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  • #61
     







     




    AOF isn’t using a normal or lognormal distribution.  That would be simplistic, but definsible.  He’s using a distribution consisting of an equal weighting of all historical returns.  It’s a worthless exercise.  No takeaways possible.
    Click to expand…


    Can you explain this thought a little more?  Do we know if annual stock market returns are distributed in a normal or lognormal fashion?  If this is all the data that we have, the distribution is the distribution. Are you concerned that he is sampling without replacement?

    I’m not a statistics whiz but the real limitation to better analysis seems to be limited data.  Is it possible to do a better assessment of stock market returns with the current data we have?  More than a few of us here have experience with robust scientific methods and and statistical analysis.  Can we show them how it should be done?
    Click to expand…


    I don’t think that is the issue that much. I think just using historical returns doesn’t give us many other scenarios that monte carlo can since it is not only based on data we have but it generates random returns/scenarios based on the model you created. Historical return resampling/bootstrapping doesn’t have any such model. So yea its more advantageous to use MC. BUT Monte Carlo simulations are only as good as the input assumptions. Do financial planners really input even mean reversion in it? Doubtful.
    Click to expand...


    May be I should google more.

    https://www.forbes.com/sites/wadepfau/2018/01/24/monte-carlo-simulations-versus-historical-simulations-updated-to-2018/#4d3de2b2768e

    I do feel validated:

    "Which brings us to the next point: the results of Monte Carlo simulations are only as good as the input assumptions, though when thinking about future retirements, historical simulations are likely to be even more disadvantaged by this issue. Monte Carlo simulations can be easily adjusted to account for changing realities for financial markets. Overall, the advantages of Monte Carlo simulations likely more than make up for any deficiencies when compared to the results we obtain using historical simulations."

    hmmm wonder who else said that lol.

    Anyways, for those who love this stuff (I actually don't but do like the math behind); original trinity study has no simulation in it. Its just rolling 30 analysis. Market is different now and changes. I wouldn't rely on 4% as an iron rule. Additionally, Donnie above is correct: build a more sophisticated model and then project (easier said then done). [Tap your hedge fund buddy heh or just follow sage advice of AlexTT et al in the thread]

    Comment


    • #62




       







       




      AOF isn’t using a normal or lognormal distribution.  That would be simplistic, but definsible.  He’s using a distribution consisting of an equal weighting of all historical returns.  It’s a worthless exercise.  No takeaways possible.
      Click to expand…


      Can you explain this thought a little more?  Do we know if annual stock market returns are distributed in a normal or lognormal fashion?  If this is all the data that we have, the distribution is the distribution. Are you concerned that he is sampling without replacement?

      I’m not a statistics whiz but the real limitation to better analysis seems to be limited data.  Is it possible to do a better assessment of stock market returns with the current data we have?  More than a few of us here have experience with robust scientific methods and and statistical analysis.  Can we show them how it should be done?
      Click to expand…


      I don’t think that is the issue that much. I think just using historical returns doesn’t give us many other scenarios that monte carlo can since it is not only based on data we have but it generates random returns/scenarios based on the model you created. Historical return resampling/bootstrapping doesn’t have any such model. So yea its more advantageous to use MC. BUT Monte Carlo simulations are only as good as the input assumptions. Do financial planners really input even mean reversion in it? Doubtful.
      Click to expand…


      May be I should google more.

      https://www.forbes.com/sites/wadepfau/2018/01/24/monte-carlo-simulations-versus-historical-simulations-updated-to-2018/#4d3de2b2768e

      I do feel validated:

      “Which brings us to the next point: the results of Monte Carlo simulations are only as good as the input assumptions, though when thinking about future retirements, historical simulations are likely to be even more disadvantaged by this issue. Monte Carlo simulations can be easily adjusted to account for changing realities for financial markets. Overall, the advantages of Monte Carlo simulations likely more than make up for any deficiencies when compared to the results we obtain using historical simulations.”

      hmmm wonder who else said that lol.

