The use of a dynamic asset class weighting portfolio framework based on changing market liquidity conditions enhances portfolio performance. By adjusting asset allocations based on market liquidity premium environments, portfolio returns improve because higher risk asset allocations can be used in up markets and lower risk asset allocations can be used in down markets.
Market liquidity cycles anticipate economic and market cycles. The authors investigate how changes in market liquidity can be used as a leading indicator for dynamic asset allocation decision making. Liquidity cross-sectionally influences market returns and not just returns on single assets. Liquidity-related information is quite useful in enhancing performance, including reducing portfolio downside risk.
How Is This Research Useful to Practitioners?
During the onset of financial stress, market participants reduce or eliminate their riskier holdings, such as equities, in favor of safer, more liquid assets, such as Treasury bills. Liquidity providers demand higher expected returns for providing liquidity. The authors use a framework that takes a “no liquidity premium” position by selling stocks when there is little opportunity to harvest a liquidity risk premium and takes a “high liquidity premium” position by buying stocks in environments that offer more generous liquidity risk premiums. A decrease in the Amihud illiquidity measure to levels below trend corresponds with a relatively low liquidity risk premium. Market environments that are awash with liquidity typically precede market downturns that are characterized by a spike in risk aversion. The ensuing liquidity event drives market prices lower. These lower prices, even after accounting for transaction costs, offer investors the opportunity to extract high premiums for providing liquidity. Gradually, the Amihud illiquidity metric returns to normal as investor confidence recovers and markets return to a normal liquidity environment.
Analyzing a portfolio model with two asset classes for 1964–2010, the authors support their thesis that changes in market liquidity can be used to improve portfolio construction over time as liquidity cycles between high and low liquidity premium environments. The research is useful for portfolio managers and savvy investors. It provides a framework for understanding a portfolio’s sensitivity to change in liquidity risk and helps determine which assets to invest in with a high level of sensitivity to market liquidity.
How Did the Authors Conduct This Research?
To compute the Amihud and turnover liquidity measures, the authors gather data on NYSE stocks that were priced between $2 and $1,000 and had market caps greater than $100 million during January 1963 to September 2010. They get the information from the CRSP stocks database. Total shares outstanding are not adjusted for free float. Consistent with previous research, the limit on market cap excludes most micro-cap stocks, American Depositary Receipts of foreign stocks, and other similar securities.
The authors compare a dynamic asset allocation (DAA) portfolio consisting of two asset classes with a strategic asset allocation (SAA) portfolio. On the basis of the changing Amihud liquidity metric and turnover measure, the DAA portfolio consistently generates higher returns than the SAA portfolio with the same level of risk. They use total returns for the comparisons, and the portfolios are rebalanced monthly.
The average monthly asset class weightings, standard deviation, and maximum draw-down risk levels of each portfolio are kept roughly the same to maintain equality. The model chooses high-liquidity premium (no liquidity premium) assets when the Amihud liquidity signal is greater (less) than −0.065. The Amihud threshold function is easily adjusted to achieve a desired risk target. The liquidity signal is binary, so when the signal value equals 0 (1), the DAA portfolio is placed into a no liquidity premium (high-liquidity premium) position (i.e., more low-risk assets/sell stock). The values of the Amihud liquidity and turnover measures are similar to those used in earlier studies. Regression of the DAA portfolio’s monthly returns against those of the SAA portfolio for the full sample period reveals the DAA portfolio’s monthly alpha. Alpha for each of the DAA portfolios is statistically significant at the 5% level, which confirms superior returns.
Previous researchers have demonstrated that stock prices relate cross-sectionally to fluctuations in aggregate liquidity. The authors opportunistically rebalance a portfolio with two asset classes to extract compensation in the form of a liquidity premium by investing in risky assets in situations of high market liquidity returns, while avoiding risky assets when liquidity returns are expected to be relatively low. But exactly how much extra return is attributable to only the liquidity premium is not covered in the current analysis. Future research could investigate how aggregate market liquidity might be used in dynamic asset allocation for international portfolios.