Monday, January 23, 2023

Bullshift

In his book Bullshift: How Optimism Bias Threatens Your Finances, Certified Financial Planner and portfolio manager John De Goey makes a strong case that investors and their advisors have a bias for optimistic return expectations that leads them to take on too much risk.  However, his conviction that we are headed into a prolonged bear market shows similar overconfidence in the other direction.  Readers would do well to recognize that actual results could be anywhere between these extremes and plan accordingly.

Problems in the financial advice industry

The following examples of De Goey’s criticism of the financial advice industry are spot-on.

“Investors often accept the advice of their advisers not because the logic put forward is so compelling but because it is based on a viewpoint that everyone seems to prefer. People simply want happy explanations to be true and are more likely to act if they buy into the happy ending being promised.”  We prefer to work with those who tell us what we want to hear.

Almost all advisers believe that “staying invested is good for investors -- and it usually is. What is less obvious is that it's generally good for the advisory firms, too.”  “In greater fool markets, people overextend themselves using margin and home equity lines of credit to buy more, paying virtually any price for fear of missing out (FOMO).”  When advisers encourage their clients to stay invested, it can be hard to tell if they are promoting the clients’ interests or their own.  However, when they encourage their clients to leverage into expensive markets, they are serving their own interests.

“There are likely to be plenty of smiling faces and favourable long-term outlooks when you meet with financial professionals.”  “In most businesses, the phrase ‘under-promise and over-deliver’ is championed. When it comes to financial advice, however, many people choose to work with whoever can set the highest expectation while still seeming plausible.”  Investors shape the way the financial advice industry operates by seeking out optimistic projections.

“A significant portion of traditional financial advice is designed to manage liabilities for the advice-givers, not manage risk for the recipient.”

“Many advisers chase past performance, run concentrated portfolios, and pay little or no attention to product cost," and they "often pursue these strategies with their own portfolios, even after they had retired from the business. They were not giving poor advice because they were conflicted, immoral, or improperly incentivized. They were doing so because they firmly believed it was good advice. They literally did not know any better.”

De Goey also does a good job explaining the problems with embedded commissions, why disclosure of conflicts of interest doesn’t work, and why we need a carbon tax.

Staying invested


On the subject of market timing, De Goey writes “there must surely be times when selling makes sense.”  Whether selling makes sense depends on the observer.  Consider a simplified investing game.  We draw a card from a deck.  If it is a heart, your portfolio drops 1%, and if not it goes up 1%.  It’s not hard to make a case here that investors would do well to always remain invested in this game.

It seems that the assertion “there must surely be times when selling makes sense” is incorrect in this case.  What would it take for it to make sense to “sell” in this game?  One answer is that a close observer of the card shuffling might see that the odds of the next card being a heart exceeds 50%.  While most players would not have this information, it is those who know more (or think they know more) who might choose not to gamble on the next card.

Another reason to not play this game is if the investor is only allowed to draw a few more cards but has already reached a desired portfolio level and doesn’t want to take a chance that the last few cards will be hearts.  Outside of these possibilities, the advice to always be invested seems good.

Returning to the real world, staying invested is the default best choice because being invested usually beats sitting in cash.  One exception is the investor who has no more need to take risks.  Another exception is when we believe we have sufficient insight into the market’s future that we can see that being invested likely won’t outperform cash.

Deciding to sell out of the market temporarily is an expression of confidence in our read of the market’s near-term future.  When others choose not to sell, they don’t have this confidence that markets will perform poorly.  Sellers either have superior reading skills, or they are overconfident and likely wrong.  It’s hard to tell which.  Whether markets decline or not, it’s still hard to tell whether selling was a good decision based on the information available at the time.

Elevated stock markets

Before December 2021, my DIY financial plan was to remain invested through all markets.  As stock markets became increasingly expensive, I thought more about this plan.  I realized that it was based on the expectation that markets would stay in a “reasonable range.”  What would I do if stock prices kept rising to ever crazier levels?

