improvement, especially with technical indicators. The findings suggest that combining market information with NBA-related signals provides meaningful predictive power, especially during high-attention game periods. This work offers a foundation for future research incorporating higher-frequency betting data, injury reports, and sentiment indicators. Such extensions could enhance understanding of how sports-event dynamics interact with financial market behavior in real time.
NBA signals; DraftKings; Stock prediction; XGBoost; Volatility forecasting.
The Core of the Business Operation of Game Companies
The global game market has exceeded 200 billion US dollars, and the industry has entered the stage of stock competition. Small and medium-sized companies are facing challenges such as scarcity of version numbers and high customer acquisition costs. Commercial operation has become the key to the survival and development of enterprises. This article focuses on the core logic of the business operation of game companies and uses multi-research methods to systematically analyze their operation practices. The study found that the core of business operation is "user-centered", and product research and development and iteration, profit model construction, and brand Intellectual Property (IP) operation are the three core elements; product differentiation is based on the foundation, and needs to maintain vitality through precise positioning and continuous iteration; the profit model needs to adapt to product attributes and user needs. It presents diversified and mixed characteristics such as buyout system, internal purchase, and advertising implantation; IP long-term operation and brand precipitation are long-term growth guarantees, and value can be amplified through cross-field ecological extension. This research fills the research gap in the systematic integration of the core elements of industry operation and provides differentiated operation paths for game enterprises of different sizes
Game Company, Business Operation, Core Elements, Profit Model, IP Operation.
Analysis and Comparison of Fresh Sales Forecasting Models Based on FreshRetailNet-50K Dataset
In the context of the increasingly intertwined digital economy and traditional retail businesses, the role of data-driven decision-making has become a vital strategy to improve the efficiency of the fresh produce chain. In this paper, the authors systematically analyse the differences between the following three popular sales prediction models: Prophet, Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory Model (LSTM), based on the openly available FreshRetailNet-50K dataset. The dataset covers the multidimensional feature set information, as well as the external influencing factors such as weather information, promotional activities, and holiday information. From the experiment results, the authors find that the prediction accuracy of the XGBoost model ranks the highest among the three sales prediction methods, with the lowest Root Mean Square Error (RMSE) at 0.714 and the highest R-Square (R²) at 0.816 compared to the Prophet and LSTM counterparts. Moreover, the authors apply the SHapley Additive exPlanations (SHAP) method to explain the interpretability of the above results and find that the time series-based features like “Lag Sale” and “Rolling Average Sale” play the most important role in determining the prediction results and demonstrate the "inertia effect" of the sales in the very short term.
Sales Forecasting, XGBoost, LSTM, Prophet, SHAP Explainability.
The Predictive Effect of ESG Scores on Stock Volatility: A Comparative Study of Polynomial Regression and Machine learning Models
This paper investigates the empirical relationship between environmental, social, and governance scores and stock volatility in the context of growing sustainable investment, using data on S and P 500 index constituents from 2019 to 2023. It compares the predictive performance of second order polynomial regression with machine learning techniques including Random Forest classification and regression models as well as a Support Vector Machine regressor, focusing on key measures of goodness of fit such as test R squared, cross validated R squared, and root mean squared error. The results show a pronounced nonlinear and convex relationship in which higher ESG scores are associated with lower stock volatility, although the marginal reduction in volatility diminishes at higher ESG levels, and they indicate that the second order polynomial regression model delivers the best overall fit with a test R squared of 0.3382, clearly outperforming both linear regression and the machine learning models. Feature importance analysis further corroborates that ESG scores account for approximately ninety two percent of the explained variation in volatility when isolating the contribution of each predictor across the statistical and machine learning approaches.
ESG, Stock Volatility, Polynomial Regression, Machine Learning, Predictive Modeling.
