Are there easily implementable life cycle investing strategies reliably superior to the conventional glidepath from equities toward bonds? In their June 2013 paper entitled “The Glidepath Illusion… and Potential Solutions”, flagged by a subscriber, Robert Arnott, Katrina Sherrerd and Lillian Wu summarize flaws in the conventional glidepath approach and explore simple alternatives that address some of the flaws. Specifically, they compare the follow six strategies:
- 80–>20: the conventional linear glidepath from 80% stocks-20% bonds to 20% stocks-80% bonds at retirement, with market capitalization weighting.
- 20–>80: inverse of the conventional linear glidepath.
- 50-50: constant 50% stocks-50% bonds, with market capitalization weighting.
- Dynamic Bond Duration: the 50-50 strategy, but: (a) hold 20-year bonds for the first 21 years; (b) shift linearly to 10-year bonds during the next ten years; and, (c) shift linearly from 10-year bonds to T-bills during the last 10 years before retirement.
- Dynamic Value/Low Beta: the 50-50 strategy, but: (a) stocks are weighted by book value for the first 21 years (from the 1,000 U.S. stocks with the highest book value); and, (b) shift linearly to low-volatility stocks (the 1,000 largest U.S. companies by market capitalization, weighted by inverse volatility) during the next 20 years.
- Dynamic Combined: the 50-50 strategy, but use Dynamic Bond Duration and Dynamic Value/Low Beta for bonds and stocks, respectively.
Comparison tests assume that: (1) each individual makes inflation-adjusted $1,000 annual contributions to a retirement portfolio over a 41-year career; and, (2) portfolio rebalancing is annual, frictionless and tax-free. Using simulations based on long-term samples of U.S. stock index, bond index and U.S. Treasury bill (T-bill) returns through the end of 2011, they find that: More…
Do upside (downside) market volatility surprises scare investors out of (draw investors into) the stock market? In the November 2013 version of his paper entitled “Dynamic Asset Allocation Strategies Based on Unexpected Volatility”, Valeriy Zakamulin investigates the ability of unexpected stock market volatility to predict future market returns. He calculates stock market index volatility for a month using daily returns. He then regresses monthly volatility versus next-month volatility to predict next-month volatility. Unexpected volatility is the series of differences between predicted and actual monthly volatility. He tests the ability of unexpected volatility to predict stock market returns via regression tests and two market timing strategies. One strategy dynamically weights positions in a stock index and cash (the risk-free asset) depending on the prior-month difference between actual and past average unexpected index volatility. The other strategy holds a 100% stock index (cash) position when the prior-month difference between actual and average past unexpected index volatility is negative (positive). His initial volatility prediction uses the first 240 months of data, and subsequent predictions use inception-to-date data. He ignores trading frictions involved in strategy implementation. Using daily and monthly (approximated) total returns of the S&P 500 Index and the Dow Jones Industrial Average (DJIA), along with the U.S. Treasury bill (T-bill) yield as the return on cash, during January 1950 through December 2012, he finds that: More…
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Here is this Friday’s installment of Avoiding Investment Strategy Flame-outs, a short book we are previewing for subscribers. Chapter previews will continue for the next six Fridays.
Chapter 3: “Avoiding or Mitigating Snooping Bias”
“Snooping bias, also called mining bias and more loosely benefit of hindsight, is a notorious artificial booster of backtest performance. It takes multiple forms:
- Picking the best of many rules/indicators (strategies, models) for a given data sample
- Optimizing rule parameters for a given data sample
- Restricting a data sample to find favorable performance of a given rule
- Running an investment contest among many individuals
“A sentiment shared among researchers in stochastic fields is: “If you torture the data long enough, it will confess to anything.” Because returns are noisy (substantially random), trying many combinations of rules, parameter settings and data samples will generate strategies that outperform benchmarks by extreme good luck. A prosecutor (an investor) satisfied with false confessions is likely to lose in court (the market).
“To illustrate, Figure 3-0 depicts the net cumulative values of $1.00 initial investments in each of 12 variations of the simple asset class momentum strategy introduced in Figure 1-1. This strategy shifts each month to the one of nine asset class proxies with the highest total return over a past return measurement (ranking) interval. Most of the proxies are exchange-traded funds (ETF). The 12 variations differ by the length of the ranking interval, from one to 12 months. All variations impose a switching friction of 0.25% whenever the strategy switches funds.
