Frequently Asked Questions

What is adaptivwealth?

adaptivwealth is a web site that shows you how to make more adaptive and diversified asset allocation decisions. We believe in the power of asset allocation, or deciding what proportion of one's money he or she should put into which asset classes, which include stocks, bonds, real estate, and commodities. Currently, we display the performance of a portfolio constructed using our proprietary methods compared against the performance of the S&P 500. We also display a pie chart showing what proportion of the portfolio is invested into what secruity.

What's so special about asset allocation?
  • Greater diversification

    Asset allocation allows for greater diversification, which can potentially produce more favorable return and risk characteristics than just picking stocks to invest in, for example. Different asset classes like stocks, bonds, and commodities can react differently to the same economic phenomenon, such as increasing inflation, lower interest rates, or even financial crisis. So, investing in different asset classes can better protect a portfolio from these risks.
  • Rules-based nature of asset allocation removes investor biases and emotions

    Asset allocation is governed by quantitative rules that tells the investor how much money to invest in what. These rules are implemented with computer algorithms that calculate optimal allocations quickly. Investor biases and emotions can negatively affect performance; asset allocation's rules-based nature takes this subjectivity out.
  • "Set it and forget it" means less transaction costs and more time for you

    No more spending hours upon hours analyzing stocks or world economies. Since asset allocation is implemented with computer algorithms, finding out how much money to invest in what assets is quick and automatic. Many asset allocation models trade or rebalance monthly, which can mean incurring smaller transaction costs (e.g. broker trading fees).
What does adaptivwealth bring to the table?

adaptivwealth uses proprietary models that make traditional asset allocation more adaptive to market changes, such as taking both asset return variance and momentum into account. Reacting faster to market changes means that a portfolio could be better protected from impending risks. By providing the information that we do online, we hope to help individual investors learn more about making adaptive and diversified asset allocation decisions.

adaptivwealth uses a basket of exchange traded funds (ETFs) in its models. ETFs are low cost, diversified investment vehicles (like mutual funds) that are liquid and readily accessible to individual investors everywhere.

What is the Minimum Variance Portfolio (MVP)?
Simply put, the Minimum Variance Portfolio is the portfolio of assets that has the smallest (historical) variance, assuming we invest 100% of our assets (for the math, visit Wikipedia's entry on Modern Portfolio Theory). Of course, the portfolio that had the smallest variance in the past will not always be the same as the portfolio that has the smallest variance in the future; we try to mitigate this effect by making our Minimum Variance Portfolio adaptive, as described in the question above. From the historical performance graph on the site's main page, the adaptive Minimum Variance Portfolio seems to have decent returns and, more importantly, much less risk than the S&P 500.
What ETFs are used?
Several ETFs representing different asset classes are used in the Minimum Variance Portfolio. In general, ETFs are selected for use in the model because they have low expense ratios, are liquid, and have long track records.
  • VTI Vanguard Total Stock Market ETF

    Why VTI?
    In addition to tracking its benchmark very closely, VTI is also much more liquid and has a much longer track record than similar ETFs such as the SPDR Dow Jones Total Market ETF (TMW) and Schwab U.S. Broad Market ETF (SCHB). VTI's low expense ratio is comparable with that of SCHB.
  • EWJ iShares MSCI Japan Index ETF

    Why EWJ?
    EWJ is by far the most liquid ETF tracking Japanese equities in the market today, and has a low expense ratio that is comparable with competing ETFs that also track Japanese equities.
  • RWX SPDR Dow Jones International Real Estate ETF

    Why RWX?
    RWX and the Vanguard Global ex-US Real Estate ETF (VNQI) are the two most actively traded international real estate ETFs. However, RWX is significantly more liquid and has a longer track record than VNQI, which is why it is used in the model instead.
  • IEF iShares Barclays 7-10 Year Treasury Bond ETF

    Why IEF?
    IEF has a very low expense ratio and is the most liquid ETF tracking intermediate-term US Treasuries.
  • TLT iShares Barclays 20+ Year Treasury Bond ETF

