April 9, 2026

Episode 404: The Finance Paper that Changed Everything

April 9, 2026

What if the way we think about investing—and expected returns—was fundamentally incomplete?

In this episode, Ben Felix and Dan Bortolotti take a deep dive into one of the most influential papers in financial economics: Fama and French (1993). With nearly 15,000 citations, this research reshaped how we understand asset pricing by showing that market beta alone isn’t enough to explain returns. Instead, multiple factors—specifically size and value—play a critical role.

Ben and Dan unpack how this paper challenged the dominance of CAPM, introduced the now-famous Three-Factor Model, and laid the foundation for decades of empirical asset pricing research. They explore how factor investing evolved, why anomalies may not be anomalies at all, and what this means for evaluating portfolios and active managers today.

The conversation also connects theory to practice—highlighting how modern fund providers implement factor strategies and what it means for investors trying to improve expected returns without abandoning diversification.

Key Points From This Episode:

(0:00:00) Introduction to the episode and why this is a long-awaited deep dive into factor investing.

(0:01:12) Overview of Fama and French (1993) and its massive impact on finance and portfolio management.

(0:03:55) Origins of factor investing and how it connects to index investing and academic research.

(0:04:46) Core premise: multiple factors drive expected returns and asset prices.

(0:06:08) He explains why different assets can have different expected returns, and why that matters for investors.

(0:07:24) Ben introduces the CAPM as the dominant model that linked expected return to market beta.

(0:08:53) Dan reflects on how revolutionary CAPM and portfolio theory were when they were first introduced.

(0:10:51) Ben describes today as a “golden age of investing,” where theory and implementation tools are widely accessible.

(0:11:17) He explains how anomalies emerged that CAPM could not explain.

(0:12:10) Ben introduces the joint hypothesis problem: we cannot cleanly separate market efficiency from model accuracy.

(0:13:47) He identifies the three big issues with CAPM: size, value, and the weak relationship between beta and returns.

(0:15:29) Ben introduces the three-factor model: market, size (SMB), and value (HML).

(0:17:37) He explains that these factors are built as long-short portfolios designed to capture systematic return variation.

(0:18:02) Dan notes that the model did not really address the low-volatility anomaly.

(0:18:36) Ben agrees and explains that later work, including the five-factor model, went further on that front.

(0:19:03) Ben describes how Fama and French formed 25 portfolios sorted by size and book-to-market.

(0:20:00) He explains their use of time-series regression to test how well the model explained portfolio returns.

(0:21:12) Ben walks through factor loadings, alpha, and R-squared, and why those outputs matter.

(0:23:31) He highlights the model’s strong explanatory power, with average R-squared around 0.93 across test portfolios.

(0:25:00) Dan clarifies that unexplained return could reflect skill, luck, or another missing factor.

(0:25:27) Ben emphasizes how dramatic the jump was from CAPM’s explanatory power to the three-factor model’s.

(0:26:11) He points to small-cap growth as the major area the model struggled to explain.

(0:27:09) Ben explains how the model also absorbed dividend-to-price and earnings-to-price “anomalies.”

(0:28:01) Dan discusses why dividend strategies may simply act as rough value screens rather than offering something unique.

(0:28:52) Ben expands on how later research, especially profitability, sharpened value investing implementation.

(0:30:37) He notes the unresolved debate over whether factors are true risk exposures or persistent mispricing.

(0:32:16) Ben explains how factor models changed the way investors evaluate active managers and fees.

(0:33:16) Dan raises the possibility that some early active managers may have intuitively identified factor opportunities before the research formalized them.

(0:34:09) Ben discusses whether factor premiums have shrunk after publication and why the evidence is still noisy.

(0:34:59) He describes how the paper helped launch the boom in empirical asset pricing research.

(0:35:35) Ben introduces the “factor zoo” problem and the explosion of published factors.

(0:36:49) He explains the five-factor model and the addition of profitability and investment.

(0:38:21) Dan asks about the intuition behind profitability and investment, especially why profitable firms might have higher expected returns.

(0:39:38) Ben explains profitability through a multi-factor lens and inferred discount rates.

(0:42:15) He argues that combining factors matters because single-factor portfolios can have offsetting exposures.

(0:44:05) Dan points out that layering too many factors naively can just bring you back toward the market portfolio.

(0:44:56) Ben discusses the tradeoff between diversified tilts and concentrated factor bets.

(0:46:29) Dan describes factor tilting as a subtle adjustment around a diversified core portfolio.

(0:46:47) Ben cites Fama’s idea that investors need to “talk themselves out of the market portfolio.”

