Risk for planners
How “other optimizers” create risk
for clients and for planners.

PATHFINDER analyses and graphs reveal that "other optimizers" are fatally flawed -- unacceptably dangerous for investors with long-term goals, and unacceptably dangerous for financial planning advisors.

For investors, the danger is being misled to select portfolios with very poor prospects for meeting needs of retirement and other long-term goals, compared to what better portfolio selection would offer. For financial planners, the danger is liability for the misguidance that "other optimizers" present.

In a nutshell, the problem is that for portfolio comparison and selection, "other optimizers" use incomplete and grossly inadequate analyses that omit the client's time horizon and long-term plan, and hide this inadequacy with a mix of pseudoscience, concealment, and deception.

The sections below outline specifics of this problem, with links to graphs on other pages that illustrate key points.

An analogy --
Condemning clients to Cleveland?

Imagine a client with a long-term goal of spending retirement skiing in Aspen.

The client is located on a curving road from which he can turn to any one of a hundred highways going in various directions. The client asks a travel planner for guidance on which highway to take. To guide the client, the travel planner relies on a “travel optimizer” software tool.

Instead of displaying and showing the planner and client a map that reveals best routes toward Aspen, the “travel optimizer” displays a graph that compares the highways in quality of pavement, and asks the client to answer a “bump tolerance questionnaire”. Through a processing of the answers based on previously conducted incantations, the “travel optimizer” determines a suitable smoothness of ride, and on this basis reveals the recommended highway.

Unfortunately, instead of heading toward Aspen and Vail, the road with the chosen pavement heads the client toward Akron and Cleveland.

This is analogous to what the “other optimizers” do -- misapply Modern Portfolio Theory by selecting portfolios for long-term goals based on probabilities for bumps along the way instead of destinations.

What makes the “other optimizers” approach so especially dangerous is that, in comparing portfolios along the frontier curve, those with the smoothest paths along the way have greatest risk of leading toward the poorest long-term destinations.

In portfolio comparison and selection,
“other optimizers” omit the time horizon

After applying Modern Portfolio Theory to find the range of best-diversified portfolios, presented on the efficient frontier, "other optimizers" use the efficient frontier to compare these portfolios and select one for recommendation. But this compares the portfolios in annual rate or return, for the single year -- omitting the time horizon.

Omitting the client's plan -- When portfolios are compared in rates of return for the single year, the investor's long-term plan is omitted. Times and amounts of planned investment are not considered, times and amounts of future dollar goals are not considered.

Omitting powerful long-term effects -- The single-year comparison presented by the efficient frontier also omits two powerful long-term investment effects that make the portfolios compare very differently for longer terms -- compounding and standard deviation shrinkage. For illustrations of how these effects change portfolio comparisons for longer terms, see the section of this website named Long-term advantage.

For longer time horizons, frontiers are very different -- Because of the power of these long-term effects, for longer investment time horizons the frontier curves are very different from the curve of the single-year efficient frontier. The differences in the curves show that for longer investment goals, portfolio comparison and selection is radically different from what is shown on the single-year efficient frontier. For illustrations of how great these differences are, see the section of this website labeled Frontiers vs. years.

How to reveal best portfolios for long-term plans and goals -- By applying Monte Carlo simulation to the results of Modern Portfolio Theory, PATHFINDER incorporates clients' plans for future investments and goals, and produces Goal Frontier graphs, advancing portfolio comparison from the single year to the client's time horizon. On Goal Frontiers, the planner can see, and show to clients, which portfolios are best in prospects and risks for clients' long-term goals. This process shows that by using just the single-year efficient frontier to compare and select portfolios for long-term goals, "other optimizers" deliver portfolio comparisons and selections that are based on woefully incomplete analysis and are very misleading. For an illustration of the process from efficient frontier to Goal Frontier, see the section on this website named Graphic summary.

Instead of including the time horizon,
“other optimizers” commit pseudoscience,
concealment, and deception

Deceptive presentation -- "Other  optimizers" label the axes of the efficient frontier "return" and "risk", which leads investors to think this single-year comparison is valid for any investment time horizon. This is a terrible deception.

For longer time horizons, the portfolio comparison is radically different from what the single-year efficient frontier shows. This is shown in two sections on this website: Graphic summary and Frontier vs. years.

The labeling of the efficient frontier’s return-rate standard deviation axis as "risk" is especially deceptive. For longer-term investment prospects, return-rate standard deviation is not at all a valid measure of risk. On the contrary, in comparing frontier portfolios for longer-term goals, those with larger return-rate standard deviations (and correspondingly higher expected rates) become more favorable in risk, and those with smaller standard deviations more risky. This is illustrated by red vs. blue portfolio comparisons in the same two sections on this website: Graphic summary and Frontier vs. years.