Performance Comparison of Gold Price Forecasting Models and Effectiveness of Hybrid Methods
Gold has long been regarded as an important risk-averse asset and an investment tool. Predicting its price trend has been a challenging topic for financial time series analysis. This study constructs six models, including artificial neural network (ANN), Long Short-Term memory (LSTM), bidirectional long Short-Term memory (Bi-LSTM) and their different hybrid strategies with generalized autoregressive conditional heteroscedastic (GARCH) model in gold price forecasting, and systematically compares their performance, based on the daily data between 2015/10/10 and 2025/10/10. Through multiple rounds of random seed experiments, the performance of each model is evaluated by the Root Mean Square error (RMSE), Mean Absolute error (MAE), Mean Absolute percentage error (MAPE), R²and trend prediction accuracy. The results indicate that the ANN model is the most robust in comprehensive performance. The further analysis shows that simply introducing GARCH volatility in a linear way or using bi-directional structure (Bi-LSTM) may reduce the stability of the model. However, adopting the non-linear fusion method by combining the point prediction from the LSTM and the volatility features from GARCH (LSTM-ANN) shows potential in fitting volatility and trend. The study reveals the characteristics of different models in gold price prediction, provides a basis for model selection, and points out that the introduction of external macroeconomic and news factors is a key direction for improving forecasting performance in the future.
Gold price prediction; Deep learning; Long Short-Term Memory; Generalized Autoregressive Conditional Heteroskedasticity; Hybrid model.
Predicting Bank Term Deposit Subscription Using Machine Learning Models
Banks are increasingly relying on data-driven target positioning to transfer potential customers into product buyers. By effectively identifying customer characteristics, one can reduce their marketing costs. However, converting purchase propensity models into measurable data remains challenging. This article integrates the advancements in tabular data modeling and marketing analysis to bridge this gap. It first provides an overview of the banking context and dataset characteristics, and then compares the propensity evaluation of basic linear models with ensemble methods, emphasizing the role of interpretability in compliance and on this basis, multiple models were established and evaluated, then the most suitable model was selected based on the data sets. Besides, this article discusses evaluations beyond accuracy: by using ROC/P&R analysis to link threshold selection with operational trade-offs. To support targeted actions, customer segments are defined, groups that are price-sensitive are identified for tailored interventions, so that the customer value are optimized. Thus, this article provides a practical perspective for transfer predictive data into profitable and auditable banking activities.
Bank Marketing, Purchase Propensity Prediction, Machine Learning, Interpretability.
Convex Optimization for Explainable Machine Learning: A Sparse Regularization Perspective
In recent years, the use of machine learning techniques for predicting the stock market has grown quickly. However, more and more people concern model explainability since the "black box" model problem has come up as a result. This essay examines the impact of convex optimization on explainable machine learning from the standpoint of mathematical optimization. It shows that Lasso ( ) and Ridge ( ) regularized objectives are convex, and that adding makes them strongly convex. Thus, the unique global optimal solution is obtained. This research use Apple's stock data from 2019 to 2024 to build sparse regression models like the Lasso, Ridge, and Elastic Net. Then this research compares how well those models predict and how well the model explains the feature. Finally, results show that Elastic Net strikes the best balance between explainability and generalization. It better than Lasso and Ridge in walk-forward MSE. Also, it keeps coefficients that are stable and economically meaningful. In general, using convex optimization and sparse regularization makes models so that it can be proven mathematically and explained in terms of economics. This is a principled way to make AI in finance more transparent and reliable.
Convex optimization, explainable machine learning, Stock market forecast.
