Asia Pacific Economic and Management Review

Deep Learning for Stock Performance Prediction: A Sharpe Ratio-Optimized Approach

2025-03-24T03:09:19+00:00

Accurate stock performance prediction is critical for portfolio management, risk assessment, and algorithmic trading. Traditional forecasting models often focus on minimizing prediction error but fail to consider risk-adjusted returns, making them suboptimal for real-world investment applications. Recent advances in deep learning have significantly improved financial time series forecasting, yet existing models primarily optimize for accuracy rather than maximizing risk-adjusted performance metrics such as the Sharpe ratio.

This study proposes a Sharpe ratio-optimized deep learning framework for stock performance prediction, integrating risk-sensitive forecasting mechanisms directly into model training. By embedding Sharpe ratio-based loss functions, the model prioritizes investment strategies that yield higher returns per unit of risk. The framework utilizes temporal convolutional networks (TCNs) and attention-based transformers, allowing for both short-term price trend detection and long-range dependency modeling. Additionally, reinforcement learning is employed to dynamically adjust portfolio allocation strategies based on evolving market conditions, ensuring adaptability across different asset classes.

Empirical results on real-world stock market datasets demonstrate that the proposed model outperforms traditional forecasting approaches in both predictive accuracy and financial performance. The study highlights the importance of integrating risk-sensitive optimization techniques within deep learning-based stock prediction frameworks, offering a more practical and scalable solution for quantitative investment strategies.

The Role of Artificial Intelligence in Sustainable Development and Industrial Transformation

2025-03-21T03:29:55+00:00

Artificial Intelligence (AI) is transforming global industries, offering unprecedented opportunities for sustainable development and economic growth. This paper examines AI's dual role as a driver of productivity and innovation, as well as its potential to address environmental challenges and reshape global trade. By analyzing AI's applications in agriculture, energy, manufacturing, and transportation, we highlight its ability to optimize resource use, reduce carbon emissions, and promote circular economies. Additionally, we explore AI's impact on global trade, from supply chain optimization to cross-border service innovation. However, the rapid adoption of AI raises challenges, including labor market disruptions, ethical dilemmas, and regulatory gaps. This paper concludes with policy recommendations to ensure AI's benefits are equitably distributed and aligned with global sustainability goals.

Anomaly Detection in E-Commerce Platforms via Graph Neural Networks

2025-03-24T02:58:52+00:00

The rapid expansion of e-commerce platforms has introduced significant challenges in fraud detection, including fake reviews, payment fraud, account takeovers, and product listing scams. Traditional fraud detection methods, such as rule-based systems and supervised learning classifiers, struggle to detect sophisticated fraudulent activities that evolve over time. This study proposes a graph neural network (GNN)-based anomaly detection framework to enhance fraud detection in e-commerce platforms by leveraging the graph-structured nature of user interactions, transactions, and review networks.

The proposed model constructs an e-commerce interaction graph, where nodes represent users, products, and transactions, while edges capture relationships such as purchases, reviews, and payment flows. The framework utilizes graph convolutional networks (GCN) and graph attention networks (GAT) to learn spatial dependencies within the transaction network, combined with gated recurrent units (GRU) to model temporal fraud patterns. By integrating spatial and temporal learning, the model can identify suspicious user behaviors, fraudulent transactions, and fake product listings with high accuracy.

Experiments conducted on real-world e-commerce datasets demonstrate that the GNN-based model outperforms traditional fraud detection approaches in terms of F1-score, precision, recall, and false positive rate reduction. The framework successfully detects anomalous activities with an F1-score of 0.91, significantly improving fraud detection in large-scale e-commerce environments. These results highlight the potential of graph-based deep learning in securing online marketplaces against fraudulent activities.

Competitive Analysis of The Logistics Industry in The Context of Cross-Border E-Commerce

2025-03-24T02:44:02+00:00

This paper describes the current situation of China's cross-border e-commerce logistics industry, mainly using the literature research method, SWOT analysis found that the problems of China's cross-border logistics industry are: the lack of professional cross-border e-commerce logistics personnel, cross-border e-commerce logistics information technology level is not high, the logistics infrastructure is not perfect, the lack of third-party logistics to provide specialized services, and the high cost of logistics. Threats faced in the development are: global turbulence and big reshuffle, deepening trade barriers, different status of logistics and distribution in different countries, mismatch between the speed of logistics development and demand, and complicated return and exchange of goods. According to the SWOT matrix of cross-border logistics industry, it summarizes the shortcomings of cross-border logistics development and puts forward suggestions and future development prospects.

Research on Personnel Turnover of Beijing Qinghu Software Limited Company

2025-03-24T02:32:45+00:00

With the improvement of China's economic level, the loss of enterprise personnel is very common. The large number of personnel loss in Internet enterprises has aroused more and more attention. No matter any enterprise, human resources are extremely important, an enterprise without human resources can not be said to have core competitiveness. Human resources also affect the trend of enterprises. Due to the rapid development of the Internet, the phenomenon of staff loss is quite obvious. It is of great significance to analyze the reasons of personnel turnover and put forward corresponding solutions for the sustainable and healthy development of relevant enterprises. Software co., LTD., Beijing green lake as the research object, analysis software co., LTD., Beijing green lake personnel loss situation, influence and reason, through on-the-spot investigation method and interview method, and other forms, from the external environment factors, internal factors and their employees, and other Angle, analyze the root cause of the high staff turnover rate, Then put forward the concrete measures to reduce the loss of personnel in Beijing Qinghu Software Co., LTD. Namely, enterprises should improve the system of performance evaluation, compensation system reform, strengthen the employees' career training, build scientific promotion mechanism, strengthen enterprise culture construction work together, should not only focus on employee's work ability, but also pay attention to employees for the identity of the enterprise, let employees to wuxi, proud of working in the enterprise, reduce staff turnover. At the same time, attracting more excellent employees to join the company aims to effectively reduce staff turnover, enhance the level of human resource management, and provide reference for similar companies to deal with staff turnover.