      Anyways, for those who love this stuff (I actually don’t but do like the math behind); original trinity study has no simulation in it. Its just rolling 30 analysis. Market is different now and changes. I wouldn’t rely on 4% as an iron rule. Additionally, Donnie above is correct: build a more sophisticated model and then project (easier said then done). [Tap your hedge fund buddy heh or just follow sage advice of AlexTT et al in the thread]
      Click to expand...


      Thats a decent article. Issue is really what we've been mentioning here and there and dancing around with all the varying models. There just isnt a lot of independent data, and just bootstrapping those results doesnt necessarily improve it much. All models are subject to parameters and thus have limitations.

      Issue is really that markets are influenced by everything and its always all changing. Population, industry, growth rates, inflation, finacialization, policy, taxes, ad nauseaum. Nothing is perfectly the same as it was so going forward is hard to predict.

      Comment


      • #63







         







         




        AOF isn’t using a normal or lognormal distribution.  That would be simplistic, but definsible.  He’s using a distribution consisting of an equal weighting of all historical returns.  It’s a worthless exercise.  No takeaways possible.
        Click to expand…


        Can you explain this thought a little more?  Do we know if annual stock market returns are distributed in a normal or lognormal fashion?  If this is all the data that we have, the distribution is the distribution. Are you concerned that he is sampling without replacement?

        I’m not a statistics whiz but the real limitation to better analysis seems to be limited data.  Is it possible to do a better assessment of stock market returns with the current data we have?  More than a few of us here have experience with robust scientific methods and and statistical analysis.  Can we show them how it should be done?
        Click to expand…


        I don’t think that is the issue that much. I think just using historical returns doesn’t give us many other scenarios that monte carlo can since it is not only based on data we have but it generates random returns/scenarios based on the model you created. Historical return resampling/bootstrapping doesn’t have any such model. So yea its more advantageous to use MC. BUT Monte Carlo simulations are only as good as the input assumptions. Do financial planners really input even mean reversion in it? Doubtful.
        Click to expand…


        May be I should google more.

        https://www.forbes.com/sites/wadepfau/2018/01/24/monte-carlo-simulations-versus-historical-simulations-updated-to-2018/#4d3de2b2768e

        I do feel validated:

        “Which brings us to the next point: the results of Monte Carlo simulations are only as good as the input assumptions, though when thinking about future retirements, historical simulations are likely to be even more disadvantaged by this issue. Monte Carlo simulations can be easily adjusted to account for changing realities for financial markets. Overall, the advantages of Monte Carlo simulations likely more than make up for any deficiencies when compared to the results we obtain using historical simulations.”

        hmmm wonder who else said that lol.

        Anyways, for those who love this stuff (I actually don’t but do like the math behind); original trinity study has no simulation in it. Its just rolling 30 analysis. Market is different now and changes. I wouldn’t rely on 4% as an iron rule. Additionally, Donnie above is correct: build a more sophisticated model and then project (easier said then done). [Tap your hedge fund buddy heh or just follow sage advice of AlexTT et al in the thread]
        Click to expand…


        Thats a decent article. Issue is really what we’ve been mentioning here and there and dancing around with all the varying models. There just isnt a lot of independent data, and just bootstrapping those results doesnt necessarily improve it much. All models are subject to parameters and thus have limitations.

        Issue is really that markets are influenced by everything and its always all changing. Population, industry, growth rates, inflation, finacialization, policy, taxes, ad nauseaum. Nothing is perfectly the same as it was so going forward is hard to predict.
        Click to expand...


        Thats why quants make the big bucks! (and are still wrong - see LTCM)

        Comment


        • #64




           







           




          AOF isn’t using a normal or lognormal distribution.  That would be simplistic, but definsible.  He’s using a distribution consisting of an equal weighting of all historical returns.  It’s a worthless exercise.  No takeaways possible.
          Click to expand…


          Can you explain this thought a little more?  Do we know if annual stock market returns are distributed in a normal or lognormal fashion?  If this is all the data that we have, the distribution is the distribution. Are you concerned that he is sampling without replacement?