(0:47:16) He notes that there is still active debate over which factors and models truly make sense.

(0:48:31) Dan explains why momentum is harder to implement in practice because of turnover, taxes, and trading costs.

(0:49:23) Ben says even simple-sounding factors like value and profitability remain heavily debated in academia.

(0:50:20) He brings the discussion back to practical relevance: how investors can access factor exposure through funds.

(0:51:06) Ben explains Dimensional’s roots in academic research and its long history of implementation.

(0:52:48) He introduces Avantis as a newer competitor with similar academic foundations and newly launched Canadian ETFs.

(0:53:42) Ben discloses that PWL uses Dimensional extensively, while noting they are not paid to mention Dimensional or Avantis.

(0:54:09) He summarizes what factor investing means for investors seeking higher expected returns through systematic tilts.

(0:55:47) Dan reflects on how early PWL’s adoption of index and factor-based investing was in the Canadian market.

(0:57:07) Ben invites listeners to learn more about how PWL applies this thinking in client portfolios.

(0:57:41) The episode moves to the after show and review section.

(0:58:21) Dan reads a listener review focused on evidence-based investing, planning, and disciplined saving.

(1:00:23) Ben notes that they never actually named the paper during the main episode.

(1:00:32) Dan closes with: the paper is Common Risk Factors in the Returns on Stocks and Bonds.

Read The Transcript:

Ben Felix: This is the Rational Reminder Podcast, a weekly reality check on sensible investing and financial decision-making from two Canadians. We're hosted by me, Benjamin Felix, Chief Investment Officer, and Cameron Passmore, Chief Executive Officer at PWL Capital.

Dan Bortolotti: Happy to be back for another one. This is a topic, I think, Ben, that I feel like you've waited your whole life to finally give an overview of factor investing in this way.

Ben Felix: Yeah, we were kind of chatting about it before. It's a topic we've talked about lots, but I don't think we've ever, or we definitely have never done sort of a deep dive into the original paper that kicked off this whole way of thinking about expected returns and asset prices.

Dan Bortolotti: Yeah, it was an interesting thing for me too, because I remember learning all of this a decade ago when I first got into understanding index investing at a high level, and then this takes index investing to a different level, but I hadn't really dove into it at this depth until the last little while. Anyway, it'll be interesting to revisit it. It's come a long way in 15 years for sure.

Ben Felix: Definitely. We're calling this discussion The Finance Paper That Changed Everything. We're really talking about Fama and French's 1993 paper in the Journal of Financial Economics, which is a paper that today has nearly 15,000 citations, which is pretty crazy for an academic paper that changed financial economics and the practice of portfolio management.

It changed it forever. Eugene Fama and Ken French, they found back then, back in 1993, that a group of three factors explained the vast majority of differences in returns across diversified stock portfolios. They did actually look at both stocks and bonds in the paper, but we're mostly going to focus on the stock analysis.

Their findings in that paper, they really had and continue to have sweeping implications for the academic study of and practice of investment management. Listeners know we mention Fama and French quite a bit. Their research comes up a lot.

They've had such a big impact on this space. This paper is like one of the foundational reasons. Fama obviously has market efficiency, efficient markets, but Fama and French together, their multi-factor asset pricing work is really foundational.

I think it's research that every investor should understand whether they choose to apply its findings to their portfolios or not. Personally, this paper is really the foundation of how I think about investing in building portfolios. Their research is what led me to find Dimensional, which is what led me to get in touch with Cameron and find PWL Capital, whatever that was, 13 years ago, almost 14 years ago.

I was in a corporate finance class and we were talking about what is the cost of capital. There was a whole big discussion in the class and Professor Vijay Jog talked about the CAPM and how we could find the cost of capital that way. Then he's like, but there's some more recent research that says that you can look at these other factors that might tell you a little bit more about the cost of capital and these other characteristics of companies that might change the cost of capital relative to what the CAPM would say.

That's neat. Then I ended up, I think, reading an article by Rob Carrick that mentioned the Fama and French research and tied it to Dimensional. I probably don't have to preface this by saying that this is going to be a nerdy episode.

Let's be honest with the type of listeners that we have. That's probably why people are listening, but I think it's a discussion that's worthwhile. We'll cover the paper's methodology, the results, the enduring impacts.

Then we do have some comments near the end about how investors today can pretty easily apply this information. Any comments before we jump in here, Dan?

Dan Bortolotti: It's funny. I have a very different origin story, of course, how I found PWL. This is why it's an interesting approach for me because I came at it when I first started getting interested in investing.

