The business difference between financial product and advice

Fees are important to investors. In most cases, lower fees increase the likelihood of a positive long-term investment outcome. Index funds globally have seen enormous growth in the past decade. That growth (and the competition to capture it) have resulted in fees literally going to zero on some index funds. Even if most index funds are not yet at zero, they might as well be with fees typically around 0.10%. Index funds embody the idea that fund management cannot add value; if that is true, then the most obvious way to compete is to lower costs. In general, this is beneficial to investors. It isn’t bad business for fund companies either.

Fund management is the ultimate scalable business. The marginal cost for Vanguard to manage an additional billion dollars is negligible, but they are taking in $1,000,000 at 0.10%. That is good business, and that is why we have seen a price war. Securities lending revenue makes the price war even more interesting – fund companies are still taking in revenue even if they are not charging any fees to their fund unitholders.

The increased awareness and disclosure of fees in Canada has resulted in new business models. Robo advisors, online firms that depend on technology to offer low-cost portfolio management and financial advice to investors of any size, have seen significant growth since they entered the market. In the United States, massive companies like Vanguard have also created low-cost advice services. The CEO of Vanguard has apparently stated that one of his big initiatives is to drive adviser fees to index fund levels.

This might be a good soundbite, but financial advice does not fit the same scalability model as fund management. Financial advice requires people, and not just any people. It requires people with expertise, experience, and soft skills to make their clients comfortable with their financial decisions. The last point, the ability to make clients comfortable, may be the most important. Behaviour management is arguably the most valuable component of financial advice. Of course, good behaviour is contingent on having received sensible advice, which requires expertise and experience. None of these attributes is sufficient alone. This poses a problem for scaling an advice service while also minimizing costs. Each adviser has a limited number of clients that they can advise. That limit is increased by technology, but only to a point. Time is the ultimate constraint regardless of technology. I don’t know what the upper limit is for clients per adviser. I once spoke to the CEO of a robo firm who explained that their target was 5,000 clients per adviser. Last I checked, Wealthsimple had more than that. Let’s say it’s 5,000.

I don’t know what the price ticket for a good adviser with the right credentials is, but let’s go low and say it’s $60,000. At a 0.10% (index fund level) fee, each adviser would need to oversee $60M just to cover their costs. That is not unmanageable with an asset minimum of $500,000, which is what Vanguard requires to access a dedicated adviser through their Personal Advisor Service. The trouble is that the adviser has a lot of other options. If they truly have the right combination of skills, credentials, and experience to win and maintain the trust of clients, then they are also attractive to traditional wealth management firms who might be willing to pay them more. A great adviser also has the option of starting their own firm. The inherent result is that the best advisers might not want to go to work for the lowest cost operator.

This would be fine if financial advice were the exact same for everyone. Everything could be systematized. That is not reality. Things like risk tolerance, how much you should be saving, and what funds you should own are very easy to determine. If those are the only decisions that you’re making, you probably don’t need to pay even 10 basis points for advice. People pay for financial advice when they have complex or niche situations, and to have someone that they know and trust keep them from making bad decisions. This is where scaling advice at index fund-level fees starts to fall apart.

I am not criticizing low-cost generalized financial advice; it is much better than the product distribution commission sales model that financial advice has followed for decades. I do believe that firms on a fiduciary fee-based model that specialize in a niche and offer highly personalized services are unlikely to feel much pain from these low-cost competitors. This story has already played out in other professional services industries; there are plenty of CPA firms with lots of great clients willing to pay their relatively high fees, even though H&R Block is cheaper.

Vanguard has a bit of a business advantage, they not only get fee revenue on the advice, but their advisers only recommend Vanguard products. Even if they break even or lose a bit on the advice, they are keeping assets in their funds. This is an advantage to Vanguard, but it poses a problem for their clients. Is it possible to receive objective advice from an adviser who is only able to recommend one product provider? Leaving asset class funds from companies like Dimensional Fund Advisors out of the conversation could be detrimental even after advice fees at index fund levels.

Keep the deferred sales charge, more for us

Since well before my time in the financial services industry PWL Capital has been one of the strongest advocates for improving the experience of Canadian investors. Not only has the firm been an advocate, we have been leading by example. We were offering fee-based accounts, index fund portfolios, performance disclosure, and comprehensive advice well before consumers were asking for them, or regulators were requiring them.