The deceptive labeling of the efficient frontier that "other optimizers" present is not acceptable to financial planners. With this labeling, “other optimizers” create far too much danger of misleading clients.

Investor’s overconcern with short-term ups and downs is terribly hard to overcome. When their technical measure, return-rate standard deviation, is awarded the ultimate word of investor terror -- risk -- this makes the problem much worse. For standard deviation we need another layperson’s term -- one that better communicates what return-rate standard deviation really measures. Instead of conveying ultimate terror, the layperson’s term for standard deviation should convey a hint of scorn, to help instead of hindering guidance of clients to look beyond toward their long-term goals.

Our layperson’s term for return-rate standard deviation is “short-term wobble”.

Focusing on short-term fears instead of long-term goals -- Financial planners know that in guiding investors with long-term goals, it is most important to educate and guide the clients to look beyond the short-term ups and downs and focus on the long-term goals. "Other optimizers" violate this most-important rule completely.

Instead of comparing portfolios in prospects and risks for the client's long-term goals, "other optimizers" carry out portfolio selection on a measure of investors' short-term fears, deceptively called "risk tolerance". What this measure really means is tolerance of short-term ups and downs along the way to the goal -- not the risks for the long-term goal that the client should be focusing on. This misleads the client toward exactly the opposite of the long-term focus the planner wants to guide and educate the client toward.

Pseudoscience and concealment -- To develop a recommended portfolio, "other optimizers" use "risk tolerance questionnaires". From these they mysteriously divine a conclusion as to a recommended size of probable short-term ups and downs the client will tolerate, as measured by return-rate standard deviation, and recommend a portfolio along the efficient frontier chosen according to this measure of the client's short-term fears.

This process may give an appearance of scientific validity, but it amounts to selecting portfolios in the dark -- without comparing the portfolios in prospects and risks for clients' long-term goals. Investors cannot see how the recommended portfolio compares to the others along the frontier curve in prospects and risks for the clients’ long-term goals -- nor can the financial planner. In this way, "other optimizers" present portfolio recommendations through a process financial planners cannot explain or justify.

Since the portfolio recommendations are developed without comparing the alternatives in prospects and risks for clients’ long-term goals, such recommendations have no valid justification.

How to deal with short-term fears the right way -- Investors' fears of short-term ups and downs are not the logical basis for selecting portfolios for long-term goals, but these fears are real and strong and must be dealt with. But what "other optimizers" do is not the way.

For this, live graphic Monte Carlo simulations are ideal. With simulations of the future for a plan, year by year from here to the long-term goals, planners can show clients clearest pictures of what is most important for clients to see and understand. First, the client can see examples of what the year-to-year ups and downs may be along the way to the goal. Second and more important, by showing the client simulations for different frontier portfolios, the planner can show the client most vividly that by tolerating bigger short-term ups and downs along the way, the client can greatly improve prospects for long-term results, for the goal. For an illustration of how effectively a Monte Carlo simulation graph can show these things, go to the page in this website's Using PATHFINDER section showing PATHFINDER’s Monte Carlo graphs.

The final deception -- Only after comparing portfolio selections without consideration of the client's time horizon, "other optimizers" turn to the client's time horizon -- and there present a final deception. For a client's plan and selected portfolio, "other optimizers" print out a table with a dollar number for investment return for every future year from here to the end of the plan.

But what return-rate standard deviations mean and show is that for each future year, returns will vary and cannot now be known. Future returns and resulting investment values may take any of many paths, which can be described in probabilities but not a specific projection. To see this illustrated, and see how unjustifiable it is for "other optimizers" to print a single year-by-year projection, see the graph in this website's Graphic summary section showing Monte Carlo simulation.

There is NO conceivable chance that the kind of projection "other optimizers" produce will be met, but very high danger that seeing it printed, the client will expect it to be met -- and when it inevitably is proven wrong, feel misled. In this way, "other optimizers" produce another serious danger of deceiving clients and placing financial planners in liability risk. For financial planners, this is not acceptable.

Observations on the problem

Modern Portfolio Theory -- value and misapplication

It is widely known that Modern Portfolio Theory was originated by Harry Markowitz and described by him in an article published in 1952. His article describes a method for finding, for a set of investments, a range of mixes of the investments that offer various expected return rates each at minimal return-rate variation or standard deviation. This method is now commonly applied to sets of asset-class investments, for which the mixes are called portfolios, and the method’s results are commonly presented as a curve on an efficient frontier graph. This graph compares the range of best-diversified portfolios in probabilistic dimensions of single-year or annual return rate: expected return rate and return-rate standard deviation.

