基于PCA-k-means和PSO-SVM的AI上市公司财务预警

通过探索影响人工智能上市公司市值增长率的财务指标,建立融合主成分分析(PCA)和k-means的PCA-k-means模型,并将指标归类为盈利、效率、负债、成长因子.围绕ST公司和非ST公司的分类建立融合粒子群算法和支持向量机(PSO-SVM)模型,为财务预警工作提供支撑.实验选取了2019年人工智能行业42家非ST公司和8家ST公司的数据.结果表明,营运能力是人工智能上市公司最核心的成长能力,市场投资不仅关注短期利润更注重长期回报,行业投资趋于理性和稳健.

Financial Early Warning of AI Listed Companies Based on PCA-k-means and PSO-SVM

By exploring the financial indicators that influence the market value growth rate of AI listed companies, the PCA-k-means model integrating principal component analysis and k-means is established, and the indicators are classified into profit, efficiency, debt and growth factors. Based on the classification of ST companies and non-ST companies, a PSO-SVM model integrating particle swarm optimization algorithm and support vector machine is established to provide support for financial early warning. The experiment selects the data of 42 non-ST companies and 8 ST companies in the AI industry in 2019. The results show that operating ability is the core growth ability of listed AI companies. Market investment not only pays attention to short-term profits but also pays attention to long-term returns, and industry investment tends to be rational and stable.