Global financial markets have been experiencing low-risk anomalies for decades. In a low-risk anomaly, low-risk stocks offer better returns than high-risk stocks, violating the fundamental tenets of many financial theories. We developed an optimal portfolio strategy that exploits low-risk anomalies in the Black–Litterman framework. Our view is that low-risk assets will outperform high-risk assets. Forecasting volatility is the most important factor in constructing a view portfolio and in determining portfolio performance. To increase the predictive power regarding volatility, the best-performing prediction model should be selected.
We compared predictive power between three state-of-the-art machine-learning prediction models (GPR, SVR, and ANN) and the GARCH and historical volatilities. SVR and ANN showed better predictive power than GARCH in all error metrics. ANN was chosen as the best model because it showed higher predictive stability than SVR. We predicted the volatility levels of each asset by the chosen ANN model and used these to construct a Black–Litterman portfolio in order to exploit the low-risk anomaly. We compared the performance of the low-risk Black–Litterman portfolio with the market portfolio and the CAPM-based market equilibrium portfolio that excludes the low-risk view in the Black–Litterman framework.
Reflecting the low-risk view was found to improve the performance of the market equilibrium portfolio, which dominated the market portfolio. The equilibrium portfolio showed a lower Sharpe ratio than the market portfolio and a negative alpha. However, reflecting the low-risk view in the portfolio greatly improved the Sharpe ratio and the alpha. In addition, the estimation error of the expected returns and covariance matrix with the low-risk view decreased as τ decreased, contributing to the improvement of the portfolio's performance.
Since low-risk anomalies are global phenomena, the market for volatility strategies is expected to be enormous. We can also combine low-risk anomalies in each market to form an optimal portfolio.