Research has shown that investment success is largely driven by asset allocation (Brinson et. al (1991)). Although many investors choose exchange traded funds (ETFs) and mutual funds over individual stocks, most must make asset allocations because they hold multiple positions in their portfolios. One rule of thumb that is commonly used is to allocate 60 percent of the investable asset in stocks and 40 percent in bonds (Chaves et al. (2011), Brinson et al. (1991); and Ziemba (2013)). As ETFs have expanded beyond simply indexing equities or bonds to include indices based on currencies and commodities, it has become simple and affordable for investors to employ non-traditional asset classes to gain further diversification. In the aftermath of the 2008 global financial crisis, investors are looking to non-traditional investment alternatives to further diversify their portfolios. Simple allocation strategies such “60/40” are no longer valid to the extent they overlook the potential for further diversification. With a universe of some 2,792 ETFs available in 2021 (Bloomberg LP, 2021), investors have a wide variety of to choose from in gaining representation for many asset classes within their portfolios. The willingness of investors to use ETFs is reflected in the fact that ETF assets grew from less than one hundred billion in 2000 to seven trillion dollars in 2021 (Board of Governors of the Federal Reserve System US).
While many studies have shown that asset allocation is of more strategic importance than active security selection, few has examined efficient asset allocation within a portfolio made up exclusively of ETFs, This paper aims to create efficient portfolios using only ETFs and to compare the performance of these portfolios with naive allocation schemes. Producing efficient portfolios from EFTs alone is feasible given the acceptance of ETFs by the investor community and the proliferation of ETFs that make it possible to find an ETF for virtually any objective, focus, or asset class.
The motivation for this research study is to make useful and simple investment recommendations for individual investors, Although ETFs have received considerable attention among researchers and practitioners, there is little literature addressing asset allocation using ETFs. Only a few studies have used ETFs exclusively to provide allocation recommendations for individual investors. Our study attempts to fill this void in the literature by producing asset allocations among 34 ETFs using Markowitz’s mean-variance optimization technique along with Ledoit and Wolf’s (2004) modification for structured and unstructured covariance matrices, This study is a meaningful contribution to a very few research that seeks optimal asset allocations using ETFs exclusively. Our objective is to provide useful information for individual investors, particularly in the form of simple allocation recommendations, Individual investors often make arbitrary allocations among asset classes that result in poor diversification and higher management costs, At the same time, wealth management companies charge substantial fees for recommending efficient allocations, Efficient portfolios made up entirely of ETFs can provide exposure to broad indices for equities, bonds, as well as to non-traditional asset classes at a relatively low cost. Our expectation is portfolios that use an optimized allocation among several ETFs will provide individual investors with more effective diversification and potentially higher returns.
Based on ex-post analysis from 2012 to 2017, the top performing optimized portfolio produced an average annualized return comparable to that of the top performing ETF Similarly, another optimized portfolio produced a higher Sharpe ratio than 85% of the ETFs in our study. Nevertheless, when the same optimization methods is implemented using ex-ante returns, the optimized portfolios do not provide superior future performance relative to the ex-post analysis and also underperforms the best performing ETFs during the same time period. Further, these results are sensitive to the length of the historical time period used to determine the optimized allocation weights or the frequency of portfolio rebalancing. Using the modified shrinkage variance-covariance matrix to solve the issue of assigning excessive weight to certain ETFs (a general occurrence with the Markowitz optimization approach) improves the portfolio performance.
Section 2 of this paper provides an overview of the literature on ETFs and portfolio optimization, In section 3, the ETFs, the data sources, and the fund selection criteria are described with summary statistics. In section 4, the empirical model used to determine the optimal weights for the portfolio based on Markowitz Optimization is described. In section 5, the empirical results of optimized portfolio performance in comparison with the individual ETFs are presented. In addition, the performance of the optimized portfolio using the modified variance covariance matrix with shrinkage technique and average optimal weight are also evaluated, Concluding remarks are provided in section 6.
Asset allocation has often been cited as a more important factor than active security selection in contributing to investment success, Brinson et. al (1991) found that 91% of the variance in pension fund returns is explained by asset allocation. This result is also supported by Ibbotson and Kaplan (2000), who report that asset allocation alone explains 87.6% of mutual fund returns and 90.7% of pension fund returns, The literature addressing efficient allocation began with the seminal paper of Markowitz (1952), who demonstrated that allocating assets among indices provides superior return ex post, Over the years, many studies applied these principles to find an ideal portfolio allocation but with mixed results. An issue with the Markowitz model stems from its use of a historical variance covariance matrix to determine the efficient allocation. As a result, the Markowitz process often leads to so called “error maximization” which is reflected in the assignment of unusually high or low allocation weights driven by outliers in the data. Consequently, the weights produced are not stable and are very sensitive to the data.
Optimization process does not perform well out of sample, Therefore, alternatives and solutions are used in the literature to overcome these issues. One common alternative is to use naïve or simplistic approaches as an alternative to portfolio optimization. Some of these approaches are 60/40 equity/ bond portfolio mentioned earlier and equal weighting. For example, Jacobs, et.al (2014) shows that simple heuristic allocations offer substantial benefits and often produce better results than classical Markowitz’s optimization. There are many more studies show that the naïve portfolio allocation techniques outperform the mean-variance optimization (See: Chaves et al. (2011) and DeMiguel et al. (2009)).
The second branch of approaches attempt to fix issues with relying on historical variance-covariance matrix that leads to unstable estimates. Bayesian methods and factor models are used to overcome these problems. Recently, robust optimization technique is also used to solve the same problems. See Kolm et al. (2014) for a more thorough review of the literature. Bayesian methods allows a researcher or investor to input his/her view into parameter estimation, With this input, the estimation becomes more robust. Many different versions of Bayesian models are used in the literature, All these models essentially combine a structured covariance matrix with a sample covariance matrix. Putting all the weight in the sample covariance matrix would lead to an unbiased estimation with potentially large estimation error, On the other hand, putting all the weight in a structured matrix would minimize the estimation error but increase the estimation bios potentially due to misspecification of the structured matrix, Single index models, for example, capital asset pricing model (CAPM) is an example of a model with a highly structured covariance, The other extreme would be regular Markowitz optimization with an unstructured covariance. Also, sample covariance model could be considered a multifactor model considering each asset in the sample as a unique factor, A significant part of the literature searches for the sweet spot between these two extremes (Marakbi 2016), Examples of such models are Treynor and Black (1973) and Black and Litterman (1992) models, All Bayesian or shrinkage models differ because of the different weights chosen between the structured and unstructured covariance matrixes (also called shrinkage constant) and the composition of the structured covariance matrix, For example, Frost and Savarino (1986) obtained a better out of sample performance using Bayesian approach to optimization, Ledoit and Wolf (2003 and 2004) offers a widely accepted shrinkage methodology, Our paper also utilizes this approach, The weight put into structured matrix is called the shrinkage constant, This approach helps to overcome the error maximization and the invertibility of variance covariance matrix, In other words, the number of assets could be more than the number of periods, Out of the sample performance with the shrinkage approach is better than the performance obtained from using only sample covariance in optimization process.
