The Robust Asset Allocation (RAA) Solution

The Robust Asset Allocation (RAA) Solution

December 2, 2014 Architect Academic Insights, Introduction Course, Key Research, Tactical Asset Allocation Research, White Papers
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The Robust Asset Allocation (RAA) Solution

Robustness: Strong and effective in all or most situations and conditions.

Executive Summary:

Many investors are faced with a fundamental problem: What should I do with my money?

People usually pursue one of four broad solutions to this problem–each has costs and benefits:

  1. Hire an expensive investment advisor that offers a limited value proposition. (generally a bad idea, although quite common)
  2. Hire an affordable advisor that delivers a strong value proposition.  (reasonable idea)
  3. Hire a cheap robo-advisor that will deliver a generic, but reasonable value proposition.  (reasonable idea)
  4. Do-it-yourself asset allocation. (reasonable idea)

We think educated investors can successfully move down the “do-it-yourself” path when equipped with the tools and knowledge to move forward. There are a few critical areas to get familiar with, but once that’s complete, DIY is perfectly viable for many. For those who are not comfortable with the DIY approach, we recommend approach 2 or 3, depending on personal preferences and circumstances.

I want to be a DIY investor, so what do I do next?

First, grab a copy of DIY Financial Advisor, which outlines the specifics discussed in this post, but in much greater detail. Or listen to my Google Talk if you want a walk through on the DIY philosophy.

Next, consider the following:

Easiest solution: 50% BND; 50% VTI (or your own special weights depending on your risk tolerance, individual circumstances).

  • Insanely cheap; insanely easy; throw away the key. Not an unreasonable approach.

More complex solution:

Meb Faber and Eric Richardson in their book, “The Ivy Portfolio,” hint at a do-it-yourself model that utilizes a simple 10-month trend-following rule  to run a risk-managed portfolio allocated across US equity, developed equity, REITs, commodities, and US Treasury bonds. These 5 asset classes are commonly referred to as the “IVY 5,” since they form the basic building blocks for the endowments of the ivy league schools and others. This is a reasonable solution for a DIY investor, but the live performance of a related strategy has been underwhelming the past few years.

However, not all is lost–simple still is the key to success, we just need a few tweaks…

We explore a more robust asset allocation solution that improves upon the IVY 5 model in three ways:

  1. We introduce security selection (value and momentum).
  2. We improve upon the IVY 5 risk-management system.
  3. We focus on fee and tax-management.

The outcome of our solution highlights what we call Robust Asset Allocation (RAA).

RAA Mission: A low-cost, low-complexity, high-liquidity, diversified, tax-efficient, risk-managed retirement portfolio.

RAA Goal: A One-Stop Retirement Solution.

Let’s get started…

1. What is Your Portfolio’s Mission?

1.1 The Purpose-Driven Portfolio

Wealth is often built by concentrated holdings, but wealth is protected by diversification. Most people accumulate wealth by working, which is a concentrated position, as this usually requires time and effort.

Having a clear purpose is the premise of portfolio investment.

Example:

  • Purpose of the portfolio: Preserve and compound wealth to assure financial security.
  • Return objective: RF (10Yr) + 400bps, AFTER TAX.
  • Risk appetite: As low as practical to achieve objective.
  • Taxes:  Can be huge drag on returns for private individuals –> minimize damage.
  • Human capital: Don’t confuse frenetic human activity, or a large staff, with higher risk-adjusted return potential.

1.2 Strategy Assessment: Stick to FACTS

“Get your facts first, then you can distort ‘em as you please.”

— Attributed to Mark Twain

While there is no “one-size fits all” strategy assessment and allocation model, a systematic framework for decision-making can help simplify the process and maximize returns. For every allocation contemplated, and each strategy that needs to be critically assessed, the FACTS framework (consisting of Fees, Access, Complexity, Taxes and Search) can be employed to clarify important considerations for the prospective investor. 

Robust Asset Allocation solution stop retirement plan
Click to read our white paper: Stick to the FACTS

A in-depth look at the FACTS framework is here.

The high level summary is as follows: Create a portfolio that minimizes fees, increases access to your own capital (e.g., liquid investments), is easy to understand (low complexity), minimizes taxes, and minimizes manager search costs. Our goal is to propose a portfolio that optimizes across all 5 key points.