From Price to Returns: The Performance Evolution of Machine Learning Models in Stock Prediction Tasks
More and more machine learning models have been used in financial forecasting. However, its application in real life has encountered many difficulties, mainly due to the non-stationary nature of the stock price data. This paper systematically examines the important effect that problem formulation has on predictive quality by looking at two different modeling approaches. The first paradigm makes direct end-to-end prediction on raw non-stationary price sequences. On the other hand, the second is theoretically informed, and focused on forecasting stationary log returns of carefully engineered features. A suite of models (LSTM, Random Forest, Ridge) were compared to a Naive Forecast in both frameworks. Empirical tests resulted in very poor performance from models of the first paradigm: the model performed abysmally and the predictions were a lot worse than the baselines. On the other hand, they applied to stationary returns are extremely predictive. The paper’s main idea is that making data stationary is more than something people do to improve the model, it is essential to obtain any form of significant financial forecast. The selection of a well founded methodology over the selection of a particular complex algorithm is much more significant.
Stock Price Prediction, Machine Learning, Time Series Forecasting, Data Stationarity.
Comparative Study of Machine Learning Classification Models on Loan Approval Prediction
As people’s demands for loans with various purposes have increased drastically over the years, the process for banks to determine whether applications should be approved or not has become increasingly time-consuming and complicated. Therefore, models that can automatically provide banks with initial decisions for reference will significantly improve the efficiency of the process. Reviewing the results provided by the model may also offer an insight into potential biases in their decision-making procedures. The study will focus on determining better models based on evaluations and further manipulations of 6 machine learning methods: Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Random Forest, XGBoost, and Light-GBM. After comparisons among different classifiers established through different combinations of methods, such as oversampling and grid search, the study finally identifies two models that are most balanced on multiple metrics: the XGBoost model that undergoes oversampling and grid search, and a voting classifier, i.e., an ensemble of XGBoost, Random Forest, and Light-GBM models. In the comparison, it is found that, particularly when the original data set is imbalanced, the oversampling leads the models to make fewer conservative decisions on whether a loan application should be approved by decreasing the precision score while increasing the recall score.
Loan Approval Prediction, Machine Learning, Binary Classification, Ensemble Learning, SMOTE.
Stock Return Prediction Based on Random Forest
With the deepening application of machine learning technology in the financial sector, using algorithms to capture non-linear market patterns has become a significant direction in quantitative investment. This study aims to utilize the Random Forest algorithm from ensemble learning to predict the short-term returns of representative stocks across multiple markets. The research acquired daily trading data for the past five years for targets such as Kweichow Moutai, Contemporary Amperex Technology (CATL), and Apple Inc. (AAPL) from Yahoo Finance (yfinance) and AkShare. Feature variables comprising technical indicators and volatility factors were constructed, with the subsequent five-trading-day return as the prediction target. Using Python's Pandas library for data cleaning and feature engineering, the Random Forest model was built based on the Scikit-learn library and evaluated using metrics such as MSE, MAE, and Directional Accuracy. Backtesting and visual analysis results indicate that the Random Forest model possesses a certain level of feasibility and application potential in short-term return prediction, offering investors a data-driven and objective decision-support tool. This study also discusses the model's limitations and proposes directions for future improvement.
Random Forest, Quantitative Investment, Return Prediction, Machine Learning, Feature Engineering.
Analysis of the Application of Large Language Models in Three Major Fields: Trading and Portfolio Management, Financial Risk Management, and Financial Text Mining
With the iteration of artificial intelligence, large language models, with their powerful capabilities in natural language understanding, logical reasoning and structured output, are influencing various fields in the financial ecosystem. This article will focus on the application of analytical large language models in three major fields: trading and portfolio management, financial risk management, and financial text mining. In trading and portfolio management, large language models understand market sentiment during the trading and investment process by building different models and can comprehensively and rationally judge whether it is suitable for trading. Through training large language models, future applications are more likely to be a "human-machine combination" model. In terms of financial risk management, different types of large language models have different performance differences. By comparing large language models, more accurate risk analysis can be obtained, and it can help people complete risk management more accurately. In the field of financial text mining, large language models, with their massive structured and unstructured data and their ability to quickly read texts, can obtain more reliable and detailed text reports. This article will systematically review the applications of large language models in three major fields.
Trading and Portfolio Management, Financial Risk Management, Financial Text Mining.