Minimum-Variance Portfolio (MVP) strategies are studied by many papers, It may not be desirable by many investors to have a low-risk portfolio (since usually the return will be lower also) initially. However, estimation problems we mentioned above is a lesser problem with a global minimum portfolio, In other words, theoretically, minimum variance portfolio may have a low return expectation, but in practice its return could be better than even a tangency portfolio, (Clarke et al. (2011), Frahm (2010), and Richardson (1989)).
There are some papers which study the portfolio allocation using exclusively mutual funds like our approach in this paper limiting the allocation to ETFs, Given the longer history of mutual funds, these results would provide a good perspective of the potential of portfolios constructed exclusively with ETFs.
Pastor and Stambaugh (2002) used equity mutual funds to create better portfolios, The study finds that investing in active mutual funds along with passive indexes could increase the Sharpe ratio of the portfolio, Louton and Saraoglu (2008) also used exclusively mutual funds to find out how many mutual funds is necessary for a well-diversified fund, Their findings suggest that 10 to 12 mutual funds are required to get reduction of most of the risk and drawn down in the portfolio, They did not offer an allocation strategy, Moreno and Rodríguez (2013) find that most of the actively managed mutual funds are not well diversified. The study used several optimization techniques to minimize the idiosyncratic risk, The optimal portfolio that minimizes the idiosyncratic risk had a good out of sample performance and this allocation provided the best alpha for the overall portfolio, Saraoglu and Detzler (2002) recommends using the analytic hierarchy process (AHP) to make asset allocation decisions using mutual funds, The study does not offer an out of sample performance result.
Using 36 Swiss ETFs, Milonas and Rompotis (2006) find that ETFs underperform their underlying indices in terms of both risk and return, Miffre (2007) compares the performance of country-specific ETFs with that of open or closed-end country funds and finds that ETFs are superior due to lower costs, lower tracking error, and being more tax efficient.
More closely related to our paper, Ma, MacLean, Xu, and Zhao (2011) employ a regime-switching risk factor in determining that sector ETFs allocations perform better than naïve allocation strategies. Furthermore, DiLellioa and Stanley (2011) state a similar conclusion after comparing several ETF strategies with the Standard and Poor’s 500 index as well as other benchmarks and finding that the ETF strategies outperform the benchmarks. Agrrawal (2013) demonstrates that multi asset class ETF portfolios dominate stock only investment options. Utilizing neural networking models, Zhao, Stasinakis, Sermpinis, & Shi (2018) are able to improve the portfolio efficiency of three ETFs compared to traditional mean-variance optimization.
Hlawitschka and Tucker (2008) used exclusively ETFs to test whether stock selection has any merit in addition to optimal asset allocation. The study finds a justification for investors to choose active stock selection over asset allocation strategies.
This study uses monthly price data obtained from Bloomberg for the period beginning August 2007 and ending December 2017. Because portfolio optimization is typically based on 60 monthly observations, it was necessary to limit the number of ETFs in this study, There are too many ETFs with similar objectives. In fact, some of the ETFs dominates others if it compared in most important categories (see Brown et.al. (2021)), The optimization process computes the ETF weights based on the sample covariance. For the solution, the inverse of the sample covariance matrix is necessary. The sample covariance matrix could be singular if the number of observations is less than the number of variables. In other words, there will not be a solution and the weights can’t be computed, Therefore, the number of ETFs used needs to be under 60 which is the number of observations used in the estimation, However, by modifying the covariance matrix, this issue can be resolved, For example, Moore-Penrose inverse can be used to solve this problem (Pappas, Kiriakopoulos, and Kaimakamis (2010)). In choosing a subsetfor our study from the 2,792 or so U.S. ETFs, we applied three screening criteria. First, the largest ETFs by market cap were selected because they are popular and meaningful from an investor’s perspective. Second, this study includes the ETFs with the longest possible history. The final criteria implemented is to include ETFs from as many asset categories as possible to improve the diversification potential of our portfolios. Where possible at least one ETF is included from each category within an asset class. For example, if two ETFs tracked the performance of the S&P 500 index, the larger of the two is included in the study and the other is eliminated. By applying the three criteria, we selected 34 ETFs for our study, Table 1 provides information on key characteristics of the 34 ETFs included in the study, Among 34 ETFs under investigation, SPDR S&P 500 ETF Trust (SPY) is the largest ETF in the US with more than 420 billion dollars in assets (in December 2021) and has been in the market for the longest period of time, for about 27 years. The ETF with the smallest market capitalization is the United States Oil Fund (USO) which tracks U,S. crude oil prices, Currently, the USO has a total asset value of about 2 billion dollars. The fund with the shortest history in our sample, Vanguard FTSE Developed Markets ETF (VEA) has been around for 10 years. Despite its short life, VEA is among the top four ETFs in our study in terms of market capitalization, VEA targets the performance of the FTSE Developed All Cap ex US Index and tracks stocks in developed market other than the U.S.
