This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Forecast horizons range from 1 week up to 1 month. The machine learning algorithms employed are ridge regression, decision tree learning, adaptive boosting,...

Advances in volatility and beta forecasting are extended to the setting of volatility timing of market neutral portfolios. Key features of the study include short horizon forecasting from models with higher accuracy levels than previously documented in the literature. A trade-off in the joint...

Insurers and pension funds face the challenges of historically low interest rates and volatility in equity markets, that have been accentuated due to the COVID-19 pandemic. Recent advances in equity portfolio management with a target volatility have been shown to deliver improved on average risk...

Neutralizing portfolios from overall market risk is an important part of investment management particularly for hedge funds. In this paper we show an economically significant improvement in the accuracy of targeting market neutrality for equity portfolios. Key features of the approach are the...

This paper demonstrates that the low volatility anomaly exists in Australian stock returns. Consistent with previous literature on other countries, low realized volatility stocks earn superior risk-adjusted returns than high realized volatility stocks. Our key findings show value-weighted...

Generating one-month-ahead systematic (beta) risk forecasts is common place in financial management. This paper evaluates the accuracy of these beta forecasts in three return measurement settings; monthly, daily and 30 minutes. It is found that the popular Fama-MacBeth beta from 5 years of...