Our unparalleled investment process has been designed to offer a holistic means of investment growth through a combination of portfolio management, long and short term goals, and rounding up passive savings under a single mobile application.
If you’re considering using Allio’s investment portfolios to save and invest for your financial goals, it’s only natural that you might be curious about our investment process and how that compares against other robo-advisors out there.
After all, it’s your money—you deserve to know how it’ll be invested.
But before diving into how our portfolios are constructed, let’s first take a look at who’s behind them.
Our portfolios have been designed by a team whose members have managed portfolios and monitored risk at multibillion dollar hedge funds, bulge-bracket investment banks, and pioneering asset management firms over the past two decades.
Our mission? To provide financial wellness for all. We believe anyone can start building wealth—no financial expertise required—with our enhanced automated investment portfolios.
Allio’s investment process can best be summed up in three key steps, which we discuss in greater detail below:
First, we seek to understand the secular correlations between asset classes
Next, we estimate and specify our expected returns for these asset classes
Finally, we use full-scale optimization to construct our portfolio, paying particular attention to potential downside scenarios
Is this approach foolproof? Certainly not—no investment process ever is. But based on rigorous modeling, testing, and experimentation, we believe it’s a strategy that can potentially yield powerful advantages.
Moreover, our process isn’t static. Just as markets evolve, our process is constantly evolving as we gain insight from research—both our own and the work of other practitioners.
Diversification—the act of investing in multiple assets and asset classes—is an important part of constructing an investment portfolio. But being diversified is about more than just holding multiple investments. The correlation between those investments is also important.
Correlation refers to how closely two different assets tend to move in relation to one another.
When assets are positively correlated, they tend to move in the same direction as each other. When assets are negatively correlated, they tend to move in the opposite direction. Assets that fall in the middle show no correlation.
Generally speaking, the closer you can get the assets in your portfolio to zero correlation, the better off you will typically be from a diversification point of view. But this is much easier said than done–as asset classes are often driven by the same macroeconomic factors.
Most robo-advisors address diversification by building portfolios that consist of stocks and bonds, with the exact percentage allocated to each bucket varying based on the individual’s investment timeline, risk tolerance, and financial goals. They do this because, over large portions of market history, stocks and bonds have been negatively correlated. When stock prices increased, bond prices tended to decrease, and vice versa.
Unfortunately, correlations are not set in stone. They can, and do, change. In fact, beginning in April 2020, it appears that the correlation between stocks and bonds has become positive. In other words, for the past 2+ years, stocks and bonds have largely moved in the same direction as one another. (This behavior is not uncommon when inflation becomes a driving force in markets.)
Because the correlation between stocks and bonds has shifted, portfolios consisting entirely of just those assets are much less diversified than they have been in the past.
That’s why, before we make any investment decision, we seek to understand the correlation between asset classes—as they currently are, depending on the secular market regime that we are in, and not as they have been or as they “should be.”
It is because of this process that our portfolios do not solely consist of stocks and bonds. They also include other asset classes like real estate, commodities, and precious metals, which tend to hold up better during secular market regimes driven by inflation.
The need for diversification is just one of the factors that we consider in deciding upon asset allocation in our portfolios.
Other critical questions that we ask ourselves include: How do we expect each of these asset classes to perform in the short, medium, and long term? What is a realistic rate of return? What is the best case scenario? What is the worst case scenario?
The answers to these questions ultimately form the bedrock of our asset allocation strategy. Once we have a sense of the expected rate of return (found using empirical models of asset class behavior), it is possible to build and manage portfolios that meet the needs of our investors—balancing their investment timeline and risk tolerance.
While nobody can forecast investment returns with complete accuracy, with an understanding of the current secular regime, enough data, and a long enough forecasting horizon, it may be possible to forecast returns within a reasonable margin of error. The analysis that we use to accomplish this relies upon a combination of both established and proprietary forecasting models.
Finally, whereas most robo-advisors use mean-variance optimization (MVO) in constructing their investment portfolios, Allio uses an entirely different method known as full-scale optimization (FSO).
While both of these portfolio construction techniques seek to maximize the tradeoff between a portfolio’s expected return and its “risk,” they do this in different ways—and to different results. To understand the difference between mean-variance optimization and full-scale optimization, we need to talk about statistical distributions and a concept known as “fat tails.”
Mean-variance optimization works under the assumption that investment returns are normally distributed, similar to the distribution in a typical bell-curve. But the reality is that the distributions of asset returns are not normal; there’s a greater possibility of large gains and large losses than you’d find under a normal distribution.
The higher probability of large gains and losses leads to what is known as “fat tails” when plotted on a chart. The chart below shows fat tail distribution (purple) compared against normal distribution (green).
Source: Allio Finance
Full-scale optimization acknowledges the existence of fat tails and incorporates them as a factor in portfolio construction. The result, as studies have shown, is a portfolio that is typically more robust to various downside possibilities compared to portfolios constructed using mean-variance optimization.
Indeed, full-scale optimization may be particularly critical for portfolios, such as Allio’s, that hold various alternative asset classes such as commodities, real estate, and gold.
With this in mind, Allio uses full-scale optimization—paired with robust simulations of different market scenarios—to construct our investment portfolios, in an effort to make them as resilient as possible to various downside market scenarios.
Here at Allio, every single investment decision that we make is guided by data, decades of cumulative Wall Street experience, and an understanding of how markets work. Our goal is to design investment portfolios that, we believe, will bring as much benefit to our users as possible, while also limiting downside risk.
Is our way the only way of doing things? Of course not. But, we believe our methodology is a sound one for the unique challenges facing investors today.