It was much more the plain vanilla, Bogle-type indexing strategies. It was only later when I started to do a lot more reading when I came upon people like Larry Swedroe, Rick Ferri, a number of others who are proponents of taking that a step further and exploring factor investing specifically through Dimensional. That, for me, came a few years later.

It definitely deepened, I think, my understanding of how all of this comes together, why it works, the limitations of active management. All of those things are tied into this.

Ben Felix: It's all so connected. You mentioned this earlier. It's all cut from the same cloth.

Dan Bortolotti: Yeah.

Ben Felix: We'll jump in here. The fundamental premise of this paper, Fama and French's 1993 paper, is that multiple factors affect asset prices and expected returns. These factors can help to explain why different types of stocks and bonds and therefore different investment portfolios have different expected returns and different realized returns.

Expected returns, a funny term, it's kind of what it sounds like. It's the return that you expect from investing in a stock or bond. The study of expected returns is often referred to as asset pricing because those two things are directly related.

An asset's price is based on its expected return and its expected return can be inferred from its price. Expected returns are, of course, not guaranteed outcomes, but as we'll see, they do contain information and we can kind of infer that because different types of assets have had systematically different realized returns. It's all kind of funny stuff.

Capital Asset Pricing Model, CAPM is like theory, but the rest of the stuff, it's often called empirical asset pricing research because there's some sort of light theory, but it's really empirically derived. You see, oh, small stocks have higher returns. Maybe there's a difference in expected.

Anyway, the idea that different assets have different expected returns and systematically different realized returns, it kind of makes sense. You'd pay more for a safer asset than a riskier one, all else equal. It's commonly known that stocks are riskier than bonds.

That's something that I don't think anybody really disagrees on. Therefore, they have higher expected returns. What's kind of less commonly understood is that different types of stocks can have systematically higher expected returns than others.

In Fama and French's original framing in the paper, they talk about common undiversifiable risks that a lot of investors are sensitive to that help to explain why some stocks have higher returns than others. I mentioned this earlier, but I do want to just say again that this paper talks about five different factors because they look at both equity and fixed income factors, but we're just going to talk mostly about the three equity factors that they looked at. If a stock is exposed to more of a certain type of risk that a lot of investors are sensitive to, that stock must have a higher expected return to entice investors to invest in it, all else equal.

The idea that investors might care about multiple types of risk wasn't a new idea when this paper came out. This is something that Robert Merton and a few other people had written about theoretically, but figuring out what those risks might be and how to measure them, that was a new thing that Fama and French introduced in this paper. Now, I think to understand why Fama and French's perspective in this paper was so impactful, it's important to understand what came before it.

In 1964 and 1965, researchers, including Bill Sharpe, developed the Capital Asset Pricing Model or the CAPM, which I mentioned briefly earlier. This model, the CAPM, connected a stock's expected return to how its price moves relative to the overall market, which is something expressed as its market beta or just its beta commonly. A beta of one, for example, means that a stock moves pretty much in lockstep with the market.

A higher beta, a beta of more than one, means the stock will tend to move up more when the market is up and down more when the market is down and vice versa for a beta less than one. According to this single factor model, the CAPM, where exposure to market risk is the only risk that investors care about, stocks with higher betas should, on average, deliver higher returns. That's what the model predicts.

CAPM was like, it was a huge deal in finance. It was a big enough deal to win Bill Sharpe the Nobel Memorial Prize in Economic Sciences in 1990 for his work on it. Because that model, the CAPM, it formalized a relationship between risk and expected return.

It actually did a pretty good job empirically explaining the observed returns of stocks. You got to think, before the CAPM, there wasn't a model for what should a stock's expected return be or what is a stock's expected return?

Dan Bortolotti: It is funny how we take that for granted now. It seems so obvious, but I guess in the 60s, no one had ever articulated it in that way, and it demonstrated it with the math. This is similar when we talk about Markowitz and portfolio theory.

It's like, if you combine uncorrelated assets into a portfolio, you reduce the risk without necessarily decreasing the expected return. We all know that today, but I mean, that was revolutionary when it came out. You have to go back 60 years, but all of this stuff is just really crystallizing, I think, around this time.

Ben Felix: And Sharpe builds on Markowitz. CAPM really builds on Markowitz portfolio theory and it creates testable predictions that are really based on Markowitz portfolio theory. That idea of having testable predictions, I mean, it flows into all sorts of other stuff too, how do you evaluate the performance of an active manager?

Before the CAPM, there was no model to say, well, how much risk did they take and what were their returns relative to what we would expect based on the amount of risk that they took? We just didn't have a model for that before the CAPM. And there's some interesting research that I'll mention in a bit that did that.

