Even Dimensional Fund Advisors Struggles with Inflation Statistics

Inflation is a risk we have to face in financial planning, particularly in retirement.  We need to measure inflation risk correctly to be able to make reasonable financial plans.  The best guide we have to the future takes into account past inflation statistics.  But the field of statistics is full of subtleties, and even Dimensional Fund Advisors (DFA) can make mistakes.

DFA creates good funds, and their advisors tend to do good work for their clients.  I’d prefer to find errors in the work of a less investor-friendly investment firm, but they provided a clear example to learn from.  They misapplied a statistical rule, and as a result, they misinterpreted the history of inflation over the past century.

I discussed this issue with Larry Swedroe in posts on X.  I respect Larry and have followed his work as he tirelessly explains evidence based investing to the masses.

A Simple Example

To explain the problem, let’s first begin with a simpler example.  Someone might offer the following as a rule: when you add two numbers, you get a total that is larger than either of the numbers you added.  This seems sensible.  After all, when two people put their money together, they end up with more together than either of them had individually.

But what if one of the numbers you add is negative?  Then the rule doesn’t work.  If one person is in debt, merging finances with another person leaves that other person with less money than they started with.  The real rule: for positive numbers, when you add them, you get a total larger than either of the numbers you added.  The qualifier “for positive numbers” is important.

Monthly Inflation Data

The amount of inflation we will get next month is a type of random variable.  This random variable has what is called a standard deviation, which is a measure of how widely the inflation outcome can vary.  The larger the standard deviation, the wider the range of possible values we could get.

Over the past century, monthly U.S. inflation has averaged 0.24% with a standard deviation of 0.52%.  For 77% of the months, inflation was within one standard deviation of its average value, i.e., between 0.24% - 0.52% = -0.28% and 0.24% + 0.52% = 0.76%.

Annual Inflation

We tend to be more interested in annual figures than monthly figures.  It’s not hard to find the average annual inflation over the past century; we just multiply the monthly average of 0.24% by 12 to get 2.9%.  But what about the standard deviation?  It doesn’t get 12 times bigger, because sometimes low inflation months tend to cancel high inflation months.

There is a rule in probability and statistics that when you add independent and identically distributed (IID) random variables, the standard deviation grows as the square root of the number of random variables you added together.  So, if you add 100 months of inflation together, this rule says that the uncertainty (standard deviation) of the total would grow by a factor of 10.  Average total inflation for 100 months would be 0.24% x 100 = 24%, and the standard deviation would be 0.52% x 10 = 5.2%.

DFA started with this rule and the fact that there are 12 months in a year.  Then they multiplied the monthly inflation standard deviation of 0.52% by the square root of 12 (roughly 3.46) to get 1.82% as the annual standard deviation.  This is a fairly low figure, and it indicates that inflation has been fairly predictable.  Unfortunately, this isn’t correct, and inflation has been substantially less predictable than this.

The Error

Recall that the standard deviation rule required that the random variables be “independent.”  This means that knowing the inflation in previous months tells you nothing about next month’s inflation.  However, this isn’t true.

There is a measure of the degree to which one month’s data predicts the next month's data.  It’s called autocorrelation or serial correlation, and is related to the idea of momentum.  The autocorrelation coefficient is a number between -1 and +1.  When random variables are independent, the autocorrelation coefficient is zero.

Monthly U.S. inflation over the past century has had an autocorrelation coefficient of 0.47, which is far too high to treat them as independent.  Knowing last month’s inflation narrows down the uncertainty of next month’s inflation.

When we calculate all the actual annual inflation figures for each of the 100 years and find the standard deviation directly instead of calculating it from monthly figures, the result is 3.7%.  This is more than double the 1.82% figure that DFA calculated.

The author of the DFA report seemed to recognize the potential for a problem.  The following note was attached to the reported annual standard deviation of 1.82%.

“Annualized number is presented as an approximation by multiplying the monthly number by the square root of the number of periods in a year.  Please note that the number computed from annual data may differ materially from this estimate.”