“Does the top-performing variation (dotted line) represent a premium earned by extracting truly valuable information from market prices, or just the payout from being the lucky winner of a lottery? The following sections address this question.”
Figure 3-0: Performance of 12 Asset Class Momentum Strategy Variations
The deadline for submission of papers for the 2014 Wagner Award, presented by the National Association for Active Investment Management (NAAIM), is February 28, 2014. Per the “Call for Papers”:
“The competition is open to all investment practitioners, academic faculty and doctoral candidates in the field. …Papers must be of practical significance to practitioners of active investing. The prize will be awarded to a paper resulting from research into active investment management, which NAAIM broadly defines as investment strategies and techniques that improve upon the risk-adjusted return obtainable from a passive, buy-and-hold, investment strategy. …Three prizes will be awarded. The best paper will receive the Wagner Award valued at $10,000; second place will receive $3,000 and third will receive $1,000. …the grand prizewinner will be invited to present his / her paper at the NAAIM annual conference: “NAAIM Uncommon Knowledge 2014,” May 5–7 at the Hyatt Regency Pier Sixty-Six in Ft. Lauderdale, Florida. Free conference attendance, U.S. air travel and lodging will be provided.”
See “Equity Sector Selection Based on Credit Risk”, “Volatility Trading Strategies” and “Taking the Noise Out of Technical Trading” for summaries of the 2013 Wagner Award first, second and third place papers, respectively.
See “Melding Momentum, Diversification and Absolute Return”, “Mutual Fund Alpha Momentum” and “Active Asset Allocation via Drawdown Control” for summaries of the 2012 Wagner Award first, second and third place papers, respectively.
See “Capital Management with Clustered Signals”, “Which Kind of (ETF) Momentum Is Best?” and “Enhancing/Streamlining Asset Rotation” for summaries of the 2011 Wagner Award first, second and third place papers, respectively.
See “Exploiting the Predictability of Volatility” and “Selling Calls or Puts According to Trend” for summaries of the 2010 Wagner Award first and second place papers, respectively.
CXOadvisory.com has no affiliation with NAAIM or the Wagner Award.
Do popular style-based exchange-traded funds (ETF) confirm the existence of a reliably exploitable value premium? To investigate, we compare the difference in returns (value minus growth) for each of the following three matched pairs of value-growth ETFs:
- iShares Russell 2000 (Smallcap) Growth Index (IWO)
- iShares Russell 2000 (Smallcap) Value Index (IWN)
- iShares Russell Midcap Growth Index (IWP)
- iShares Russell Midcap Value Index (IWS)
- iShares Russell 1000 (Largecap) Growth Index (IWF)
- iShares Russell 1000 (Largecap) Value Index (IWD)
Using monthly adjusted closing prices (incorporating dividends) for these ETFs during August 2001 (the earliest month available for IWP-IWS) through October 2013 (147 months), we find that: More…
Do popular capitalization-based exchange-traded funds (ETF) confirm the existence of a reliably exploitable size effect? To investigate, we compare the difference in equally weighted returns (small minus large) for the following matched pair of small-large ETFs:
- iShares Russell 2000 Index (Smallcap) Index (IWM)
- iShares Russell 1000 (Largecap) Index (IWB)
Using monthly adjusted closing prices (incorporating dividends) for these ETFs during May 2000 (the earliest month available for both) through October 2013 (162 months), we find that: More…
Is diversification across stock and bond factors superior to diversification across asset classes? In their August 2013 report entitled “Investing in Systematic Factor Premiums”, Kees Koedijk, Alfred Slager and Philip Stork measure the gross performances of widely used stock and bond factors and pit portfolios diversified across those factors against portfolios diversified across asset classes. For equities, they examine market, size, value, momentum and low-volatility factors. For bonds, they examine market, term spread, credit spread, high-yield, short-term credit yield and short-term government yield factors. They consider both U.S. and European data as available. They take an institutional perspective and therefore restrict consideration to simple, long-only portfolios. For asset class diversification, they consider stocks-bonds and stocks-bonds-commodities-real estate. They ignore all trading frictions involved in constructing factor portfolios and in rebalancing multi-asset and multi-factor portfolios. Using monthly prices for U.S. and European stocks, bonds, Real Estate Investment Trust (REIT) indexes and a common global commodity index as available through mid-to-late 2012, they find that: More…
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