They took the CAPM and they applied it to active mutual funds at the time and asked exactly that question. Do they earn returns higher than what we would expect based on the risk they took?

Dan Bortolotti: To take that a step further too, there was a time, not all that long before this, where no one really understood what market risk was. You had to have properly constructed, easily replicable indexes before you could do that. We take all this stuff for granted, but if you were a stock picker in Benjamin Graham's day, what were you measuring your performance against?

There was no benchmark. A lot of this stuff during this period, the mid-century is when all of this stuff starts to be formalized and now we can actually start evaluating the performance of investments and investment managers in a way that just wasn't possible before.

Ben Felix: Totally. It really speaks to this idea that we are currently in this golden age of investing, where all of this theory is beneath us. We're sitting on top of the foundation of this powerful theory.

Not only are we aware of it, but we have products that are just at our fingertips to take that information and implement it in investment portfolios, which is pretty cool. Even though the CAPM defined and then dominated the study of asset pricing from its inception through the 70s and the 80s, research had consistently come out after the CAPM was published, showing that certain types of stocks had higher returns than what could be explained by the CAPM. Once we had this model, people started testing stuff.

I mentioned the active managers. The active managers generate returns in excess of what would be expected based on the risk they took. Then there's also like, oh, look at this certain type of stock.

It actually performs better than the CAPM would predict. That's interesting. People started doing all kinds of those types of tests.

Now, at the time when those observations could not be explained by the CAPM, the asset pricing model at the time, they would be referred to as anomalies. An asset pricing anomaly could mean one of two things. This is a return that can't be explained by the CAPM.

An asset pricing anomaly could be one of two things. If we believe the CAPM is a perfect model for explaining expected returns, anomalies in that case must mean that markets are not efficient. Some of the papers at this time, I think one of them was even titled. Persuasive Proof of Market Inefficiency.

It could mean that. Or if we know that markets are efficient, the CAPM in that case must just be the wrong model. This tension between those two possibilities turns out to be impossible to resolve.

This is something that Fama wrote about. It's called the joint hypothesis problem. Basically, we can't say whether markets are efficient without having an asset pricing model to test market efficiency.

We can't prove whether an asset pricing model is right without knowing whether markets are efficient. That's the joint hypothesis problem. It makes all of this really non-testable, at least in a way that definitively tells us whether markets are efficient.

Even still, the CAPM asset pricing research was hugely important. That is where Fama and French start their 1993 paper. They focus on three key problems with the CAPM.

One is that small stocks earn higher average returns than large stocks, unexplained by differences in their CAPM betas. The other one is that high book to market or value stocks earn higher average returns than low book to market or gross stocks. Again, unexplained by differences in their CAPM betas.

Then the relationship between beta and average returns was weaker than CAPM predicted. People may be familiar with the anomaly. There were low beta stocks earning higher returns than the CAPM would suggest that they should earn.

In simple terms, the CAPM suggested that two companies with the same beta should have similar or identical expected returns, regardless of their size or value characteristics or really any other characteristic for that matter. Anything not following the model would be considered a mispricing or an asset pricing anomaly. Fama and French were looking at this in this paper and saying, how can we resolve this?

How can we resolve these anomalies? They had noted these patterns in returns that I just talked about. They say right in the front of their paper, "The cross section of average returns on US common stocks shows little relation to the market betas of the Sharpe-Lintner asset pricing model."

Pretty blunt language. They're pretty straight up about CAPM is not working. Then they follow up.

This is the part that's really interesting. They follow it up with the suggestion that maybe the standard model was ignoring risk factors that are actually priced by the market. It's on that basis that Fama and French build their now famous three-factor asset pricing model.

Rather than just looking at the market factor like the CAPM, Fama and French developed a three-factor model that relates a stock's expected returns to the market factor, which is similar to the CAPM, but they add a size factor and a relative price or value factor. The market factor, again, basically same idea as the CAPM, how much the stock or portfolio moves when the overall market moves. That shows whether the stocks went up or down with the market.

It's still a super important factor because broad market movements do tend to affect most stocks, regardless of their size or value characteristics. The CAPM in Fama and French's paper, it still explains around 60, maybe 70, in some cases, 80% of differences in return. It's still a hugely important factor.

The size factor is pretty straightforward. It's based on the size of companies. More specifically, it's denoted as SMB or small minus big.

This factor refers to the difference in returns between small company stocks and large company stocks. If small stocks outperform large stocks in a given period, the SMB premium is positive, which it had been at the time that they were doing this research, which is why it was identified as an anomaly or a risk factor. If large stocks do better than small stocks, which has happened over a lot of the periods of time since this paper came out, the premium is negative.

