公司金融指标的波粒子二元性
Wen Zhu1, Junmin Lyu1, Xiangyuan Li1
1Shenhua AI Vision Research Institute, Guangzhou Huashang College, Guangzhou, China.
PloS one
|November 21, 2025
概括
本研究引入了波粒子二元模型,用于金融指标,如股权回报率 (ROE) 和资产回报率 (ROA). 它揭示了历史金融极端和波动性在很大程度上预测了公司未来的业绩.
科学领域:
- 金融经济学 金融经济学
- 企业金融公司财务
- 计量经济学 计量经济学
背景情况:
- 公司财务指标,如股权回报率 (ROE) 和资产回报率 (ROA),表现出复杂的行为.
- 传统的金融模型往往无法捕捉这些指标中观察到的极端变化和跳跃.
研究的目的:
- 为企业财务指标提出一种新的"波粒子二元性"模型.
- 在中国A股制造业公司的ROE和ROA中,共同描述持续的波动和离散的跳跃.
- 确定影响金融指标极端变化和跳跃的因素.
主要方法:
- 利用了2009年至2024年805家上市制造商的面板数据集.
- 适用于ROE和ROA的离散时间差异方程规范.
- 增强模型与金融指标向量的欧几里德规范,并采用多变量线性回归.
主要成果:
- 在ROE和ROA中确认了明显的重尾和跳跃活动,拒绝正常.
- 证明仅基于传统时刻和跳跃率的线性模型是不够的.
- 发现历史极端,波动性和初次差异的平均值显著解释了随后的公司业绩 (调整后R2=0.430).
- 确定了ROE跳跃 (战略调整,行业周期) 和ROA跳跃 (一次性收益/损失,会计政策转变) 的驱动因素.
结论:
- 拟议的"波粒子二元性"模型为理解金融指标动态提供了一个统一的框架.
- 经验证据支持该模型用于风险识别和评估高质量的企业发展的实用性.
- 历史金融极端和波动是未来公司业绩的关键预测因素.
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