tag:blogger.com,1999:blog-5621914599310831423.post8277619976830393778..comments2024-03-28T09:15:32.976-07:00Comments on The Retirement Café: Monte Carlo and Tales of Fat TailsDirk Cottonhttp://www.blogger.com/profile/05616143752082768155noreply@blogger.comBlogger2125tag:blogger.com,1999:blog-5621914599310831423.post-10489795106137280152018-08-02T12:00:28.560-07:002018-08-02T12:00:28.560-07:00I don't blame you for not trusting MC for reti...I don't blame you for not trusting MC for retirement planning. I don't find many sources that do it well. <br /><br />However, I will reiterate from a previous post that calculating your odds of a successful retirement isn't a good use of MC. Creating a large number of possible outcomes that you can become aware of, evaluate and prepare for is a much better use. <br /><br />I think low-probability one-time events like a crash of an individual's retirement portfolio are impossible to predict. Nassim Taleb testified before Congress as to the predictability of low-probability events. Measuring the frequency of failure among a large population of retirees over a long time period isn't the same as predicting which particular households will lose the bet.<br /><br />The problem with MC models of p(ruin) is that the random normal number generator produces random uniform <i>sequences</i> of returns (it doesn't care about sequences) and that doesn't correctly model the historical data. That will always be worst case. You'll need to explain the shrug for me.<br /><br />Machine learning is essentially pattern recognition. The key problem here is a lack of data and AI can't fix that.<br /><br />I overlaid the log-normal curve because a terminal portfolio value is the product of a sequence of normal distributions (annual market growth factors) and the product of a sequence of <i>n</i> normal distributions is a log-normal distribution, not as the result of a goodness-of-fit test (although, it passed that, too). As I stated, a log-normal distribution is what we should expect.<br /><br />Sorry, it took so long to respond, your comment got lost in the tons of spam this website receives. I appreciate your thoughts. Thanks for writing, Francis! Always a pleasure to hear from you.Dirk Cottonhttps://www.blogger.com/profile/05616143752082768155noreply@blogger.comtag:blogger.com,1999:blog-5621914599310831423.post-26905304238868617522018-07-26T20:30:37.613-07:002018-07-26T20:30:37.613-07:00I don't think much of MC analysis as a retirem...I don't think much of MC analysis as a retirement planning tool, because the one I received did not do a lot for me as I planned my escape from the 40-hour work-week. I know that “experience is not evidence,” and n=1 doesn’t mean much. Still, about all I gleaned from the MC I received was that the odds of outliving my savings probably were small, while the odds of my savings growing probably were large. So, I’m afraid I shrugged when I read your conclusion that, “the major flaw in the analysis appears to be the use of a naive Monte Carlo model based solely on normally-distributed market returns.” <br /><br />However, I do have a comment and a question. Both are more statistical nits than they are responses to your critique of the Nerd's Eye paper. <br /><br />My first statistical nit is your assertion that, "... I would argue that estimating a probability of ruin metric is a poor use of MC models since low-probability events are unpredictable." I think that is an overly strong statement. Low-probability events are *difficult* to predict, but they are not unpredictable. It is common to need to predict whether an event or outcome that lands in the far end of a tail (e.g., fraud detection, anomaly detection, medical diagnosis, oil spillage detection, fault detection). There are lots of techniques (e.g., over-sampling, under-sampling, hybrids) to overcome the “class imbalance” problem.” I expect some of them eventually will show up in the retirement finance literature. Perhaps there eventually will be a machine-learning formula relating MC predictions of ruin to the characteristics of those who exceed a specified probability of failure. <br /><br />Second, in commenting on terminal portfolio values you say that, “TPVs, as you can see in the chart above, are log-normally distributed, not normally-distributed, and should be expected to be larger than a normal distribution predicts.” I suspect you have a good reason for fitting a log-normal curve to the histogram of TPVs created by the historical data model. Still, I could visualize other types of distributions fitting that curve. Are you fitting a log-normal distribution because of the number of trials in your simulations, or did you perhaps use some goodness-of-fit statistic? It seems that other types of distributions (Bernoulli?) might be used, since you are asking whether outcomes are successes or failures. <br /><br />Best regards,<br /><br />FrancisFrancishttps://www.blogger.com/profile/18147039767761041234noreply@blogger.com