Lists and Characteristics of Exchange Traded Funds (ETFs) (June 2007 to December 2017)
Ticker | Name | Total Assets (million Dollars) | Fund Objective | Fund Geographical Focus | Fund Asset Class | Fund Strategy | Market Cap Focus | History Length (Days) | |
---|---|---|---|---|---|---|---|---|---|
1 | SPY | SPDR S&P 500 ETF TRUST | 272,676 | Large-cap | United States | Equity | Blend | Large-cap | 9107 |
2 | VTI | VANGUARD TOTAL STOCK MKT ETF | 93,194 | Broad Market | United States | Equity | Blend | Broad Market | 6063 |
3 | EFA | ISHARES MSa EAFE ETF | 85,325 | International | International | Equity | Blend | Large-cap | 5985 |
4 | VEA | VANGUARD FΓSE DEVELOPED ETF | 69,281 | International | International | Equity | Blend | Large-cap | 3816 |
5 | VWO | VANGUARD ELSE EMERGING MARKE | 68,452 | Emerging Markets | International | Equity | Blend | Broad Market | 4684 |
6 | QQQ | POWERSHARES QQQ TRUST SERIES | 57,747 | Large-cap | United States | Equity | Growth | Large-cap | 6876 |
7 | AGG | ISHARES CORE U.S. AGGREGATE | 53,629 | Aggregate Bond | United States | Fixed Income | Aggregate | N.A. | 5215 |
8 | UH | ISHARES CORE S&P MIDCAP ETF | 44,841 | Mid-cop | United States | Equity | Blend | Mid-cap | 6433 |
9 | IWM | ISHARES RUSSELL 2000 ETF | 42,166 | Small-cap | United Stales | Equity | Blend | Small-cap | 6433 |
10 | IWD | ISHARES RUSSELL 1000 VALUE E | 41,631 | Large-cap | United States | Equity | Value | Large-cap | 6433 |
11 | IWF | ISHARES RUSSELL 1000 GROWTH | 40,961 | Lorg©-cap | United States | Equity | Growth | Large-cap | 6433 |
12 | LQD | ISHARES IBOXX INVESTMENT GRA | 38,431 | Corporate | United States | Fixed Income | Corporate | N.A. | 5642 |
13 | GLD | SPDR GOLD SHARES | 35,343 | Precious Metals | Global | Commodity | Precious Metals | N.A. | 4796 |
14 | VNQ | VANGUARD RETT ETF | 34,626 | Real Estate | United Stales | Equity | Blend | Broad Market | 4846 |
15 | TIP | ISHARES TIPS BOND ETF | 24,339 | Inflation Protected | United States | Fixed Income | Inflation Protected | N.A. | 5145 |
16 | BSV | VANGUARD SHORT-TERM BOND ETF | 23,884 | Aggregate Bond | United States | Fixed Income | Aggregate | N.A. | 3923 |
17 | VEU | VANGUARD FTSE ALL-WORLD EX-U | 23,484 | International | International | Equity | Blend | Large-cap | 3956 |
18 | VGK | VANGUARD FTSE EUROPE ETF | 18,598 | European Region | European Region | Equity | Blend | Large-cap | 4684 |
19 | HYG | ISHARES IBOXX USD HIGH YIELD | 17,946 | Corporate | United States | Fixed Income | Corporate | N.A. | 3922 |
20 | PFF | ISHARES US PREFERRED STOCK E | 17,653 | Preferred | United States | Fixed Income | Preferred | N.A. | 3934 |
21 | BIV | VANGUARD INTERMEDIATE-TERM B | 15,303 | Aggregate Bond | United States | Fixed Income | Aggregate | N.A. | 3923 |
22 | VBR | VANGUARD SMALL-CAP VALUE ETF | 12,763 | Small-cap | United Stales | Equity | Value | Small-cap | 5089 |
23 | MBB | ISHARES MBS ETF | 11,859 | Mortgage-Backed | United States | Fixed Income | Mortgage-Backed | N.A. | 3948 |
24 | SHY | ISHARES 1-3 YEAR TREASURY BO | 11,261 | Government | United States | Fixed Income | Government | N.A. | 5642 |
25 | IWS | ISHARES RUSSELL MIDCAP VALU | 11,125 | Mid-cap | United States | Equity | Value | Mid-cap | 6009 |
26 | IWO | ISHARES RUSSELL 2000 GROWTH | 9,188 | Smalkcap | United States | Equity | Growth | Small-cap | 6370 |
27 | IWP | ISHARES RUSSELL MIDCAP GROW | 8,615 | Midcap | United States | Equity | Growth | Mid-cap | 6001 |
28 | SHV | ISHARES SHORT TREASURY BOND | 8,057 | Government | United States | Fixed Income | Government | N.A. | 4012 |
29 | TLT | ISHARES 20+ YEAR TREASURY BOND | 7,185 | Government | United States | Fixed Income | Government | N.A. | 5642 |
30 | SLV | ISHARES SILVERTRUST | 5,454 | Precious Metals | Global | Commodity | Precious Metals | N.A. | 4270 |
31 | RWX | SPDR DJ INTERNATIONAL REAL E | 3,765 | Real Estate | International | Equity | Blend | Broad Market | 4039 |
32 | EFG | ISHARES MSCI EAFE GROWTH ETF | 3,600 | International | International | Equity | Growth | Large-cap | 4536 |
33 | DBC | POWERSHARES DB COMMODITY IND | 2,292 | Broad Based | Global | Commodity | Broad Based | N.A. | 4354 |
34 | USO | UNITED STATES OIL FUND LP | 2,077 | Energy | United States | Commodity | Energy | N.A. | 4288 |
Notes: The trading price observations of each ETF Is obtained from Bloomberg from June 2007 to December 2017. Out of approximately2200 funds. the 34 ETFs funds are used based on three screening criteria Including funds with the largest market capitalization, longest history, and within each category asset class. The ETFs are ranked from the largest size to smallest size in terms of total assets value expressed in billion dollars.
Beginning in June 2007 and ending in December 2017, a total of 126 monthly prices were obtained for each of the study’s 34 ETFs, Using the monthly price observations (Pt,i) of each ETE, the monthly returns for a given ETF
Historical Performances of Exchanges Traded Funds (August 2007 to December 2017)
Ticker | Name | Mean | Standard Deviation | |
---|---|---|---|---|
1 | QQQ | POWERSHARES QQQ TRUST SERIES | 11.4000 | 18.0800 |
2 | IWF | ISHARES RUSSELL l000 GROWTH | 8.0300 | 15.3500 |
3 | IWO | ISHARES RUSSELL 2000 GROWTH | 7.9000 | 20.1800 |
4 | IJH | ISHARES CORE S&P MIDCAP ETF | 7.7200 | 17.7100 |
5 | IWP | ISHARES RUSSELL MIDGAP GROW | 7.430C | 18.0600 |
6 | IWM | ISHARES RUSSELL 2000 ETF | 6.5400 | 19.6100 |
7 | VBR | VANGUARD SMALLGAP VALUE ETF | 6.2700 | 19.6000 |
8 | VTI | VANGUARD TOTAL STOCK MKT ETF | 6.1900 | 15.5900 |
9 | GLD | SPDR GOLD SHARES | 6.0600 | 19.0600 |
10 | SPY | SPDR S&P 500 ETF TRUST | 5.8100 | 15.0600 |
11 | IWS | ISHARES RUSSELL MIDGAP VALU | 5.6600 | 17.9600 |
12 | IWD | ISHARES RUSSELL l000VALUE E | 3.9000 | 15.7400 |
13 | TLT | ISHARES 20+YEAR TREASURY BOND | 3.5500 | 13.9300 |
14 | VNQ | VANGUARD REIT ETF | 2.3300 | 25.5100 |
15 | SLV | ISHARES SILVER TRUST | 2.1300 | 34.1 l00 |
16 | LQD | ISHARES IBOXX INVESTMENT GRA | 1,5700 | 7.7400 |
17 | TIP | ISHARES TIPS BOND ETF | 1.1900 | 6.3400 |
18 | BIV | VANGUARD INTERMEDIATE-TERM B | 1.1100 | 5.5500 |
19 | AGG | ISHARES CORE U.S. AGGREGATE | 0.9500 | 3.7800 |
20 | EFG | ISHARES MSCI EAFE GROWTH ETF | 0.7300 | 18.6200 |
21 | MBB | ISHARES MBS ETF | 0.6900 | 2.9700 |
22 | BSV | VANGUARD SHORT-TERM BOND ETF | 0.4600 | 2.4300 |
23 | SHY | ISHARES 1 -3 YEAR TREASURY BO | 0.3800 | 1.2800 |
24 | SHV | ISHARES SHORT TREASURY BOND | 0.0600 | 0.3300 |
25 | VWO | VANGUARD FTSE EMERGING MARKE | -0.2800 | 23.7500 |
26 | VEU | VANGUARD FTSE ALL-WORLD EX-U | -0.2800 | 19.9500 |
27 | VEA | VANGUARD FTSE DEVELOPED ETF | -0.6300 | 19.0900 |
28 | HYG | ISHARES IBOXX USD HIGH YIELD | -1, 0400 | 11.5100 |
29 | EFA | ISHARES MSCI EAFE ETF | -1.1100 | 19.2800 |
30 | VGK | VANGUARD FTSE EUROPE ETF | -2.1700 | 20.9500 |
31 | PFF | ISHARES US PREFERRED STOCK E | -2.2500 | 19.1300 |
32 | PWX | SPDR DJ INTERNATIONAL REAL E | -3.9500 | 21.1800 |
33 | DBC | POWERSHARES DB COMMODITY IND | -4.3700 | 20.6900 |
34 | USO | UNITED STATES OIL FUND LP | -15.2000 | 33.7700 |
Notes: ETFs funds are ordered from the highest to lowest average monthly returns from August 2007 to December 2017, The monthly return is the percentage change In price of each ETF. Out of approximately 2,200 funds, the 34 ETFs funds are used based on three screening criteria Including funds with the largest market capitalization, longest history, and limiting ETFs from each category of asset classes. The ETFs are ranked from the highest to lowest returns. The average monthly return and standard deviation are expressed in percent.