          I’m not a statistics whiz but the real limitation to better analysis seems to be limited data.  Is it possible to do a better assessment of stock market returns with the current data we have?  More than a few of us here have experience with robust scientific methods and and statistical analysis.  Can we show them how it should be done?
          Click to expand…


          I don’t think that is the issue that much. I think just using historical returns doesn’t give us many other scenarios that monte carlo can since it is not only based on data we have but it generates random returns/scenarios based on the model you created. Historical return resampling/bootstrapping doesn’t have any such model. So yea its more advantageous to use MC. BUT Monte Carlo simulations are only as good as the input assumptions. Do financial planners really input even mean reversion in it? Doubtful.
          Click to expand…


          May be I should google more.

          https://www.forbes.com/sites/wadepfau/2018/01/24/monte-carlo-simulations-versus-historical-simulations-updated-to-2018/#4d3de2b2768e

          I do feel validated:

          “Which brings us to the next point: the results of Monte Carlo simulations are only as good as the input assumptions, though when thinking about future retirements, historical simulations are likely to be even more disadvantaged by this issue. Monte Carlo simulations can be easily adjusted to account for changing realities for financial markets. Overall, the advantages of Monte Carlo simulations likely more than make up for any deficiencies when compared to the results we obtain using historical simulations.”

          hmmm wonder who else said that lol.

          Anyways, for those who love this stuff (I actually don’t but do like the math behind); original trinity study has no simulation in it. Its just rolling 30 analysis. Market is different now and changes. I wouldn’t rely on 4% as an iron rule. Additionally, Donnie above is correct: build a more sophisticated model and then project (easier said then done). [Tap your hedge fund buddy heh or just follow sage advice of AlexTT et al in the thread]
          Click to expand...


          I agree that MC is only as good as its assumptions, but this is a truism.  AOF used very poor assumptions, hence the lack of utility in the article.

          Comment


          • #65







             







             




            AOF isn’t using a normal or lognormal distribution.  That would be simplistic, but definsible.  He’s using a distribution consisting of an equal weighting of all historical returns.  It’s a worthless exercise.  No takeaways possible.
            Click to expand…


            Can you explain this thought a little more?  Do we know if annual stock market returns are distributed in a normal or lognormal fashion?  If this is all the data that we have, the distribution is the distribution. Are you concerned that he is sampling without replacement?

            I’m not a statistics whiz but the real limitation to better analysis seems to be limited data.  Is it possible to do a better assessment of stock market returns with the current data we have?  More than a few of us here have experience with robust scientific methods and and statistical analysis.  Can we show them how it should be done?
            Click to expand…


            I don’t think that is the issue that much. I think just using historical returns doesn’t give us many other scenarios that monte carlo can since it is not only based on data we have but it generates random returns/scenarios based on the model you created. Historical return resampling/bootstrapping doesn’t have any such model. So yea its more advantageous to use MC. BUT Monte Carlo simulations are only as good as the input assumptions. Do financial planners really input even mean reversion in it? Doubtful.
            Click to expand…


            May be I should google more.

            https://www.forbes.com/sites/wadepfau/2018/01/24/monte-carlo-simulations-versus-historical-simulations-updated-to-2018/#4d3de2b2768e

            I do feel validated:

            “Which brings us to the next point: the results of Monte Carlo simulations are only as good as the input assumptions, though when thinking about future retirements, historical simulations are likely to be even more disadvantaged by this issue. Monte Carlo simulations can be easily adjusted to account for changing realities for financial markets. Overall, the advantages of Monte Carlo simulations likely more than make up for any deficiencies when compared to the results we obtain using historical simulations.”

            hmmm wonder who else said that lol.