Unfortunately, 1.82% is a terrible “estimate” for 3.7%.  This note didn’t help Larry Swedroe who said in our exchange on X, “Fact is though inflation volatility [is] historically low.”  The DFA report led him to believe that inflation is much more predictable than it really is.

Treating financial numbers over time as independent of each other is pervasive in the financial industry.  
They often do this for stock and bond returns, inflation, and other types of returns.  An exception is the work on momentum in asset prices.  We usually see investment models take into account correlations between asset types, but within an asset type over time, independence is often assumed.  However, we should always check autocorrelation to see whether the independence assumption is justified.  

As Larry observed, inflation is “regime related, and regimes last,” and private equity has high autocorrelation due to “stale prices.”  No doubt there are many other causes of high autocorrelation that make the independence assumption inappropriate.

It Gets Worse

What really matters to a financial plan is how inflation builds over decades, not just one year of inflation.  The autocorrelation coefficient for annual inflation is 0.67, which is even higher than it was for monthly inflation.  This means that uncertainty about inflation over a decade is much higher than we might predict knowing the standard deviation of annual inflation.

There are 120 months in a decade.  If we misapply the standard deviation rule and multiply the monthly standard deviation of 0.52% by the square root of 120, we get 5.7%.  However, when we find the standard deviation of inflation over decades directly, it is 25%, a far cry from only 5.7%.

Putting Decade Standard Deviation of Inflation into Context

Suppose we buy a basket of goods for $100, and we want to know what it will cost in a decade.  If we misapply the standard deviation rule and assume what is called a lognormal distribution of inflation outcomes, we think there is only one chance in a thousand that the basket will cost more than $160 a decade later.  

Within the past century, there are a total of 1081 decade-long periods with one decade starting each month.  When we directly calculate how the price of $100 worth of goods grows during each of these 1081 rolling decades in the past century, we find that 23% of the time it exceeds $160.  That’s not a typo.  Somehow 1 in a thousand in theory became 230 in a thousand in reality.

How Relevant is Old Inflation Data?

Some might argue that old inflation data from 50 to 100 years ago isn’t relevant in today’s world.  There may be some truth to this.  However, if we’re going to discount the standard deviation of inflation to reflect reality in the modern world, we have to start  from the correct figure.  The standard deviation of inflation over decades has been 25%, not 5.7%.  Whatever discounting we do, it should start from 25%.

However, we can’t discount inflation uncertainty too far.  As the recent bout of rising prices we had starting in 2021 showed us, central banks cannot fully control inflation.  Future spikes of inflation are possible, and the models we use to account for inflation in financial planning have to reflect this reality.

Annuities

An annuity is a contract where someone hands a lump sum of money to a bank or insurance company in return for guaranteed payments for life, which provides protection against longevity risk and return risk.  Typically, annuity payments are not inflation protected, but as Larry pointed out, inflation-protected annuities now exist.

Decumulation experts know that investors don’t like annuities, even though simulations show that annuities improve the odds that a financial plan will meet client goals.  They call this the annuity puzzle.  

However, if the problem of not modelling inflation properly is pervasive, then maybe annuities (that don’t have inflation protection) aren’t as helpful in lowering portfolio risk as they appear.  Annuities have not fared well in the simulations I do for my own portfolio, but that is because I model the full “wildness” of inflation.

Implication for Financial Planning

Financial planners often use a tool to run what are called Monte Carlo simulations of a financial plan.  The idea is that the simulator creates many plausible histories of investment returns, and checks how often your plan meets your goals.  You might be told that your plan has, say, a 95% chance of success.

However, if the inflation model the simulator uses is wrong, this success rate will be wrong too.  The main error simulators with bad inflation parameters make is to underestimate the uncertainty in the long-term buying power of fixed income securities, such as medium to long-term bonds and annuities and pensions that don’t have inflation indexing.

