Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 6, 2023 erstellt
Description:
Till date, empirical works on the utility of social media sentiments in predicting financial market outcomes have yielded mixed results. This paper contributes to this discourse by providing new insights into the limited salience of uncertainty sentiments on Twitter for a broad range of financial assets across various asset classes and geographic regions. Using sophisticated statistical techniques in the time-frequency domain, we demonstrate the weak correlation and causal information flow between economic and market sentiments versus financial outcomes. Furthermore, we offer new perspectives on the delayed nature of the salience and the specific time periods in which it is active or dormant. Our findings are also subjected to robustness tests for volatility and extreme events, and we find that the main results remain consistent. Additionally, we create an alternative search intensity index from Google trends data and uncover a bias towards large economies where English is the primary language