            Anyways, for those who love this stuff (I actually don’t but do like the math behind); original trinity study has no simulation in it. Its just rolling 30 analysis. Market is different now and changes. I wouldn’t rely on 4% as an iron rule. Additionally, Donnie above is correct: build a more sophisticated model and then project (easier said then done). [Tap your hedge fund buddy heh or just follow sage advice of AlexTT et al in the thread]
            Click to expand…


            I agree that MC is only as good as its assumptions, but this is a truism.  AOF used very poor assumptions, hence the lack of utility in the article.
            Click to expand...


            Trinity study also just uses historical data. It has some utility otherwise why the hullabaloo?

            Comment


            • #66










               







               




              AOF isn’t using a normal or lognormal distribution.  That would be simplistic, but definsible.  He’s using a distribution consisting of an equal weighting of all historical returns.  It’s a worthless exercise.  No takeaways possible.
              Click to expand…


              Can you explain this thought a little more?  Do we know if annual stock market returns are distributed in a normal or lognormal fashion?  If this is all the data that we have, the distribution is the distribution. Are you concerned that he is sampling without replacement?

              I’m not a statistics whiz but the real limitation to better analysis seems to be limited data.  Is it possible to do a better assessment of stock market returns with the current data we have?  More than a few of us here have experience with robust scientific methods and and statistical analysis.  Can we show them how it should be done?
              Click to expand…


              I don’t think that is the issue that much. I think just using historical returns doesn’t give us many other scenarios that monte carlo can since it is not only based on data we have but it generates random returns/scenarios based on the model you created. Historical return resampling/bootstrapping doesn’t have any such model. So yea its more advantageous to use MC. BUT Monte Carlo simulations are only as good as the input assumptions. Do financial planners really input even mean reversion in it? Doubtful.
              Click to expand…


              May be I should google more.

              https://www.forbes.com/sites/wadepfau/2018/01/24/monte-carlo-simulations-versus-historical-simulations-updated-to-2018/#4d3de2b2768e

              I do feel validated:

              “Which brings us to the next point: the results of Monte Carlo simulations are only as good as the input assumptions, though when thinking about future retirements, historical simulations are likely to be even more disadvantaged by this issue. Monte Carlo simulations can be easily adjusted to account for changing realities for financial markets. Overall, the advantages of Monte Carlo simulations likely more than make up for any deficiencies when compared to the results we obtain using historical simulations.”

              hmmm wonder who else said that lol.

              Anyways, for those who love this stuff (I actually don’t but do like the math behind); original trinity study has no simulation in it. Its just rolling 30 analysis. Market is different now and changes. I wouldn’t rely on 4% as an iron rule. Additionally, Donnie above is correct: build a more sophisticated model and then project (easier said then done). [Tap your hedge fund buddy heh or just follow sage advice of AlexTT et al in the thread]
              Click to expand…


              I agree that MC is only as good as its assumptions, but this is a truism.  AOF used very poor assumptions, hence the lack of utility in the article.
              Click to expand…


              Trinity study also just uses historical data. It has some utility otherwise why the hullabaloo?
              Click to expand...


              What hullabaloo? Trinity analyzed the historical data and came to the conclusion that 4% withdrawals with specific asset allocations did not exhaust a portfolio over specific time periods. That’s it.  It’s not an ironclad rule, but it is a helpful guideline that is easy to remember, so its use is widespread.

              People later developed probability distributions based on the historical data to provide additional nuance to the original Trinity analysis.  AOF is taking the analysis a step backwards.  He didn’t even compare his results to the Trinity study, but if you got something out of it, great.

              Comment


              • #67













                 







                 




                AOF isn’t using a normal or lognormal distribution.  That would be simplistic, but definsible.  He’s using a distribution consisting of an equal weighting of all historical returns.  It’s a worthless exercise.  No takeaways possible.
                Click to expand…


                Can you explain this thought a little more?  Do we know if annual stock market returns are distributed in a normal or lognormal fashion?  If this is all the data that we have, the distribution is the distribution. Are you concerned that he is sampling without replacement?

