For years, Wall Street has relentlessly promoted the simple investment advice of buying and holding. Over time, this strategy morphed into the quasi holy grail of investing for a number of reasons: first, its winning streak over a fairly long period of time (thanks to one of the most powerful bull runs in equities ever recorded from 1982 to 2000) and second, its simplicity and cost efficiency.
The buy-and-hold strategy also fits rather well with the technical needs of Wall Street. It creates a constant and stable inflow of money into equities and it frees stockbrokers and financial advisors from the rigor of actually managing portfolios, thus allowing them to concentrate on asset gathering, a now-predominant function in a space where the deregulation of executing commissions and newly found legal liabilities has changed the business model forever. The buy-and-hold mantra also helps mutual funds from a tactical, strategic angle by securing stable flows of capital into their investment products while minimizing their liquidity issues.
The success of buy-and-hold has also helped validate passive investing and indexing (i.e., beta replication), an investment technique that does not bother with market timing, active asset allocation, or stock picking; rather, it is strictly concerned with replicating benchmark performance at low execution and management costs.
Investment strategies, including buy-and-hold and indexing, go through market cycles where macro and structural conditions are favorable. Inevitably, these market cycles are followed by periods of unfavorable contingencies. Since the tech bubble burst in March 2000, the performance of the equity market has been inconsistent, and the market has underperformed compared with most other asset classes. In effect, a strictly passive strategy has garnered very little performance after almost a decade. The Standard & Poor’s 500 Index (S&P 500) peaked on March 27, 2000, with a value of 1523.86 (on a closing basis). Over eight years later on July 2, 2008, the S&P 500 stood at 1251.62 (after briefly reaching a new high of 1554.41 on October 11, 2007)a loss of almost 18 percent.
On a rolling basis, holding U.S. stocks passively for at least 10 years has rarely produced a significantly negative performance in real terms. In fact, there have only been three general periods of underperformance since 1880. But if the stock market does not change its tune radically in the next 18 months, we could witness the first largely negative decade since the mid-1970s. To make things even more depressing for the indexing crowd, it is unlikely that economic conditions will favor the passive approach in the foreseeable future.
The long-term implications of the credit deleveraging process, alongside the inflationary pressures that have been steadily building over the last year, would indicate that an uneven performance by equities can be expected for quite some time. Not so shockingly to the astute investor, active risk management and active asset allocation seem to be back in vogue. Alpha investing, the technique that actively seeks alternative and possibly uncorrelated sources of return beyond the passive performance produced by beta exposure, is going to be the central and pivotal element of every successful portfolio.
This article looks at an alternative, simple, yet cost-effective way to actively manage an equity portfolio. The study focused on using exchange-traded funds (ETFs) as low-cost allocation vehicles to help investors build diversified portfolios that could be actively managed based on simple rules. The process invoked two major tactical investment styles: momentum and contrarian.
While these techniques can be construed in a myriad of different ways, the philosophy behind them can be simply deconstructed:
- Momentum will overweight the portfolio toward those sectors/asset classes showing price outperformance; and
- Contrarian will overweight the portfolio toward those sectors/asset classes suffering price underperformance.
For the exact rules and composition of the portfolio, please refer to the Methodology section below, which describes our strategy in further detail.
The idea was to create a model that would allow for an active sector rotation in the search for market anomalies. In line with the premise that economic and equity performance will be significantly uneven in the future, this study looked for an active strategy that would capitalize on such sector discrepancies.
A decision was made to utilize ETFs from iShares, one of the original issuers of ETFs, with the intent of satisfying the following requirements: low-cost execution, allocation simplicity, and instrument liquidity. Thirty-six ETFs were chosen from the available menu with the objective of diversifying amongst various sectors.
One drawback inherent in this approach was the relatively short historical period available for analysis. Most ETFs have less than five years of price data, which forced us into running two different types of studies: one with fewer sectors but a longer history (five years) and two with more sectors but shorter price histories (18 and 24 months).
It was also determined that other constraintsthe limited number of sectors offered by iShares and the liquidity of various ETFswere not a significant problem, as the available ETFs were diversified enough to validate the project. In fact, this study selection included all of the sectors represented in the S&P 500, with three additions: International (four ETFs), Fixed Income (two ETFs), and Commodities (one ETF). However, further studies should include more sectors as more ETFs come to the market and achieve tradable liquidity.
It was also decided not to run correlation studies but, instead, to build and rebalance portfolios strictly for the sectors selected according to our general rules. The idea behind this decision was to concentrate on developing a strategy that would significantly outperform the benchmark (S&P 500), and then analyze the portfolio’s risk by looking at how the largest losses compared to the index.
Analysis of Results
After back-testing the two different strategiesmomentum and contrarianover the three time periods, along with three varying degrees of diversification, we found the results were indeed consistent across different tests, and, therefore, validated our premise.
The best risk-adjusted strategy was momentum with a three-month rebalancing. This strategy outperformed SPY (the ETF for the S&P 500 index) over all three studies by a significant amount while showing similar draw-down risk. Momentum also significantly outperformed in the one-year rebalancing study with comparable draw-down risk.
In general, the momentum strategy outperformed the contrarian strategy by large degrees, with the only notable exception being the five-year test with one-month rebalancing. In this study, momentum underperformed SPY slightly and underperformed and the contrarian method more significantly. However, even within this rebalancing framework, momentum considerably beat both SPY and contrarian style in studies 2 and 3, which tested shorter historical periods (18 and 24 months), albeit during a very volatile period that included a bull-and-bear cycle, with wider diversification.
