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How to deliver financial planning value
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For assessing individuals' long-term financial plans, Monte Carlo simulation offers power to translate uncertain inputs into probabilities for long-term results. New software products, which we shall call "Other Monties", offer financial planners this power.

But for guiding individuals toward their long-term goals, full and effective use of Monte Carlo simulation requires three applications. When these are stated in order of execution, the application stated above is number 2:

1. Long-term portfolio optimizing -- Identify best portfolios for long-term plans and goals, by comparing the frontier portfolios in prospects and risks for the long-term plans and goals.

2. Prospects for plan -- With a chosen portfolio, assess probabilities for results of each client's long-term plan, to guide adjustment of clients' plans to best balance and meet their short- and long-term priorities and needs.

3. Client education on short-term / long-term -- Educate clients on prospects for short-term ups and downs ahead for the chosen plan, and the long-term advantage of tolerating these ups and downs, to build client commitment to best long-term plans.

While "other Monties" offer application 2, they do not precede it with application 1 or follow it up with application 3. Applications 1 and 3 are so critical to effective financial planning guidance that without them, the value of application 2 is reduced to a fraction of what it should be, and can even have negative effect.

Monte Carlo Application 1 -- Revealing best portfolios for long-term plans

In long-term prospects for a financial plan, portfolio choice has overwhelming effect. Assuming efficiently-diversified portfolios, for a typical plan one portfolio choice might offer expected results of millions of dollars and goal-meeting probabilities over 90%, while with another portfolio choice the expected result hits zero before the end of the plan and goal-meeting probabilities are under 10%. For illustrations, see the comparisons for portfolios shown as red and blue on probability curves and Goal Frontier graphs in this website's Using PATHFINDER section, starting at the Probability-curves comparison.

For revealing best portfolios for long-term plans, Monte Carlo simulation is a terribly inefficient tool. To assess a plan with just one portfolio selection, thousands of simulation runs are required -- and the number of different portfolios is infinite. What financial planners need, to see and show their clients best portfolios for long-term plans, is a tool that (a) uses Modern Portfolio Theory to narrow the search to the range of best-diversified, then (b) compares these portfolios in long-term prospects and risks by applying Monte Carlo simulation, and (c) reveals the comparison as PATHFINDER does on Goal Frontier graphs.

With the comparisons shown on these graphs, the planner can see which portfolios are best for clients' long-term plans and goals, show the clients why the selected portfolios are best -- and in Monte Carlo application 2, assessment of prospects for a client plan, use a portfolio that is best for the plan.

But "other Monties" do not provide such portfolio-selection comparisons. Some merely provide for user portfolio entry and go directly to application 2, producing assessment of a plan with specs for whatever portfolio the user enters. This may well amount to showing prospects for the client with a portfolio as poorly suited for the plan as the one this website's graphs show in blue -- showing the client and guiding her toward a financially gloomy future, when with a best portfolio the prospects are vastly better like those the graphs on this website show in red.

Some "other Monties" do much worse -- applying the worst in misconceived and deceptively presented misdirection borrowed from “other optimizers”. These tools guide the planner and client to choose the portfolio based on a questionnaire to assess the client's "risk tolerance", which amounts to selecting portfolios for long-term goals based on short-term fears instead of prospects for long-term goals. In "other Monties" applying this approach, the portfolios offering greatest risk of lower long-term results are labeled as having least "risk". And by purporting to determine an appropriate portfolio for the client scientifically, tools that apply this approach discourage planners and clients from even exploring other portfolios, where they might discover others much better for the plan.

When "other Monties" proceed to assessment of the plan with portfolios determined these ways, their output has very high probability of seriously misleading both the planner and the client. In addition to probably leaving planner and client planning a less-than-best portfolio, the tool is most likely to show them long-term prospects far below what they would be with a suitable portfolio. Clients may be led or scared to take inappropriate actions.

For long-term plans, portfolio selection is so important that for many plans, perhaps for most, it’s the most important part of the financial planner’s advice. Yet “other Monties” fail to address it, or do it wrong and present it deceptively.

Monte Carlo Application 3 -- Educating clients on short-term / long-term

Financial planners know very well that when investments go through short-term ups and downs, clients may lose their commitments to portfolios that are best for their long-term goals. Now, new snowstorms from the media make this problem more difficult than ever before -- stock-price ups and downs are reported up to the day, the hour, the minute; suppliers of investment-churning services and tools run ads suggesting that wise investors check and change their investments wirelessly at traffic stops on the daily commute.

If from excessive concern with short-term ups and downs, clients abandon long-term plans, even the best Monte Carlo applications described above are for naught.

For client education to build the understanding essential for enduring client commitment to long-term plans, Monte Carlo simulation offers unrivaled power -- if used in the most effective ways for this purpose. By showing the client simulation runs from here to the end of the long-term plan -- year by year -- the planner can show the client previews of ups and downs that are likely to be encountered along the way. To see how vividly such a graph can do this, in this website's Using PATHFINDER section see the illustration of the Monte Carlo simulation graph.

And by showing simulations for the client plan with each of two portfolios -- the best portfolio for the plan, and another with smaller standard deviation -- the planner can show the client most vividly that by tolerating the larger ups and downs of the best portfolio, the client can stay on a path to far better prospects for the long-term goal. For an illustration, in this website's Using PATHFINDER section, see the Monte Carlo graph comparing two portfolios.

"Other Monties" do not apply Monte Carlo simulation effectively for this essential client education. Without such education, the value of what they do produce is likely to  dissipate when ups and downs ahead scare clients into abandoning their long-term plans.

PATHFINDER offers full Monte

Portfolio PATHFINDER offers full application of Monte Carlo simulation -- all three most-important Monte Carlo applications, all delivered in interactive graphs for fullest and easiest planner use and clearest client communication and education.

Monte Carlo application 1, comparing portfolios to reveal the best for long-term plans --  portfolios all along the frontier are assessed for the client’s time horizon and long-term plan using Monte Carlo simulation for each. Resulting comparisons are presented so planners and clients can see which portfolios are best for long-term plans and goals --  in interactive, scrollable PATHFINDER Goal Frontier graphs.

Monte Carlo application 2, fullest assessment of probabilistic prospects for the plan -- with the selected portfolio, PATHFINDER shows probabilities of meeting-or-beating vs. falling short for targets throughout the range of likely results, visually -- in interactive, scrollable PATHFINDER Probability-curve graphs.

Monte Carlo application 3,  fullest client education on short term and long term -- PATHFINDER shows vivid pictures of likely short-term ups and downs ahead, and shows that best portfolios offer far better long-term prospects than others with smaller short-term ups and downs -- on dynamic PATHFINDER Monte Carlo simulation graphs.