2. Does Complexity Enhance Asset Allocation?

2.1 Do Fancy Models Work? Not Exactly…

“Of the 14 models we evaluate across seven empirical datasets, none is consistently better than the 1/N rule…”

— DeMiguel, Garlappi and Uppal.

Victor Demiguel and his colleagues have explored many sophisticated methodologies to optimize asset allocation. They have solutions that can possibly beat an equal-weight allocation, but these alternative solutions add a high degree of complexity. Prof. DeMiguel has an outstanding outline of asset allocation research here if you’d like to explore further. We’ve discussed the academic rational for following a simple asset allocation model in the past, here, and here. An equal-weight portfolio–also promoted by DeMiguel et al.–would seem to fly in the face of modern portfolio management, which in general has suggested that investors rely on mean-variance type allocation, or other highly engineered schemes. However, while mean-variance-analysis has reverse engineered the best historical Sharpe ratio (i.e. the tangency portfolio), this solution relies on a correlation matrix input, which is highly unstable and difficult to estimate. Indeed other approaches have this same weakness. The results below, taken from the DeMiguel et al. paper on asset allocation, highlight the incredible robustness associated with an equal-weight, or 1/N, type of asset allocation regime.

Robust Asset Allocation_Optimal vs Naive Diversification
Source: DeMiguel, V., L. Garlappi, and R. Uppal, 2009, Optimal Versus Naïve Diversification: How Inefficient is the 1/N Portfolio Strategy? Review of Financial Studies 5, 1915-1953.

DeMiguel et al. are not the only researchers pointing towards the 1/n solution. Even the great Harry Morkowitz, a Nobel Prize winner and the founder of portfolio management, is quoted as saying:

“I should have computed the historical covariance of the asset classes and drawn an efficient frontier…I split my contributions 50/50 between bonds and equities.”

A few quotes attributed to Einstein sum up the key lessons learned from piles of disinterested research compiled on the subject of asset allocation.

  1. Keep it simple.
  2. Complexity does not imply value.

Robust Asset Allocation_Keep it simple

2.2 Simplify the Allocation Problem: Simplify; Simplify; Simplify

“Don’t try anything fancy. Stick to a simple diversified portfolio, keep your costs down and rebalance periodically to keep your asset allocations in line with your long-term goals.”

–David Swensen, Yale Endowment CIO.

The “IVY 5” portfolio, described by Faber (2007) and then further elaborated by Faber and Richardson (2009), includes 5 asset classes:

  • SP500 = SP500 Total Return Index
  • EAFE = MSCI EAFE Total Return Index
  • REIT = FTSE NAREIT All Equity REITS Total Return Index
  • GSCI = GSCI Index
  • LTR = Merrill Lynch 7-10 year Government Bond Index
Robust Asset Allocation_IVY5
Click to enlarge.

At a high level, one can think of the IVY 5 portfolio as a 40% equity, 40% real asset, and 20% fixed income portfolio. I consider it a goldilocks portfolio: Not too simple; not too complex; just right.

2.3 Simplify Risk-Management

In general, while efforts to time the market should be viewed with skepticism, certain systematic timing strategies that have been explored in academia appear to reduce risk, without significantly impacting long-run returns. In particular, the application of simple moving average rules has been demonstrated to protect investors from large market drawdowns, which is defined as the peak-to-trough decline experienced by an investor. Jeremy Siegel, in his book, “Stocks for the Long Run,” explores the effect on performance on the Dow Jones Industrial Average from 1886 to 2006, when applying a 200-day moving average rule. Applying the rule is straightforward. If the market is above the 200-day moving average rule, hold, otherwise go to cash. Siegel found that this simple technical rule outperforms a buy-and-hold approach, both in absolute terms and on a risk-adjusted basis.

The simple moving average trading rule can be used across asset classes and is suggested in the Faber/Richardson Ivy Portfolio book. Below I outline how this rule might look over the past few years on the S&P 500 (12-month SMA depicted).

Robust Asset Allocation_Moving average
Click to enlarge

Below is a diagram of how the IVY 5 with a moving average rule might look in practice.

Robust Asset Allocation_IVY5 Moving average example
Click to enlarge.

The IVY 5 with moving average model sounds good in theory, but what does this portfolio look like historically?