The contrarian method produced its best result in the first study (five-year testing) with the one year rebalancing. In this instance, it beat momentum and SPY and it did not show a negative draw-down, either. However, in the shorter historical testing period, it lost more than the SPY while momentum produced solid positive results.
For a complete review of all the results, please look at Appendix B.
We first looked at the available sectors for trading via iShares and chose 36 ETFs representing 13 main sectors: basic materials, consumer goods, energy, financials, healthcare, industrials, natural resources, technology, telecommunications, utilities, fixed income, international, and commodities. As explained previously, this menu was a very close representation of the S&P 500 with additional exposure to sectors and asset classes that should all be monitored by an active investor.
It was believed that in order for this research to have a solid foundation, we had to back-test at least five years of performance. Due to the fact that a large portion of ETFs were introduced in May 2006, with some more introduced in September 2006, only 18 of the 36 selected ETFs had available data going back at least five years (see the table below for the complete universe). Therefore, we decided to expand the investigation by dividing the research into three separate studies in order to compare how results would vary when more ETFs were included in the sample.
Each study had different testing periods, but they were all recalibrated according to the same rules: rebalancing every month, rebalancing every three months, and rebalancing every twelve months. At the end of each period (monthly, quarterly, and annual, respectively), we would rank the performance of the available ETFs for that period. Then, for the momentum portfolio, we would invest in the top 25th percentile for the following period. For the contrarian portfolio, a decision would be made to invest in the worst 25th percentile in the next period.
The study equally weighted the positions of the ETFs forming the portfolio, adding only one filter: no more than 25 percent was to be invested in each of the 13 sectors. In line with the study’s practical approach, a decision was made to use SPY as a benchmark, which is the State Street ETF that replicates the S&P 500.
Study 1 included data from May 2003 to April 2008, for which 18 ETFs were available and a total of four ETFs formed our portfolio. Study 2 included data from June 2006 to May 2008, for which 30 ETFs were available and 7 ETFs formed our portfolio. Study 3 ran from October 2006 to May 2008, for which 36 ETFs were available and 9 formed our portfolio.
Due to the time period and associated lack of available data, the authors could not test the 12-month rebalancing strategy in Study 3. The study used adjusted closing monthly prices provided by Yahoo! Finance. We also used adjusted closing daily prices for testing maximum daily loss within the worst period.
The results of this study are gross and do not incorporate commissions cost or slippage cost.
Improving Portfolio Management
As touched upon in the introduction, the motivation behind the analysis undertaken in this project was to test a simple and cost-effective way to produce an active alpha strategy to replace or at least complement more traditional beta-driven portfolios.
Over the years, the investment community has spent a considerable amount of resources directing portfolios toward more or less rigid index exposures. Initially, there was a sound intellectual foundation for such ideas: Capital Asset Pricing Model (CAPM) and Markowitz’s Market Portfolio. In the first section of this article, we also identified some of the practical reasons underlying this approach
The CAPM framework operated from the point of view of a single beta, the global market portfolio beta. However, the realization of consistent deviations from equilibrium in investment subsets (i.e., emerging markets, small caps, and so on) that offered returns uncorrelated to the market created the first friction with CAPM: exotic betas and the multiple beta approach, which eventually led to the proliferation of ETFs.
This study supports the idea that the ultimate portfolio optimization comes from the ability to identify sources of return produced by active and skilled investment management. The consistent ability to enhance performances with superior market timing and security selection or, more simply, alpha, is today, more than ever, a central tenet of an optimal portfolio.
As described initially, indiscriminate exposure to a generalized beta has produced negative returns over the last decade. Furthermore, future conditions do not seem to indicate a change to this situation. In today’s world of disappearing traditional returns, what can an investor do to optimize her/his portfolio? It would seem that the need to actively incorporate skill-based or alpha-seeking strategies in traditional portfolios should become a priority.
As an answer to the quest for cheap and simple alpha, we looked at momentum and contrarian strategies executed via ETFs. There is a considerable amount of research to validate both strategies as solid starting points for investors.
Momentum strategies generally work across boundaries due to a number of potential factors: under-reaction to the dissemination of news (a practical discovery that stands in clear contrast to the efficient market hypothesis), the difficulty large investment funds experience in deploying capital quickly, and ultimately, their simplicity of execution offered, which can lead to easy replication and self-fulfilling results. Interestingly, studies show that momentum strategies seem to be more successful in shorter-term periods while value strategies seem to outperform over longer time horizons. Our study validated that finding: While momentum outperformed the benchmark and contrarian strategy in most scenarios, it underperformed in the five-year simulation with annual rebalancing (the longest time frame with the least frequent rebalancing).
This fluctuating outperformance over time horizons could be exploited in a core-satellite portfolio, where the core is dedicated to value beta exposure and the satellite is comprised of alpha-seeking momentum investing.
In conclusion, this research indicates that a simple momentum strategy, based on quarterly or monthly sector rotation, executed via ETFs should provide a cost-effective way to obtain that much-sought-after alpha component, which every portfolio will need to counterbalance the current negative conditions for traditional beta investing.
 R. Schiller, “From Efficient Markets Theory to Behavioral Finance,” Journal of Economic Perspectives, 17, (Winter 2003).
 “SPY” is the ticker symbol of the exchange-traded fund (ETF) that replicates the performance of the S&P 500 market index. It is a tradeable version of the index and therefore (among other reasons) an applicable benchmark for this practical portfolio.
 “State Street” refers to the investment company that issued the SPY (there are other ETFs that replicate indices with similar profiles, but they are issued by other institutions).
 Harry Markowitz, “Portfolio Selection,” Journal of Finance, 7, No. 1, (March 1952).
 H. Hong and J. Stein, “A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets,” The Journal of Finance, LIV, no. 6, (December 1999).