I pointed out to Larry that the annual inflation standard deviation has been 3.7% and not 1.8%.  He replied “IMO [this] makes little difference because even if [it] was 1.8 not 3.7 one should still consider left tail risks making the portfolio resilient to that, especially given our fiscal situation and the risk of inflation it creates.”

So Larry is saying that even if the Monte Carlo simulations are wrong, financial planners should be checking financial plans for large shocks known as “left tail risks,” such as high inflation, and these checks will uncover any problems.

I agree that we should check financial plans for left tail risks, but I’m not comfortable with this answer.  Suppose we leave simulators unchanged and continue treating inflation as tame.  Suppose further that a financial planner tells a client that their plan has a 95% chance of meeting their goals and has been checked against left tail risks.  Will the planner go on to say that the 95% figure is not really correct, but not to worry because the left tail check will cover things?  Not likely.  If the planner did bring it up, the client would likely ask how far wrong the success probability is.  The planner wouldn’t know.  The client would wonder what the point of the simulation is if the results are known to be wrong.  If the planner doesn’t tell the client that their probability of success isn’t really 95%, the main purpose of the simulations would be to impress clients rather than inform them; Monte Carlo simulations would be marketing rather than substance.

I’m not sure how much of a difference it will make in Monte Carlo simulations to fix the inflation modelling.  I’ve never run simulations with “tame” inflation, and I’ve never seen others run simulations with “wilder” inflation.  But those who have never modelled inflation properly can’t be sure what difference it will make either.

Conclusion

A well known fact about how to calculate the standard deviation of sums of random variables, such as investment returns or inflation, is widely misused in contexts where it does not apply.  Dimensional Fund Advisors did this with inflation data, and this calls into question the accuracy of financial planners’ simulations of their clients’ financial plans.

Book Review: The Algebra of Wealth

Scott Galloway uses a unique style in his book The Algebra of Wealth: A Simple Formula for Financial Security.  Rather than offer generic advice to choose a career that pays well, Galloway takes the tone of someone telling you privately what he really thinks of various career options, for example.  He takes a similar approach to other topics as well.  Readers may not agree with all of his advice, but they can’t say his opinions weren’t clear.  

The book is divided into chapters on focus, good habits, and avoiding mistakes; choosing a career and developing skills; spending, saving, and budgeting; and investing.  The blunt commentary on choosing a career was the most interesting part of the book.  

For those concerned that this is some sort of math book, it isn’t.  The few formulas in the book are mostly not intended to be taken literally.  For example, “focus + (stoicism x time x diversification),” and “value = (future income + terminal value) x discount rate.”  That latter formula is vaguely close to a value formula, but is unlikely to be helpful to anyone who doesn’t already know the actual formula.

Career choice

Many writers have taken sides on whether to “follow your passion” in picking a career.  Galloway’s take is interesting.  “Don’t follow your passion, follow your talent.” “Passion careers suck,” and “work spoils passion.”  “Focus on mastery; passion will follow.”  I’ve certainly suspected that the reason I had passion for my career and the sports I’ve played is because I was good at them.

Because few entrepreneurial ventures succeed, working for a large organization “offers better risk-adjusted returns.”  “As a society, we romanticize entrepreneurship.” “The defining characteristic of an entrepreneur” is lacking “the skills needed to succeed in a large organization.”  Most entrepreneurs “did not start companies because they could, but because they had no other options.”

Jobs in the trades can be very lucrative, but “We have shamed an entire generation into believing a trades job means things didn’t work out for you.”  Pursuing a college or university degree isn’t the best option for everyone.

Loyalty

These days, employers offer little loyalty to their employees, “and that has made our loyalty to one another, as individuals, even more important.”  “Mike Bloomberg once said, ‘I have always had a policy: If it’s a friend and they get a promotion, I don’t bother to call them; I’ll see them sometime and make a joke about it.  If they get fired, I want to go out for dinner with them that night.  And I want to do it in a public place where everybody can see me.”