3. Exploring a Simple IVY5 Model

3.1 Simulation Background:

Simulated Historical Performance: 1/1/1979 to 12/31/2015

Results are gross of management fee and transaction costs for illustrative purposes only. These are simulated performance results and do not reflect the returns an investor would actually achieve. All returns are total returns and include the reinvestment of distributions (e.g., dividends). Data is from Bloomberg and publicly available sources. Portfolios are annually rebalanced.

The following 5 asset classes are used in the back-test (referred to as the “IVY 5”):

  • SP500 = SP500 Total Return Index
  • EAFE = MSCI EAFE Total Return Index
  • REIT = FTSE NAREIT All Equity REITS Total Return Index
  • GSCI = GSCI Index
  • LTR = Merrill Lynch 7-10 year Government Bond Index (prior to 6/1982, Amit Goyal Data)

3.2 Benchmark Summary Statistics

10-Year Bonds and US Equity have performed the best over the past 30+ years. No wonder everyone is so enamored with the 60/40 S&P 500/10-year portfolio…

Robust Asset Allocation figure 1
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

 

3.3 Strategy Summary Statistics

Below we depict the simple IVY 5 portfolio, the IVY 5 portfolio with a moving average rule applied, a 60/40 SP500/LTR portfolio, and a SP500 portfolio.

  • IVY5_MA: 5 assets, equal-weight, annual-rebalance, MA rule –> Tough to beat.
  • 60/40: 60% SP500; 40% LTR, annual-rebalance –> Tough to beat.
Robust Asset Allocation figure 2
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

4. The Robust Asset Allocation Solution (RAA)

Unfortunately, we can’t buy IVY5 index returns, but can only run a backtest on the IVY5 portfolio. Investment vehicles cost money in the real world, so we need to choose wisely. To replicate the IVY5 we could do the following with iShares products, which happen to have an ETF for all of the IVY 5 exposures:

  • IVV (7bps): iShares Core S&P 500
  • EFA (34bps): iShares MSCI EAFE Total Return Index
  • IYR (46bps): iShares US Real Estate
  • GSG (75bps): iShares S&P GSCI Commodity-Indexed Trust
  • IEF (15bps): iShares 7-10 Year Treasury Bond

In our backtests above, we looked at gross of fee performance, but we clearly can’t invest in an index for free. Using iShares, the ETF fee costs are 35.4bps, on average, plus transaction costs and RIA fees.

All that said, one could certainly implement the IVY 5 portfolio with moving average strategy with minimal brain damage. Yahoo Finance charting allows you to run a monthly 12-month moving average test for each asset class, and with a yearly rebalance across asset classes, you would be in DIY heaven.

But can we do better?

4.1 Three Simple, but Important, Improvements Over IVY5

1. We believe value and momentum can work.

  • How do we get access to these exposures without buying the asset manager a new yacht each year?
    • Focus on affordable exposures that take active bets (not closet index) on value and momentum.

Value and Momentum strategies have been shown to outperform, historically.  Luck for investors, there are a variety of value investing etfs and momentum investing etfs offered in the market. Since they are far and away the strongest anomalies explored in academic research, they are probably worth getting exposure to, but we want to confirm this.

Here we present simulated gross of fee performances of these two strategies from 1/1/1963 to 12/31/2015, using publicly available data from Ken French’s website.

First, we examine momentum:

  • MOM_10 = Top Decile Momentum (Data); Momentum has worked!
Robust Asset Allocation figure 3
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.
  • VAL_10 = Top Decile Value (Data); Value has also worked!
Robust Asset Allocation figure 4
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

The implication is that we should look at replacing our market-weight passive exposures with high-conviction value and momentum strategies–but only if we can access these exposures in an affordable, tax-efficient, and transparent way. Otherwise, much of the purported “edge” will go to the croupiers and Uncle Sam.

2. We believe we can deliver a simple and effective risk management system.

We have examined hundreds of risk-management platforms over the past 5 years. You name it–we’ve backtested it, thought about it, or implemented it.

Conclusion: Asset allocation models are 99% noise; 1% signal.

