Dynamic portfolio optimization with risk-aversion adjustment utilizing technical indicators

We propose a dynamic portfolio rebalancing approach within the mean-risk framework, in which the riskaversion coefficient is adjusted according to market trend information captured by a technical indicator. We employ Gini's Mean Difference as the risk measure and the moving average as the technical indicator. We conduct a thorough empirical evaluation with a rolling horizon approach using the S&P 500 market data. The empirical results reveal that the proposed portfolio rebalancing strategy with time-varying risk-aversion adjustments generates portfolios with higher returns than those obtained with a strategy in which the risk-aversion coefficient is fixed.