Morningstar Advisor - April/May 2013 - (Page 65)
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.
Exhibit 1 Liquidity-Driven Asset Allocation
Performance Summary (December 1980–September 2010), Amihud Measure*
DAA
DAA
Arithmetic
Standard
Return (%) Deviation (%)
SAA
SAA
Arithmetic
Standard
Return (%) Deviation (%)
SAA
Sharpe
Ratio
Alpha (%)
t-stat
Large Growth
10.48
9.17
9.06
9.12
0.71
0.55
1.62
3.28
Large Value
10.97
7.76
9.93
7.76
0.90
0.76
1.35
3.16
Small Growth
10.64
11.78
8.42
11.65
0.56
0.38
2.31
3.64
Small Value
12.11
8.69
10.65
8.82
0.93
0.75
1.94
3.86
REIT
11.71
9.06
10.41
8.90
0.85
0.72
1.41
2.91
EAFE
10.28
9.05
9.38
9.06
0.69
0.59
1.23
2.44
*Figures are annualized
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.
Benchmark
50% equity/50% bond
High Liquidity Premium
60% equity/40% bond
No Liquidity Premium
30% equity /70% bond
Large Growth (LG)
Russell 1000 Growth
Large Value (LV)
Russell 1000 Value
Small Growth (SG)
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
DAA
Sharpe
Ratio
Russell 2000 Growth
Small Value (SV)
Russell 2000 Value
REIT
FTSE NAREIT Equity REITs
EAFE
MSCI EAFE
Bonds
BarCap US Govt/Credit 1–3 Yr
In choosing which of the two portfolios
to use in forming our DAA portfolio each month,
we first calculate the market environment
based on the degree of liquidity risk and
use this to inform our asset-allocation decision.
Here, our portfolio decision is determined
by one of two possible states of the world: low
or no expected liquidity premium or high
expected liquidity premium. The two market
states are governed by the level of liquidity risk
in accordance with the thresholds shown
in Exhibit 1. In calculating our liquidity
time-series measurements, we use a six-month
average value.
The liquidity-risk-regime thresholds were
chosen in a straightforward way in order
to equalize the portfolio asset-allocation risk
postures over time between the dynamic
portfolio and the static benchmark portfolio.
That is, the average monthly weightings
for both the SAA and DAA portfolios over the
full study period equal 50% stocks/50% bonds.
First, we determine the threshold triggers using
the entire sample period, 1980–2010, which
allows our regime changes to reflect the reality
of market-liquidity conditions. Exhibit 2
relates the Amihud measure over time versus
the fixed threshold trigger. The model
chooses high liquidity premium (or no liquidity
premium) when Amihud is greater (or less) than
MorningstarAdvisor.com 65
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Table of Contents for the Digital Edition of Morningstar Advisor - April/May 2013
Morningstar Advisor - April/May 2013
Contents
Contributors
Letter From the Editor
The Pursuit of Happiness and Financial Advice
What Strategies Do You Use to Control Risk?
Driven to Succeed for Clients and Family
How to Assess a Portfolio’s Bond Risk
Luck, Skill, and Investing
Investments á la Carte
Investment Briefs
Investing’s No- Brainers Have Costs
A Defensive Ride
Risk On/On Risk
The Risk of Being Overconfident
Year of Living Dangerously
The Risk-Parity Approach
A Guide to Mutual Funds Running Risk-Parity Strategies
What Moats Tell Us About Risk
Risk’s Wake-Up Call
Seeing Is Believing
Why Investors Lag the Returns of Their Funds
Liquidity Signals
Pump Them Up
Golden Oldies Keep on Truckin’
Our Favorite Mutual Funds
50 Most-Popular Equity ETFs
Undervalued Stocks With Wide Moats
Our Social Blind Spot
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