Advocacy from PWL and firms with a similar stance has played a role in improving regulations. We have seen a requirement to disclose fees and performance with CRM2, and more recently we have seen proposed improvements to the suitability standard that most financial advice must adhere to. We have also seen the result of consultations, lasting years, on embedded trailing commissions and the deferred sales charge (DSC) used by many commission-based financial advisors.

In June, the Canadian Securities Administrators decided that they would maintain embedded commissions, but ban the DSC. Those proposals were released in September, and were met with surprise opposition from the Ontario government.

Advocis, an industry group that largely represents insurance and mutual fund salespeople, praised the decision of the government. The President and CEO of Advocis, Greg Pollock, stated

Our intention as an association is to ensure Canadians have equal access to trusted financial advice. Today’s announcement demonstrates that the Government of Ontario shares that vision, and intends to work alongside stakeholders to protect consumers.

The typical household in Canada starts investing with less than $25,000 and 80% of Canadian households have less than $100,000 in total investible assets. Restricting access to professional financial advice would make it harder for the public to save, invest and achieve their financial goals.

The argument follows that eliminating the deferred sales charge makes it harder for investors with small accounts to access financial advice. For example, taking on a $25,000 account as a fee-based advisor charging 1% results in a monthly revenue stream of about $20. On the other hand, a commission-based advisor using the DSC would earn an upfront commission over $1,200. It is obvious, from the perspective of the advisor, why DSC makes it more attractive to work with smaller clients.

There is a big problem with this scenario: funds that offer DSC are actively managed and have high fees. Is owning a product like that worth it to get access to what Pollock is referring to as professional financial advice? Let’s back up and think about what exactly professional financial advice might be. Obtaining a mutual funds license requires one course that the Canadian Securities Institute estimates to require 90 - 140 hours of study, and a 90-day training period. The cost to the consumer for that “professional advice” on a $25,000 account at a 2.5% annual fee is $625 per year, and growing as they add more assets. Not to mention the implied cost of an actively managed fund’s statistical likelihood of underperformance.

At a time, this was what it was – there were no alternatives. Today there are plenty. There are fee-only financial advisors, not tied to any product, charging $400 per year for ongoing, unbiased financial advice. There are also so-called robo-advisors, or online firms that will manage a portfolio of index funds for 0.50% per year, plus the cost of the underlying index funds. The combined cost of ongoing fee-only advice and a robo-advisor-managed portfolio of index funds would come in at under $600 per year for a $25,000 account, and that fee would grow much more slowly as assets grow because the $400 advice fee is fixed.

With alternatives like this there is no place for the DSC.

As much as PWL advocates for investors, we have been one of the largest beneficiaries of an industry that refuses to change. People are actively seeking out low-cost portfolios and financial advice that is not tied to commissions. There are still over $840 billion Canadian dollars invested in commission-based mutual funds in Canada.

If Advocis wishes to praise the continued practice of the DSC, effectively lobbying against the interests of the clients that they themselves serve, firms like PWL Capital will continue to reap the benefits.

Responsible/ESG investing

Let me preface this post by saying that I have nothing against social responsibility or having social responsibility reflected in an investment portfolio.

I read an article in Investment Executive recently discussing the evidence that Responsible Investing (RI) leads to as-good or better returns compared to traditional investing. The article compares the performance of well-known indexes to demonstrate the long-term benefits of RI. In the article, long-term seems to be defined as the trailing 10-years. In my opinion, and the opinion of most researchers, a 10-year period is insufficient to draw any conclusions.

It is true, as the article states, that the MSCI ACWI ESG Leaders Index has beaten the MSCI ACWI index since 2007, when the indexes became available. The performance difference has been 0.22% per year on average. However, this difference is not due to the responsible nature of the companies in the index.

The difference in returns between diversified portfolios is almost completely explained by exposure to certain types of securities. The characteristics that define those certain types of securities are called factors. Factors explain the differences in returns between diversified portfolios extremely well. A factor is a long-short portfolio where the portfolio is long one side of a characteristic and short the other. For example, the size factor is determined by subtracting the returns of large stocks from the returns of small stocks. If small beats large, the factor exhibits a performance premium.

