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《长安大学学报（社科版）》[ISSN:1671-6248/CN:61-1391/C]

卷:

期数:

2024年02期

页码:

82-97

栏目:

经济学·“数据要素×”研究

出版日期:

2024-04-20

文章信息/Info

Title:

Commodity price risk supervision in the context of “data element ×”

文章编号:

1671-6248(2024)02-0082-16

作者:

徐亮¹; 文婧¹; 朱禹臣²; 唐显博³

(1. 西南财经大学 工商管理学院,四川 成都 611130; 2. 澳门城市大学 商学院,澳门 999078; 3. 悉尼大学 文学与社会科学学院,亚新南威尔士州 悉尼 2006)

Author(s):

XU Liang¹;  WEN Jing¹;  ZHU Yuchen²;  TANG Xianbo³

(1. School of Business Administration, Southwestern University of Finance and Economics,Chengdu 611130, Sichuan, China; 2. Faculty of Administration, City University of Macau, Macau 999078, China; 3. Faculty of Arts and Social Sciences,University of Sydney, Sydney 2006, NSW, Australia)

关键词:

“数据要素×; ”;  大宗商品;  数字经济;  风险监测;  机器学习

Keywords:

“data element ×; ”;  commodity;  digital economy;  risk supervision;  machine learning

分类号:

F49

DOI:

-

文献标志码:

A

摘要:

随着数字经济时代的到来,大宗商品市场面临诸如价格波动大、供应链不确定性等风险。为研究“数据要素×”背景下大宗商品价格风险监管问题,对大宗商品的关键地位和政策举措、“数据要素×”对大宗商品市场的塑造力、大宗商品交易风险的挑战和前景展望、大宗商品价格风险监测的紧迫性进行分析。研究发现,数据分析和人工智能等新兴技术正在彻底改变中国大宗商品市场,大宗商品面临数据采集、价格数据标签需求、知识图谱构建、风险动态预警等四大核心问题。研究表明,应借助深度学习对多源异构数据进行采集、借助知识元的标引和集成技术建立数据标签、借助数据挖掘等构建知识图谱、借助分级校准建立风险动态预警系统,提高投资者和决策者在大宗商品市场中的风险应对能力。

Abstract:

With the advent of the era of digital economy, the commodity market faces risks such as significant price fluctuations and supply chain uncertainties. To examine the commodity price risk supervision challenges in the context of “data element ×”, this paper analyzes the pivotal role and policy measures concerning commodities, the influential force of “data element ×” on the commodity market, the challenges and prospects related to commodity trading risks, and the pressing need for commodity price risk supervision. The study reveals that emerging technologies such as data analysis and artificial intelligence are reshaping China's commodity market entirely. Commodities encounter four central issues: data collection, requirements for price data labeling, construction of knowledge maps, and dynamic risk alerts. Research suggests leveraging deep learning for gathering multi-source heterogeneous data, employing knowledge element indexing and integration technology to establish data labels, utilizing data mining for knowledge map construction, and implementing hierarchical calibration to establish a dynamic risk alert system. These measures aim to enhance the risk response capabilities of investors and decision-makers in commodity markets.

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备注/Memo

备注/Memo:

收稿日期:2024-02-03
基金项目:国家自然科学基金项目(71971171); 大商所“百校万才”工程研究项目(DECYJ202301)
作者简介:徐亮(1983-),男,四川乐山人,教授,博士研究生导师,管理学博士。

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更新日期/Last Update: 2024-04-20