    Why TLT?
    TLT has a very low expense ratio and is the most liquid ETF tracking long-term US Treasuries
  • IAU iShares Gold Trust ETF

    Why IAU?
    IAU is a very liquid ETF that tracks gold, albeit being less liquid than its competing ETF, the SPDR Gold Shares ETF (GLD). IAU and GLD move in lockstep; however, IAU's expense ratio is almost half as much as GLD's, which is why it is used instead of GLD.
  • DBC PowerShares DB Commodity Index ETF

    Why DBC?
    DBC is much more liquid than its competing ETF, the iPath DJ-UBS Commodity Index ETN (DJP). The expense ratios of the two are about the same. DBC's prospectus states that the index is managed to mitigate the effects of contango, which is a large issue for all commodity funds. Also, unlike DBC, DJP is an exchange traded note (ETN) and so its survival depends on the credit of its issuing bank, Barclays Bank.
  • VGK Vanguard MSCI Europe ETF

    Why VGK?
    VGK has a significantly lower expense ratio and higher liquidity than its ETF competitor, the iShares S&P Europe 350 Index ETF (IEV).
  • VNQ Vanguard REIT Index ETF

    Why VNQ?
    VNQ is a very liquid ETF that tracks the performance of US real estate investment trusts. It has a much lower expense ratio than the iShares Dow Jones US Real Estate ETF (IYR).
  • VWO Vanguard FTSE Emerging Markets ETF

    Why VWO?
    VWO and the iShares MSCI Emerging Markets Index ETF (EEM) both track their underlying indices very closely, and are very liquid. VWO has a significantly lower expense ratio than EEM, which is why it is included in the model instead of EEM.
Is there any research backing the effectiveness of adaptivwealth's "adaptive Minimum Variance Portfolio"?

Yes, there is!

An investor who cares only the mean and variance of asset returns can allocate his assets optimally and maximize his Sharpe Ratio (Markowitz, 1952); this is called Modern Portfolio Theory, and Markowitz would later win a Nobel Price for it and some of his other work.

In Modern Portfolio Theory, there is the idea of the Minimum Variance Portfolio. Traditionally, mean-variance optimization (to find the the optimal asset allocation) in MPT is done with expected returns and expected covariance as inputs into the model, where the optimizer minimizes the variance of the portfolio given a target level of expected return. However, expected returns are notoriously difficult to forecast and often cause optimized portfolios to perform poorly out of sample. As a result, researchers often ignore expected returns altogether (Jagannathan and Ma, 2003; Chan, Karceski, and Lakonishok, 1999) and only focus on minimizing the overall variance of the portfolio--hence we have the Minimum Variance Portfolio

Our version of the also MVP selects assets that are more likely to perform well in the future, specifically by selecting assets with price momentum. The momentum anomaly is the phenomenon that assets that have performed well in the recent past tend to continue to perform well in the near future. The momentum anomaly has been shown to be a significant effect and to have persisted across time, asset classes, and even countries (Asness, Moskowitz, and Pedersen, 2009)

The implementation of the Minimum Variance Portfolio using momentum as an input into the model, in addition to monthly portfolio rebalancing, makes it adaptive and reactive to changes in the market environment, resulting in improved risk-adjusted returns.

  • Asness, Clifford S., Moskowitz, Tobias J. and Pedersen, Lasse Heje, Value and Momentum Everywhere (March 6, 2009). AFA 2010 Atlanta Meetings Paper.
  • Chan, Louis K.C., Karceski, Jason J. and Lakonishok, Josef, On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model (March 1999). NBER Working Paper No. w7039.
  • Markowitz, Harry. "Portfolio Selection." The Journal of Finance 7.1 (1952): 77-91.
  • Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1684, 08.
If there are ten ETFs, then why does the adaptive MVP only trade around three ETFs every month?

Remember that the adaptive MVP uses momentum as an input into the model. So every month, some ETFs are filtered out because of poor recent performance.

Next, this reduced list of ETFs is plugged into an optimizer that finds the allocation that minimizes the portfolio's overall variance. This results in some ETFs having a 0% allocation (in addition to those that were filtered out because of poor recent performance).