Including this factor captures whether being a small company is related to any systematic return variation that investors are compensated for. Then the third one is the value factor. This one's denoted as HML or high minus low, which is referring to the book to market valuation ratios of companies.

Book to market is basically the company's accounting value. It's like on paper value compared to its stock market value. What's it worth on paper versus what are people, investors willing to pay for it?

High book to market ratio means it's a value stock. They're cheaper in market price relative to the book value of their assets. Then a low book to market stock is a growth stock.

A growth stock is where investors are willing to pay more for future potential, which pushes prices further above the book value of the company's assets. HML, high minus low, measures the difference in returns between value stocks and growth stocks. Again, similar to the SMB premium, when value stocks outperform the HML premium, or the value premium is positive and then vice versa.

That captures the effect of any return premium that investors receive for holding value stocks rather than growth stocks. Each of these factors, including the market factor, are called long short portfolios. SMB, for example, is long small cap stocks and short big stocks

You could have a whole discussion about that, about long short portfolios. There's some other funny things about the way that the factor portfolios are constructed too, which is even too nerdy, I think, for this discussion. The main idea is that they constructed these factor portfolios that are really designed to capture the return variations related to company size and relative price, the systematic return variations, which are things that at the time had been well documented as return anomalies.

Dan Bortolotti: The interesting thing about this though is, correct me if I'm wrong, but it doesn't seem like they addressed the third issue, which was basically what I would call the low volatility anomaly, where you had these low beta stocks that the model suggested would underperform, but in fact, they outperformed. It's not one of the new factors added to explain. Low volatility has become a investment strategy.

There are funds in that that try to capture it. I'm not sure that it has the same kind of academic rigor that the other factors have.

Ben Felix: I think it does. It's a little bit different. Fama and French's Three-Factor Model, you're right, Dan, it didn't really address the low volatility anomaly.

Their later Five-Factor Model, which we'll touch on in a little bit, it did go quite a bit further in addressing or explaining the low volatility anomaly, but yeah, the Three-Factor Model didn't really touch it. You're right. The next thing they do in this paper, which is really neat, is they create this model, but then they need to take it for a spin.

To do that, to take it for a test drive, they formed diversified portfolios by sorting stocks based on size and value characteristics. They split stocks, these are US stocks, into five size groups and five book to market groups. We get a total 25 test portfolios.

This really just allowed them to capture every combination of the two size and value factors. We've got small value stocks on one end, we've got big gross stocks on the other end, and then all the possible combinations in between. They show that these groups of stocks do have pretty significant variation in average returns.

There is something going on here for sure. Then they test whether their three factor asset pricing model is able to explain that variation. This is where they did something that at the time was pretty groundbreaking and still shows up in tons of academic finance papers and practical investment analysis today.

They used time series regression to test how well their asset pricing factors explained the returns of their 25 test portfolios. Time series regression looks at the returns of a portfolio over time and asks how much of the variation in returns is explained by the asset pricing factors being used in the model. It's pretty straightforward to run a time series regression.

You can do it pretty easily in Excel. There's also free tools online like Portfolio Visualizer, where you can just drop in a ticker and it'll run a three or a five factor regression. On Portfolio Visualizer, you can even try different asset pricing models.

Fama and French have their asset pricing models, but there are other competing models out there. Anyway, the tools we have talk about a golden age of investing. You can just, with a couple of clicks run analysis that would have taken Fama and French a lot longer to do back in 1993.

Dan Bortolotti: Let alone Bill Sharpe in 1965.

Ben Felix: Yeah, right.

Dan Bortolotti: It's very easy for us to kind of look back at a model like CAPM and say, oh, it's simplistic.

It doesn't explain that much. It's like, yeah, but there's a reason why nobody did it until him. We really do take a lot of this for granted.

Ben Felix: Yeah. The output of a time series regression, it's going to spit out factor loadings is what we call them or coefficients. It really just tells you how the portfolio being looked at moves relative to each factor.

It's also going to spit out an alpha, which is the portion of returns that was not explained by the factors in the model. Now that last term alpha turns out to be pretty important to the practice of investment management for sure, but also to academic analysis. Alpha was first used to describe excess risk adjusted returns in a 1968 paper.

Now this is a Michael Jensen paper. It's another banger of a paper if you're into this kind of thing. It's actually a really cool paper.

It's the one I mentioned earlier where they took the CAPM, this new thing at the time, and applied it to active fund performance. They took the single factor Capital Asset Pricing Model and asked whether actively managed mutual fund managers were generating returns in excess of what should be expected based on the amount of risk they were taking. It's tangential to this discussion, but it's worth mentioning that active managers in that study were not able to beat the market.