To investigate whether optimized ETF portfolios yield superior returns as compared to individual ETFs, we employed classical Markowitz optimization (1952) which identifies the minimum variance portfolio for a given return. First, the return data from the first 60 months was used to identify the optimal ETF allocations for 10 optimized portfolios, These 10 portfolios represent the entire efficient frontier for our 34 ETFs from the minimum risk-return portfolio (Portfolio 1) to the maximum risk-return portfolio (Portfolio 10), Second, month 61 returns are calculated for each of our ten optimized portfolios and the portfolios are ranked based on average annualized return from the maximum. Third, at the end of each month beginning with month 61, the ETF composition of each optimized portfolio is re-estimated using returns from a 60-month rolling period (the oldest price observation is removed and the most recent (from the prior month) is added). The returns on the ten newly optimized portfolios (based on the re-estimated ETF allocations) are then calculated and ranked for the subsequent month. The re-estimation process for the ETF allocations of each portfolio and the calculation of returns based on those re-estimated ETF allocations are repeated for each month until December 2017, a total of 65 times. Next, a model ETF allocation is determined for each of our ten portfolios by averaging the 65 monthly weights assigned to each ETF in each of the ten portfolios. Finally, the annualized average returns of the ten model portfolios over the 65-month period are compared with the average returns of individual ETFs during the same time period. Note that the replication of the efficient asset allocation for 65 times also allows us to examine if there are any persistent weights assigned to a certain asset class. See the Appendix to understand the methodology in more detail.
Based on the classical Markowitz optimization, the efficient allocation (Markowitz 1952) can be determined as follows:
The objective function is to minimize the risk with certain constraints.
Objective = Min(
Subject to
where
Using the Matlab optimization package, 10 portfolios are constructed on the efficient frontier formed by the 34 ETFs in the study, Because these 10 portfolios represent the whole efficient frontier, investors can choose a particular portfolio along the efficient frontier that suits their personal risk tolerance. The returns of the 10 optimized portfolios are also compared with naïve portfolio allocation strategies as well as the individual ETFs, The naive portfolio can be constructed in such a way that all 34 ETFs are equally weighted.
Table 3 presents the comparison of the performance of the ten optimized portfolios with the performance of the individual ETFs during the period of August 2012 to December 2017, The optimized portfolios performed quite well producing Sharpe ratios higher than mostof the individual ETFs, As shown in Panel A of Table 3, the Sharpe ratios of Portfolio 7 (1.01) and 8 (1.00), which are on the far-right corner of efficient frontier, are larger than most ETFs except 5 top ETFs. Portfolio 10 has higher return than any ETF except QQQ.
Comparison of Performances of Optimized Portfolio and Individual Exchange Traded Funds Based on Actual Return (August 2012 to December 2017)
Portfolio | Return | Risk | Sharpe Ratio | |
---|---|---|---|---|
Lowest Risk/Return | Portfolio 1 | 0.0004 | 0.0012 | -18.3066 |
Portfolio 2 | 0.0177 | 0.0134 | -0.3074 | |
Portfolio 3 | 0.0350 | 0.0268 | 0.4910 | |
Portfolio 4 | 0.0523 | 0.0404 | 0.7544 | |
Portfolio 5 | 0.0695 | 0.0540 | 0.8840 | |
Portfolio 6 | 0.0868 | 0.0677 | 0.9608 | |
Portfolio 7 | 0.1041 | 0.0818 | 1, 006l | |
Portfolio 8 | 0.1214 | 0.0994 | 1.0018 | |
Portfolio 9 | 0.1387 | 0.1246 | 0.9387 | |
Highest Risk/Return | Portfolio 10 | 0.1560 | 0.1804 | 0.7441 |
QQQ | POWERSHARES QQQ TRUST SERIES | 16.2000 | 12.1000 | 1.1 600 |
IWF | ISHARES RUSSELL 1000 GROWTH | 13.8000 | 9.8000 | 1.1900 |
IWO | ISHARES RUSSELL 2000 GROWTH | 13.5000 | 14.1000 | 0.8000 |
IWP | ISHARES RUSSELL MID-GAP GROW | 13.2000 | l0.6000 | 1.0400 |
IJH | ISHARES CORE S&P MIDCAP ETF | 13.0000 | l0.9000 | 0.9900 |
VBR | VANGUARD SMALL-GAP VALUE ETF | 12.4000 | 12.0000 | 0.8500 |
VTI | VANGUARD TOTAL STOCK MKT ETF | 12.3000 | 9.6000 | 1, 0600 |
IWM | ISHARES RUSSELL 2000 ETF | 12.3000 | 13.4000 | 0.7500 |
SPY | SPDR S&P 500 ETF TRUST | 12.2000 | 9.3000 | 1.0800 |
IWS | ISHARES RUSSELL MIDGAP VALU | 12.0000 | 10.0000 | 0.9900 |
IWD | ISHARES RUSSELL 1000 VALUE E | l0.9000 | 9.5000 | 0.9200 |
EFG | ISHARES MSCI EAFE GROWTH ETF | 7.5000 | 11.1000 | 0.4800 |
VEA | VANGUARD FTSE DEVELOPED ETF | 6.4000 | 11,3000 | 0.3800 |
EFA | ISHARES MSCI EAFE ETF | 6.3000 | 11,6000 | 0.3500 |
VGK | VANGUARD FTSE EUROPE ETF | 5.9000 | 12.8000 | 0.2900 |
VEU | VANGUARD FTSE ALL-WORLD EX-U | 5.2000 | 11,4000 | 0.2700 |
VNQ | VANGUARD REIT ETF | 4.0000 | 13.2000 | 0.1400 |
VWO | VANGUARD FTSE EMERGING MARKE | 2.5000 | 14.5000 | 0.0200 |
RWX | SPDR DJ INTERNATIONAL REAL E | 1.2000 | 12.7000 | -0.0800 |
LQD | ISHARES IBOXX INVESTMENT GRA | 0.0000 | 4.9000 | 0.4400 |
SHV | ISHARES SHORT TREASURY BOND | 0.0000 | 0.l000 | -21,3300 |
SHY | ISHARES 1-3 YEAR TREASURY BO | -0.1000 | 0.7000 | -3.2100 |
TLT | ISHARES 20+YEAR TREASURY BOND | -0.4000 | 11, 0000 | -0.2400 |
MBB | ISHARES MBS ETF | -0.4000 | 2.4000 | -1, 0600 |
BSV | VANGUARD SHORT-TERM BOND ETF | -0.5000 | 1,3000 | -2.0l00 |
AGG | ISHARES CORE U.S. AGGREGATE | -0.5000 | 3.0000 | -0.9200 |
PFF | ISHARES US PREFERRED STOCK E | -0.6000 | 4.5000 | -0.6300 |
HYG | ISHARES IBOXX USD HIGH YIELD | -0.9000 | 5.1000 | -0.6100 |