Research on the Promoting Role of Rural Financial Innovation in Rural Economic Growth under the Rural Revitalization Strategy

2025-03-24T04:05:52+00:00

The Rural Revitalization Strategy is a major strategic measure to solve the "Three Rural Issues" in China, and rural financial innovation plays a crucial role in this process. This paper deeply analyzes the internal relationship between rural financial innovation and rural economic growth, elaborates in detail on the main models and development status of rural financial innovation, explores its promoting mechanism for rural economic growth, including aspects such as improving the efficiency of resource allocation, promoting agricultural industrialization, and facilitating the upgrading of rural consumption. At the same time, it analyzes the challenges faced by current rural financial innovation, and based on theoretical analysis, puts forward targeted policy suggestions. The aim is to provide theoretical support and practical guidance for further promoting rural financial innovation and achieving the sustainable and healthy growth of the rural economy.

Analysing the Costs Faced by Consumers in the Consumer Market

2025-03-21T03:29:51+00:00

Upgrading the consumption structure is an important condition for achieving sustainable economic growth. On the basis of reviewing the frontier research on consumption cost at home and abroad, this paper gives an overview of the connotation of consumption cost and analyses its impact on consumption; at the same time, combining with the development trend of the digital economy, it tries to introduce the rational negligence theory into the consumption cost, and analyses the theoretical mechanism of the impact of consumption cost on the upgrading of the consumption structure from the perspective of rational negligence.

Exploring the Refinement of Cost Management Practices Driven by Big Data

2025-03-24T03:03:50+00:00

With the expansion of enterprise scale and the increasing complexity of business operations, cost management, as a core aspect of enterprise management, directly affects an enterprise’s profitability and market competitiveness. Traditional cost management methods often rely on experience-based judgment and manual operations, making it difficult to cope with massive and complex data environments, which in turn limits the accuracy and efficiency of cost control. Therefore, how to fully leverage big data technology to achieve refined and intelligent cost management has become a key objective for modern enterprises. This paper focuses on the refined cost management practices driven by big data, analyzing the application scenarios and implementation paths of big data technology in cost management. The goal is to promote the digital transformation of enterprises in cost management, thereby enhancing management efficiency and competitiveness.

Building HVAC Electric Load Demand Prediction: Balancing Learning Rate and Hidden Layers for Improved Model Performance

2025-03-21T03:29:58+00:00

This study examines the performance of a predictive model for building HVAC electric load demand under three distinct conditions. The analysis focuses on two key metrics: the coefficient of variation of the root mean square error (CVRMSE) and the coefficient of determination (R²). Results indicate a notable disparity in model fitting across the conditions. For conditions 1 (learning rate =0.0001, hidden layer =7) and 3 (learning rate =0.0001, hidden layer =5), an increase in iteration rounds leads to a decrease in CVRMSE, signifying enhanced prediction accuracy. Conversely, condition 2 (learning rate =0.01, hidden layer =7) exhibits an increase in CVRMSE with more iterations, suggesting reduced accuracy. The R² values consistently rise with additional iterations across all conditions, indicating improved model fit. However, condition 2 presents a slightly larger discrepancy between the training and test sets compared to conditions 1 and 3. These findings highlight the varying impacts of iteration on model performance across different scenarios. The study underscores the importance of tailoring model parameters, such as learning rate and hidden layers, to specific conditions to optimize predictive accuracy. This research contributes to the understanding of how iterative processes and model configurations affect the accuracy and reliability of HVAC load predictions, offering insights for future model development and application in energy management systems.

Supplier Quality Management and Green Technology Innovation

2025-03-24T02:49:33+00:00

Supplier quality management is an effective way to promote the green transformation of enterprises, which is limited by the degree of supplier quality improvement, and there are big obstacles for traditional manufacturing enterprises to improve production efficiency. From the perspective of quality management, we explore the realization path of green technological innovation of suppliers through quality management for green technological innovation.

Construction of Enterprise Financial Performance Evaluation System

2025-03-24T02:38:24+00:00

This article is dedicated to discussing the construction methods and implementation processes of the enterprise financial performance evaluation system, as well as the challenges and corresponding countermeasures. Firstly, it elaborates on the concept of enterprise financial performance evaluation and its construction factors. Secondly, it describes the theoretical basis of the components of the enterprise financial performance evaluation system, including relevant theoretical models and models. Subsequently, it expounds on the methods and techniques for constructing the enterprise financial performance evaluation system, including the selection of KPI indicators and the determination of weights. Then, it presents the implementation process of constructing the enterprise financial performance evaluation system, including preparatory work, indicator design, and the establishment of evaluation models. Finally, it discusses the difficulties faced in constructing the enterprise financial performance evaluation system and proposes corresponding countermeasures. Through the exploration in this article, it can provide specific theoretical and practical guidance for the continuous improvement of the enterprise's financial performance evaluation system.

Financial Risks and Measures in Corporate Mergers and Acquisitions

2025-03-24T01:57:11+00:00

In recent years, with the general strengthening of China's national strength, mergers and acquisitions have become more frequent. Mergers and acquisitions can strengthen a company's strength and achieve maximum value big. Mergers and acquisitions are complex economic activities with many interests that can involve some financial risks. Therefore, preventing financial risks has become a challenge for companies. Key factors for successful mergers and acquisitions. This article analyses various risks and their causes in the process of mergers and acquisitions and proposes some control measures.