Smoothing the Subjective Financial Risk Tolerance: Volatility and Market Implications

Abstract

This study explores smoothing techniques to refine financial risk tolerance (FRT) data for the improved prediction of financial market indicators, including the Volatility Index and S&P 500 ETF. Raw FRT data often contain noise and volatility, obscuring their relationship with market dynamics. Seven smoothing methods were applied to derive smoothed mean and standard deviation values, including exponential smoothing, ARIMA, and Kalman filter. Machine learning models, including support vector machines and neural networks, were used to assess predictive performance. The results demonstrate that smoothed FRT data significantly enhance prediction accuracy, with the smoothed standard deviation offering a more explicit representation of investor risk tolerance fluctuations. These findings highlight the value of smoothing techniques in behavioral finance, providing more reliable insights into market volatility and investor behavior. Smoothed FRT data hold potential for portfolio optimization, risk assessment, and financial decision-making, paving the way for more robust applications in financial modeling.