Do investors respond to what fund managers say, or how they look saying it? Using 2,000 live-streamed sessions by Chinese ETF managers and multimodal machine learning, we show that managers’ facial expressions, not their words, drive fund flows. A one-standard-deviation increase in positive facial affect raises next-day flows by 0.17pp (260% of mean). Vocal tone shows weak effects; textual sentiment shows none. Critically, facial expressions predict flows but not returns, indicating pure persuasion rather than information transmission. Effects strengthen when investors are emotionally vulnerable (down markets, retail-heavy funds) and persist 2-3 weeks before dissipating. Our findings challenge the emphasis on textual disclosure in finance and raise questions about investor protection as video communication proliferates.

The emergence of deep learning has yielded substantial advancements in computer vision, hence offering novel opportunities for the interdisciplinary exploration of finance and computer science. This paper adopts a cognitive dissonance theory viewpoint to investigate the impact of managers face emotion on market performance and risk in Chinese listed companies from 2016 to 2022. We employ deep learning model to analyze managers’ facial emotion. We find that the more positive facial expressions of managers in earnings conference call predict better market performance, lower volatility and share price crash risk. This study deepens the application of cognitive dissonance theory.