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Retirement Calculators Show 'Dramatically Different' Results
Another duh, entirely obvious article: 'entirely to be expected', 'there isn't a right answer', 'investors and advisers should use them as a guide rather than take them at face value.' etc. Jeez louise. MJG can remind us again what monte carlo means.
@davidmoran: Your such a breath of fresh air, and a ray of sunshine, I don't know how MFO can get along without you ! Lead, follow, or get out of he way !!!!!! Regards, Ted
The fact that various on-line retirement survival planners yield disparate projections should not shock anyone. Given the complexity of that projection and its sensitivities to hugely speculative inputs like savings and investment returns, I would be shocked if they generated highly correlated outputs. Uncertainties of the future is the nature of the beast.
Folks planning for retirement should take advantage of the Internet access to these many tools. Of course they should also be somewhat familiar with their shortcomings. No codes are universally perfect. All these codes have assumptions that must be recognized. One direct solution to this minor dilemma is to deploy several of these tools and/or to do a bunch of sensitivity analyses. Decisions should be based on the magnitude of result differences.
Since uncertainty pervades any future estimates, sensitivity analyses should always be the rule of the road when doing any study. The richness in the many available retirement resources should be exploited using reasonable input variations.
When I retired about two decades ago, these tools were not readily available, so I wrote my own Monte Carlo based retirement code. I did thousands of simulations. I’m sure they were far less sophisticated compared to those currently accessible and formulated by professionals. But my early struggles gave me insights into just how sensitive portfolio survival rates were to seemingly innocuous assumptions.
Playing what-if games in these planning tools will demonstrate those sensitivities. Back in those glory days, my goal was to project a 95% portfolio survival rate. I also gave myself a cost-of-living adjustment that reflected inflation estimates. In some scenarios, my portfolio failed the survival target. I discovered that I could improve that outcome by simply not awarding myself that cost-of-living adjustment after a market down year. Small adjustments matter.
Flexibility during retirement is a key to success. The possible level of needed flexibility can be explored with all these now available computer simulation tools. I welcome them, even given their shortcomings. I recommend you do the same. The more flexible the code inputs, the more likely the simulations will be useful as a retirement planning tool.
As is always the case, buyer (user) beware. Know what you are doing before attempting to do it is always a good habit to practice. The many options are a positive.
Edit: As a sample, allow me to direct your attention to a useful retirement planning website that uses Monte Carlo methods. It may not be the “best” whatever that means, but it is simple to use and is fast:
Thanks for reading my reply. It’s oftentimes hard to infer the true intent of your comments. Are they sincere or are they sarcastic? Regardless, I’m a true believer of using Monte Carlo tools to explore the possibilities of uncertain market returns in terms of their impact on portfolios over an extended timeframe.
I suspect that this uncertainty is shared by almost all MFOers, and is a rather persistent source of worry or even fear.
Monte Carlo methodology was specifically designed, especially for the WW II atomic bomb development, to address uncertainty. Since market return uncertainty is a dominant characteristic of the investment process, it was only a short time when this tool was adapted for investment projection purposes.
I found it a most useful tool when making my retirement decision, when constructing a portfolio, and when exploring possible outcome scenarios. Of course, these are merely paper studies, and in the end require execution and a commitment to stay the course. Not all financial consultants agree with the utility of Monte Carlo. Just like an auto, it must be used with care and caution to reach your destination. That’s true for any tool.
My simple goal is to introduce Monte Carlo to MFO participants. Each individual gets to choose if it might be constructive for his or her purposes.
Charles Darwin observed that “Ignorance more frequently begets confidence than does knowledge”. When investing, ignorance can do immense harm to a portfolio. Monte Carlo can work to reduce that ignorance by putting numbers on possible negative outcomes. By itself, that information serves as a wakeup alert.
“While we are free to choose our actions, we are not free to choose the consequences of our actions.” That’s not me talking; that’s from “The 7 Habits of Highly Effective People” by Steven Covey. Carefully practiced Monte Carlo analysis shows the potential range of results that are possible from a portfolio. That’s helpful when making portfolio decisions. Given its input simplicity and execution speed, the accessible Monte Carlo codes allow a user to examine alternate portfolio options. That too should be helpful.
As always, MFOers get to choose their own poison. One of my selected poisons is to deploy Monte Carlo analysis when making portfolio adjustments. I hope other MFOers likewise decide to do the same.