TIP | ISHARES TIPS BOND ETF | -1.2000 | 4.5000 | -0.7400 |
BIV | VANGUARD INTERMEDIATE-TERM B | -1.3000 | 4.3000 | -0.8l00 |
GLD | SPDR GOLD SHARES | -4.3000 | 15.8000 | -0.4l00 |
DBC | POWERSHARES DB COMMODITY IND | -9.1000 | 14.7000 | -0.7700 |
SLV | ISHARES SILVER TRUST | -9.8000 | 25.2000 | 0.4700 |
USO | UNITED STATES OIL FUND LP | -18.5000 | 29.0000 | -0.7l00 |
Notes: The optimized portfolio return is calculated based on optimal allocation in each asset class using 7-month rolling period return and historical variance-covariance matrix. These performance was achieved using the last 60 months returns to find the optimal weights and applying these weights to the current month’s returns and repeating the process for the following month by adding the latest month and dropping the first month, and keeping 1he sample at 60 months. This process was repeated 65 times. 65 optimal returns are averaged for each portfolio and standard deviations are calculated and both annualized. Optimized portfolio returns are ordered from the lowest risk/lowest return (Portfolio 7) to the highest risk/highest returns (Portfolio 10). Returns on each individual ETFs are the average monthly returns of the same time period as optimized portfolio individual ETFs Performance are ordered from the highest to lowest returns. Sharpe ratio is the return on portfolio or ETFs, minus risk-free rate divided by their standard deviation, where 70-year Treasury rate of 2,18% is used as the risk-free rate, All returns and standard deviations are expressed In percent.
To better understand the ETF allocations of each of these 10 efficient portfolios, we calculate the average ETF weights over the 65-month period. Table 4 presents the average weights of each ETF in the 10 efficient portfolios. These average weights could provide guidance to investors in forming a better performing portfolio which provides superior returns. For example, the minimum risk portfolio (Portfolio 1) is dominated by SHV (Short-Term Treasury Bond) and only four ETFs entered in the highest return portfolio (Portfolio 10). These ETFs are QQQ (Large Cap Growth), SLV (Silver), GLD (Gold), and IWO (Small Cap Growth). During the study period from August 2007 to December 2017, it is interesting to note that SHV accounts for 99.70% of lowest return portfolio (Portfolio 1) as SHV generated an average annual return close to 0%. Similarly, QQQ constitutes 78,50% of Portfolio 10, This is due to the fact that QQQ with an average annual return of 16.2% is the top performer of the 34 study ETFs, As is evident in Table 4, investors who want to achieve higher returns along the efficient frontier should increase their allocation to QQQ while decreasing their allocation to SHV. In moving from portfolio 1 to portfolio 10 the average weights of QQQ increase from 0,0001 to 0.7895 while the weights of SHV decrease from 0,9970 to 0.0020.
Optimal Allocation based on Average Weights in each ETFs for Efficient Corner Portfolios (August 2012 to December 2017)
Ticker | Name | Portfolio 1 Low Risk/Return | Portfolio 2 | Portfolio 3 | Portfolio 4 | Porlfolio 5 | Portfolio 6 | Portfolio 7 | Portfolio 8 | Portfolio 9 | Portfolio 10 Higt Risk/Return | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | TLT | ISHARES 20+ YEAR TREASURY BOND | 0.1100 | 0.1690 | 0.2290 | 0.2890 | 0.3200 | 0.2630 | 0.1210 | |||
2 | SHV | ISHARES 1G YEAR TREASURY BO | 0.9970 | 0.7210 | 0.5020 | 0.3590 | 0.2370 | 0.1200 | 0.0290 | 0.0020 | ||
3 | IWS | ISHARES RUSSELL MID-GAP VALU | 0.0160 | 0.0320 | 0.0480 | 0.0640 | 0.0800 | 0.1000 | 0.1000 | 0.0930 | ||
4 | SHY | ISHARES 1-3 YEAR TREASURY BO | 0.1180 | 0.1740 | 0.1520 | 0.1060 | 0.0600 | 0.0210 | 0.0020 | |||
5 | IWD | ISHARES RUSSELL 1000 VALUE E | 0.0100 | 0.0200 | 0.0300 | 0.0390 | 0.0490 | 0.0460 | 0.0040 | |||
6 | IWP | ISHARES RUSSELL MIDGAP GROW | 0.0040 | 0.0080 | 0.0120 | 0.0160 | 0.0190 | 0.0100 | 0.0030 | 0.0020 | ||
7 | IWF | ISHARES RUSSELL 1000 GROWTH | 0.0020 | 0.0030 | 0.0040 | 0.0050 | 0.0050 | 0.0060 | 0.0020 | |||
8 | MBB | ISHARES MBS ETF | 0.0020 | 0.0030 | 0.0050 | 0.0060 | 0.0030 | |||||
9 | IJH | ISHARES CORE S&P MIDCAP ETF | 0.0010 | 0.0020 | 0.0030 | 0.0030 | 0.0040 | 0.0040 | 0.0050 | |||
10 | LQD | ISHARES IBOXX INVESTMENT GRA | 0.0010 | 0.0020 | 0.0020 | 0.0030 | 0.0020 | |||||
11 | IIP | ISHARES TIPS BOND ETF | 0.0010 | 0.0020 | 0.0020 | 0.0010 | ||||||
12 | QQQ | POWERSHARES QQQ TRUST SERIES | 0.0010 | 0.0640 | 0.1240 | 0.1840 | 0.2450 | 0.3050 | 0.3830 | 0.5120 | 0.6390 | 0.7850 |
13 | SLV | ISHARES SILVER TRUST | 0.1080 | |||||||||
14 | GLD | SPDR GOLD SHARES | 0.0090 | 0.0210 | 0.0330 | 0.0460 | 0.0590 | 0.0770 | 0.1030 | 0.1370 | 0.0770 | |
15 | IWO | ISHARES RUSSELL 2000 GROWTH | 0.0010 | 0.0010 | 0.0020 | 0.0310 |
Notes: Portfolio optimization conducted for 65 times. Each of these optimization processes computes the optimal weights in each of these 34 ETFs for 10 model portfolios. In other words, each process produces a weight table with 34 rows and 10 columns. In most cases, the weights are zero. To summarize, we averaged weights by columns and rows. For example, the average weight of SHV (I-share’s 1-3-year Treasury bonds ETF) in portfolio 1 Is 0.997. This is the average weight of SHV in portfolio 1 for 65 estimations. It dominates portfolio 1 since this is a portfolio with minimum risk. It Is natural to see that low-risk Treasury bonds makes up the bulk of this minimum risk portfolio. Some ETFs have zero average weight across the spectrum of portfolios. Therefore, we did not Include these ETFs In the table since all the values are zero.