Here is a sampling of the various ideas we have tested:

Macroeconomic Fundamental Indicators

  • Wealth Ratio — Lettau and Ludvigson (2001) , “Consumption, Aggregate Wealth, and Expected Stock Returns”
  • Adaptive Macro Indexes — Bai (2010) ,”Equity Premium Predictions with Adaptive Macro Indexes”
  • Sum of Macro Variables — Ferreira and Santa-Clara (2011),  “Forecasting Stock Market Returns: The Sum of the Parts is More than the Whole”
  • Industrial Mental Return — Jacobsen, Marshall and Visaltanachoti (2013), “Stock Market Predictability and Industrial Metal Returns”
  • Implied Cost of Capital — Li, Ng and Swaminathan (2013), “Predicting Market Returns Using Aggregate Implied Cost of Capital”
  • Oil Factor — Kilian and Park (2007), “The Impact of Oil Price Shocks on the U.S. Stock Market”
  • CAPE — Robert Shiller (2013)

Technical Indicators

  • Historical Data Forecast — Maheu and McCurdy (2006), “How Useful are Historical Data for Forecasting the Long-Run Equity Return Distribution?”
  • Time-Varying Sharpe Ratio — Tang and Whitelaw (2011), “Time-Varying Sharpe Ratios and Market Timing”
  • Return Dispersion — Maio (2012), “Return dispersion and the predictability of stock returns”
  • Mean Reversion Indicator — Huang, Jinag, Tu and Zhou (2012), “Mean Reversion, Momentum and Return Predictability”
  • Cross-sectional Technical Analysis–Han, Yang, Zhou, “A New Anomaly: The Cross-Sectional Profitability of Technical Analysis”
  • Simple MA Rule –Faber (2007), “A Quantitative Approach to Tactical Asset Allocation.”
  • Challenging MA rule #1 –Scholz and Walther (2011), “The trend is not your friend! Why empirical timing success is determined by the underlying’s price characteristics and market efficiency is irrelevant”
  • Challenging MA Rule #2–Zakamulin, “The Real-Life Performance of Market Timing with Moving Average and Time-Series Momentum Rules”
  • Challenging MA Rule #3–Marmi, Pacati, Risso, Reno, “A Quantitative Approach to Faber’s Tactical Asset Allocation”
  • Overview of Technical Analysis — Park and Irwin (2007), “What we know about the profitability of technical analysis?”

Sentiment and Variance Indicators

  • Investor Sentiment — Huang, Tu, Jiang and Zhou (2014), “Investor Sentiment Aligned: A Powerful Predictor of Stock Returns”
  • Cross-Section of Volatility — Ang, Hodrick, Xing and Zhang (2006), “The Cross-Section of Volatility and Expected Returns”
  • Variance Risk Premia — Bollerslev, Tauchen and Zhou (2009), “Expected Stock Returns and Variance Risk Premia”
  • VIX Term Structure — Johnson (2011), “Equity Risk Premia and the VIX Term Structure”
  • Equity Shares — Baker and Wurgler (2000), “The Equity Share in New Issues and Aggregate Stock Returns”
  • Broker-Dealer Leverage — Adrian, Etula and Muir (2013), “Financial Intermediaries and the Cross-Section of Asset Returns”
  • Implied Volatility Spread — Atilgan, Bali and Demirtas (2011),”Implied Volatility Spreads, Skewness and Expected Market Returns”

Comprehensive Summary of Different Predictors

  • Gupta, Modise and Uwilingiye (2012) — “Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors”
  • Welch and Goyal (2008) — “A Comprehensive Look at The Empirical Performance of Equity Premium Prediction”
  • Rapach and Zhou (2011) — “Forecasting Stock Returns”
  • Combo — Neely, Rapach, Tu and Zhou (2011), “Forecasting the Equity Risk Premium: The Role of Technical Indicators”

The sad conclusion is that none of these ideas stand up to intense robustness tests, except for the simplest, technical rules. You just can’t beat them. It’s kind of crazy when you think about it. We had hoped that having tested every model and approach under the sun that we would be able to triumphantly announce that we had identified a way to reliably predict the market using fancy algorithms derived from 100’s of academic researchers. But it just wasn’t the case. We’ve built these models: They aren’t reliable; they aren’t robust; and they are littered with data-mining. A large swath of the financial services industry would love to have you believe in their magic. I’m here to tell you that it doesn’t exist. Sorry.

But after all the years of research and analysis, what have we concluded?