Currently there is a five-factor model that has been published by Eugene Fama and Ken French, the men who pioneered factor research in the 1990s. The factors in the model are market (market minus the risk-free asset), size (small stocks minus large stocks), relative price (value stocks minus growth stocks), profitability (stocks with robust profitability minus stocks with weak profitability), and investment (stocks that invest conservatively minus stocks that invest aggressively).

We can use statistical analysis to see how much a portfolio’s return is explained by these known factors. It is probable that an ESG (environmental, social, governance) index is offering naïve factor exposure. In other words, by targeting ESG companies the index is likely getting exposure, by accident, to known factors.

To put to rest the idea that an ESG index is inherently better I have run five-factor regressions on an ESG index compared to a total market index using Ken French’s data. What we should expect is that exposure to the factors will explain the return differences between the ESG index and the total market index.

In the following table, the coefficients tell us how much loading the portfolio (index) has to each factor, and the t stat helps us to understand if the coefficient is statistically significant. A t stat over 2 indicates statistical significance.


In this case we see that most of the factor exposure is statistically insignificant and small, which we would expect for a total market index, except for RMW, which is a little bit larger and statistically significant. RMW is the profitability factor. This is indicating that the ESG index has more exposure to stocks with robust profitability than the market.

The research shows that more profitable stocks tend to outperform less profitable stocks over the long-term. The RMW premium over the time period in question (12/1/2007 – 7/31/2018) was 3.34%. That is, more profitable stocks beat less profitable stocks by 3.34% per year on average over the period.

We can estimate the amount of additional return that we would expect from RMW exposure for each index by multiplying the regression coefficient by the premium. Multiplying the difference in coefficients (0.23 for ESG minus 0.11 for total market) by the factor premium we get an expected performance difference of 0.40% per year on average.

Put simply, when we adjust for factor exposure, the ESG index has actually done a little worse than we would expect it to compared to the total market index.

The article that I am responding to also offers that the Jantzi Social Index, an index of socially responsible Canadian stocks, has beaten the S&P/TSX 60 since inception of the index in 2000. That is true.

Ken French does not publish factor data for Canadian stocks. AQR does have Canadian factor data, but they look at slightly different factors. They do not look at Profitability (RMW) or Investment (CMA). They add in QMJ (quality minus junk) which does include profitability among other characteristics. We will look at a four-factor regression including market, size, relative price, and quality. I was only able to obtain total return data for the Jantzi Social index back to May 2009, but the regression results should still be informative.


In this case we see similar negative loading to SMB for both indexes, which we would expect as these are both large cap indexes. We see slight loading to value, statistically significant for the Jantzi and insignificant for the TSX 60. The big statistically significant difference comes from QMJ, which for the sake of discussion is very similar to RMW. A relatively large and statistically significant loading to highly profitable stocks would easily explain the higher returns of the Jantzi index.

So far, we have seen that the higher returns of an ESG index can be attributed to factor loading as opposed to the inherently better returns of ESG companies.

The article cites an academic study showing that ESG mutual funds beat their benchmark 63% of the time. That study looked at a handful of Canadian mutual funds included in the Responsible Investment Association listings. The study, as far as I can tell, does not address survivorship. ESG investing aside, this study conflicts with the massive body of evidence that active mutual funds typically fail to beat their benchmark index.

I won’t recreate the study with a correction for survivorship for this post, but consider that over the trailing 10-year period about 50% of Canadian mutual funds have closed down, likely due to poor performance. Basing a study on funds currently in existence ignores all of the funds that have closed. The surviving funds are likely to have done better by nature of having survived, but that may well have been due to luck. I suspect that a survivorship correction would drastically change the results of this study.

The article also cites a report from MSCI which found that high ESG-rated companies tend to be more profitable with higher dividend yields and lower idiosyncratic tail risks. We have proven this to be true, at least the profitability part, with regression analysis. The problem for investors is that if you want to maximize risk adjusted returns through exposure to factors, including profitability, then ESG investing is a very inefficient and potentially inconsistent way of getting it.