In the end I formed a plan that had me tapering stock ownership as the blended CAPE of world stocks exceeded 25.  So, during “normal” times I would stay invested, and during crazy times, I would slowly shift out of stocks in proportion to how high prices became.  I was a market timer.  My target stock allocation was 80%, but at the CAPE’s highest point after making this change, my chosen formula had dropped my stock allocation to 73%.  That’s not much of a shift, but it did reduce my 2022 investment losses by 1.3 percentage points.

So, I agree with De Goey that selling sometimes makes sense.  Although I prefer a formulaic smooth taper rather than a sudden sell-off of some fraction of a portfolio.  I didn’t share De Goey’s conviction that a market drop was definitely coming.  I had benefited from the run-up in stock prices, believed that the odds of a significant drop were elevated, and was happy to protect some of my gains in cash.  I had no idea how high stocks would go and took a middle-of-the-road approach where I was happy to give up some upside to reduce the possible downside.  “Sound financial planning should involve thinking ahead and taking into account positive and negative scenarios.”  “Options should be weighed on a balance of probabilities basis where there are a range of possible outcomes.”

As of early 2022, “the United States had the following: 5 percent of global population, 15 percent of global public companies, 25 percent of global GDP, 60 percent of global market cap, 80 percent of average U.S. investor allocation, the world's most expensive stock markets.”  These indicators “point to a high likelihood that a bubble had formed.”  I see these indicators as a sign that risk was elevated, but I didn’t believe that a crash was certain.

When markets start to decline

“If no one can reliably know for sure what will happen, why does the industry almost always offer the same counsel when the downward trend begins?”

Implicit in this question is the belief that we can tell whether we’re in a period when near future prices are rising or falling.  Markets routinely zig-zag.  During bull markets, there are days, weeks, and even months of declines, but when we look back over a strong year, we forget about these short declines.  But the truth is that we never know whether recent trends will continue or reverse.

De Goey’s question above assumes that we know markets are declining and it’s just a question of how low they will go.  I can see the logic of shifting away from stocks as their prices rise to great heights because average returns over the following decade could be dismal, but I can’t predict short-term market moves.

Conviction that the market will crash

‘In the post-Covid-19 world, there was considerable evidence that the market run-up of 2020 and 2021 would not end well.  Some advisers did little to manage risk in anticipation of a major drop.”

I’ve never looked at economic conditions and felt certain that markets would drop.  My assessment of the probabilities may change over time, but I’m never certain.  I have managed the risk in my portfolio by choosing an asset allocation.  If I shared De Goey’s conviction about a major drop, I might have acted, but I didn’t share this conviction.

Back on 2020 Jan. 6, De Goey announced on Twitter that “I’m putting a significant portion of my clients’ equity positions into inverse notes.”  Whether this was the right call based on the information available at the time is unanswerable with any certainty.  Reasonable people can disagree.  However, the results since then at least show that market timing is a difficult game: in the past 3 years, my unleveraged portfolio is up an annual compound average of 6.1% nominally, and 1.9% in real terms.  If De Goey had reversed his position near the bottom of the short-term market crash, he could have profited handsomely.  On the other hand, those who simply held on fared reasonably well.

“Advisers, like everybody else, need to be more humble.”  This is inconsistent with DeGoey’s 2021 May 11 call to “Get Out!”.  Staying invested because we don’t know what will happen is more consistent with being humble than making a high-conviction call that markets will crash.

CAPE as a market predictor

“CAPE readings are often extremely accurate in predicting future long-term annualized returns.”  This isn’t true.  What little data we have shows some correlation, but it is weak.

We should listen when “Shiller says his cyclically adjusted price earnings (CAPE) calculations are not useful for the purpose of market timing.”

DALBAR


The author discusses DALBAR’s annual analysis of investor behaviour.  DALBAR's methodology is so shockingly bad that most people find it hard to believe when it’s described.  Using DALBAR's methodology to analyze your returns over the past decade, if you got an inheritance 5 years ago, you’d be judged a poor investor for missing the returns in the first half of the decade.  In fact, all the money you've saved from your pay to invest should have been invested on the day of your birth.  Anything less is a sign that your lifetime investment behaviour is poor.

