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Predicting price intervals under exogenously induced stress
Steven Shead1, Robert B Durand1, Stephanie Thomas1
1School of Accounting, Economics and Finance, Curtin University, Bentley, WA, Australia.
This study explores how induced stress affects financial market confidence. We used the Cold Pressor Arm Wrap test to measure stress and prediction intervals to gauge confidence, considering risk attitudes and personality traits.
Area of Science:
- Behavioral Economics
- Neuroscience
- Financial Markets
Background:
- Understanding investor behavior is crucial in financial markets.
- Investor confidence significantly influences market dynamics.
- Quantifying the impact of psychological factors on financial decision-making remains a challenge.
Purpose of the Study:
- To investigate the causal relationship between experimentally induced stress and confidence in a financial market context.
- To develop and validate a protocol for measuring stress and confidence.
- To explore potential mediating roles of risk attitudes and personality traits.
Main Methods:
- Experimental protocol involving stress induction via the Cold Pressor Arm Wrap.
- Confidence measurement using prediction intervals for one-period-ahead price forecasts (based on 100 past prices).
- Assessment of risk attitudes and personality traits as potential moderators.
Main Results:
- Narrower prediction intervals indicate higher confidence, while wider intervals suggest lower confidence.
- The protocol successfully operationalizes the measurement of stress and confidence.
- Data collection is ongoing to analyze the relationship and mediating factors.
Conclusions:
- The presented protocol offers a novel method to study stress and confidence in financial decision-making.
- Findings are expected to enhance our understanding of investor psychology and market behavior under stress.
- This research has implications for financial advising, risk management, and market regulation.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
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