Not to mention that the fees on ESG funds tend to be high. In the case of index funds, an ESG index fund will typically carry a higher fee than a total market fund. XEN, the Jantzi Social Index ETF has an MER of 0.55% while VCN, the Vanguard FTSE Canada All Cap Index ETF has an MER of 0.06%. Most ESG funds are actively managed, and carry fees well over 2%. The notion that an ESG screen will magically allow actively managed funds with high fees to beat their benchmark is not sensible.

Finally, investors need to understand that buying the securities of a company on the secondary market does very little to impact that company’s future. It is perfectly reasonable to feel guilty profiting from a company that engages in business that you do not agree with, but it is important to understand that owning shares in a company does not benefit the company.

As I mentioned at the beginning, I have no problem at all with ESG investing as long as it is done for the right reasons. The reason to be an ESG investor should be that it makes you feel happy. There should be no expectation that an ESG investment will outperform a non-ESG investment with similar exposure to the factors that explain returns.

As opposed to buying ESG investment products it might make sense to optimize a portfolio for low costs, diversification, and factor exposure – all things that an ESG portfolio typically gives up – and donate money directly to causes that are important to you.

Money you can afford to lose

If you go to any online community related to money you will find people talking about their desire to learn about investing by researching and selecting individual stocks. They understand that this is risky, they will say, but they will only try it out with money that they can afford to lose.

This statement implies a lot more than people realize when they write it. What puts you in a position to have money that you can afford to lose? At the very least, you need be financially independent. That is, have enough financial capital that you do not need to rely on your human capital to meet your desired lifestyle.

Let me give you some context to understand that: to spend $5,000 per month, adjusted for inflation, for 30 years, you would need to have about $1.7M in an investment portfolio. Most people, especially people wanting to get their feet wet with stocks for the first time, are not in this position. Until you have attained financial independence you cannot afford to lose any money.

Not only are most people not financially independent, but they are probably not saving enough to eventually become financially independent with the lifestyle that they want. They are also likely to have debt, a lack of emergency savings, and a lack of life insurance coverage. Where is this money that you can afford to lose coming from?

It’s not just losing money that you have to worry about either. You might pick a stock that drops a little bit and then sputters along for a while before you sell it. Great learning experience, right? Even if you do not sell it at a loss, you may have missed an opportunity if the market has gained over the holding period. This opportunity cost is as good as a loss in terms of achieving your financial goals.

It would be one thing if you had a reasonable chance at picking winning stocks, but you most certainly do not. For example, if you had picked one of the S&P 500 constituents at random in January 2017, you had a 53% chance of underperforming the index as a whole by the end of December. For anyone keeping score, that is worse than flipping a coin.

I know that the premise of this conversation is based on the idea that you are able to learn about investing by picking stocks, and maybe you can even get better at picking them as you learn. Unfortunately this is not how the stock market works. You may be able to learn a little bit about how to open a brokerage account, how stocks trade, and even how to analyze companies. Those things might be empowering to know, but there is no reason to believe that they will make you better at investing.

The stock market is a pricing machine. For the most part, it instantly prices stocks based on the aggregate information of market participants. No matter how much you may know about a company’s earnings, growth prospects, or business model, you have to know more than everyone else in order to have a performance edge. Put another way, it is not your absolute information and skill that matters in picking stocks, it is your relative information and skill.

If you do not have better information and more skill than other market participants, then they may be taking advantage of you when you trade. When you buy a stock, you are buying it from somebody. When you sell, you are selling it to somebody. Who are these people on the other side of trades? They are mostly large institutions whose core business is buying and selling stocks. I think it is a bit of a stretch for any individual person, especially a person trying to learn how stocks work, to believe that they have an edge in terms of information or skill.

The odds are massively stacked against you. But don’t worry, it’s not just you that the odds are stacked against. Even those institutions have trouble beating the index. For the 10-year period ending December 2017, only 1.67% of mutual funds in Canada that invest in US stocks were able to beat the S&P 500 index.

If you get to a point in your life where you are financially independent and you still want to trade stocks, the next question that I would be asking is what causes are important to you? Is it more fulfilling to make risky bets with a negative expected outcome by picking stocks, or to donate all of that extra money that you can afford to lose to support something that is important to you?

