Current Issue

All articles published in this issue have undergone a thorough peer review process, and stringent checks for repetition rates have been implemented to ensure the integrity of the content.

Total number of articles in this issue: 3
Total number of pages in this issue: 23

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Year

2025

Volume

2

Number

2

Status

Archived

Published

2025 April 17

Articles

Open Access Research ArticlePublished 17 April 2025

The Theoretical Logic and Practical Path of Promoting High-Quality Development In Xinjiang with Historical and Cultural Resource Endowments

Si Chen

Committee of the Political Bureau of the Central Committee, Secretary of the Secretariat of the Central Committee, and President of China, emphasizes that ' culture is an important fulcrum to promote high-quality development '. he also emphasized that "culture is a crucial fulcrum for promoting high - quality development." In the new stage of development, we should adhere to the Party's leadership over culture, deeply study and understand the rich connotations of General Secretary Xi Jinping's cultural ideology, the spirit of his important speeches on the "two combinations", and his important expositions on high - quality development. Taking "fostering a strong sense of community for the Chinese nation" as the main line, we should implement the spirit of General Secretary Xi Jinping's important speeches and instructions during his inspection tour in Xinjiang.Adhering to cultural empowerment, promoting industry with culture, cultivating people with culture, giving full play to the advantages of historical and cultural resource endowment, continuously promoting the creative transformation and innovative development of excellent traditional culture, transforming the spiritual core of culture into the kinetic energy of high-quality development, and solidly promoting the high-quality development of Xinjiang Uygur Autonomous Region.

Disciplines:Development EconomicsSubjects:Cultural EmpowermentReferences:8

DOI10.70393/6a6574626d.323830ARKark:/40704/JETBM.v2n2a01
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Open Access Research ArticlePublished 17 April 2025

Big Data-Driven ESG Quantitative Investment Strategy

Bingxing Wang

With sustainability becoming more important worldwide, investors are looking more closely at environmental, social, and governance (ESG) factors. This paper looks at how big data could help investors use ESG information effectively in quantitative investing. It discusses how the use of big data techniques can lead to more accurate and transparent ESG analyses. Using regression models, the study identifies a positive relationship between companies' ESG scores and their expected stock returns. It also illustrates how detailed big data analysis can enrich the evaluation of corporate ESG performance. Despite these advantages, the practical use of such methods still faces several significant hurdles. Data quality issues, a lack of standardized ESG metrics, and dynamic market conditions can undermine model accuracy and stability. To address these obstacles, we propose improved data cleaning procedures, the promotion of industry-wide ESG standards, and enhancements to model adaptability. Looking forward, new technologies such as artificial intelligence and blockchain are likely to help ESG investing become simpler and more efficient. Using these tools can make processing ESG data faster and clearer, giving investors stronger support when making decisions. In general, applying big data in ESG investing can help promote sustainability in financial markets and may also offer steady, long-term returns. Yet, there’s still uncertainty about how easily these technologies can actually be used in practice.

Disciplines:FinanceSubjects:Investment BankingReferences:15

DOI10.70393/6a6574626d.323837ARKark:/40704/JETBM.v2n2a02
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Open Access Research ArticlePublished 17 April 2025

Real-time Early Warning of Trading Behavior Anomalies in Financial Markets: An AI-driven Approach

Boyang Dong, Toan Khang Trinh

This paper introduces a novel AI-driven approach for real-time early warning of trading behavior anomalies in financial markets. The proposed system integrates advanced deep learning architectures with traditional statistical methods to enhance detection accuracy and processing efficiency. Our framework employs a multi-layered neural network design optimized for high-frequency trading pattern recognition, incorporating feature extraction mechanisms specifically calibrated for financial market data streams. The system demonstrates exceptional performance, achieving a 97.5% detection rate for known trading anomalies while maintaining false positive rates below 1%. Performance evaluation confirms the system's ability to process approximately 150,000 transactions per second with average latencies of 15 milliseconds. Comprehensive testing against 24 months of historical market data validates the system's effectiveness across diverse market conditions, including high volatility and low liquidity scenarios. Comparative analysis reveals significant performance improvements over conventional surveillance methods, with detection accuracy increasing by 28% and processing efficiency improving by 45%. The system's adaptive learning capabilities ensure continuous evolution based on emerging trading patterns. Experimental results confirm robust performance across different market sectors, including stress-tested environments and cross-asset scenarios. This research advances market surveillance technology by establishing a new benchmark for real-time anomaly detection in complex financial ecosystems.

Disciplines:FinanceSubjects:Investment BankingReferences:47

DOI10.70393/6a6574626d.323838ARKark:/40704/JETBM.v2n2a03
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Copyright © 2026 The Author(s). Published by Southern United Academy of Sciences.
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Journal of Economic Theory and Business Management (JETBM)
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