We periodically get queries about how to invest in the latest hot area (3D printing a few years ago, blockchain and cannabis more recently).  We believe that fads (from tulip bulbs to blockchain) are generally poor investments.

To profit from new technology is harder than first appears. There are two ways to profit:

First, you can recognize the technology revolution – but if everyone recognizes it, then all the investment opportunities are already at least fully valued, if not overvalued (investors tend to get carried away). So you must recognize a new technology has enormous potential while others don’t see it. That is difficult to say the least.

Second, you can figure out which company is going to be the winner in the gold rush. Again, you must be better at predicting than everyone else. Another extremely difficult challenge.

History is replete with life-changing technologies, but in virtually every case, investors (in aggregate) lost money trying to pick the winners (though of course a lucky few made fortunes). Examples include the railway mania of the 19th century, the automobile industry in the early 20th century, the PC revolution in the early 1980’s, the dot com and internet boom of the late 1990’s, etc.

Railroads and cars might not seem like cutting-edge technology, but they certainly were at the time.

As Wikipedia notes, “There were over 1,800 automobile manufacturers in the United States from 1894 to 1930. Very few survived.” Even Henry Ford’s first attempt failed.

So, while we will probably agree that the current whatever is likely to be huge in the future, we would also recommend not investing in it. It is much more likely to be overvalued than undervalued.

One sign that the investment opportunities may be experiencing excessive enthusiasm is that you want in on the action!  FOMO is almost always a sure way to lose money.

Following are three simple formulas that can help you know if you are saving and spending appropriately. These shouldn’t take the place of a comprehensive financial plan as these metrics can be inadequate (or even wrong) in some specific situations. These are based on well-known rules of thumb but I have improved (i.e. complicated) them so they apply to a wider range of situations.

Savings Rate. The traditional rule of thumb is that in order to maintain a retirement lifestyle similar to what it was prior to retirement, retirement savings should be 10% of your income. In my opinion this is correct for people with modest incomes, but I think the rule can be improved.

My rule of thumb would be 10% of income plus 1% for every $10,000 of income over $50,000 but not more than 25%. For example, if an individual’s income is $70,000 per year, the savings rate should be 10 + (7 – 5) = 12%. If the income level is $200,000 the savings rate should be 10 + (20 – 5) = 25%. Households with incomes over $200,000 can remain at 25%.

The reason for my adjustment is that Social Security provides a substantial amount of a lower earner’s retirement income but much less of a higher earner’s.

Spending Rate. The traditional rule of thumb is that retirement spending should be no more than 4% of initial portfolio value (and then be adjusted annually for inflation). In my opinion this is roughly correct at the beginning of retirement but of course becomes increasingly wrong as the retiree ages.

My rule of thumb would be spend no more than your portfolio value divided by 60 minus half your age. In other words, at age 66 that would be 60 – 66/2 = 27. So you shouldn’t spend more than 1/27th (roughly 3.7%) of your portfolio at that age. At age 90 it would be 60 – 90/2 = 15 (1/15th is roughly 6.7%).

You could also use the IRS required minimum distribution tables which are similar to my figures.  (My version has very slightly higher spending initially, but then gets lower at older ages.)

Home Purchase. The value of your residence should not exceed two and half times your annual income. In other words, if you make $200,000 per year the maximum home value should be $500,000. This simple rule is likely too restrictive in very high-cost locations like San Francisco or Manhattan and of course is inapplicable for retirees.

My latest quarterly ramblings to my Financial Professionals list are out: Financial Professionals Winter 2020

I wrote this a few years ago with my fellow financial professionals as the intended audience.  I thought it would make a good post here as well.

I was thinking about what goes into quality wealth management. The goal, in our view, is to use wealth management to maximize long-run client happiness in the face of an uncertain future.

I think there are three inputs into the process:

1. Quantitative and Qualitative Data – you have to know the facts about the client, about the tax code, about capital market return history and drivers, etc. You also need to know the “soft stuff” about the client to maximize their happiness.

2. Analytical Ability – you have to be able to “do the math” to calculate whether or not a mortgage should be paid off, an IRA converted to a Roth, how Social Security or a pension should be claimed, etc.

3. Wisdom – exposition below.

The first two items I think are (or should be!) just “table stakes” – they aren’t a differential advantage, a unique selling proposition, or whatever you want to call it. But I think wisdom is what separates the high quality advisor from the typical one. The Socratic paradox is the statement (based on Socrates, but not a direct quote), “I know that I know nothing.” Supposedly this made him the wisest man in Athens. Another great observation (attributed to many sources, but probably from Josh Billings originally) is, “It ain’t what you don’t know that gets you into trouble, it is what you know for sure just ain’t so.”

The problem is acknowledging our ignorance doesn’t make clients very comfortable – it might even prevent us from having any. Imagine if we re-branded Financial Architects like this:

Financial Architects, LLC
“Embracing ignorance since 2005”

But we are ignorant, particularly in our predictions of the future, whether that is the tax code, the yield curve, investment returns, etc. As the Danish proverb (not Yogi Berra!) says, “It is difficult to make predictions, especially about the future.” So, in light of our ignorance, here are a few things that I think are prudent:

1. Spend lots of time trying to become wiser by reading, writing, and thinking. See here for example. Schedule time for thinking, or, even better, empty your schedule like Charlie Munger and Warren Buffett. Bill Gates, and others, take “think weeks.” Leonardo da Vinci observed, “Men of lofty genius, when they are doing the least work, are most active.” I don’t know if any of us would qualify as “men of lofty genius” but I think having free time (like I do today so I can think about this and write this) is important.