Is market timing all for nothing? Perhaps…but…

We’ve boiled robust market-timing mechanisms down to two rules that are the best of a bad bunch:

1. Time Series Momentum Rule (MOM)

  • Excess return = total return over past x months less return of T-bill
    • If Excess return >0, go long risky assets. Otherwise, go alternative assets (T-bills or Zero).
    • Popularized by Gary Antonacci and rigorously examined by Moskowitz et al.

2. Simple Moving Average Rule (MA)

  • Moving Average (N) = average N month prices
    • If Current Price – Moving Average (N) > 0, go long risky assets. Otherwise, go alternative assets (T-bills or Zero).

We have actually identified the mathematical relationship between MOM and MA Rules, outlined in the chart below:

Robust Asset Allocation_Momentum and MA rule

As the equations highlight, MOM and MA rules are tied together: Time-series momentum rules (MOM) are a function of MA rules. And while MOM and MA triggers are highly correlated, there are circumstances where the rules have a difference of opinion.

It turns out that this “difference of opinion,” where one zigs when the other zags, adds some robustness to a comprehensive risk-management system.

3. We believe we can minimize taxes.

  • Annually rebalance taxable accounts.
  • Systematically harvest losses (book short-term losses to offset gains in other parts of the portfolio).
  • Tax manage risk management events (i.e., MA rule triggers a move to cash).

Uncle Sam is increasing tax rates year by year. For individual investors, taxes are extremely important–much more important than asset allocation or so-called “alpha.” This important reality is often overlooked by consumers of Wall Street products. Nobody can withstand Uncle Sam’s 50%+ carried interest program, also known as tax rates. Only the most sophisticated and intelligent proprietary traders can earn such a performance. Uncle Sam makes hedge fund fees look cheap with his 50%+ performance fee hurdle. But our goal is to minimize Uncle Sam’s fees. We can do this by minimizing capital gains, maximizing short-term capital losses, and deferring taxes as far out into the future as possible.

Robust Asset Allocation_Tax issues

An example workflow for tax management is outlined in the figure below:

Robust Asset Allocation_Tax Management

Taxes are everything for taxable investors. A failure to plan for taxes is to fail as an investor.

4.5 Three (3) RAA concepts (Pick your poison or create your own)

In general, thinking outside of a model is hazardous to one’s wealth. But every investor is different. The three options below are meant to suffice for a wide swath of investors whose primary goal is to protect against inflation, preserve capital, and grow their real wealth by 3-4% a year, if they’re lucky.

But perhaps you are an investor with an extremely bearish view on bonds; or perhaps you are extremely bullish on equities; or maybe you think the value anomaly is a fraud. As someone who has earned their hard-earned wealth, you also maintain the right to allocate as you see fit. Go for it. We merely present the three options below as a reasonable starting point.

Robust Asset Allocation_three concepts

How do these models perform, in theory?

4.6 Example RAA Process

Keep things as simple as possible, but no simpler.

Step 1 in the RAA process is to replace generic passive allocations to domestic and international equity with affordable high-conviction tax-efficient value and momentum alternatives. As we mentioned earlier, there are value investing etfs and momentum investing etfs offered in the marketplace.

Step 2 in the process is to calculate MA and MOM risk-management calculations on each “line” of the system (e.g., REITs, Domestic Value, etc.). If MA=MOM = 1 , 100% long; if MA=MOM = 0, 100% cash; otherwise, if MA<>MOM, go 50% long, 50% cash.

Step 3 in the process involves the implementation of tax-management techniques: harvest short-term losers; tax-manage hedging events; and annually rebalance such that one maximizes long-term capital gain and minimizes short-term capital gains. If an investor is running this system across qualified and non-qualified accounts, concentrate taxable dollars in tax-efficient equity exposures; concentrate non-taxable dollars in tax-inefficient fixed income and real asset exposures.

Robust Asset Allocation_Moderate plan

4.7 Simulation Background for the Robust Asset Allocation Solution

Simulated Historical Performance: 1/1/1992 to 12/31/2015 (limited due to international momentum data limitations).

Benchmark results (4.8) are gross of management fee and transaction costs for illustrative purposes only.

Strategy results (4.9) are net of 50bps management fee and 50bps transaction costs (1% total annual costs). These are simulated performance results and do not reflect the returns an investor would actually achieve. All returns are total returns and include the reinvestment of distributions (e.g., dividends). Data is from Bloomberg and publicly available sources. Portfolios are annually rebalanced. MA and MOM risk-management rules applied on a monthly basis.