Comments
MFO can get along without you ! Lead, follow, or get out of he way !!!!!!
Regards,
Ted
The fact that various on-line retirement survival planners yield disparate projections should not shock anyone. Given the complexity of that projection and its sensitivities to hugely speculative inputs like savings and investment returns, I would be shocked if they generated highly correlated outputs. Uncertainties of the future is the nature of the beast.
Folks planning for retirement should take advantage of the Internet access to these many tools. Of course they should also be somewhat familiar with their shortcomings. No codes are universally perfect. All these codes have assumptions that must be recognized. One direct solution to this minor dilemma is to deploy several of these tools and/or to do a bunch of sensitivity analyses. Decisions should be based on the magnitude of result differences.
Since uncertainty pervades any future estimates, sensitivity analyses should always be the rule of the road when doing any study. The richness in the many available retirement resources should be exploited using reasonable input variations.
When I retired about two decades ago, these tools were not readily available, so I wrote my own Monte Carlo based retirement code. I did thousands of simulations. I’m sure they were far less sophisticated compared to those currently accessible and formulated by professionals. But my early struggles gave me insights into just how sensitive portfolio survival rates were to seemingly innocuous assumptions.
Playing what-if games in these planning tools will demonstrate those sensitivities. Back in those glory days, my goal was to project a 95% portfolio survival rate. I also gave myself a cost-of-living adjustment that reflected inflation estimates. In some scenarios, my portfolio failed the survival target. I discovered that I could improve that outcome by simply not awarding myself that cost-of-living adjustment after a market down year. Small adjustments matter.
Flexibility during retirement is a key to success. The possible level of needed flexibility can be explored with all these now available computer simulation tools. I welcome them, even given their shortcomings. I recommend you do the same. The more flexible the code inputs, the more likely the simulations will be useful as a retirement planning tool.
As is always the case, buyer (user) beware. Know what you are doing before attempting to do it is always a good habit to practice. The many options are a positive.
Edit: As a sample, allow me to direct your attention to a useful retirement planning website that uses Monte Carlo methods. It may not be the “best” whatever that means, but it is simple to use and is fast:
http://www.flexibleretirementplanner.com/wp/planner-launch-page/
Good luck!
Best Regards.
MFO can get along without you !
I know! Thanks!
And I am glad MJG filled us in again on multivariate possibilities and outcomes!
Thanks for reading my reply. It’s oftentimes hard to infer the true intent of your comments. Are they sincere or are they sarcastic? Regardless, I’m a true believer of using Monte Carlo tools to explore the possibilities of uncertain market returns in terms of their impact on portfolios over an extended timeframe.
I suspect that this uncertainty is shared by almost all MFOers, and is a rather persistent source of worry or even fear.
Monte Carlo methodology was specifically designed, especially for the WW II atomic bomb development, to address uncertainty. Since market return uncertainty is a dominant characteristic of the investment process, it was only a short time when this tool was adapted for investment projection purposes.
I found it a most useful tool when making my retirement decision, when constructing a portfolio, and when exploring possible outcome scenarios. Of course, these are merely paper studies, and in the end require execution and a commitment to stay the course. Not all financial consultants agree with the utility of Monte Carlo. Just like an auto, it must be used with care and caution to reach your destination. That’s true for any tool.
My simple goal is to introduce Monte Carlo to MFO participants. Each individual gets to choose if it might be constructive for his or her purposes.
Charles Darwin observed that “Ignorance more frequently begets confidence than does knowledge”. When investing, ignorance can do immense harm to a portfolio. Monte Carlo can work to reduce that ignorance by putting numbers on possible negative outcomes. By itself, that information serves as a wakeup alert.
“While we are free to choose our actions, we are not free to choose the consequences of our actions.” That’s not me talking; that’s from “The 7 Habits of Highly Effective People” by Steven Covey. Carefully practiced Monte Carlo analysis shows the potential range of results that are possible from a portfolio. That’s helpful when making portfolio decisions. Given its input simplicity and execution speed, the accessible Monte Carlo codes allow a user to examine alternate portfolio options. That too should be helpful.
As always, MFOers get to choose their own poison. One of my selected poisons is to deploy Monte Carlo analysis when making portfolio adjustments. I hope other MFOers likewise decide to do the same.
Best Wishes.