Having observed that the optimized ETF portfolios produced better performance compared with individual ETFs on an ex-post basis, we now investigate whether the optimization process can produce better portfolio performance in the future. Using an ex-ante approach, we calculated the ETF allocations for ten efficient portfolios using four different approaches. The first three approaches rebalance ten optimized portfolios monthly to allocations determined by using 1) returns from the prior 60-months, 2) returns from the prior 40-months, and the 3) the monthly cumulative returns. The fourth approach rebalances each of the 10 portfolios annually using the returns from the prior 60 months.
Panel A of Table 5 presents the results of the ex-ante performances from August 2012 to December 2017 of 10 efficient portfolios based on past 60-months return. Portfolio optimization using the historical variance and covariance matrix for out of sample forecasting does not appear to produce useful results. The best Sharpe ratio produced using this strategy is 0.572 from optimized Portfolio 8. This is significantly lower than the Sharpe ratio of 1.01 for Portfolio 7 based on applying optimal allocation on ex post returns and is also lower than the Sharpe ratio of 1.16 for the QQQ ETE Further, the highest return forecasted from efficient portfolio is only 8.00 %, which is almost the half of the top performing portfolio using the ex post approach or the QQQ ETF alone.
Out of Sample Forecast of Optimized Portfolios Performance Using Historical Variance-Covariance Matrix (August 2012 to December 2017)
Return | Risk | Sharpe Ratio | ||
---|---|---|---|---|
Lowest Risk/Return | Portfolio 1 | 0.0000 | 0.1000 | -20.0670 |
Portfolio 2 | 1, 0000 | 1,3000 | -0.9660 | |
Portfolio 3 | 1.9000 | 2.5000 | -0.1210 | |
Portfolio 4 | 2.7000 | 3.8000 | 0.1470 | |
Portfolio 5 | 3.6000 | 5.0000 | 0.2790 | |
Portfolio 6 | 4.4000 | 6.3000 | 0.3600 | |
Portfolio 7 | 5.5000 | 7.4000 | 0.4550 | |
Portfolio 8 | 7.0000 | 8.5000 | 0.5720 | |
Portfolio 9 | 8.0000 | l0.4000 | 0.5560 | |
Highest Risk/Return | Portfolio 10 | 5.5000 | 16.5000 | 0.2040 |
Lowest Risk/Return | Portfolio 1 | 0.0000 | 0.l000 | -20.7580 |
Portfolio 2 | 1.0000 | 1,3000 | -0.8730 | |
Portfolio 3 | 2.0000 | 2.6000 | -0.0490 | |
Portfolio 4 | 3.l000 | 4.0000 | 0.2430 | |
Portfolio 5 | 4.2000 | 5.3000 | 0.3810 | |
Portfolio 6 | 5. 1000 | 6.6000 | 0.4330 | |
Portfolio 7 | 6.0000 | 7.9000 | 0.4840 | |
Portfolio 8 | 7.7000 | 9.2000 | 0.6010 | |
Portfolio 9 | 8.9000 | 11.1000 | 0.60l0 | |
Highest Risk/Return | Portfolio 10 | 9.7000 | 14.4000 | 0.5230 |
Lowest Risk/Return | Portfolio 1 | 0.1000 | 0.1000 | -17.3720 |
Portfolio 2 | 0.5000 | l.l000 | -1.4840 | |
Portfolio 3 | 1.0000 | 2.3000 | -0.5110 | |
Portfolio 4 | 1.5000 | 3.7000 | -0.1900 | |
Portfolio 5 | 2.0000 | 5.0000 | -0.0420 | |
Portfolio 6 | 2.5000 | 6.4000 | 0.0430 | |
Portfolio 7 | 2.9000 | 7.8000 | 0.0890 | |
Portfolio 8 | 2.4000 | 9.2000 | 0.0260 | |
Portfolio 9 | 2.3000 | 11.0000 | 0.0110 | |
Highest Risk/Return | Portfolio 10 | 0.3000 | 15.6000 | -0.1220 |
Portfolio 1 | 0.0000 | 0.l000 | -I9.0260 | |
Portfolio 2 | 0.8000 | 1,4000 | -0.9990 | |
Portfolio 3 | 1,5000 | 2.7000 | -0.2540 | |
Portfolio 4 | 2. l000 | 4.2000 | -0.0260 | |
Portfolio 5 | 2.7000 | 5.6000 | 0.0950 | |
Portfolio 6 | 3.3000 | 7.0000 | 0.1660 | |
Portfolio 7 | 4.3000 | 8.5000 | 0.2470 | |
Portfolio 8 | 5.4000 | 10.4000 | 0.3110 | |
Portfolio 9 | 5.8000 | 12.3000 | 0.2970 | |
Portfolio 10 | -1.0000 | 20.3000 | -0.1560 |
Notes: All the performances are calculated based on out-of-sample forecast and historical variance covariance matrix. Panel A uses historical 60 months observations on a 1-month rolling basis to determine the optimal weight for each month, while Panel B uses past 40-months observations. Panel C uses a cumulative sampling. The Initial sample starts with 60-months observations and the current month’s observation is added each month without removing the oldest observation. Unlike performances reported In Panels A, B, and C where portfolio composition Is changing once a month, the portfolio performance reported in Panel D using a proccess that reshuffles the portfolio annually.
