Throughout my career, which has spanned more than 21 years, I have invested a considerable amount of time studying all of the most advanced theories and methodologies that have been developed to invest money. Practitioners have developed sophisticated computer models and software tools to apply these key concepts to real-world investing, in an attempt to generate alpha — positive performance above the returns generated by the appropriate benchmark.
For example, if the investor was investing 100 percent in stocks, positive alpha would be the return in excess of the return that the Standard & Poor’s 500 generated (assuming the benchmark is the S&P 500). Definitions for alpha can get pretty involved, but for the purposes of this article, the definition provided above will suffice. The problem is that to make these software tools function, software developers must make lots of simplifying assumptions, otherwise there would be far too many variables and the software tool would not generate usable results. In making these simplifying assumptions, however, much of the real-world realities of investing are lost.
The use of simplifying assumptions is not limited to the financial world. In fact, I see them being used throughout corporate America on a regular basis. At large companies, upper management is continuously looking for ways to cut costs to improve bottom-line profits. This has been especially true and these efforts have been especially aggressive during this long recessionary period. If we look at corporate profits over the past 13 quarters, since the fourth quarter of 2008 when the financial markets imploded and the recession officially started, companies across the economy have been cutting costs dramatically to make their earnings (net income). This is easy to see if we simply look at top-line revenue growth, which has not been impressive, versus bottom-line earnings growth, which overall has been pretty strong. What this means is that companies are achieving positive earnings performance, not by increasing their sales, but by cutting their expenses.
To cut costs, upper management teams look for ways to streamline operations. Often this involves laying off workers and consolidating departments. This has been especially true for the financial industry as so many large companies have failed, and consequently were swallowed up by other firms. When this happens, there are duplications of departments, so it is logical to think that by consolidating departments, the new combined company can save money.
The problem with this strategy is that all people are not created equal. The real world is not simple!
If we could correctly assume that all employees within two departments that provide the same exact level of service as the next employee, then it makes perfect sense that the company could (simply) close one of the departments and have the other (identical) department handle the needs of the entire company. If only the world were so simple.
The problem is that when you get down to ground level, where the real work gets done every day, you realize that within these departments, you have a broad range of skill sets and experience levels. What makes a department at a large company function properly is typically a few key individuals who have been doing their jobs for many years, and know exactly what to do in any situation. More important, they know exactly who to call when there is any problem to get it resolved quickly and accurately.
When upper managements shut down entire departments, they are treating their employees as if they are all the same — as if they are commodities that can be interchanged or easily replaced. This is naïve, counterproductive and highly damaging to an organization and to its employees — not just those that get fired, but those that remain who lose those problem solvers, mentors and leaders who drive the overall productivity of their departments.
In the investment world, software programs that use simplifying assumptions make the same mistake — all investments within a certain asset class are not created equal! One large-cap stock is not identical to 1,000 others. Further, stocks that perform a certain way during a specific economic time, under a specific (large) set of economic, political and financial circumstances, can perform completely differently in a different economic environment. Asset allocation software tools typically use averages for performance and risk factors, for each asset class. The theory is that over the long term, each asset class generally performs the same, so taking an average for each asset class is fine. No, it’s not! This is a false assumption.
There are literally hundreds of variables that direct and indirectly affect the performance of a given asset class, depending on what is happening in the world and in the financial markets at any given time. To make these asset allocation software tools function, the writers must make a ton of simplifying assumptions, such as using average rates of return and risk parameters for each asset class. Just to underscore my point, if they only use historical averages, by definition, they are not incorporating any future assumptions into the analysis. How can any investment recommendation be valid without including expectations for future performance?
Keep in mind also that the people writing these software tools are not market or investment experts. They are software engineers with absolutely no market experience whatsoever. Is that the person an investor should be relying upon for investment advice? Ultimately, we all have to find ways to make decisions in life without the luxury of knowing all of the information that will affect the outcomes. Admittedly, we all have to make assumptions when we make any decision about the future.
My point is writing this is that too often decisions are made using simplifying assumptions without the people who are directly affected by the results understanding what those simplifying assumptions are, or having any real input into the decision-making process. For employees of large companies, managers are deciding their futures. For investors relying on asset allocation software tools, or advisers using these tools, the danger is in trusting the output of these tools implicitly, or the advice of those using the tools, without understanding their limitations (the tools and the advisors).
Those limitations are primarily contained within the simplifying assumptions used to create the software tool. It would be a mistake to blindly accept the output of these software tools or the advice an adviser provides to a client based on these tools, without understanding the assumptions that went into their development and the limitations that result.