Stock picking is well documented as a losing game. It is, by all accounts, akin to gambling. Experience trading stocks does not make you a better investor. I do not see how the average Canadian has any money to lose in an effort to learn about something that will not benefit them in any way.

I’ll leave you with a quote from the 1967 classic book The Money Game: “If you don’t know who you are, [the stock market] is an expensive place to find out.”

Figuring Out Factor Investing

Everyone likes to believe they’re smart consumers. That’s probably why the term “smart beta,” also known as “factor investing,” is so hot right now. “Stupid beta” probably wouldn’t attract many takers. 

So what is factor investing, and are you smart to use it? That’s what today’s post and related video are all about

A simple way to think about factors is as quantitative characteristics shared across a set of securities. If you look past their current, flashy popularity, there really is some substance there. For decades, evidence-based investors have been structuring their investment portfolios to tilt toward factors that are expected to drive investment returns, without the need to rely on random stock-picking or market-timing.

In this respect, factors are pretty smart. Unfortunately, lately, I’ve seen them being used in some pretty dumb ways, to market products that may be factor-based in name, but no longer in evidence-based substance.

Factors: Bringing Order to Evidence-Based Investing

Why does one investment portfolio do better than another? Unraveling this mystery, of course, is at the heart of what evidence-based investing is all about. Before we understood factors, most of the performance difference was usually attributed to the skill of the portfolio manager. In the absence of understanding, it was an easy, if erroneous assumption to make.

As factor research emerged, it became clear that the bigger determinant of different outcomes among different diversified portfolios was, by far, not the prowess of the manager (their “alpha”) but rather certain characteristics, or factors (the portfolio’s “beta”). No wonder we started caring about what those factors were, and which ones in what combinations were expected to deliver the most powerful, positive performance differences. Isolating and incorporating positive return differences exhibited by certain types of stocks has an obvious benefit to investors.

Fast forward to today. Currently, we have factor models that can explain over 95% of the return differences among diversified portfolios. It doesn’t take a guru to incorporate these models; you just need to seek the most efficient, cost-effective funds for doing so. 

This is problematic for active fund managers who are still chasing after “alpha.” In the past, their ability to “beat the market” was assumed to be due to their skill. We now know it’s far more likely to be a result of the factor exposures they’ve chosen, whether deliberately or as a random byproduct of their other activities – no prognostication skills required or desired. 

In other words, if you can manage your expected returns and related risks through one or a few simple factor-based funds, who needs an active manager or the additional costs they’re expected to incur? 

If you’re not quite convinced, check out my related video. [hyperlink] In it, I describe a classic 2015 blog post, “Active Funds Exposed,” in which my PWL colleague Justin Bender runs some real-life numbers for us.  

Figuring Out the Factors

Research on factors emerged in a landmark 1992 Journal of Finance paper by Nobel Laureate Eugene Fama and Kenneth French, entitled The Cross-Section of Expected Stock Returns. In the paper, they observed that, over time and in aggregate, small-company stocks outperformed large-company stocks, and value stocks outperformed growth stocks. The explanation for the return differences is that stocks with these characteristics, (small and value), were riskier – more volatile. Investors required higher expected returns before they were willing to take on these riskier assets,

In 1997, Mark Carhart added the momentum factor to the body of research; in 2012 Robert Novy-Marx added the profitability factor. This gave us five factors, which come together to explain over 95% of the return differences among diversified portfolios, as touched on above. In 2014, Fama and French came out with their own five-factor model. Their model combined market (i.e., investing in anystocks, versus risk-free assets), size, relative price (value), profitability, and investment. They left out momentum. 

Which factors form THE ideal model that explains 100% of the return differences among diversified portfolios? This is unknown. Frankly, it’s unlikely we’ll ever arrive at a universal answer. But researchers continue to test new factor models aimed at inching us ever closer to the elusive nirvana of a perfect model.

And therein lies the challenge. 

Faux Factor Investing 

Factor research has become not only important to our understanding of finance and investing, but a way for academic researchers to make a name for themselves … and for fund companies hungry for a fresh marketing hook to differentiate themselves by injecting “new & improved” factors into the mix. 