While prior 60-month return data is commonly used in reallocating portfolios and measuring their performance, one particular interest of this study is to see whether the performance of optimized portfolio can be improved by relying only on more recent return data, Thus, we next utilized return data from the prior 40 months to recalculate the weights for our optimized portfolios. The use of 40 month return data is acceptable given that the study is limited to portfolios formed from only 34 ETFs inasmuch as the number of observations that can produce an efficient optimization procedure has to be greater than 34. Panel B of Table 5 reports the performance results for the 10 portfolios optimized using return data from the prior 40 months. Using the 40-month data, the highest Sharpe ratio produced by an optimized portfolio improved to 0.601. The highest return produced by an optimized portfolio using 40-month data is 9.70%, which is significantly lower than 15.6 % return produced by an optimized portfolio using ex post data.
To examine whether historical data from longer periods increase the effectiveness of our optimized portfolio strategy, a cumulative return method is used where each month’s return data added without eliminating the earliest observation. For example, while the optimized ETF weights for month 61 were based on 60-month return data, the optimized weights for month 62 portfolios are based on return data from the prior 61 months and so on until month 125 when the optimized ETF weights were based on return data from the prior 124 months. Results reported in Panel C of Table 5 show a surprising finding where longer history does, in fact, decreases the effectiveness of this strategy. When cumulative history of the returns up to the optimization month is used instead of just past 60 months, the Sharpe ratios of each of the 10 optimized portfolios are lower, The best performing portfolio (Portfolio 7) has the Sharpe ratio of only 0.089 with a return of only 2,9%.
Monthly portfolio rebalancing is costly for investors due to the transactions costs incurred and the higher tax rate imposed on short term capital gains as comparted to long term gains, In reallocating portfolio weights monthly, the investor is forced to taxes currently on any gains and thereby loses the ability to defer those potential tax liabilities. To address this issue, we investigate how reallocating portfolios annually rather than monthly might affect the performance of the 10 optimized portfolios. The results of portfolio performance based on annual optimization are reported in Panel D of Table 5. The portfolio performance based on annual reallocation of the optimized portfolios, as reported in Table 5, is worse than when rebalancing occurred monthly. The best performing portfolio in terms of Sharpe performance is Portfolio 8 with a Sharpe ratio of 0,31, declining from 0,57 and 0,60 when monthly optimization based on 60-months and 40-months returns are implemented, When looking at the performance from return perspective, the best performing portfolio generates only 5.40%, which is significantly lower than highest returns from efficient portfolio based on monthly asset allocation obtained from 60-month (7.00%) and 40-months (8.90%) optimization process.
Overall findings suggest that emphasis on more recent return observations and increased frequency in rebalancing portfolio will improve efficiency of portfolio optimization. This is the case for the ex-ante approach. However, the best performing portfolio based on out-of-sample forecast cannot beat the performance based on ex post approach. In addition, portfolio performance using optimization methodology did poorly compared to the top performing ETFs. These disappointing results could be due to a known flaw in Markowitz’s portfolio allocation method that relies on the historical variance covariance matrix, To address this concern, we construct optimized portfolios and gauge their performance using a modified sample covariance matrix.
The results presented in Table 5 indicate that the ex-ante, out of sample performance of the optimized portfolios not as good as the ex-post performance of an optimal portfolio. One well-known problem with the Markowitz optimization method is its assigning the excessive weights to specific asset classes. In fact, this can be observed from the weights assigned to specific ETFs in several of the optimized portfolios. As shown earlier in Table 4 of average weight of each ETF in each of 10 efficient portfolios, only 15 out of 34 ETFs had a non-zero weight in any of the optimized portfolios. Ledoit and Wolf (2003) suggests the use of a shrinkage technique to modify the covariance matrix, The shrinkage combines sample covariance with a highly structured covariance matrix. Ledoit and Wolf used constant correlation matrix as a proxy for a highly structured covariance matrix. A shrinkage constant is used to combine these two covariance matrixes.
Where λ is the shrinkage constant, X is the structured covariance matrix, and Y is the sample covariance matrix2.
We estimated shrinkage constant and constant correlation matrix. The shrinkage constant estimate is 0,1514, Using Ledoit and Wolf technique, a shrunken covariance matrix was created and used in repeating the optimization process. The optimization ETF allocations are calculated based on 60-months historical data and out of sample forecast for portfolio returns. New performance metrics for 10 optimized portfolios obtained using the shrunken covariance matrix technique are reported in Table 6, Eight out of ten optimized portfolios show a marginally improved performance in terms of returns and Sharpe ratio when compared with the performance of optimized portfolios determined using the historic variance-covariance matrix and reported in Panel A of Table 5. Only portfolios 7 and 8 show poorer performance when the shrunken covariance matrix is used. The return on the best performing portfolio (portfolio 9) has improved from yielding 8.00 percent to 8,20 percent return and the highest Sharpe ratio increased from 0.572 to 0.576.
The Performance of Optimized Portfolios Using The Modified Variance Covariance Matrix with the Shrinkage Technique (August 2012-Decemer 2017)
Portfolio | Return | Risk | Sharpe Ratio | |
---|---|---|---|---|
Lowest Risk/Return | Portfolio 1 | 0.0000 | 0.1000 | -20.7570 |
Portfolio 2 | 1.0000 | 1.2000 | -0.9540 | |
Portfolio 3 | 2.0000 | 2.5000 | -0.0880 | |
Portfolio 4 | 2.9000 | 3.7000 | 0.1830 | |
Portfolio 5 | 3.7000 | 5.0000 | 0.3130 | |
Portfolio 6 | 4.6000 | 6.3000 | 0.3940 | |
Portfolio 7 | 5.5000 | 7.5000 | 0.4500 | |
Portfolio 8 | 6.9000 | 8.5000 | 0.5520 | |
Portfolio 9 | 8.2000 | 10.4000 | 0.5760 | |
Highest Risk/Return | Portfolio 10 | 5.5000 | 16.5000 | 0.2040 |
Notes: This table presents the performance of optimized portfolios in the 65-month period starting from August 31,2012 to December 29, 2017. At the beginning of each month, the optimization process configured the most efficient allocation. The money is invested based on the best allocation and the performance is measured at the end of the month on the ex ante basis. The procedure Is repeated for the 65 months. In this optimization, the sample variance and covdridnce matrix is modified with a shrinkage technique. Risk Is measured as standard deviation. Return and risk are expressed in percent.