We've talked about that paper in the past when we were making the case for index funds, I think. That model only accounted for market risk. It only accounted for CAPM.

We'll come back to the effects of including other factors in that type of analysis later. In the context of Fama and French's 1993 paper, they took each of the 25 test portfolios and tracked their performance from 1963 to 1991. Then they used their model to try to explain the differences in their returns.

For each portfolio, the regression estimated how its returns co-moved with the three factors in the model and whether there were any large alphas, excess returns unexplained by the model. They also measured the R squared values. It measured the explanatory power of a regression model.

What percentage of a portfolio's ups and downs over time could be attributed to the factors. An R squared of one would mean perfect explanatory power and zero would mean that the factors explained basically none of the returns. What Fama and French found in terms of alphas and R squareds across their 25 test portfolios was pretty incredible.

Keeping in mind, this is an empirical model. They had the market factor and we've got this small companies are doing some weird stuff. Value companies are doing some weird stuff.

Let's test them all out and see if there's something going on here. They find that the R squared values ranged from across these 25 test portfolios range from 0.83 to 0.97. There's about 0.93 on average across the test portfolios, which is very high. In 21 of the 25 portfolios tested, the R squared value of the regression was over 0.9. This is where we get the common, at least in our world of factor nerdiness, the common description that the Three-Factor Model explains around 90% of the differences in returns between diversified portfolios. There are two other key data points that came out of these tests. One was that each test portfolio had a beta very close to one. That was interesting because it reinforced the fact that the variation in returns is explained by more than just exposure to market beta.

Since the market beta, exposures were basically the same across the board, but the returns varied widely. Now that would be very surprising, that finding, if market beta fully priced all assets. Instead, we see things following the pattern that Fama and French predicted with their Three-Factor Model.

This is where, again, in these tests, we see that CAPM explains 60 to 80%. I usually hear it described as around 60% differences in returns across diversified portfolios.

Dan Bortolotti: Just to clarify here, if you say, for example, that the Three-Factor Model explains 90%, the 10% that is unexplained could be skill, but it could also be luck. The model does not differentiate between those two.

Ben Felix: Skill, luck, or some yet to be determined factor.

Dan Bortolotti: It means unexplained. It doesn't mean explained by something specific other than those three factors. It just means we don't know.

Ben Felix: Correct. Something outside of the model being tested. Even still, when they add more factors later, we get up to maybe 90, 93, 94, maybe 95% explanatory power, but there's always going to be that idiosyncratic component of whatever. Idiosyncratic risk, or skill, or luck, or whatever you want to call it.

I do think it's worth reiterating that we went from being able to explain somewhere between 60, 70, maybe 80% of the differences in returns between diversified portfolios with the CAPM, to being able to explain over 90% with the Three-Factor Model. Pretty crazy. Another important observation in their regressions was that the three factor regressions resulted in near zero intercepts, or alphas, on almost all test portfolios.

The one exception, the one that they really struggled with, and actually their Five-Factor Model continued to struggle with, was small cap growth stocks. They had and have much lower returns than the Three-Factor Model could explain. That left a lot of room for future research.

A lot of which has been done, but there's still a funny anomaly there, where small cap growth stocks continue to tend to have lower returns than asset pricing models predict. The minimal alphas in most cases, except for small growth, is important, and it might be one of the most compelling validations of the model. Fama and French demonstrate that once you account for market size and value factor exposures, there are basically no persistent unexplained returns remaining in most portfolio sorts.

Again, that's the 90%. We're explaining most of the differences in returns across all these portfolios that vary across a couple of important dimensions. The other thing they did in this paper that I love, so they had their 25 test portfolios sorted on size and value, cool, but they also had two other characteristics that had been associated in academic literature as being associated with higher returns.

They, again, fire up the Three-Factor Model to see if they can explain those anomalies. Those were dividends to price and earnings to price. They, again, find that the Three-Factor Model explains the returns across these portfolios as well.

This is one of the places where Fama and French really shattered the beliefs of dividend focused investors and showed that there's nothing special about dividends even if the high dividend to price portfolio does perform well. It's because it's a value portfolio, not because dividends are special.

Dan Bortolotti: That's what I was going to say. There's a lot of overlap here. A lot of high dividend stocks have high dividends because they have low prices.

That's right. It's just the inverse way of looking at it. I agree with this is something that I think has not always resonated with dividend investors is not that the high dividend is good.

It's that the dividend is high because the price is low and the low price is what's good. High dividends can be a proxy for value. My understanding of this is that Fama and French showed that it's not a very good proxy.

