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Automated investment services – more commonly known as robo-advisory accounts – are relatively simple to understand, on the surface at least.
We talked about this last week – robo-advisories were created by millennials in response to the dot-com collapse, the financial crisis, and traditional fee-based advisory services.
But all is not what it seems. And if you dare dig into how they actually work, you'd be surprised by how complex they are.
Today, I want to show you how these services actually automate portfolio selection and perform rebalancing acts, and help you understand some of the complex portfolio management theories providers have to use.
Because what you don't know can really hurt you – and you should know how your money is being managed.
Let me break it down for you…
Two Approaches, One Goal: Diversification
There are two general approaches to how automated investing services construct portfolios. Either they're based on a long-term timeline, or they're built on goals-based investing.
For example, two of the most popular robo-advisory service providers, Wealthfront and Betterment, go in different directions. Wealthfront has a long-term timeline investing platform, while Betterment's platform focuses on goals-based investing.
Betterment explains its goals-based investing focus this way:
When you invest with Betterment you'll notice that we don't give you a risk-tolerance questionnaire. Instead, we ask about time as it pertains to your investments: your age in relation to your goal (with retirement, say) or the time horizon to reach your goal (e.g. three years to save up a Safety Net fund). That's because a goal-specific time horizon is an objective measure of the potential range of outcomes which you should be exposed to.
While you can choose a service based on how long you'll be in the market that rebalances your portfolio based on your changing age, like what target-date funds do, all robo-advisors construct diversified portfolios for you.
The common denominator with a diversified portfolio is they theoretically reduce portfolio risk without sacrificing expected returns.
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Unfortunately, there's a problem with diversification. Providers mostly diversify you based on the same portfolio management theories.
That means that you may not be special just because you're on a long-term investing platform or on a goals-based platform.
Portfolio diversification is generally achieved through mean-variance analysis or optimization.
Mean-variance analysis quantifies risk and expected returns by applying concepts from statistics, where the values of assets (stock prices) are taken to be random variables with various expected values.
Mean-variance optimization was introduced by Nobel laureate Harry Markowitz in his 1952 paper "Portfolio Selection." It was the first mathematical formalization of the idea of diversification of investments. It considers a set of risky assets and calculates portfolios for which the expected return is maximized for a given level of portfolio risk, where the risk is measured as variance.
Mean variance analysis became known as Modern Portfolio Theory (MPT), where optimized portfolios result in the "efficient frontier," a band of portfolios that dominate all other feasible portfolios in terms of their risk-return tradeoff.
Generally, the return on all securities is correlated to the market return through a constant called beta. The market's beta, a measure of how it moves relative to itself, is always 1 (because it is a measure of itself). Variance analysis includes measuring how individual securities and portfolios vary from the market's beta of 1. A portfolio's beta, the weighted average of the betas of the securities in a portfolio, measures the portfolio's correlation to the market.
But each security's return is also subject to an "idiosyncratic" (non-systematic) term that is independent of the market return and the idiosyncratic terms of all other securities. A portfolio's idiosyncratic term is the weighted average of the idiosyncratic terms for each of the securities.
Since the idiosyncratic term of each security is assumed to be independent of all other securities, the variance of the idiosyncratic term of the portfolio is not the weighted sum of the constituent securities' idiosyncratic variances. It is less than the weighted sum, since the idiosyncratic terms tend to diversify – some are positive while others are negative, canceling each other out. With a sufficiently large number of securities, meaning diversification, idiosyncratic risk can be theoretically completely eliminated.
The One Thing They Won't Tell You
About the Author
Shah Gilani boasts a financial pedigree unlike any other. He ran his first hedge fund in 1982 from his seat on the floor of the Chicago Board Options Exchange. When options on the Standard & Poor's 100 began trading on March 11, 1983, Shah worked in "the pit" as a market maker.
He helped develop what has become known as the Volatility Index (VIX) - to this day one of the most widely used indicators worldwide. After leaving Chicago to run the futures and options division of the British banking giant Lloyd's TSB, Shah moved up to Roosevelt & Cross Inc., an old-line New York boutique firm. There he originated and ran a packaged fixed-income trading desk and established that company's "listed" and OTC trading desks.
Shah founded a second hedge fund in 1999, which he ran until 2003.
Shah's vast network of contacts includes the biggest players on Wall Street and in international finance. These contacts give him the real story - when others only get what the investment banks want them to see.
On top of the free newsletter, as editor of The 10X Trader, Money Map Report and Straight Line Profits, Shah presents his legion of subscribers with the chance to earn ten times their money on trade after trade using a little-known strategy.
Shah is a frequent guest on CNBC, Forbes, and MarketWatch, and you can catch him every week on FOX Business' "Varney & Co."