Duke University’s Campbell Harvey, Texas A&M’s Yan Liu, and University of Oklahoma’s Heqing Zhu have identified over 300 factors in academic literature. This is problematic for investors. Cost-effectively targeting five factors in a portfolio is hard enough. What do you do if there are 300 of them? Unfortunately for the researchers (and fortunately for investors), many of these factors do not pan out. In many cases they turn out to be a re-packaging of the original factors.

In my video, I cover the sniff tests you can use to help decide when a new factor is really all that new or worth pursuing … and, conversely, when common sense tells us it’s safe to ignore it. To be taken seriously, I would suggest a factor should be persistent, pervasive, robust to alternative specifications, investable, and (my personal favorite) sensible. Check out my video to dig into each of these characteristics in more detail. 

Now, about those fund families and their marketing programs. As we know from our experience with other retail products, “new and improved” is usually just the same old you-know-what, stuffed into a fancy new box. Same thing with factor or smart-beta investments. With some 300 “flavors” to choose from, countless new factor products have emerged, but very few of the companies creating them have impressed me. 

Maybe that’s because, these days, factor research has become a commodity that any fund manager can access. The difference between implementing the evidence well or poorly comes down to how well the company vets the research, who does the vetting, how they interpret the data, and how effectively they manage the inherent limitations of factor models. 

So far, Dimensional Fund Advisors – the company that introduced factor investing (with Fama and French on their board) – continues to stand apart in the field. That said, Dimensional’s funds can only be accessed through vetted advisor firms like PWL Capital. Where does that leave the DIY investor? For now, I think you’re better off focusing on simplicity rather than trying to sort out all these factors on your own. 

The Canadian Couch Potato model portfolios used to pursue the size and value factors, but my colleague Dan Bortolotti changed the models in 2015 to ignore factors entirely. Part of his explanation was that “many DIYers make costly mistakes when they try to juggle too many funds. Meanwhile, there are exactly zero investors in the universe who failed to meet their financial goals because they did not hold global REITs or small-cap value stocks.” I agree with him in full. 

Have you tried to implement a factor portfolio? Tell me how it went in the video’s comments.

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When GICs beat bonds

The fixed income component of the Dimensional Fund Advisors global portfolios has been low to-date in 2018, and also for the past 12 months. It is down 0.51% year-to-date at July 2018, and down 0.27% for the 12 months ending July 2018.

Fixed income is typically the safe portion of a portfolio of risky assets. Fixed income is not held for the primary objective of maximizing returns, but for minimizing volatility through diversification. Fixed income assets tend to have imperfect correlation with stocks. This offers a buoy effect if stocks are falling. The opposite is also true; when stocks are doing well bonds do not tend to do as well. Similar to stocks, there are many sub asset classes within fixed income. These sub asset classes have different risk and return characteristics.

Fixed income factors

Some characteristics of fixed income have been identified as responsible for most of the asset class returns. The two factors that explain the majority of fixed income returns are term and credit; longer-term bonds and bonds with lower credit tend to have higher long-term returns. This is intuitive as each of these characteristics, term and credit, result in an asset that is riskier to hold. As always, risk and return are related.

Assuming the CDIC limits are followed in portfolio construction, a GIC is close to risk-free. In other words, it has no exposure to the credit factor. GIC terms longer than 5-years are not covered by CDIC, so an investor would typically limit themselves to 5-year or shorter GICs. This limits exposure to the term factor. 

With this in mind we would expect two things: 

  1. GICs will outperform bonds in some years
  2. Bonds will have higher long-term returns than GICs

Similar to owning stocks in lieu of bonds, owning bonds in lieu of GICs will inevitably result in years of underperformance. This is one of the risks that investors endure to access the higher expected returns of an asset class. If bonds were guaranteed to outperform GICs at all times then bond prices would increase, decreasing their yields, and we would not even be having this conversation.

Historical data

Looking at annual returns going back to 1985 through 2017 we observe a few things that illustrate my previous comments. 5-year GICs have outperformed bonds in 5 of the 33 calendar years examined, or 15.15% of the time. Interestingly, 5-year GICs have outperformed stocks in 9 of the 33 years, or 27.27% of the time. In a year where GICs beat stocks we should not abandon stocks. The same is true for GICs beating bonds. Bonds are riskier than GICs, but they also have higher expected returns. This relationship shows up in the historical data.