That price to book is a better measure if you're trying to find value. (28:41) There's lot of different ways to measure value stocks. They have a focus, I believe, on price to book as being the most reliable way of measuring this factor.

Ben Felix: It's come a long way since this 1993 paper where once they bring in profitability and combine that with value and you're looking at those two metrics together, I think that they're precision in looking at what is a cheap company, but a cheap company that actually is a good investment opportunity. Looking at both those factors is quite good, which has an interesting interaction with dividends as well because if you look at a dividend portfolio, it's probably going to have a value tilt. There's a good chance it's got a profitability tilt too.

Dividends turned out to be a pretty good naive filter for a couple of very robust, academically robust factors. I think where it gets interesting from the dividend investing perspective is that you could build that portfolio that loads on value and profitability without needing to sort by dividends because you're excluding a whole bunch of companies that don't pay dividends to build a dividend focus portfolio when if you're really just trying to tilt toward value and profitability, you can do that without the dividend focus. With the dividend tilt, you'll often end up with larger companies as well and you might not want that. You might want to tilt toward size or not, but when you focus on dividends, you sort of get naive exposure to the other factors when you could be focusing on the factors that are actually driving returns and get a more diversified, more customizable portfolio.

One thing that is worth mentioning is it's still debatable, like Fama and French, the language they use in this paper is that we're modeling these as systematic risk factors. That's still debatable. We don't really know if they're risk factors or if they're artifacts of mispricing and that's another one of those things that's really, really hard to test definitively.

Do these systematic factors that we can see in the data, do they exist because of mispricing or do they exist because of risk? I don't even know if it matters. I think it matters if you believe that mispricing would be arbitraged away, which some people do believe.

We've had some guests talk about how they think that has happened. If you believe that there are limits to arbitrage and that systematic mispricings won't go away for that reason, I don't think it's really relevant whether we're looking at risk or mispricing as long as we believe these things are going to persist, which is really the big question from a practical perspective. Because if we can look at these factors and say, cool, they exist and hey, cool, they explain differences in returns across diversified portfolios.

We can't know. If we don't think that the premiums for these factors are going to be positive going forward, we may not want to tilt toward them. I mean, you may even want to tilt away from them if you knew that the premiums were going to be negative.

They're kind of separate, I guess, where you can have a model that does a good job explaining differences in returns, but the premiums don't have to be positive. If you're going to tilt toward them, you kind of hope they're going to be positive. The big thing Fama and French showed is that size and value don't need to be anomalies.

We don't have to look at the world from a CAPM perspective and say, hey, these things don't make sense. What Fama and French said is maybe these things are systematic factors, risk or otherwise, that we need to account for when we're looking at portfolios and assessing different types of stocks. When they include those in their model, they are able to almost fully explain return differences across this broad range of diversified portfolios.

That really changed how we viewed markets. We went from in a CAPM world, that's sort of whatever you want to call it, 60% explanatory power to, with the Three-Factor Model, 90% plus explanatory power. It's a very powerful increase.

Other area that this has an impact is not just, it's like, okay, cool. We can explain differences in returns. That's neat.

These things don't all have to be anomalies, okay? It gets really interesting again when we say, why have some active managers outperformed? If they've outperformed because they're skilled, then maybe we should be giving them more money to invest.

If they've outperformed because they tilted toward value or size, maybe that's not as interesting and maybe they shouldn't command a high fee for that service. It's basically like, if you know a manager is just tilting toward value stocks, they're going to charge you one and a half or 2% to do that. You could just buy a value index for whatever 20 basis points.

Fama and French did look at that in later research using the Three-Factor Model. That's their famous paper on luck versus skill in active fund management.

Dan Bortolotti: One does wonder, before this was really understood, there were managers who outperformed. Looking back with hindsight bias, we can say, well, you outperformed because you tilted more to small stocks or value stocks. Those managers identified those opportunities, presumably before the academic research showed that they had premiums.

They might have been lucky, but they might have just had very good intuition. Once the factors get publicized, there is some potential for them to shrink because the market is mostly efficient. If some of these things were indeed mispricings, misunderstandings of risk, that would presumably get smaller in the future as people understood that.

This is all very difficult to tease out from real-world fund and manager performance.

Ben Felix: Oh yeah, very much so. That is true that post-publication factor premiums have tended to get smaller. I don't think they've gone to zero.

We've had one guest, Andrew Chen, who thinks that they have. He's looked at the US and said that maybe they've gone to zero for exactly that reason. You look outside the US and the factor premiums have still been quite positive and even post-Andrew's sample in the US, the value premium, for example, has been positive over that specific period.