“I have asked their representatives for a breakdown between the performance of investors with advisers and investors without. DALBAR says the research does not offer that degree of granularity.”  De Goey is right to be skeptical of DALBAR’s results, but the problems are far worse than a lack of granularity.  DALBAR’s flawed methodology would unfairly make adviser results look bad too.  If you had handed your inheritance over to an adviser, that adviser would have missed the returns in the first half of the decade as well.

Other bad outcomes


“The concern is how people might react to what could go down as the biggest, deepest, longest downturn of their lives. What if the drop was more than 60 percent and the markets were nowhere close to their previous levels five years after the drop started?”  

I can play that game too.  What if governments start printing money like crazy causing massive inflation and making hoarded cash and other fixed income products worthless?  What if the only things left with value are businesses, real estate, and physical objects?  In this scenario, being in the stock market is what will save you.

It’s not that I believe this scenario is likely.  It’s that we can’t go too far down the road telling ourselves a single story.  There are many possibilities for what will happen in the future.

Cheated

In parts of the book, it becomes apparent that De Goey feels strongly that he made the right calls and that he was somehow cheated out of being proven right.  The following quotes illustrate this feeling.

“Even before anyone ever heard of Covid-19, many felt a recession was possible or probable. The pandemic merely hastened what these people felt was inevitable when markets tumbled in early 2020. Then, like fairy-tale heroes, central bankers came riding to the rescue.”

“By rights, the world should have entered a recession in early 2020, but central bankers delayed that recession.”

“Instead of allowing for the traditional ebb and flow of market cyclicality, central bankers and finance ministers seemed determined to keep the good times rolling for as long as possible using whatever means they had.”

These quotes make the following observation seem ironic: “Individuals low in self-awareness might attribute failure externally.”

Conclusion

At its best, Bullshift warns investors about their own biases as well as biases in the investment industry.  At its worst, it is an extended attempt to justify a market call that didn't work out.  Readers would do well to be wary of their preference for rosy predictions, but they should also be wary of doomsday predictions.

Monday, January 16, 2023

My Investment Return for 2022

My portfolio lost 4.9% in 2022, while my benchmark return was a loss of 6.2%.  This small gap came from my decision to shift to bonds based on a formula using the blended Cyclically-Adjusted Price-to-Earnings (CAPE) ratio of the world’s stocks.  After deciding on this CAPE-based approach, all the portfolio adjustments were decided by a spreadsheet, not my own hunches.  I started the year 20% in fixed income, it grew to a high of 27% as the spreadsheet told me to sell stocks, and now it’s back to 20% after the spreadsheet said to buy back stocks.  This cut my losses in 2022 by 1.3 percentage points.

My return also looks good compared to most stock/bond portfolios because I avoided the rout in long-term bonds.  My fixed income consists of high-interest savings accounts (not at big banks), a couple of GICs, and short-term bonds.  If long-term bonds ever look attractive enough, I may choose to own them.  My thinking for now is that I prefer the safe part of my portfolio to be very safe, and I certainly didn’t want to own long-term bonds back when yields were insanely low.

Another thing that helped my results look a little better is that I measure my returns in Canadian dollars, and the U.S. dollar appreciated relative to the Canadian dollar during 2022.  Even though my U.S. stocks lost money, they appeared to lose less money when measured in Canadian dollars.

One measure that doesn’t look very good this year is that my real return (after adjusting for inflation) was a loss of 11.0%.  I prefer to think in terms of real returns because what matters to me is what I can buy with my money.  So, while I hope to achieve somewhere close to a 3% average annual compound return, I fell behind significantly this year.  However, stocks have performed well since I retired, so I’m still on the upside of sequence-of-returns risk.

