Incorporating asset growth potential and bear market safety switches in international portfolio decisions

In the paper the impact of the growth potential index (GPI) of risky assets and bear market safety switches in portfolio decisions is discussed. A recursive formulation based on out-of-sample time series predictions of the underlying assets is applied in the empirical testing. It is demonstrated that the multiple representations framework provides useful forecasts for portfolio management. A number of alternative forecasting methods are included. The best forecast for each individual asset serves as input to the portfolio optimization module. The recursive time series estimation-optimization system is embedded in the genetic hybrid algorithm to improve the prediction accuracy. In contrast to single-period equilibrium models, the mathematical program recognizes cardinality constraints required in institutional banking, the opportunity cost, fixed and variable transactions costs, liquidity, the risk profile of the investor and the entry/exit time for risky investments. The database consists of the daily market indexes of 12 global stock exchanges in local and Euro converted currencies based on the daily European interbank exchange rates. Time series regressions indicate that GPI-constrained recursions outperform the buy-and-hold strategy. The downside risk of the portfolio is effectively controlled by crisp or fuzzy distress indicators to switch between cash or low-risk interest bearing instruments and risky assets.

Graphical abstractGPI-constrained weekly recursions including safety switches. Time period: 1999.12.08–2008.12.31.Figure optionsDownload full-size imageDownload as PowerPoint slideHighlight► Safety switches and growth potential of individual stocks reduce the downside portfolio risk. ► The yield of the system exceeds the buy-and-hold benchmark with statistical significance. ► A number of representative vector-valued forecasting methods have been included. ► The optimal forecast for each individual asset serves as input to the optimization module. ► The mathematical program is flexible and allows the specification of a large variety of risk formulations.