Public attention, sentiment and the default of Silicon Valley Bank

Abstract

We assess the interplay between public attention and trading of the Silicon Valley Bank (SVB) stock before its default on March 10, 2023. Based on intra-day data in 15-min intervals, we estimate SVB market excess returns and match these with intra-day measures of investor attention based on the relative number of tweets and Google searches. Wavelet analysis reveals bilateral lead–lag patterns between both series and demonstrates that a higher level of attention led to a significant decrease in SVB returns. Thereby, the results provide evidence that Twitter sentiment and media attention ultimately fueled and accelerated the crash dynamics of Silicon Valley Bank. Economically, Twitter provides information at lower costs and higher effectiveness than newspapers and allows direct communication without potential distortions from media bias or timing lags in reporting. Hence, individuals can coordinate and communicate their run beliefs at a much faster pace, emphasizing the importance for financial stability.