Based on the findings, rebalancing the optimized portfolio by using the actual optimized weights for each month for each ETFs does not appear to create better portfolio performance, The out of sample forecast performance does not yield a better return than ex post performance even when using a modified covariance matrix. Investigating the asset allocation weight closely, we further examine whether some informed strategies that provide optimized return to investors can be achieved using different weight measure. Alternatively, the overage weights for each composition of ETF in each portfolio is utilized instead of the actual weights optimized each month. The portfolio weights will first be created by the optimization process at the beginning of each month using 60-month historical data. Portfolio return for that month is determined based on ex ante approach, Optimized weight for next month is re-estimated on a 1-month rolling basis by the most recent month and the dropping the first month in the data to keep the number of past observations to be constant at sixty observations, Then, the optimized weight for each ETF composition in each portfolio for the second month is the average of the first two months and investment allocation is constructed accordingly, The process is repeated till the end of study period. In particular, the optimized weight for 65-month periods will be the average of 65 optimized weights obtained for each month. The performance of 10 efficient performance is simply the average of monthly optimized portfolio performance.
The average weights over the last 65 months of each ETF in these 10 efficient portfolios was presented in Table 4, As pointed out earlier, fewer than 50% of ETFs under investigation are given consideration based on the optimized weights. QQQ is the dominant ETF as average weight increases with the portfolio return. On the other hand, average allocation in SHV decreases when investors prefer to invest in riskier portfolio with higher returns. This is understandable since QQQ is an ETF investing in large-cap growth stocks in the U.S, and provides the highest return of 16.20%, while SHV focusing on short-term T-Bond returns nothing to investors.
Utilizing the average weights methodology outlined above, the performances of each of these 10 portfolios based on 60-month historical data and ex ante approach reported in Table 7 are lower than ex post optimized portfolio performance. However, the performance is much better than ex ante optimized portfolio results reported in Panel A of Table 5. The Sharpe ratio of top performing portfolio (Portfolio 9) analyzed from applying average weight on one-month ahead return is 1.008 is practically the same from applying actual weight on end of the month return as reported in Table 4. In comparison with ex ante optimized portfolio performance analyzed from actual weight, the top performing portfolio shows Sharpe ratio of only 0.5729, which is less than half of top performing performance from using average weight, In addition, the performance of best portfolio when optimized weights are averaged remains superior to any of top performing portfolios regardless of the length of historical data used and the frequency of portfolio rebalance conducted, As reported in Table 5, using shorter period of 40-month historical data, the Sharpe ratio is 0.60l0 and it is even worse with cumulative data which yields 0.089 Sharpe ratio, Rebalancing portfolio once a year to avoid transaction cost and tax issues does not lead to better Sharpe performance which remains at 0.3110, When comparin gwith individual ETF performance, the average weight method provides the portfolio return that is higher than the returns of 70% (24/34) of individual ETF and the Sharpe ratio that is higher than 85% (29/34) of ETF under investigation, Overall, the main information to be gleaned from Table 7 is overaging the weights is better than using the actual optimized weights for the month.
Out of Sample Forecast Performance of Optimized Portfolios Using The Average Optimal Portfolio Weights (August 2012-December 2017)
Portfolio | Return | Risk | Sharpe Ratio | |
---|---|---|---|---|
Portfolio 1 | 0.0000 | 0.1000 | -20.8870 | |
Portfolio 2 | 1.3000 | 1.200C | -0.7410 | |
Portfolio 3 | 2.6000 | 2.3000 | 0.1840 | |
Portfolio 4 | 3.9000 | 3.4000 | 0.4950 | |
Portfolio 5 | 5.2000 | 4.6000 | 0.6510 | |
Portfolio 6 | 6.4000 | 5.7000 | 0.7460 | |
Portfolio 7 | 7.7000 | 6.7000 | 0.8220 | |
Portfolio 8 | 9.2000 | 7.6000 | 0.9180 | |
Portfolio 9 | 10.9000 | 8.7000 | 1.0080 | |
Portfolio 10 | 11.7000 | 10.6000 | 0.8990 |
Notes: This table presents the performance of optimized portfolios In the 65-month period starting from August 31,2012 to December 29,2017. This performance is obtained with average optimal weights, Using the average of the optimal weights, 65 month returns are calculated. The average of these 65 returns and standard deviations are reported In this table.
In this article, efficient asset allocation among ETFs are investigated based on the Markowitz’s portfolio optimization approach. Using the data from 2007 to 2017, we empirically examine whether investors can achieve a better portfolio performance with various risk and return levels by simply allocating investment among different ETFs, The optimized portfolios are compared with best performing individual ETFs during the same time period, To determine optimal weight, for each ETF composition in each of 10 efficient portfolios, we utilize 1-month rolling returns of past 60 months, past 40 months, and 60 months cumulative returns.
Markowitz’s portfolio optimization approach is known for putting too much weight on a certain asset class, We remedy this issue by implementing the modified variance-covariance matrix in optimization process, The true test of a portfolio allocation strategy is the out of sample ex-ante portfolio selection, We compared the performance of out of sample mean-variance efficient portfolios with the actual ETFs performance.
Overall findings suggest that optimization procedure using historical 60-months data provides an efficient portfolio performance that is comparable with individual top performing ETFs, On the ex-post basis, the top performing portfolio yields the highest average monthly returns of 15,6% with highest Sharpe ratio of 1.01, similar to investing 100% in QQQ ETF that emphasizes on the large-cap high growth stocks in the U.S. However, this efficient allocation using the Markowitz’s portfolio optimization approach, does not predict a superior future performance. In 2012-2017 period, this approach underperformed many ETFs, The Sharpe ratio of the top performing optimized portfolio of 0.5720 is less than half of the Sharpe ratio of top performing QQQ, The out of sample forecast of optimized portfolio is a little better when only more recent historical data is used in determining optimal weight because of quicker feedback. The performance is worse when portfolio is rebalanced annually instead of monthly. When a modified shrinkage covariance matrix was employed to reduce the problem of excessive weights assigned to some ETFs, the performance of optimized portfolio improved slightly by 0.2% in terms of returns and 0.004 in Sharpe ratio. Lastly, constructing efficient portfolios based on the average of optimized weights improves the returns of top performing portfolio by 370 basis points and increases Sharpe ratio significantly (+0.5). Efficient portfolios using the average of optimized weight performs better than 85% of the individual ETFs in terms of Sharpe ratio, In conclusion, the average weight approach to portfolio optimization represents the best overall strategy. This is the only out of sample portfolio optimization approach that was comparable to the best ETFs in our list in terms of mean-variance efficiency, Investors should consider using this technique as an investment strategy. While this study is limited to the extent to which Markowitz portfolio theory holds for ETFs, it has clear practical applications for financial professionals as a possible alternative to other portfolio allocation approaches. Additionally, the US-based research provides a useful platform for further research in the Australian market.