Savings Goals

“Ambitious savings goals … can backfire.”  Research shows “people set overambitious goals for savings in the future.”  “Set a goal for saving this month, and you are likely to be realistic and achieve your goal.  Set a goal for saving in six months, and you are likely to set an unrealistic goal and fail to meet it.”  I can’t say I found this to be true for me about saving money, but it’s definitely true for my exercise goals.

Financial planning

“Failing to consider inflation is a common but severe oversight in financial planning.”  Ignoring inflation entirely can be devastating, but assuming it will remain at some fixed low level is dangerous as well.

“You aren’t paying [financial advisers] for investment returns.  Over the long term, nobody beats the market.  And if someone does have the secret to above-market returns, they aren’t going to be sharing it with you for a fixed percentage.  You’re paying an adviser for planning, accountability, and confidence.”

Investing

Invest “in a half dozen low-cost, diversified exchange-traded funds (ETFs) that put the majority of your money in U.S. corporate stocks.”  This is solid advice, but 6 ETFs may be high these days.  There are many good solutions that use 1-3 ETFs.

Galloway’s take on the passive/active investing debate.  Once they get to $10,000 in long-term savings, he tells people to invest $8000 passively, and $2000 actively, and to invest anything past $10,000 passively.  The $2000 “is enough that when you lose, you’ll feel the pain, but it’s not so much that you’re putting your future economic security at unnecessary risk.”  The idea is to learn whether you’re cut out for active investing without risking too much.  Galloway recommends avoiding active funds.

Those who delve into stock picking need to look out for EBITDA.  “CEOs like to emphasize EBITDA [rather than actual profit figures] for the simple reason that it makes their business look more profitable.”  “There has been a trend toward even more aggressive metrics, especially among early stage companies, usually described as ‘adjusted EBITDA.’”  Galloway describes the justification for such metrics as “dubious.”

One short section gives one of the best explanations I’ve seen of what bonds are, why they exist, and what players are involved in bonds.

Options markets are “dominated by sophisticated professionals.”  “Retail investors buying individual [option] contracts are minnows who these big fish swallow up for easy profit.”

Conclusion

This book has interesting takes on a variety of important personal finance topics.  Some of it is specific to Americans (mainly tax matters), but the majority of the book is useful to Canadians as well.  Whether you agree or disagree with the opinions expressed, it will make you think.

Inflation is Much Riskier than Financial Planning Software Makes it out to be

As we’ve learned in recent years, inflation can rise up and make life’s necessities expensive.  Despite the best efforts of central bankers to control inflation through the economic shocks caused by Covid-19, inflation rose significantly for nearly 3 years in both Canada and the U.S.  

Uncertainty about future inflation is an important risk in financial planning, but most financial planning software treats inflation as far less risky than it really is.  This makes projections of the probability of success of a financial plan inaccurate.  Here we analyze the nature of inflation and explain the implications for financial planning.

Historical inflation

Over the past century, inflation has averaged 2.9% per year in both Canada and the U.S.(*)  However, the standard deviation of annual inflation has been 3.6% in Canada and 3.7% in the U.S.  This shows that inflation has been much more volatile than we became used to in the 2 or 3 decades before Covid-19 appeared.  In 22 out of 100 years, inflation in Canada was more than one standard deviation away from the average, i.e., either less than -0.7% or more than 6.5%.(**)  Results were similar in the U.S.

Historical inflation has been far wilder than the tame inflation we experienced from 1992 to 2020.  And the news gets worse.  Within reason, a single year of inflation is not a big deal to a long-term financial plan; what matters is inflation over decades.  It turns out that inflation is wilder over decades than we’d expect by examining just annual figures with the assumption that each year is independent of previous years.

The standard deviation of Canadian inflation over the twenty 5-year periods is 14%, and over the ten decades is 27%.  Based on assuming independent annual inflation amounts, we would have expected these standard deviation figures to be only 8% and 11%.  How could the actual numbers be so much higher?  It turns out that inflation goes in trends.  This year’s inflation is highly correlated with last year’s inflation.  Rather than a correlation of zero, the correlation from one year to the next is 66% in Canada and 67% in the U.S.(***)

Even successive 5-year inflation samples have a correlation of 60% in Canada and 56% in the U.S.  It’s only when we examine successive decades of inflation that correlation drops to 23% in Canada and 21% in the U.S.  This is low enough that we could treat successive decades of inflation as independent, but we can’t reasonably do this for successive years.

