基于人工智能的服装纺织行业上市公司盈利能力综合评估指标构建  

Construction of Comprehensive Evaluation Indicators for Profitability of Listed Companies in the Clothing and Textile Industry Based on Artificial Intelligence

作　　者：徐汪洋 

机构地区：[1]上海数道信息科技有限公司,上海

出　　处：《统计学与应用》2024年第3期1018-1026,共9页Statistical and Application

摘　　要：财务指标分析对于公司运营情况的评价有着至关重要的作用。以往传统的财务指标分析,一般都只能通过流动比率、资产负债率、总资产周转率、资产报酬率等指标,相对单一、孤立地进行对比分析与评价,无法以一个整体、全面的指标进行综合性的评价与度量。本论文创新性地将分析侧重于公司的盈利能力水平,依托主成分分析算法将包括净利润率、总资产报酬率在内的6个代表盈利能力的指标进行整合,提炼形成经营获利能力指标和投资获利能力指标,以及一个直观简洁的盈利能力综合评价指标,建立起面向服装纺织行业上市公司盈利能力的三级量化评估体系,并进一步确定了其余各方面指标和盈利能力之间存在的潜在关联。从而帮助相关管理者与分析者切实提升从庞大的财务信息数据中获得关键信息的速度与效率。Financial indicator analysis plays a crucial role in evaluating a company’s operational situation. In the past, traditional financial indicator analysis could only be conducted through indicators such as current ratio, asset liability ratio, total asset turnover rate, and asset return rate, which were relatively single and isolated for comparative analysis and evaluation, and could not be comprehensively evaluated and measured as a whole and comprehensive indicator. This paper innovatively focuses on the profitability level of the company, integrates six representative profitability indicators (including net profit rate, total asset return rate etc.) based on principal component analysis algorithm, extracts and forms indicators for business profitability and investment profitability, as well as an intuitive and concise comprehensive evaluation indicator for profitability, and establishes a three-level quantitative evaluation system for the profitability of listed companies in the clothing and textile industry, and further determines the potential correlation between other indicators and profitability. So as to help relevant managers and analysts effectively improve the speed and efficiency of obtaining key information from massive financial information data.

关 键 词：盈利能力 主成分分析 关联网络 综合量化评估 

分 类 号：F27[经济管理—企业管理]