                I’m not a statistics whiz but the real limitation to better analysis seems to be limited data.  Is it possible to do a better assessment of stock market returns with the current data we have?  More than a few of us here have experience with robust scientific methods and and statistical analysis.  Can we show them how it should be done?
                Click to expand…


                I don’t think that is the issue that much. I think just using historical returns doesn’t give us many other scenarios that monte carlo can since it is not only based on data we have but it generates random returns/scenarios based on the model you created. Historical return resampling/bootstrapping doesn’t have any such model. So yea its more advantageous to use MC. BUT Monte Carlo simulations are only as good as the input assumptions. Do financial planners really input even mean reversion in it? Doubtful.
                Click to expand…


                May be I should google more.

                https://www.forbes.com/sites/wadepfau/2018/01/24/monte-carlo-simulations-versus-historical-simulations-updated-to-2018/#4d3de2b2768e

                I do feel validated:

                “Which brings us to the next point: the results of Monte Carlo simulations are only as good as the input assumptions, though when thinking about future retirements, historical simulations are likely to be even more disadvantaged by this issue. Monte Carlo simulations can be easily adjusted to account for changing realities for financial markets. Overall, the advantages of Monte Carlo simulations likely more than make up for any deficiencies when compared to the results we obtain using historical simulations.”

                hmmm wonder who else said that lol.

                Anyways, for those who love this stuff (I actually don’t but do like the math behind); original trinity study has no simulation in it. Its just rolling 30 analysis. Market is different now and changes. I wouldn’t rely on 4% as an iron rule. Additionally, Donnie above is correct: build a more sophisticated model and then project (easier said then done). [Tap your hedge fund buddy heh or just follow sage advice of AlexTT et al in the thread]
                Click to expand…


                I agree that MC is only as good as its assumptions, but this is a truism.  AOF used very poor assumptions, hence the lack of utility in the article.
                Click to expand…


                Trinity study also just uses historical data. It has some utility otherwise why the hullabaloo?
                Click to expand…


                What hullabaloo? Trinity analyzed the historical data and came to the conclusion that 4% withdrawals with specific asset allocations did not exhaust a portfolio over specific time periods. That’s it.  It’s not an ironclad rule, but it is a helpful guideline that is easy to remember, so its use is widespread.

                People later developed probability distributions based on the historical data to provide additional nuance to the original Trinity analysis.  AOF is taking the analysis a step backwards.  He didn’t even compare his results to the Trinity study, but if you got something out of it, great.
                Click to expand...


                Thats fine but doesn't qualify as less than worthless for me. Point is if you criticize AOF work that way then Trinity study deserves the same blame.

                Look I got no dog in this fight, I'm just pointing out no method is perfect. If you think its worst, fine. To me he's not building a model. Trinity study didn't either. They are just using historical data and saying what could've been. If people project it to future prediction well then they should be careful.

                I would like to see this though:

                1. Run MC with your de jeur parameters. Find your SWR.

                2. Compare to AOF bootstrap.

                 

                Comment


                • #68
                  You say AOF isn’t trying to predict the future, isn’t accurately describing the past, and is instead describing what might have been.   That is the definition of beyond useless to me.

                  Comment


                  • #69




                    You say AOF isn’t trying to predict the future, isn’t accurately describing the past, and is instead describing what might have been.   That is the definition of beyond useless to me.
                    Click to expand...


                    What do you think of the life cycle (nalebuff/ayers) approach to this? It kind of ties in with dynamic allocation/glidepaths. We all know that higher equities allocation actually gives one the best chance of the longest withdrawal period and highest ending or spending allowance. However, that comes with extreme SORR. You have the majority of your wealth in just the last decade or so.