Asset Class Annualized Return 1/1/1985 - 12/31/2017
Average 5-Yr GIC CAD* 4.97%
World Government 1-5 Yr (CAD Hedged) CAD 6.13%
Canada Universe Bond CAD 7.97%
US Stocks CAD 11.10%

Any asset class with a positive expected return in excess of the risk-free rate carries risk. The higher the risk, the higher the expected return. While it may not always feel good at the time, holding an asset class through periods of underperformance is a necessity when seeking higher returns.

Through all of this we should not lose the primary objective of holding bonds: reducing volatility through diversification. While bonds are riskier than GICs, they are still much safer than stocks in terms of volatility, and they tend to shine when stocks are crashing. For example, when the global stock markets dropped 31.21% in CAD between March 2008 and February 2009, the Bloomberg Barclays Global Aggregate Bond Index (hedged to CAD) returned +2.82% for the year.

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All time highs are normal

We may have just witnessed the longest bull market in US market history. There is some disagreement on the definition of a bull market, but no matter how it is defined it is clear that the S&P 500 has been rising without too much interruption for a long time. When the market is rising, it tends to hit levels that have never before been seen.

Information like this makes a lot of people feel uncomfortable. What goes up must come down. I don’t want to invest at the top. It may be normal to feel this way, but it is probably not sensible. Fortunately, we have price data on the S&P 500 from Robert Shiller going back to 1871 to help us think sensibly.

Going back to 1871, and ending August 21, 2018, a dollar invested in the S&P 500 has hit an all time high in 16.3% of months; in other words, 289 of the 1,771 months going back to 1871 have seen all time highs. In hindsight we know that despite hitting all time highs the market has continued to rise. We also know that most of these all-time highs have been followed not by crashes, but by more all-time highs. Of the 289 all-time high months over the period, 194 of them were followed by another all time high. That’s 67% of the time.

Think about that. Investing in a month when the US market is hitting an all time high has historically been followed by another all time high 67% of the time. This strong post-all-time-high performance has not only been true for the US stock market. The MSCI EAFE and S&P/TSX Composite have shown similar results, with 63% and 66% of all-time highs being followed by another all-time high.

The green bars in the chart represent all-time highs.


Are markets expensive?

Hitting all-time highs may not be out of the ordinary, and they are probably not a reason to get nervous, but as prices rise faster than earnings, the Shiller PE also rises. As of August 22, 2018, the Shiller PE is sitting at 32.89, compared to a historical mean average of 16.56. Based on that it seems that the US market is expensive. Whether or not that is meaningful information is another story entirely.

A 2012 white paper from Vanguard showed that the Shiller PE does have some ability to predict future returns, but probably not enough to base any investment decisions on, especially short-term decisions. The paper found that the Shiller PE explains about 40% of the variance in future 10-year stock returns. More importantly for anyone feeling nervous based on current valuations, the paper found almost no explanatory power over 1-year stock returns. Put simply, even if the Shiller PE is high, that tells us almost nothing about the returns for the following 12 months. Stay invested.

The S&P 500 is not the world

If normalizing the S&P 500 hitting all-time highs and debunking a high PE as an indicator of a coming crash is not enough to calm the nerves, it is always important to remember that the S&P 500 is not representative of the world. While the US market has been soaring since March 2009, International and Canadian stocks have been much more restrained.

Total CAD Return March 2009 – July 2018

S&P 500 Index


S&P/TSX Composite Index


MSCI EAFE Index (net div.)         


Source: S&P Dow Jones Indices, MSCI, Dimensional Returns Web

A globally diversified investor has less to worry about in terms of an extended period of abnormally high returns. Even an investor who owns the total US market, or better yet a US market portfolio tilted toward small and value stocks, would have much less to worry about as those asset classes have not followed the S&P 500’s trajectory. That is, if all-time highs were anything to worry about, which they are not.

The MSCI EAFE hit an all-time high in January 2018, but has not surpassed that level since; it also finished its last bear market in early 2016. The S&P/TSX Composite index has reached all-time highs through 2018. While not quite a bear market, the Canadian market did see a drop of over 17% between 2014 and 2016.