How relevant is older inflation data?

Some might argue that old inflation data isn’t relevant; we should use recent inflation data as more representative of what we’ll see in the future.  After all, central banks had a good handle on inflation for a long time.  Let’s test this argument.

From 1992 to 2020, inflation in Canada averaged 1.72% with a standard deviation of 0.94%.  Using this period as a guide, the inflation that followed was shocking.  In the 32 months ending in August 2023, inflation was a total of 15.5%.  Using the 1992 to 2020 period as a model, the probability that the later 32 months could have had such high inflation is absurdly low: about 1 in 10 billion.(****)

It may be that older inflation data is less relevant, but our recent bout of inflation proves that the 1992 to 2020 period cannot reasonably be used as a model for future inflation.  There is room for compromise here, but any reasonable model must allow for the possibility of future bouts of higher inflation.

Implications

It’s important to remember that once a bout of inflation has been tamed, the damage is already done.  Prices have jumped quickly and will start climbing slower from their new high levels.  If there has been 10% excess inflation over some period, all long-term bonds and future annuity payments will be worth 10% less in real purchasing power than our financial plans anticipated.  This is a serious threat to people’s finances.

We often hear that government bonds are risk-free if held to maturity.  This is only true when we measure risk in nominal dollars.  Because spending rises with inflation, our consumption is in real (inflation-adjusted) dollars.  Bonds held to maturity are exposed to the full volatility of inflation.  We need to acknowledge that bonds have significant risk.  Only inflation-protected government bonds are free of risk.

When financial planning software uses a fixed constant for inflation, like 2% or 2.5%, it is understating the risk posed by inflation.  With constant inflation, bonds held to maturity look risk-free when they aren’t.

Most annuities are also exposed to inflation risk.  Annuities are good for removing longevity risk, but future payments are not as stable in real dollars as they appear to be in nominal dollars.

When software performs Monte Carlo simulations to determine the probability that a financial plan will fail, a poor model of inflation overstates the protection offered by bonds and annuities.  The probability that bonds and annuities will fail to perform their main function of providing safety is higher than these simulators estimate.

It’s fairly easy to write software that performs Monte Carlo portfolio simulations.  The challenge is in correctly modelling investment returns and inflation with reasonable parameters.  Unfortunately, software outputs look equally slick whether this modelling is done well or not.  It’s easy to tinker with model parameters to get the success percentage you want for a financial plan, even if you don’t intend to cheat.

Remedies

One way to address inflation risk is to model it better and simulate it along with stock and bond return simulations.  However, stock and bond returns are not independent of inflation.  If a particular simulation run has high inflation, it’s not reasonable to assume that subsequent nominal stock and bond returns are unaffected.

Along with high inflation, we often get interest rate changes, which affects future bond returns.  Businesses typically raise prices in response to inflation, which can raise future nominal stocks returns.  The interplay between inflation and investment returns is complex.

Some financial planners recognize the problem of fixed inflation assumptions and they run their Monte Carlo simulations with different fixed values for future inflation as a further test of a financial plan.  This helps to some degree, but they are punishing the returns from bonds, annuities, and stocks equally, which doesn’t reflect the reality of inflation’s effects on different types of investment returns.

Because we spend real inflation-adjusted dollars, it’s better to model the real returns of stocks, bonds, and other investments directly.  Instead of studying nominal stock returns to create simulation models, we should study and model real stock returns.  The same is true for bonds and other types of investments such as real estate.

We would still need to model inflation to estimate capital gains taxes and anything else that is based on nominal dollars, but directly modelling the real returns of investments tends to make it easier to properly simulate and test a financial plan.