                    Supposedly, and the math seems to work, using the life cycle way they espouse and "diversifying across time", which is a pretty eloquent solution or at least illusion, you have similar downside risks as to a static portfolio, yet the average portfolio and best case scenario results are significantly better than a fixed scenario. Nice, if you can take the 100%+ equity approach. Few can do 200% for considerably long periods of time, but I am trying to think of it as filling up my 80-90% equity bucket first, then filling up bonds right at the end.

                    I will add the caveat I do not think this is a good strategy for your average retail mom/pop investor. Prudent account management and paying attention to your leverage and having a systematic plan makes sense as well. I dont think there is a single person that can truly take a whole portfolio massive draw down just sitting there, no matter if they understand its early/not much overall % of retirement wealth and they will be better long term. Humans dont work like that, I know I dont. So stops or some kind of less risk or risk off asset makes sense.

                    Comment


                    • #70
                      Well, Donnie, I'm sure AOF is crushed by your opinion.

                      Comment


                      • #71




                        Well, Donnie, I’m sure AOF is crushed by your opinion.
                        Click to expand...


                        I hope he is and that it causes him to lift his actuarial game.  It’s not an opinion though. It’s a demonstrable fact.







                        You say AOF isn’t trying to predict the future, isn’t accurately describing the past, and is instead describing what might have been.   That is the definition of beyond useless to me.
                        Click to expand…


                        What do you think of the life cycle (nalebuff/ayers) approach to this? It kind of ties in with dynamic allocation/glidepaths. We all know that higher equities allocation actually gives one the best chance of the longest withdrawal period and highest ending or spending allowance. However, that comes with extreme SORR. You have the majority of your wealth in just the last decade or so.

                        Supposedly, and the math seems to work, using the life cycle way they espouse and “diversifying across time”, which is a pretty eloquent solution or at least illusion, you have similar downside risks as to a static portfolio, yet the average portfolio and best case scenario results are significantly better than a fixed scenario. Nice, if you can take the 100%+ equity approach. Few can do 200% for considerably long periods of time, but I am trying to think of it as filling up my 80-90% equity bucket first, then filling up bonds right at the end.

                        I will add the caveat I do not think this is a good strategy for your average retail mom/pop investor. Prudent account management and paying attention to your leverage and having a systematic plan makes sense as well. I dont think there is a single person that can truly take a whole portfolio massive draw down just sitting there, no matter if they understand its early/not much overall % of retirement wealth and they will be better long term. Humans dont work like that, I know I dont. So stops or some kind of less risk or risk off asset makes sense.
                        Click to expand...


                        I hadn’t seen that paper / work, but just looked it up.  Now that is an interesting and informative analysis.  I would have to think through it more, but the concept is right.  People do not typically include future savings in their current portfolio composition and that is a mistake.

                        Comment


                        • #72







                          Well, Donnie, I’m sure AOF is crushed by your opinion.
                          Click to expand…


                          I hope he is and that it causes him to lift his actuarial game.  It’s not an opinion though. It’s a demonstrable fact.







                          You say AOF isn’t trying to predict the future, isn’t accurately describing the past, and is instead describing what might have been.   That is the definition of beyond useless to me.
                          Click to expand…


                          What do you think of the life cycle (nalebuff/ayers) approach to this? It kind of ties in with dynamic allocation/glidepaths. We all know that higher equities allocation actually gives one the best chance of the longest withdrawal period and highest ending or spending allowance. However, that comes with extreme SORR. You have the majority of your wealth in just the last decade or so.

                          Supposedly, and the math seems to work, using the life cycle way they espouse and “diversifying across time”, which is a pretty eloquent solution or at least illusion, you have similar downside risks as to a static portfolio, yet the average portfolio and best case scenario results are significantly better than a fixed scenario. Nice, if you can take the 100%+ equity approach. Few can do 200% for considerably long periods of time, but I am trying to think of it as filling up my 80-90% equity bucket first, then filling up bonds right at the end.

