Unstructured multimedia data (text and audio) provides unprecedented opportunities to derive actionable decision-making in the financial industry, in areas such as portfolio and risk management. However, due to formidable methodological challenges, the promise of business value from unstructured multimedia data has not materialized. In this study, we use a design science approach to develop DeepVoice, a novel nonverbal predictive analysis system for financial risk prediction, in the setting of quarterly earnings conference calls. DeepVoice forecasts financial risk by leveraging not only what managers say (verbal linguistic cues) but also how managers say it (vocal cues) during the earnings conference calls. The design of DeepVoice addresses several challenges associated with the analysis of nonverbal communication. We also propose a two-stage deep learning model to effectively integrate managers’ sequential vocal and verbal cues. Using a unique dataset of 6,047 earnings call samples (audio recordings and textual transcripts) of S&P 500 firms across four years, we show that DeepVoice yields remarkably lower risk forecast errors than that achieved by previous efforts. The improvement can also translate into nontrivial economic gains in options trading. The theoretical and practical implications of analyzing vocal cues are discussed.

中文翻译：

---

释放语音对金融风险预测的力量：一种理论驱动的深度学习设计方法

非结构化多媒体数据（文本和音频）为金融行业在投资组合和风险管理等领域做出可操作的决策提供了前所未有的机会。然而，由于巨大的方法挑战，非结构化多媒体数据的商业价值承诺尚未实现。在这项研究中，我们使用设计科学方法开发 DeepVoice，这是一种用于财务风险预测的新型非语言预测分析系统，用于季度收益电话会议。DeepVoice 不仅通过利用经理所说的（口头语言提示）而且利用经理在收益电话会议中如何说（声音提示）来预测财务风险。DeepVoice 的设计解决了与非语言交流分析相关的几个挑战。我们还提出了一个两阶段的深度学习模型，以有效地整合管理者的顺序声音和语言提示。使用标准普尔 500 强公司四年来的 6,047 个收益电话样本（录音和文本记录）的独特数据集，我们表明 DeepVoice 产生的风险预测错误比以前的努力要低得多。这种改进还可以转化为期权交易中的重要经济收益。讨论了分析声音线索的理论和实践意义。这种改进还可以转化为期权交易中的重要经济收益。讨论了分析声音线索的理论和实践意义。这种改进还可以转化为期权交易中的重要经济收益。讨论了分析声音线索的理论和实践意义。