Erik Christian Montes Schütte
Biographic Data
| ID | 8920114 |
|---|---|
| NAME | Erik Christian Montes Schütte |
| GIVEN NAMES | Erik Christian Montes |
| FAMILY NAME | Schütte |
| SIGNATURE | SCHÜTTE E C M |
| AFFILIATIONS | Aarhus University |
| ORCID | 0000-0001-7662-9460 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Quantifying investor narratives and their role during Covid‐19
This paper elicits and quantifies narratives from open‐ended surveys sent daily to US stockholders during the first wave of the COVID‐19 pandemic. Using textual analysis, we extract 13 narratives and measure their prevalence over time. A validation analysis confirms the behavioral and economic relevance of the retrieved narratives. Moreover, we find that the narratives contain predictive information for future excess stock and bond returns, and t…
In Search of a Job
We show that Google search activity on relevant terms is a strong out-of-sample predictor for future employment growth in the United States over the period 2004–2019 at both short and long horizons. Starting from an initial search term “jobs,” we construct a large panel of 172 variables using Google’s own algorithms to find semantically related search queries. The best Google Trends model achieves an out-of-sample R2 between 29% and 62% at horizo…
No prominent works on this page.
In Search of a Job
We show that Google search activity on relevant terms is a strong out-of-sample predictor for future employment growth in the United States over the period 2004–2019 at both short and long horizons. Starting from an initial search term “jobs,” we construct a large panel of 172 variables using Google’s own algorithms to find semantically related search queries. The best Google Trends model achieves an out-of-sample R2 between 29% and 62% at horizo…
Quantifying investor narratives and their role during Covid‐19
This paper elicits and quantifies narratives from open‐ended surveys sent daily to US stockholders during the first wave of the COVID‐19 pandemic. Using textual analysis, we extract 13 narratives and measure their prevalence over time. A validation analysis confirms the behavioral and economic relevance of the retrieved narratives. Moreover, we find that the narratives contain predictive information for future excess stock and bond returns, and t…
COVID-19 Pandemic Impacts (2 works) · Economics (2 works) · 2019-20 coronavirus outbreak (1 work) · Biology (1 work) · Business (1 work) · Computer Science (1 work) · COVID-19 epidemiological studies (1 work) · Data-Driven Disease Surveillance (1 work) · Econometrics (1 work) · Financial Markets and Investment Strategies (1 work)