The following 9 asset classes are used in the studies that follow:

  • SP500 = SP500 Total Return Index
  • EAFE = MSCI EAFE Total Return Index
  • REIT = FTSE NAREIT All Equity REITS Total Return Index
  • GSCI = GSCI Index
  • LTR = Merrill Lynch 7-10 year Government Bond Index (prior to 6/1982, Amit Goyal Data)
  • MOM_10 = U.S.Top Decile value-weight Momentum (Data)
  • VAL_10 = U.S. Top Decile value-weight Value (Data)
  • IMOM_5 = International Top Quintile Momentum (Average Top 3 market cap value-weight quintiles, Data)
  • IVAL_5 = International Top Quintile Value (Average Top 3 market cap value-weight quintiles, Data)

4.8 Benchmark Summary Statistics

  • Domestic value and momentum outperform the buy & hold index.
  • International value and momentum outperform the buy & hold index.
  • Bonds outperform real assets.
Robust Asset Allocation figure 5
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

4.9 Strategy Summary Statistics

The results for the three permutations of the robust asset allocation solution are presented below.

  • RAA_BAL = 40% Equity; 40% Real; 20% Bonds. Equity split between value and momentum. Risk-Managed.
  • RAA_MOD = 60% Equity; 20% Real; 20% Bonds. Equity split between value and momentum. Risk-Managed.
  • RAA_AGG = 80% Equity; 10% Real; 10% Bonds. Equity split between value and momentum. Risk-Managed.
  • IVY5_MA = 40% Equity; 40% Real; 20% Bonds. Moving average rule applied.
  • 60/40 = 60% Equity; 40% Bonds.

RAA has historically outperformed the IVY 5 with MA system by 100-200bps–without excessive complication!

Robust Asset Allocation figure 6
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

5. Conclusion

Robust asset allocation solutions should be relatively simple, minimize complexity, and be robust across different market regimes. Simultaneous to these requirements, the solution must be affordable, liquid, simple, tax-efficient, and transparent, otherwise, many of the benefits of the solution will flow to the croupiers and Uncle Sam.

We recommend that investors explore our robust asset allocation framework and go for the do-it-yourself solution. You’ll be paying yourself 1%+ a year via saved RIA fees.

Is this the only solution? No. But any solution must be robust, simple, tax-manageable, and low-cost. This is our best effort to develop a simple model. Developing a complicated model is easy; simple is difficult.

We even provide some  tools to facilitate (we’ll be improving these over the next few months–stay tuned).

We will be implementing a version of the robust asset allocation model with our new automated advisor offering, Alpha Architect Advisor.

If you are simply too overwhelmed by portfolio management and have tax problems, we implement a tax-managed version of our robust asset allocation strategy for 50bps (minimum investment size is $1mm and above and we can go to 25bps for larger accounts).

Please contact us if you are interested.

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Please remember that past performance is not an indicator of future results. Please read our full disclaimer. The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. This material has been provided to you solely for information and educational purposes and does not constitute an offer or solicitation of an offer or any advice or recommendation to purchase any securities or other financial instruments and may not be construed as such. The factual information set forth herein has been obtained or derived from sources believed by the author and Alpha Architect to be reliable but it is not necessarily all-inclusive and is not guaranteed as to its accuracy and is not to be regarded as a representation or warranty, express or implied, as to the information’s accuracy or completeness, nor should the attached information serve as the basis of any investment decision. No part of this material may be reproduced in any form, or referred to in any other publication, without express written permission from Alpha Architect.

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About the Author

Wesley R. Gray, Ph.D.

After serving as a Captain in the United States Marine Corps, Dr. Gray received a PhD, and was a finance professor at Drexel University. Dr. Gray’s interest in entrepreneurship and behavioral finance led him to found Alpha Architect. Dr. Gray has published three books: EMBEDDED: A Marine Corps Adviser Inside the Iraqi Army, QUANTITATIVE VALUE: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors, and DIY FINANCIAL ADVISOR: A Simple Solution to Build and Protect Your Wealth. His numerous published works has been highlighted on CBNC, CNN, NPR, Motley Fool, WSJ Market Watch, CFA Institute, Institutional Investor, and CBS News. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.