2. Recognize that the best predictor of the future is frequently the present. The current yield curve is the best estimate of the future yield curve, the current tax code is probably the best estimate of the future one. I would be cautious about assuming reversion to some “normal” level of interest rates, equity risk premiums, PE ratios, profit margins, etc. – particularly over a short period of time. The exception to this would be if differences are profound, but even then, it is problematic. In 1995 the dividend yield of the market got lower than it had ever been in history, but it turned out to be much better to buy than to sell. In the spring of 2009, the earnings yield (the inverse of the PE) was also lower than it had ever been (due to minuscule earnings) just before the market soared.

3. Given the paucity of information, frequently the best we can do is equal-weight. Sometimes this is called the 1/n strategy. At best it looks unsophisticated, at worst, ignorant. But it is empirically grounded. If your asset allocation models have decimal points, I would (politely) suggest you are over-fitting your data. If you have more than half-dozen or so allocations in your model you have probably sliced your asset classes too finely (you may have two or three holdings in each class, but you probably shouldn’t have all that many top-level classes).

4. Be very slow to make tactical changes to a portfolio. We rarely know as much as we think we do, and we certainly will almost always know less than the collective wisdom of all market participants. It is very hard to do nothing, but doing nothing is frequently the optimal move. As Warren Buffett has said, “Much success can be attributed to inactivity. Most investors cannot resist the temptation to constantly buy and sell.” He also stated, “Lethargy, bordering on sloth, should remain the cornerstone of an investment style.”

Our goal (perhaps a New Year’s resolution) should be to end every day slightly less ignorant than the day before while remaining humbly aware of our remaining ignorance.

Humans are captivated by stories, but largely oblivious to data. In addition, we really want certainty and conclusions when generally all that is available is uncertainty and probabilities.

For example, people frequently want a prediction of what the market will do this year, and I think there are two reasonable answers based on history:

1. Most likely between a 29% loss and a 53% gain, but there is about a 1-in-20 chance it could be outside that range. (The average 12-month return from 1926-2018 for U.S. stocks was 12.05% with a standard deviation of 20.90%. 95% would be within 1.96 standard deviations so 12.05% +/- 40.96% is a range of -28.91% to +53.02%.)

2. Most likely between a 20% loss and a 45% gain, but there is about a 1-in-20 chance it could be outside that range. (If you assume that the world is safer or different now so post-WWII numbers are a better estimate of the future, the average 12-month return from 1946-2018 for U.S. stocks was 12.21% with a standard deviation of 16.52%. 95% would be within 1.96 standard deviations so 12.21% +/- 32.37% is a range of -20.16% to +44.58%.

You could also argue that equity returns will be lower by some amount – maybe 1% lower because of lower inflation and another 2-3% lower from a lower ERP (Equity Risk Premium) going forward so the whole distribution is shifted down by that amount. If so you can adjust the ranges down by 3-4%. I also do think that starting post-WWII is too aggressive, but I can understand the logic of someone using it and I wouldn’t say they are wrong. I would point out though, if that is the correct distribution then 2008 was a huge outlier. If we use from 1926 it was fairly normal. (The worst 12-months in that debacle was March 2008 to February 2009, which had a 42.48% loss – a rare but reasonable 2.53 standard deviation event (1 in 175) if we use from 1926 to the month prior to that period, but an improbable 3.26 standard deviations (1 in 1795) if we start in 1946.) So, my best answer would be: “Most likely between a 33% loss and a 50% gain, but there is about a 1-in-20 chance it could be outside that range.”

Also, if you want to know the 100-year-flood number that would be 2.58 standard deviations. 12.05% minus a 3.5% adjustment for lower returns in the future is 8.55% minus 2.58*20.90% = -45.36%. (Of course, there is also a 1-in-100 chance of a positive 62.47%) Keep in mind, the worst-case scenario that has ever happened (in any area, not just market returns) was not the worst-case just prior to it happening. Think about that for a while.

I am anticipating some questions, here are the answers:

1. You undoubtedly think those answers are wrong – you just really don’t think the range is that high. I feel the same way, but I know I’m wrong…

2. Clients must be profoundly unhappy with an answer like that. I know, but it is what it is. If I could improve on those figures I would be running a hedge fund engaged in market timing.

3. I used the CRSP 1-10 figures, not the S&P 500 because the question is “what do you think the market will do?” not “what do you think the S&P 500 will do?” Most people think it is the same thing, and substantially they really are, the correlation is above 99%, the difference in geometric returns has been 25 basis points (advantage S&P500) and average annualized difference in standard deviation was 34 basis points (advantage CRSP 1-10). So, I wouldn’t really quibble if someone used the S&P500 to do these calculations, but I didn’t.

4. I rounded off to a reasonable number of decimal places as I typed this up, but all the calculations used all the decimal points I had available – just in case you are following my math and find something slightly off.

6. The correct returns to use for this exercise are arithmetic, not geometric. If you want to convert, the rough estimate (but it’s pretty good) is given by squaring the standard deviation (to get the variance), then subtracting half of that from the return. For example, I said, “The average 12-month return from 1926-2018 for U.S. stocks was 12.05% with a standard deviation of 20.90%.” 0.2090^2= 0.0437 That divided by 2 equals 0.0218. 12.05% minus 2.18% is 9.87% geometric return, which is the figure you are more accustomed to seeing. For more on this topic you can see my calculator here.

7. I used 12-month periods, the maximum drawdown to expect is higher because it can go on for longer than 12-months. For example, from October 2007 through February 2009 was a 50.19% decline, but 2008 was just 36.71%, and as mentioned above, March 2008 to February 2009 had a 42.48% loss.

8. I used a normal distribution rather than a log-normal one because for a one-year period they are trivially different. There was already more than enough math here to make most people’s heads hurt without introducing that complication.