Unveiling Market Insights by Deciphering CEO Interview Videos

Financial investors increasingly turn to unstructured data sources to inform their decisions. This study investigates the predictive value of multimodal information extracted from CEO interview videos for forecasting post-interview market risk. By systematically aligning and aggregating textual, acoustic, and visual features—including facial expressions, physical orientation, and vocal cues—we develop a comprehensive feature representation for each interview. We evaluate the impact of these features using various machine learning models and conduct extensive ablation studies to measure the incremental contribution of each modality. Our results demonstrate that integrating multimodal data, especially through ensemble approaches, significantly enhances volatility prediction compared to models relying solely on traditional financial indicators. Notably, visual cues, when effectively processed alongside textual and audio information, provide additional explanatory power. These findings highlight the importance of utilizing unstructured video data in financial forecasting and offer valuable methodological advancements and managerial insights.