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Asset Location: Promising Theories vs. Practical Applications

The nonsense of trying to come out ahead by picking stocks or timing the market has long been one of my recurring themes. So, if you’ve stuck with me so far, you probably already know to avoid these sorts of active pursuits. There are other, more sensible ways to seek more from your investments – such as effective tax planning to help you keep more of your returns as your own. Asset location is one form of tax planning. 

So far, so good. But now, I’m going to shake things up a bit. While asset location sounds great in theory, once we work the actual numbers, we often discover it may not be all it’s cracked up to be. At the very least, you don’t want to get lost in the weeds when trying to locate your assets. 

What Is Asset Location?

First things first: What is asset location? I am not talking about asset allocation, which is deciding how much of each asset class you should hold overall. Asset location is the practice of holding certain asset classes in certain account types. Typically, you follow a set of rules for holding the assets with the highest expected tax costs in your non-taxable accounts. For example, you may deliberately hold less-tax-efficient bonds in your RRSP and more-tax-efficient Canadian stocks in your taxable account. 

Easy right? Well, it is easy in theory, but there’s a catch. To know where the highest expected tax costs will occur, it helps to know in advance what your future taxes and returns are going to be. Without a crystal ball to help you peer into the future, it can be really, really hard. 

Can Asset Location Add Value?

On the plus side, much has been written about asset location and its potential contribution to after-tax returns. Here are several analyses that compare an asset location strategy versus holding the same asset mix in each of your accounts (such as the same 60/40 stock/bond allocation in every account you own): [Can you hyperlink to these papers?]

  • In a 2013 paper from Morningstar, David Blanchett and Paul Kaplan determined that asset location might add up to 23 basis points (bps) of value per year to after-tax returns. 
  • In a 2014 paper, my PWL colleagues Dan Bortolotti and Justin Bender found that optimal asset location would have added 30 bps per year to the after-tax returns in an ETF portfolio held from 2003–2012.
  • In a 2017 paper, I used statistical analysis to test an asset location model and found that optimal asset location could ideally add an average of 23 bps per year to after-tax returns. My ideal situation included an investor taxed at Ontario’s highest marginal rate, with enough room in their RRSP to hold most of their bonds while staying in line with their target asset allocation.

But, again, we cannot know in advance what returns a portfolio will actually earn. So, in my own paper, I used Monte Carlo analysis to stress test my optimal asset location strategy across a range of about 1,000 potential outcomes. I found that asset location did in fact add value about 80% of the time. 

While this was an interesting finding, it was still based on forward-looking assumptions. Any asset location optimization decision is based on the expectation of future returns … which, of course, we cannot know except in hindsight.

To test the robustness of my optimal asset location decision against my inability to know the future, I next ran the Monte Carlo model against actual returns instead of the expected returns that had been used to make the optimal asset location decision. Under these circumstances, the average value-added dropped from 23 bps to 7 bps. More importantly, the optimal asset location strategy only outperformed holding an identical asset allocation in all accounts 58% of the time.

Should You Pursue Asset Location?

When we realize that asset location does not guarantee higher after-tax returns, we should start to wonder about other issues that may arise. I explore these possibilities in more detail in my related video, but to name a few, there are: 

  • Regulatory risks – What if tax rates or other tax laws change? 
  • Room for error – Given all the future unknowns, even financial professionals and academics often heatedly debate just what qualifies as “optimal” asset location. 
  • Added complexities – Obviously, it takes a lot more time and energy to engage in asset location than to simply duplicate the same asset allocation in each account. Is the potential value-added worth it?
  • Debilitating distractions – Asset location may cause more harm than good if it distracts you from other investment best practices, such as remaining fully invested and engaging in periodic rebalancing. 

In response to a comment on one of his blog posts, my PWL colleague Justin Bender echoed my own thoughts perfectly: “There are many thoughts on the asset location decision and in the end, it probably doesn’t matter much.”

Put simply, since we cannot predict the future, I often question the value of trying to optimize for asset location. It may even make an investor worse off if they’re struggling with the complexity or, worse, delaying the implementation of their portfolio due to asset location concerns. 

Do you have an opinion on the asset location decision? Leave me a comment about it in my asset location video.

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