Conclusion

Most financial planning software underestimates the potential for inflation to disrupt a financial plan.  Measuring volatility in nominal terms is fundamentally misguided, and treating inflation as constant implicitly treats nominal and real quantities as having the same volatility.  As a result of this distortion, bonds and annuities are over-valued as a means to control risk.  Inflation-protected bonds are under-valued.  The success percentages that portfolio simulators calculate for financial plans often have little connection to reality.

Footnotes

(*) All figures used here use the logarithm of Consumer Price Index (CPI) ratios.  This is important for good modelling of inflation and investment returns, but makes only a modest difference in the actual figures.  For example, the average logarithm of annual inflation in Canada for the past century is 2.914%, which corresponds to compound average annual inflation of exp(2.914%)-1=2.957%.

(**) For those who expected inflation to be more than one standard deviation from its mean 32% of the time instead of 22%, this is misapplying the normal distribution (also known as the bell curve).  Inflation figures are far from normally distributed.  Financial mathematics is littered with over-application of the normal distribution.

(***) When a random variable is uncorrelated with its past annual values, the standard deviation of a 5-year sum is sqrt(5) times the standard deviation of a single year.  For decades, we multiply by sqrt(10).  With inflation, the actual correlation is not zero; the autocorrelation coefficient is about 2/3.

(****) Assuming that annual inflation samples are independent and lognormal with a mean of 1.72% and standard deviation of 0.94%, our recent 32-month bout of inflation is a 6.4-sigma event, which has probability of about 10^(-10).  So, the distribution assumptions are clearly not true.

How Investing Has Changed Over the Past Century

Benjamin Graham is widely considered to be the “father” of value investing, the process of finding individual stocks whose businesses offer the prospect of future price gains while offering reasonable protection against future losses.  Graham co-founded Graham-Newman Corp. nearly a century ago.  Stock markets have changed drastically since then.

Early in Graham’s investing career, his approach was to buy stock in companies that were out-of-favour and severely undervalued.  He described these methods in his 1934 book Security Analysis.

But Graham’s investment methods were never static.  As Jason Zweig explained in Episode 75 of the Bogleheads on Investing Podcast:

“People criticize Graham all the time for being old-fashioned, for having these formulaic techniques for valuing stocks, … and then people say these things are all out-moded.  Nobody invests like that any more.  Nobody should.  And that completely misses the mark for two reasons.  First, during his lifetime, Graham revised the book [The Intelligent Investor] several times, and every time he revised it he changed all those formulas.  He updated them to reflect the new market realities at the time the new edition of the book was coming out.  … If he were still around today, he would update all those formulas all over again, and they would look nothing like what’s in the books.  … The second objection is much more basic, which is: that’s not Graham’s message.  … Graham’s message is that if you try to play the same game as Wall Street itself, you will lose.”

Graham recognized that markets change over time.  To keep beating the market averages, as he did for many years, his investment methods had to change over time.

However, in Graham’s last version of The Intelligent Investor in 1973, he wrote

“We have some doubt whether a really substantial extra recompense is promised to the active investor under today’s conditions. But next year or the years after may well be different. We shall accordingly continue to devote attention to the possibilities for enterprising investment, as they existed in former periods and may return.”

Graham expressed optimism that conditions might change so that some version of his investment approach might beat the markets.  However, that hasn’t been the trend.  Markets have become ever more competitive with each passing decade.

Another quote from Graham in the same 1973 book:

“Since anyone—by just buying and holding a representative list [a market index]—can equal the performance of the market averages, it would seem a comparatively simple matter to ‘beat the averages’; but as a matter of fact the proportion of smart people who try this and fail is surprisingly large.  Even the majority of investment funds with all their experienced personnel have not performed so well over the years as the general market.”

