The Robo-advisor Revolution: Why They’re Falling Short And A Better Alternative

Modern-based Robo-Advisory Methodology

Robo-advisors solutions debuted in the market in 2008. These investing vehicles surfaced to help many people manage their assets. By the end of 2015, the number of robo-advisers mushroomed to around 100 companies, managing approximately $60 billion in clients’ assets.

Robo-advisors have since expanded into new avenues, such as advising retail customers on how much money to spend versus save, retirement planning, and decumulating. Decumulation typically starts when people have reached retirement age and can begin to access their retirement savings without tax penalties. 

Robo-Advisory Methodology

The tools employed to manage client portfolios differ little from existing portfolio management software widely used in the industry. Robo-advisor-based portfolios typically include exchange-traded funds (ETFs), but some also offer portfolios of individual stocks. Typically they employ modern portfolio theory that is programmed to minimize risk for a given expected return.  

Robo-advisor Customer Acquisition

Traditional human advisors have a high customer acquisition cost. They operate with time constraints, leaving many middle-class investors under advised or unable to obtain portfolio management services because of the minimums imposed on investable assets. An average financial planner requires that their clients have $50,000 to invest. Compare their minimum investment amount to that of robo-advisors that start as low as $5 in the U.S. Having lower minimums on investable assets than traditional human advisors is advantageous. 

Financial advisors charge fees for advising, and the fee is typically based on the value of the assets that they are advising and actively investing. Those Assets Under Management (AUM) fees are much lower for robo-advisors than human advisors.

However, the risks of using a robo-advisor to guide investments are tied to the algorithm's quality. If the robo-advisor can anticipate and adapt to every change in the market, then robo-advisors would outperform all other advisors, but that hasn’t happened yet. With more computing power and historical trading data available to a computer algorithm, is there potential for a smarter robo-advisor?  We believe so. 

Xiggit’s robo-advisor incorporates improved optimization. How exactly? Read on to learn more.

Under the Hood of Robo-advisors

Most robo-advisors use “mean-variance optimization.” Also known as “Modern Portfolio Theory,” it is an approach that top mathematicians developed, dating back to the 1950s, which revolutionized the investment industry. The lack of computing power back then forced some very simple assumptions about investment markets for this process to work – namely that markets behave predictably, which history has repeatedly shown is a very flawed assumption.  These assumptions also lead to “estimation errors” that can underestimate the risk level investors make with their money.  In short, it was a great solution – 70 years ago, given the constraints of computing power back then. Its weak link is that it can underestimate risk in your portfolio if your robo-advisor uses this widely used yet archaic portfolio management tool.

Today, nearly all robo-advisors use math from the 1950s, passive indexing, yet claim to be using “award-winning portfolio methods.” While factually correct, these awards were given more than 70 years ago. And their performance has lagged passive indexes by over 5% per year. Fast forward 70 years, and the Q Consulting Group solved this problem, creating a “heavy tail optimization” tool that eliminates the “simplifying assumptions” of mean-variance optimization.  By removing these simplifying assumptions made in the 1950s, the risks we are calculating are mathematical truths, not assumed, and portfolios can be created using this knowledge. In short, it is better math – plain and simple; it’s also more contemporary.

Xiggit’s robo-advisor is based on heavy tail optimization. We measure, understand and manage risk more accurately than mean-variance optimization. Applying a heavy tail optimization process, we can select portfolios for our investors aiming to achieve similar returns as other robo-advisors while taking less risk with your money. Xiggit’s approach selects portfolios that can achieve higher returns while taking the same level of investment risk.

How Xiggit introduces Efficient Investing into Robo-advising 

Efficient investing isn’t just about returns. Efficient investing is about how much return you receive for the risk level you had to take to achieve it.  

According to Modern Portfolio Theory, you can limit the volatility of your portfolio by spreading out your risk among different types of investments. By putting together a basket of risky or volatile stocks, the portfolio's overall risk is less than any one of the individual stocks in the basket.

Most robo-advisors use standard models based on Modern Portfolio Theory that significantly underestimates risk. For example, the probability of a market selloff based on the old models is once in every 33 years. In contrast, Xiggit’s model shows the probability of the COVID-19 selloff (a 30% move over a 22-day period) using the Heavy tail model is once every 37 years because it is based on real data, not assumed data. A model closer to reality correlates more to accuracy and therefore better results.

Here’s an example:  

Mrs. Jones has a $1,000 investment portfolio allocated into stocks, bonds, and cash. Over time, she expects her portfolio to double in value over the next ten years. Her total return on her investment portfolio over this time frame would then be:

$1,000 x 2 = $2,000 ending value

$1,000 = starting value

Gain on investment = $2,000 - $1,000 = $1,000

Total return on the original $1,000 = $1,000 / $1,000 = 100% total return

Maximum Loss of AUM

While Mrs. Jones invested, there was a severe economic crisis, and the stock market crashed, losing 50% of its value in year five. Mrs. Jones' portfolio lost 30% of its value this year because she invested in a diversified portfolio of stocks, bonds, and cash (which is why it lost less value than the stock market overall, as bonds and cash are less risky than stocks). This amount was the biggest loss that Mrs. Jones realized in a single year during her 10-years of investing. This number is referred to as "maximum loss" on a portfolio.

Risk of Loss for Investments

When Mrs. Jones began her investment journey, she invested in a portfolio that was expected to have a 30% maximum loss during her investment period. To easily remind her of the risk in her portfolio, her advisor labeled the portfolio as "Risk Score 30," with the 30 reminding her that this combination of stocks, bonds, and cash could lose 30% in value in any given year.

Why Lowering Your Investment Risk Matters

Mrs. Jones had to be willing to take a 30% loss in value to achieve her 100% total return over 10 years. Thus, the ratio of her total return to the amount of risk she had to take is 100% / 30% = 3.33 times. Another investor, Mrs. Smith, was willing to take a 50% loss on their portfolio to achieve the same 100% rate of return, their risk/return ratio would be 100% / 50% = 2.0. 

Mrs. Jones invested in a more efficient portfolio than Mrs. Smith. She had to take fewer risks to achieve the same investment returns. This investment approach is a key concept in finance called "risk-reward tradeoff." 

Xiggit's portfolios are designed to minimize the risk amount our investors need to achieve similar total returns as the overall market. The bottom line is that clients can get better returns with a robo-advisor like Xiggit's, which incorporates modern math.