Dynamic Portfolio Choice
Andrew Ang (Columbia Business School) | July 2012
The foundation for a long-term investment strategy is rebalancing to fixed asset class positions, which are determined in a one-period portfolio choice problem where the asset weights reflect the investor’s attitude toward risk. Rebalancing is a counter-cyclical strategy that has worked well even during the Great Depression in the 1930s and during the Lost Decade of the 2000s. Rebalancing goes against investors’ behavioral tendencies and is also a short volatility strategy. When there are liabilities and asset returns vary over time, the long-term investor’s optimal portfolio consists of (i) a liability-hedging portfolio, (ii) a market (or myopic demand) portfolio that reflects optimal short-run asset positions, and (iii) an opportunistic (or long-term hedging demand) portfolio that allows a long-run investor to take advantage of changing investment returns.
The Trend is Our Friend: Global Asset Allocation Using Trend Following
Steve Thomas (City University London), et al. | June 2012
We examine the effectiveness of applying a trend following methodology to global asset allocation. The application of trend following offers a substantial improvement in risk-adjusted performance compared to traditional buy-and-hold portfolios. We also find it to be a superior method of asset allocation than risk parity. Momentum and trend following have often been used interchangeably although the former is a relative concept and the latter absolute. By combining the two we find that one can achieve the higher return levels associated with momentum portfolios but with much reduced volatility and draw downs due to trend following. We observe that a flexible asset allocation strategy that allocates capital to the best performing instruments irrespective of asset class enhances this further.
On the Style Switching Behavior of Mutual Fund Managers
Bart Frijns (Auckland University of Technology), et al. | July 2012
This paper develops an empirically testable model that is closely related to theoretical model for style switching behavior of Barberis and Shleifer (2003). We implement this model to examine the style switching behavior of US domestic equity mutual fund managers. Using monthly data for 2,044 mutual funds over the period 1961-2010, we find strong evidence for style switching behavior: on average nearly 53% of the funds in our sample engage in style switching. Overall, we find that growth funds tend to behave more as positive feedback (momentum) traders, whereas value funds tend to behave more as negative feedback (contrarian) traders. Linking the style switching behavior to fund characteristics, we typically find that funds that engage more aggressively in style switching tend to be younger and have higher total expense ratios. Linking the style switching behavior to risk-adjusted performance, we find no evidence of the ability of style switching to generate positive alpha.
Inflation Risk and Real Return
Mike Sebastian (Hewitt EnnisKnupp) | June 2012
Inflation risk is greatest in times of national or global stress; inflation risk is a form of a “tail risk.” A traditional portfolio of stocks and bonds is exposed to inflation risk. The specific nature of an investor’s liabilities and spending determines inflation sensitivity beyond that of the asset portfolio. Commodities and TIPS are the most effective short-term and long-term inflation hedges. Other traditionally recognized “inflation-hedging” assets offer more limited benefits. Investors have several attractive options for increasing inflation protection: Add or increase allocation to inflation-hedging assets, specifically commodities and TIPS, in the current asset allocation framework; Add a Real Return asset category, with a core of commodities and TIPS, funded proportionally from return-seeking and risk-reducing assets; Add inflation hedging assets to an “Opportunity Fund.” Investors can expect to pay about 0.15% of assets in the form of reduced expected returns for a reasonable level of inflation protection, before any gains from active management. Investors with inflation-sensitive liabilities or spending should consider instituting an allocation to inflation hedging assets of 10% of the total fund.
Efficient Algorithms for Computing Risk Parity Portfolio Weights
Denis B. Chaves (Research Affiliates), et al. | July 2012
This paper presents two simple algorithms to calculate the portfolio weights for a risk parity strategy, where asset class covariance information is appropriately taken into consideration to achieve “true” equal risk contribution. Previous implementations of risk parity either (1) used a naïve 1/vol solution, which ignores asset class correlations, or (2) computed “true” risk parity weights using relatively complicated optimizations to solve a quadratic minimization program with non-linear constraints. The two iterative algorithms presented here require only simple computations and quickly converge to the optimal solution. In addition to the technical contribution, we also compute the parity in portfolio “risk allocation” using the Gini coefficient. We confirm that portfolio strategies with parity in “asset class allocation” can actually have high concentration in its “risk allocation”.