The following chart shows the cumulative real returns for my portfolio and my benchmark since I started investing on my own rather than working with financial advisors.  Each dollar I had in my portfolio in 1994 that remained invested over this entire period has doubled in purchasing power three times now.  The power of compounding shows itself over long periods.


Through all of the recent market turmoil, my calculated safe withdrawal rate (adjusted for inflation) has remained amazingly stable.  This is because I adjust the assumed future stock market returns based on the current blended CAPE of the world’s stocks.  As stocks rose, my spreadsheet assumed lower future returns, and as stocks fell, the assumed future returns rose again.

I try not to look at my portfolio spreadsheet too often, but when I do, I rarely look at my net worth.  I focus on the monthly dollar amount of my after-tax safe withdrawal rate.  I find this amount much more meaningful than the net worth figure that feels disconnected from day-to-day living.

Friday, January 13, 2023

Short Takes: Private Equity Volatility Laundering, Problem Mortgages, and more

What a difference a year makes.  During the COVID-19 lockdowns, many people saved a lot of money, either from their pay (if they were lucky enough to keep their jobs) or from government payments.  As the world opened up, people started spending this money and businesses couldn’t keep up.  These businesses still can’t get all the new employees they want but the problem has eased considerably compared to a year ago.  I saw a small example in Florida recently.  I was in a burger chain restaurant and saw a sign saying they were looking for employees at $12 per hour.  Last March, the sign in this same restaurant offered $18 per hour and implored workers to “START RIGHT NOW!”

Here are some short takes and some weekend reading:

Cliff Asness accuses private equity investors and managers of “volatility laundering.”  Failing to value private equity frequently and accurately creates the illusion of smooth returns.

Scotiabank’s new President and CEO Scott Thomson explains how they identify potential problem mortgage customers.  Currently, he sees about 1 in 40 as being “vulnerable.”  It’s not clear how many of these vulnerable customers are likely to default under different interest rate scenarios.

Robb Engen at Boomer and Echo
tells us what type of investing headlines to ignore.

Friday, December 30, 2022

Short Takes: Bond Surprise and Sticking to a Plan

When people suggest topics for me to write about, more often than not I can point to an article I’ve already written, which is handy for me.  I doubt I’ll ever run out of thoughts on new topics, but it’s good to have a body of work to refer to.

Here are my posts for the past two weeks:

Car Companies Complaining about Interest Rates

RRSP Confusion


Searching for a Safe Withdrawal Rate: the Effect of Sampling Block Size

Here are some short takes and some weekend reading:


Ben Carlson
lists some things in the markets that surprised him this year.  The first thing is that stocks and bonds both went down double-digits.  Apparently, that’s never happened before.  I guess if you just look at the history of stock and bond returns, this outcome looks surprising.  However, when you look at the conditions we’ve come through, this was one among a handful of likely outcomes.  Bond markets were being artificially propped up, and the dam had to burst sometime.  As for stocks, the Shiller CAPE nearly reached 40.  We can’t know exactly when the next stock correction will happen, but the odds rise as the CAPE enters nosebleed territory.

Robb Engen sets a good example of sticking to his plan and making few trades despite a difficult 2022 for investors.

Wednesday, December 28, 2022

Searching for a Safe Withdrawal Rate: the Effect of Sampling Block Size

How much can we spend from a portfolio each year in retirement?  An early answer to this question came from William Bengen and became known as the 4% rule.  Recently, Ben Felix reported on research showing that it’s more sensible to use a 2.7% rule.  Here, I examine how a seemingly minor detail, the size of the sampling blocks of stock and bond returns, affects the final conclusion of the safe withdrawal percentage.  It turns out to make a significant difference.  In my usual style, I will try to make my explanations understandable to non-specialists.

The research

Bengen’s original 4% rule was based on U.S. stock and bond returns for Americans retiring between 1926 and 1976.  He determined that if these hypothetical retirees invested 50-75% in stocks and the rest in bonds, they could spend 4% of their portfolios in their first year of retirement and increase this dollar amount with inflation each year, and they wouldn’t run out of money within 30 years.

