Incorporating market-liquidity levels in a dynamic asset-allocation policy improves portfolios.
A longer version of this article will be published in the 2013 summer issue of the Journal of Portfolio Management.
At the micro-level, liquidity relates to the speed and ease with which an investor can trade an asset. Amihud and Mendelson (1986) used the bid-ask spread as a measure of liquidity to test the relationship between security returns and liquidity. They found evidence that investors demanded a premium for illiquid securities. Datar, Naik, and Radcliffe (1998) used the turnover rate (number of shares traded as a fraction of the number of shares outstanding) as a proxy for liquidity and found that firm-level stock returns are strongly negatively related to their turnover rates. Using autocorrelation in returns as a proxy for illiquidity, Khandani and Lo (2011) showed that illiquidity premiums are generally positive and significant, ranging from 2.74% to 9.91% per year among hedge funds and fixed-income mutual funds. These results all support the notion of an illiquidity premium: Lower levels of liquidity relate to higher average returns.
At the macro-level, liquidity has also been shown to be an important variable in the pricing of assets. Stock prices relate cross-sectionally to fluctuations in aggregate liquidity [Amihud (2002), Pastor and Stambaugh (2003), Kamara, Lou, and Sadka (2008)]. This research further shows that common measures of liquidity risk move together and that liquidity influences the market return, not just the return of a single asset, cross-sectionally.
Altogether, research suggests that the market’s aggregate liquidity relates to real economic activity. Indeed, Naes, Skjeltorp, and Ødegaard (2011) show that when aggregated to the market level, cross-sectional liquidity contains useful information about the current and future state of the economy. This is where our study comes in. Our results extend these efforts by shedding light on how changes in market-liquidity risk premiums have an impact on a portfolio’s performance.
Market Liquidity and Dynamic Portfolio Allocation
To accomplish this task, we propose a model of portfolio selection that adjusts an investor’s portfolio allocation to changing market-liquidity premiums and market conditions. We find that changes in market liquidity provide a useful “leading indicator” in dynamic asset allocation.
We apply the Amihud (2002) illiquidity measure, which is calculated for each stock for each month and then aggregated to a market level. The Amihud measure is a priceimpact measure of illiquidity and reflects the degree to which prices move in response to trading volume changes. A higher value of Amihud signifies higher illiquidity (lower liquidity) because a particular dollar volume traded is associated with a relatively high price movement. We calculate the Amihud liquidity measures using data consisting of all the stocks on the New York Stock Exchange that met our criteria over our study period of January 1980 to September 2010.
To illustrate the effectiveness of liquidity as an information signal, we employ a simple two-asset portfolio choice model composed of stocks and bonds. The benchmark and the constructions of the dynamic liquidity premium portfolios are shown in the first table. The specific asset classes we employed are shown in the second table. Each one of the six equity assets separately comprises the equity component, while the bond component is always the BarCap US Gov/Credit 1–3 Year Total Return Index. We use this simple two-asset construction to generate the performance of a dynamic asset-allocation (DAA) portfolio. We use a simple high-liquidity premium/no-liquidity premium signal to adjust the asset-allocation risk posture. We then compare the DAA performance results to the strategic, or benchmark, portfolio (SAA) allocation. In all cases, we use total returns and rebalance monthly.