By 1976, Graham become more pessimistic about beating markets:

“I am no longer an advocate of elaborate techniques of security analysis in order to find superior value opportunities. This was a rewarding activity, say, 40 years ago, when our textbook ‘Graham and Dodd’ was first published; but the situation has changed a great deal since then. In the old days any well-trained security analyst could do a good professional job of selecting undervalued issues through detailed studies; but in the light of the enormous amount of research now being carried on, I doubt whether in most cases such extensive efforts will generate sufficiently superior selections to justify their cost.”

Graham remains a hero to many value investors, despite the fact that 48 years ago he doubted whether analyzing businesses to find good value worked any more.  Graham’s methods worked from 50 to 100 years ago because of the ample dumb money flowing in stock markets.  For superior investors making excess returns to exist, there must be many inferior investors performing worse than market averages.

The proportion of money in stock markets controlled by individual investors has declined steadily over the decades.  Investors who knew little used to buy their own stocks.  Now, many such investors use mutual funds and exchange-traded funds to have their investments controlled by experts.  Dumb money has shrunk as a percentage, and the competition among investment professionals to exploit dumb money has become so sophisticated that few understand it.

Markets have reached the point where many smart investment professionals seem like dumb money when compared to their competition.  In this environment, individual investors have little chance as stock pickers.  In the third edition of The Intelligent Investor, Jason Zweig wrote

“Millions of investors spend their entire lives fooling themselves: taking risks they don’t understand, chasing the phantoms of past performance, selling their winning assets too soon, holding their losers too long, paying outlandish fees in pursuit of the unobtainable, bragging about beating the market without even measuring their returns.”

Markets have changed dramatically over the past century.  Simple methods of beating markets stopped working decades ago.  There may be some brilliant investors, such as Warren Buffett, who can still beat markets, but most investors actively investing their own money are just fooling themselves.  We could make the case that if Graham were around today, he might be a passive index investor.

Passive Investing Exists

Many people like to say that passive investing doesn’t exist.  However, these people make a living from active forms of investing and are just playing semantic games to distract us.  Active fund managers and advisors who recommend active strategies are the main people I see claiming that passive investing doesn’t exist, but what they say isn’t true.

There is a continuum between passive and active investing; they are not absolute properties.  We can reasonably call an investment approach passive even if it involves some decisions, just as we can call a person thin even if their weight isn’t zero.  We may disagree on the exact threshold between passive and active investing, but the concept of passive investing still has meaning.

By “passive investing,” most people mean some form of broadly-diversified index investing with minimal trading.  Although passive investing usually requires substantially less work than active investing, passive investors still have decisions to make.  They need to choose an asset allocation, funds, accumulation strategy, rebalancing strategy, decumulation strategy, etc.  The term “passive” comes from the fact that there is no need for day-to-day or even week-to-week decisions.  It’s possible for passive investment to run on autopilot for a year without adjustment.  In contrast, more active strategies need closer attention.

The rise of passive investing is a threat to active fund management.  Even factor-based investing that leans toward the passive end of the continuum is threatened by more passive forms of investing.  It’s hard to argue against the success of broadly-diversified index investing with minimal trading.  So, rather than trying to argue in favour of more active strategies, it’s easier to meander into a pointless discussion about how passive investing doesn’t really exist.

“Why should I pay your high fees instead of just owning a passive index fund?”  Active fund managers have a very hard time with this question.  A few meet it head on, but most can’t.  Advisors could launch into a discussion of the value of their services beyond portfolio construction, but some find it easier to launch into “well, you know, passive investing doesn’t really exist, because …”.

We could flip the argument against the existence of passive investing to prove that active investing doesn’t exist.  You’re idle for at least part of your day, so no investment strategy is purely active, and all we have is degrees of passive investing.  More absurdly, there is no pure form of red, so all we have is degrees of blue.  We need to see this claim that passive investing doesn’t exist for the distraction it is.

There is nothing wrong with explaining that even passive investors have to make important decisions.  However, phrased this way, active fund managers would have to explain why their products and services help investors make these important decisions.  It’s easier to deny the existence of passive investing and conclude “you see, there’s not much difference between the investment approach you want and what I offer.”  In reality, there are important differences that should be discussed.