Browsing by Author "Boehle, Isabel Amelia"

Risk propensity is a critical factor in how agents make decisions under uncertainty. Its influences have far reaching effects impacting individuals, institutions, and the economy. This research proposes and investigates Google Search Trends as a new signal to derive risk aversion, using a metric derived from financial market indicators as a benchline (adapted from Bekaert et al., 2019). Several structural models are evaluated for their ability to capture and forecast fluctuations in risk aversion. Finally, a simple practical test for the forecast is implemented by comparing different trading strategies to one based on forecast-derived signals. The research finds promising links between Google Search Trends and risk aversion. Furthermore, the signal based trading strategy is found to generate excess yields over random or holding strategies.
The contribution of this paper is threefold. First, it introduces a novel behavioural source for measuring risk aversion. Second, it adds to the discussion of how risk aversion can be captured structurally. And finally, it provides further motivation to investigate the effect of risk aversion on economic outcomes.

Keywords: Risk aversion, Forecasting, Google Search Trends, Decisions Under Uncertainty, Risk Aversion Based Trading Signals