                          I will add the caveat I do not think this is a good strategy for your average retail mom/pop investor. Prudent account management and paying attention to your leverage and having a systematic plan makes sense as well. I dont think there is a single person that can truly take a whole portfolio massive draw down just sitting there, no matter if they understand its early/not much overall % of retirement wealth and they will be better long term. Humans dont work like that, I know I dont. So stops or some kind of less risk or risk off asset makes sense.
                          Click to expand…


                          I hadn’t seen that paper / work, but just looked it up.  Now that is an interesting and informative analysis.  I would have to think through it more, but the concept is right.  People do not typically include future savings in their current portfolio composition and that is a mistake.
                          Click to expand...


                          I think its pretty neat as well. Time is really the only thing we have on our side. Difficulty is in implementation (not that its hard, its simple now) but in that will folks stick with it.

                          Comment


                          • #73
                            Well I'm not convinced. Wade pfau link is open to all and anyone can look up his arguments re: historical value analysis vs MC. His graph did show MC being better - life proof but didn't demonstrate bootstrapping as garbage.

                            Unless you are Wade pfau! It's possible!

                            Interesting lifecycle theory, is there a paper somehwre or you gotta buy the book zaphod ?

                            Comment


                            • #74




                              Well I’m not convinced. Wade pfau link is open to all and anyone can look up his arguments re: historical value analysis vs MC. His graph did show MC being better – life proof but didn’t demonstrate bootstrapping as garbage.

                              Unless you are Wade pfau! It’s possible!

                              Interesting lifecycle theory, is there a paper somehwre or you gotta buy the book zaphod ?
                              Click to expand...


                              Paper is totally fine. Book is typical for the genre, repeats ad nauseaum, too long.

                              Comment


                              • #75







                                You say AOF isn’t trying to predict the future, isn’t accurately describing the past, and is instead describing what might have been.   That is the definition of beyond useless to me.
                                Click to expand…


                                What do you think of the life cycle (nalebuff/ayers) approach to this? It kind of ties in with dynamic allocation/glidepaths. We all know that higher equities allocation actually gives one the best chance of the longest withdrawal period and highest ending or spending allowance. However, that comes with extreme SORR. You have the majority of your wealth in just the last decade or so.

                                Supposedly, and the math seems to work, using the life cycle way they espouse and “diversifying across time”, which is a pretty eloquent solution or at least illusion, you have similar downside risks as to a static portfolio, yet the average portfolio and best case scenario results are significantly better than a fixed scenario. Nice, if you can take the 100%+ equity approach. Few can do 200% for considerably long periods of time, but I am trying to think of it as filling up my 80-90% equity bucket first, then filling up bonds right at the end.

                                I will add the caveat I do not think this is a good strategy for your average retail mom/pop investor. Prudent account management and paying attention to your leverage and having a systematic plan makes sense as well. I dont think there is a single person that can truly take a whole portfolio massive draw down just sitting there, no matter if they understand its early/not much overall % of retirement wealth and they will be better long term. Humans dont work like that, I know I dont. So stops or some kind of less risk or risk off asset makes sense.
                                Click to expand...


                                I think people do this life cycle investing with real estate. They take on high debt early and gradually pay it off over 15-30 years. Unlike a loan on stocks there isn't generally a risk of margin call, only default if you lose your job.

                                In terms of life cycle, you are most able to absorb hits early in your life (before kids) or after they have grown up. Having high debt when you have kids is stressful. Maybe it's better for your final net worth but may not be worth it.

                                I can't see why you can't take more risk at the end of your life. If you have enough for retirement at age 55, what stops you from taking some of the excess and leveraging it if you want to swing for the fences ? In fact if you have excess for retirement at any age, you can leverage up what you can afford to lose.

                                Hopefully though you'll have more experience and not blow up. Taking on margin loan debt at a young age is a sure fire way to blow up. But maybe you're better off blowing up at a younger age and learning from this than doing this at a later age.

                                Comment

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