Researchers Anarkulova, Cederburg, O’Doherty, and Sias observed that U.S. markets were unusually good in the 20th century, and that foreign markets didn’t fare as well.  Further, there is no reason to believe that U.S. markets will continue to perform as well in the future.  They also observed that people often live longer in retirement than 30 years.  

Anarkulova et al. collected worldwide market data as well as mortality data, and found that the safe withdrawal rate (5% chance of running out of money) for 65-year olds who invest within their own countries is only 2.26%!  In follow-up communications with Felix, Cederburg reported that this increases to 2.7% for retirees who diversify their investments internationally.

Sampling block size

One of the challenges of creating a pattern of plausible future market returns is that we don’t have very much historical data.  A century may be a long time, but 100 data points of annual returns is a very small sample.  

Bengen used actual market data to see how 51 hypothetical retirees would have fared.  Anarkulova et al. used a method called bootstrapping.  They ran many simulations to generate possible market returns by choosing blocks of years randomly and stitching them together to fill a complete retirement.  

They chose the block sizes randomly (with a geometric distribution) with an average length of 10 years.  If the block sizes were exactly 10 years long, this means that the simulator would go to random places in the history of market returns and grab enough 10-year blocks to last a full retirement.  Then the simulator would test whether a retiree experiencing this fictitious return history would have run out of money at a given withdrawal rate.

In reality, the block sizes varied with the average being 10 years.  This average block size might seem like an insignificant detail, but it makes an important difference.  After going through the results of my own experiments, I’ll give an intuitive explanation of why the block size matters.

My contribution

I decided to examine how big a difference this block size makes to the safe withdrawal percentage.  Unfortunately, I don’t have the data set of market returns Anarkulova et al. used.  I chose to create a simpler setup designed to isolate the effect of sampling block size.  I also chose to use a fixed retirement length of 40 years rather than try to model mortality tables.

A minor technicality is that when I started a block of returns late in my dataset and needed a block extending beyond the end of the dataset, I wrapped around to the beginning of the dataset.  This isn’t ideal, but it is the same across all my experiments here, so it shouldn’t affect my goal to isolate the effect of sampling block size.

I obtained U.S. stock and bond returns going back to 1926.  Then I subtracted a fixed amount from all the samples.  I chose this fixed amount so that for a 40-year retirement, a portfolio 75% in stocks, and using a 10-year average sampling block size, the 95% safe withdrawal rate came to 2.7%.  The goal here was to use a data set that matches the Anarkulova et al. dataset in the sense that it gives the same safe withdrawal rate.  I used this dataset of reduced U.S. market returns for all my experiments.

I then varied the average block size from 1 to 25 years, and simulated a billion retirements in each case to find the 95% safe withdrawal rate.  This first set of results was based on investing 75% in stocks.  I repeated this process for portfolios with only 50% in stocks.  The results are in the following chart.


The chart shows that the average sample size makes a significant difference.  For comparison, I also found the 100% safe withdrawal rate for the case where a herd of retirees each start their retirement in a different year of the available return data in the dataset.  In this case, block samples are unbroken (except for wrapping back to 1926 when necessary) and cover the whole retirement.  This 100% safe withdrawal rate was 3.07% for 75% stocks, and 3.09% for 50% stocks.  

I was mainly concerned with the gap between two cases: (1) the case similar to the Anarkulova et al. research where the average sampling block size is 10 years and we seek a 95% success probability, and (2) the 100% success rate for a herd of retirees case described above.  For 75% stock portfolios, this gap is 0.37%, and it is 0.32% for portfolios with 50% stocks.

In my opinion, it makes sense to add an estimate of this gap back onto the Anarkulova et al. 95% safe withdrawal rate of 2.7% to get a more reasonable estimate of the actual safe withdrawal rate.  I will explain my reasons for this after the following explanation of why sampling block sizes make a difference.

Why do sampling block sizes matter?

