The Sound of Risk: A Multimodal Physics-Informed Acoustic Model for Forecasting Market Volatility and Enhancing Market Interpretability

📄 arXiv: 2508.18653v1 📥 PDF

作者: Xiaoliang Chen, Xin Yu, Le Chang, Teng Jing, Jiashuai He, Ze Wang, Yangjun Luo, Xingyu Chen, Jiayue Liang, Yuchen Wang, Jiaying Xie

分类: cs.LG, cs.AI, cs.SD, eess.AS

发布日期: 2025-08-26

备注: 9 pages, 6 figures

💡 一句话要点

提出多模态物理信息声学模型以增强市场波动预测能力

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 多模态融合 声学特征提取 金融风险评估 情感分析 市场波动预测

📋 核心要点

  1. 现有的金融风险评估方法在面对信息不对称和企业叙事时效果不佳，传统文本分析难以捕捉情感动态。
  2. 本文提出的多模态框架结合了文本情感与高管声音动态，通过物理信息声学模型提取情感特征，提升风险评估的准确性。
  3. 实验结果显示，多模态特征能够显著解释市场波动性，尤其是在高管从脚本演讲转向即兴问答时，情感动态的变化对波动预测影响显著。

📝 摘要（中文）

金融市场中的信息不对称常常通过精心设计的企业叙事加剧，削弱了传统文本分析的有效性。本文提出了一种新颖的多模态框架，结合了来自财报电话会议中高管声道动态的文本情感与副语言线索。核心是物理信息声学模型（PIAM），该模型应用非线性声学技术，从受信号失真影响的原始音频中稳健提取情感特征。通过分析1795次财报电话会议的数据，发现多模态特征能够解释高达43.8%的样本外30天实际波动率的方差，尽管它们未能预测股票回报的方向。该研究为投资者和监管者提供了一种增强市场可解释性和识别潜在企业不确定性的强大工具。

🔬 方法详解

问题定义：本文旨在解决金融市场中信息不对称导致的风险评估不足问题，现有方法难以有效捕捉高管情感动态，影响市场解读。

核心思路：提出一种多模态框架，结合文本情感分析与声学特征提取，利用物理信息声学模型（PIAM）从高管的声音中提取情感特征，以增强市场波动预测能力。

技术框架：整体架构包括数据采集、声学特征提取、情感状态映射和波动性预测四个主要模块。首先从财报电话会议中提取音频数据，然后应用PIAM提取情感特征，最后将这些特征与文本情感结合进行波动性预测。

关键创新：最重要的技术创新在于将非线性声学应用于情感特征提取，克服了传统方法在信号失真情况下的局限性，提供了更为稳健的情感动态分析。

关键设计：在模型设计中，采用了三维情感状态标签空间（紧张、稳定、唤醒）来映射情感特征，并通过消融实验验证了多模态方法的有效性，显示出其在金融数据分析中的独特优势。

📊 实验亮点

实验结果表明，尽管多模态特征未能预测股票回报方向，但它们能够解释高达43.8%的样本外30天实际波动率的方差。与仅使用财务数据的基线相比，提出的方法在波动性预测上显著提升，验证了声学与文本模态的互补性。

🎯 应用场景

该研究的潜在应用领域包括金融市场分析、投资决策支持和企业风险管理。通过提供更为准确的市场波动预测，帮助投资者和监管机构识别潜在风险，提升市场透明度和可解释性，具有重要的实际价值和未来影响。

📄 摘要（原文）

Information asymmetry in financial markets, often amplified by strategically crafted corporate narratives, undermines the effectiveness of conventional textual analysis. We propose a novel multimodal framework for financial risk assessment that integrates textual sentiment with paralinguistic cues derived from executive vocal tract dynamics in earnings calls. Central to this framework is the Physics-Informed Acoustic Model (PIAM), which applies nonlinear acoustics to robustly extract emotional signatures from raw teleconference sound subject to distortions such as signal clipping. Both acoustic and textual emotional states are projected onto an interpretable three-dimensional Affective State Label (ASL) space-Tension, Stability, and Arousal. Using a dataset of 1,795 earnings calls (approximately 1,800 hours), we construct features capturing dynamic shifts in executive affect between scripted presentation and spontaneous Q&A exchanges. Our key finding reveals a pronounced divergence in predictive capacity: while multimodal features do not forecast directional stock returns, they explain up to 43.8% of the out-of-sample variance in 30-day realized volatility. Importantly, volatility predictions are strongly driven by emotional dynamics during executive transitions from scripted to spontaneous speech, particularly reduced textual stability and heightened acoustic instability from CFOs, and significant arousal variability from CEOs. An ablation study confirms that our multimodal approach substantially outperforms a financials-only baseline, underscoring the complementary contributions of acoustic and textual modalities. By decoding latent markers of uncertainty from verifiable biometric signals, our methodology provides investors and regulators a powerful tool for enhancing market interpretability and identifying hidden corporate uncertainty.