One of the great fascinations during my thirty years plus in this industry has been observing how fragile and fleeting confidence can be in most every sense of the word - especially as it applies to self and to others. When things are going well and markets are rising, self-confidence reigns. Confidence in others, such as fund managers, is strong too, but it is quite fickle, landing on whomever produces the best results. And the faster markets rise, the shorter the duration that confidence remains in one place.
When the tide turns and markets begin falling, and doom and gloom replace the good news nearly overnight, self-confidence begins to fade for the stalwarts, and it evaporates like water on a hot skillet for the rest. They begin heading for the exits as fast as they can, with little regard for anything but getting into the relative safety of cash. This reaction is an understandable one. When there is nothing more than price to gauge confidence, its easy to understand how a sharp drop can elicit fear.
Others, instead of heading for the exits will choose to ‘hunker down’ and ride it out even though confidence in their managers is severely shaken, or even broken. Here is a fascinating phenomenon where people make a conscious decision to stick with managers in whom they have lost all confidence, yet still believe they are better off with them than leaving them. In other words, they have lost confidence in their own ability to choose better managers or more strangely, their need for continuity and consistency ironically supercedes their need for confidence.
There is another definition of confidence that we believe shines like a bright beacon for anyone struggling with uncertainty regarding his or her financial goals. It is that of statistical confidence. We use sophisticated probabilities analysis referred to by many as Monte Carlo analysis to provide confidence for our clients. But the process is complicated. You may not be completely clear on how it works and you might resaonably wonder if you can have confidence in the statistical results it produces.
Our process begins with a database of actual historical market returns (stock and Treasury) and statistically possible returns. As Dave Loeper (designer of our system) points out, we are “able to measure not only the uncertainty of historical returns but also potential returns. This additional step [of including possible returns] helps us make sure we are not ignoring the chance that we have not yet seen the worst (or the best) of what the markets might produce.
So while we cannot predict the future of markets and how they will impact our client’s lives, we can measure the uncertainty of our client’s plan. The system does this by ‘living’ our client’s life plan (cash flow amounts and timing) virtually through all kinds of markets and it does it for 1,000 ‘lifetimes.’ The computer randomly draws market returns and calculates the impact that distinct return has on our client’s wealth relative to his cash flows, one year at a time for every year of his life and then it does it all over again 999 more times. The purpose is to gain an understanding of how confident we can be that our client will exceed his goals considering the uncertainty of market returns, including the very worst of them. With these numerous trials, our client ends up with many more outcomes than the one he was planning for; in other words, we've modeled the uncertainty of the future.
The figure below represents the potential outcomes for a couple living out a 32-year retirement on $65,000 annually (adj. for inflation without Social Security to keep it simple) using a $2 million portfolio (60% stocks, 40% US Treasuries, and cash). Each colored line represents a 10th percentile (there are 100 virtual lifetimes between each line). While the chances for any one of the outcomes to occur are equal, notice how wide the range of potential outcomes is on the right-hand side of the graph. The analysis suggests our couple could end their lives $3.1 million in debt or they could just as likely die with $12.1 million in wealth; and there are 998 additional possibilities in between.
Others, instead of heading for the exits will choose to ‘hunker down’ and ride it out even though confidence in their managers is severely shaken, or even broken. Here is a fascinating phenomenon where people make a conscious decision to stick with managers in whom they have lost all confidence, yet still believe they are better off with them than leaving them. In other words, they have lost confidence in their own ability to choose better managers or more strangely, their need for continuity and consistency ironically supercedes their need for confidence.
There is another definition of confidence that we believe shines like a bright beacon for anyone struggling with uncertainty regarding his or her financial goals. It is that of statistical confidence. We use sophisticated probabilities analysis referred to by many as Monte Carlo analysis to provide confidence for our clients. But the process is complicated. You may not be completely clear on how it works and you might resaonably wonder if you can have confidence in the statistical results it produces.
Our process begins with a database of actual historical market returns (stock and Treasury) and statistically possible returns. As Dave Loeper (designer of our system) points out, we are “able to measure not only the uncertainty of historical returns but also potential returns. This additional step [of including possible returns] helps us make sure we are not ignoring the chance that we have not yet seen the worst (or the best) of what the markets might produce.
So while we cannot predict the future of markets and how they will impact our client’s lives, we can measure the uncertainty of our client’s plan. The system does this by ‘living’ our client’s life plan (cash flow amounts and timing) virtually through all kinds of markets and it does it for 1,000 ‘lifetimes.’ The computer randomly draws market returns and calculates the impact that distinct return has on our client’s wealth relative to his cash flows, one year at a time for every year of his life and then it does it all over again 999 more times. The purpose is to gain an understanding of how confident we can be that our client will exceed his goals considering the uncertainty of market returns, including the very worst of them. With these numerous trials, our client ends up with many more outcomes than the one he was planning for; in other words, we've modeled the uncertainty of the future.
The figure below represents the potential outcomes for a couple living out a 32-year retirement on $65,000 annually (adj. for inflation without Social Security to keep it simple) using a $2 million portfolio (60% stocks, 40% US Treasuries, and cash). Each colored line represents a 10th percentile (there are 100 virtual lifetimes between each line). While the chances for any one of the outcomes to occur are equal, notice how wide the range of potential outcomes is on the right-hand side of the graph. The analysis suggests our couple could end their lives $3.1 million in debt or they could just as likely die with $12.1 million in wealth; and there are 998 additional possibilities in between.
(C) Wealthcare Capital Management, Inc.
So how do we gain any confidence from all these lines and uncertainty? The graph clearly demonstrates how vulnerable to uncertainty one is if historical market returns are his only guide to financial confidence; he can only react to what has happened. We take a proactive approach. With this tool, we can measure the uncertainty our clients will experience as they demand from the markets the wealth required to accomplish or exceed their goals.
Notice that most of the lines end above zero. In fact, 830 of them representing virtual lives lived experienced market returns sufficient to meet or exceed our couple’s needs for $65,000 annual spending, adjusted for inflation. Remember, we used a $2 million portfolio (60%/40%) that would be drawn down to zero. Based on the analysis then, our couple can be 83% confident of meeting or exceeding their goals.
But confidence is fleeting, you might say. What happens in the real world when a 2008 comes crashing in and the S&P drops 38%? Let’s say our couple began a relationship with us December 31, 2007. During 2008 our Balanced model (60%/40%) lost 19.7% so our couple’s portfolio would be down to $1,541,000 after taking their $65,000 withdrawal for income. Certainly, that drop might rattle anyone who has nothing more than CNBC or the hollow promise that 'it will come back in time' on which to base his confidence.
During our hypothetical December client meeting, we informed our clients that there was a 17.3% chance of their portfolio becomming under-funded in its first year due to market volatility. In wealth terms that translated to $1,436,072. As their portfolio stood at $1,541,000, the couple's confidence remained above our comfort threshold of 75% so no portfolio or spending changes were required.
The plan report that we presented our clients would have included a lifetime snapshot of where their investment portfolio needed to be in today’s dollars for each of their remaining years in order to provide sufficient confidence of meeting or exceeding their income goals.
A picture of confidence is worth a thousand virtual lives !
Look again at the table entitled "Chance of Falling Outside the Comfort Zone." It indicates that just in the first year, there is a 41.6% chance our couple will be outside what we consider an acceptable range of confidence. Because of this broad range of potential outcomes in a relatively short period of time we continually stress test our clients' plans against new information to indentify opportunities or risks when their plans become over- or under-funded, respectively. Without a tool like this, determining life-plan confidence is nothing more than guesswork and guesswork is not effective life planning.
Have a nice weekend.