For finding best portfolios for investors’ long-term goals, this method provides an essential and major step along the way. It leads toward effective diversification, and narrows the search to the range of portfolios that are best-diversified.

But for finding best portfolios for long-term goals, applying what Mr. Markowitz’ article describes is only part of the job. These results are only raw material for further analysis. To select a portfolio from the range of the best-diversified, a second step is required. For any investor with long-term goals, this requires advancing the portfolio comparison from the single-year efficient frontier to probabilities for the long-term plan and goal.

The Markowitz article does not address this second step. It does not present any method for selection among the range of portfolios his method has found, and it very specifically avoids addressing investments of multiple periods such as multiple years. For valid selection of portfolios for long-term goals, a tool must go beyond what the Markowitz Modern Portfolio Theory article provides.

“Other optimizers” apply the Modern Portfolio Theory that Mr. Markowitz’ article described to identify the range of portfolios that are best-diversified. It is in the second step, which the Markowitz article does not describe, that designers of “other optimizers” apparently did not know what to do. It is here that they turned instead to focus on short-term fears, psuedoscience, and deceptive presentation, as outlined above and illustrated in other sections on this website.

Misrepresenting financial planners’ intentions

There’s an aspect of “other optimizer” misguidance that’s especially dangerous for financial planners, and for the financial planning profession.

Financial planners know that it’s most important to guide and educate investors to look beyond the short-term ups and downs and focus on the long-term goals. Financial planners also know that this is extremely difficult to do, and devote great effort to this most-important duty.

But “other optimizers” do just the opposite: they feature, and present what appears to be scientific endorsement for, comparing and choosing portfolios based on short-term fears instead of long-term goals. With this approach, ”other optimizers” make it appear that financial planners are avoiding the difficult work of educating clients to focus on the long term. In this way, “other optimizers” make it appear that financial planners are sacrificing clients’ long-term financial futures to make the planner’s job easier.

This is not what financial planners intend. This impression created by “other optimizers” is unacceptable to financial planners, and to the financial planning profession.

Conclusion

For advising clients with long-term goals, “other optimizers” are simply not acceptable. They threaten unacceptable danger to clients’ long-term futures, and to financial planners’ liabilities and reputations.

With use of Monte Carlo simulation exploding, and PATHFINDER showing its use to compare the portfolios for long-term plans and goals, “other optimizers” are now exposed. The individual financial planner and the financial planning profession cannot permit their continued misleading of clients. “Other optimizers” represent too much liability danger for financial planners, as well as too much danger to clients’ long-term financial health.

The answer

To meet the need that “other optimizers” purport to meet but fail to meet -- guiding individuals and families toward best portfolio plans for their long-term needs and goals -- Portfolio PATHFINDER is the answer.

For providing and explaining best portfolio recommendations, PATHFINDER offers the unique power of Goal Frontier graphs, to compare portfolios and see which are best in prospects and risks for clients’ long-term plans and goals. These graphs compare the best-diversified portfolios, found through Modern Portfolio Theory -- and compare them for clients’ time horizons, long-term cash flow plans and goals, through application of Monte Carlo simulation to each portfolio. On these graphs, you can see and show the clients which portfolios are safest in measures of risk for their time horizons and long-term goals.

For illustrations of these graphs and their long-term planning power, see the section on this website named Graphic summary, and the part of the section on Using PATHFINDER showing Goal Frontiers.

For educating clients to tolerate short-term ups and downs and focus on long-term goals, PATHFINDER’s dynamic Monte Carlo simulation graphs provide you most-effective visual client-communication power. By running and comparing simulations for two portfolio plans -- the recommended portfolio plan, and another with smaller standard deviation -- you can show clients most vividly that by tolerating larger short-term ups and downs along the way, clients can proceed toward far better prospects for their long-term goals. For giving clients previews of what is likely in ups and downs along the way -- the best advance preparation -- the dynamic year-by-year progressions of simulations of the plan are ideal. And then by proceeding to PATHFINDER’s graph of Probability curves, you can further illustrate the superiority of the recommended plan and further reinforce your clients’ focus on their long-term goals.

For illustration of PATHFINDER Monte Carlo simulations and resulting Probability-curve graphs, see the page in this website’s Using PATHFINDER section showing Monte Carlo graphs.

For an overview of Portfolio PATHFINDER and how to use it, go to this website’s section on Using PATHFINDER.