It is easier to understand why block size in the sampling process makes a difference if we consider a simpler case.  Suppose that we are simulating 40-year retirements by selecting two 20-year return histories from our dataset.

For the purposes of this discussion, let’s take all our 20-year return histories and order them from best to worst, and call the bottom 25% of them “poor.”

If we examine the poor 20-year return histories, we’ll find that, on average, stock valuations were above average at the start of the 20-year periods and below average at the end.  We’ll also find that investor sentiment about stocks will tend to be optimistic at the start and pessimistic at the end.  This won’t be true of all poor 20-year periods, but it will be true on average.

When the simulator chooses two poor periods in a row to build a hypothetical retirement, there will often be a disconnect in the middle.  Stock valuations will jump from low to high and investor sentiment from low to high instantaneously, without any corresponding instantaneous change in stock prices.  This can’t happen in the real world.

Each time we randomly-select a sample from the dataset, there is a 1 in 4 chance it will be poor.  The probability of choosing two poor samples when building a 40-year retirement is then 1 in 16.  However, in the real world, the probability of a poor 20-year period being followed by another poor period is lower than 1 in 4.  The probability of a 40-year retirement in the real world consisting of two poor 20-year periods is less than 1 in 16.

Of course, by similar reasoning, the simulator will also produce too many hypothetical retirements with two good 20-year periods.  So, we might ask whether all this will balance out.  The answer is no, because we are looking for the withdrawal rate that will fail only 5% of the time.

Good outcomes from the simulator are largely irrelevant.  We are looking for the retirement outcome that is worse than 95% of all other outcomes.  When the simulator produces too many doubly-poor outcomes, it drives down this 95% point.  The result is an overly pessimistic safe withdrawal rate.

In the more complex case of the simulators discussed here, we are joining return histories of varying lengths, but the problem with disconnects in stock valuations and investor sentiment at the join points is the same.  The more join points we have, the more disconnects we create.  So, the lower the average return sample length, the lower the safe withdrawal rate result.  This is what we saw in the charts above.

In more mathematical terms, the autocorrelations in actual stock prices result in poor periods tending to be followed by above-average periods, and vice-versa. This is called mean reversion.  When we select samples from the return dataset and join them together, we partially destroy this mean reversion.  The shorter the return samples, the more mean reversion we remove.

Anarkulova et al. selected fairly long samples from their dataset (a decade on average) to try to preserve mean reversion.  This helped somewhat, but mean reversion exists on the decade level as well, and choosing 10-year blocks of returns destroys mean reversion between the decades.

What is the remedy?

Anarkulova et al. aren’t misguided in the methods they use.  There just isn’t enough available historical return data to run this type of experiment without getting creative.  If we had a million years of actual stock returns rather than just a century or so, it would be much easier to determine safe withdrawal rates.

However, we can’t just ignore the problem of properly preserving mean reversion.  My best guess is that we need to take the roughly 0.3% gap I observed between Anarkulova et al. approach and the “herd of retirees” approach (described earlier) and add it to the 2.7% withdrawal rate calculated by Anarkulova et al.  This gives a base withdrawal rate of 3.0%.  Fans of the 4% rule will still find this result disappointingly low, but I believe it is reasonable.

From there a retiree can adjust for other factors.  For example, we need to deduct about half the MERs we pay.  We also need to spend less if we retire before age 65, and can spend more if we retire after age 65.  Another adjustment is that we can withdraw more initially if we are prepared to reduce spending if markets disappoint rather than blindly spend our portfolios down to zero as Bengen’s original 4% rule would have us do.  Another adjustment for me is that my total costs (including foreign withholding taxes) on investments outside Canada are lower than the 0.5% assumed by Anarkulova et al.  We can make further adjustments if our mortality probabilities are different from the average.

Safe withdrawal rates are a complex area where most of what we read is biased toward telling us we can spend more.  Anarkulova et al. used reasonable historical returns and mortality tables to provide an important message that safe withdrawal rates are lower than we may think.  However, as I’ve argued here, I think they are too pessimistic.