There's so much noise in this stuff. It's hard to say what is true. I think we have degrees of truthiness rather than truth.

Dan Bortolotti: Yeah, there's so little that we can be confident and certain about. You just try to do what makes sense based on the evidence without putting too much predictive power on any theory.

Ben Felix: Altogether, the findings in this paper, they challenge but they also built on single factor CAPM asset pricing to create a much more comprehensive framework for how investors should think about building and evaluating portfolios systematically. Then after this paper came out, it caused this explosion in the term that I mentioned earlier, which is called empirical asset pricing. It's really like looking at returns and figuring out what factors might be driving them.

That field of study really exploded. Researchers wanted to find the best factors. They wanted to create better asset pricing models.

It became a bit of a problem maybe. John Cochrane in his 2011 presidential address to the American Finance Association, he described the proliferation of factors as a zoo. That's become this famous term, the "factor zoo."

You could maybe even call it factor slop, I don't know, to use the language from my recent video on ETF slop. That's probably not very nice to some of the researchers who have discovered some of the, any factors, I don't know. There was a 2016 paper from Cam Harvey, Liu and Zhu.

They found at that time, this is 10 years ago now, they found that there were 316 distinct factors that had been published in academic journals at the time. I think later research has kind of suggested that maybe there were 316 published factors, but they really all fall into a relatively small number of categories. We found like 316 different flavors, but there's, when you really boil it down, there's only a handful still.

In any case, it did create this whole new set of academic techniques for choosing factors. How do you evaluate which factors belong in a model? Different techniques for comparing asset pricing models.

There's like a whole bunch of papers on this now. In the face of the zoo or the slop, whatever you want to call it, Fama and French did go on to update their 1993 paper. I don't know if I'd call it an update, I guess.

They created a new model in 2015 and they introduced two new factors with that update. Those were profitability and investment. Profitability is expressed as RMW or robust minus weak.

That's the excess return from companies with high profitability over those with weak profitability or robust profitability over weak profitability. Then investment is expressed as CMA, conservative minus aggressive. That's companies that grow the book value of their assets slowly, which is conservative, tend to outperform those that pour cash into rapid asset growth, which are called aggressive.

The Five-Factor Model did help to solve some of the problems that the Three-Factor Model was not able to. We talked about the low volatility anomaly. There were a couple of other ones that were still unexplained by the Three-Factor Model, but Fama and French have a separate paper.

I can't remember exactly what the title is, but it's something about dissecting anomalies with theFfive-Factor Model, where they go and take a bunch of asset pricing anomalies and say, well, these are largely explained by the Five-Factor Model now. As I mentioned earlier, it did move the explanatory power up closer to 95%. Depending on which portfolio sort we were talking about, it was up to 94% of the differences in returns across diversified portfolios.

The Five-Factor Model is really now today, I would call it the workhorse. It's like the benchmark asset pricing model in academic finance.

Dan Bortolotti: You think it's worth a quick chat about profitability? Investment to me has always been the most difficult one to understand intuitively, but profitability has that issue as well. I think it does come back to this idea of the low volatility anomaly.

In other words, I think we all understand intuitively, if small stocks have higher expected returns than large companies, it's usually because they're riskier. For value, maybe it's risk. The behavioral component, I think, explains a lot too.

People generally don't like cheap, boring companies and they like big expensive companies. They will pay more for them. Profitability is a lot harder to understand because why would investors not want to invest in companies that are clearly profitable if they were attracted to those quote unquote good companies?

Would they not drive up the price until the point where this is the old idea of you can't tell a good stock by identifying a good company because price means everything. Is there some explanatory reason why investors would not prefer profitable companies and therefore impart some kind of higher expected return on them?

Ben Felix: I've always had to think about it from a multi-factor perspective. It's really like all else equal perspective. If we take two companies that are otherwise identical on all characteristics, but one has higher profitability, but they've got the same value characteristics and all that stuff, the higher profitability company must have a higher discount rate applied to its expected future cash flows in order for their valuations to be the same for these two companies.

If we're looking at which stock should I buy and we find one that's more profitable, but it's trading at the same relative price as some other stock, the inferred discount rate or implied discount rate that the market must be pricing it for those characteristics to be the way that they are must be higher. I think that's the story that Fama and French talk about in their five-factor paper as well. Then when you think about it from a single-factor perspective, I think it's kind of similar for value actually.

If we think about just value stocks in aggregate, they have higher expected returns like you said, because they're boring companies. People aren't – they're maybe riskier. People aren't as interested in them, so they've got lower prices, but we've also got that question of do they have lower prices because they're crappy companies, in which case they're not good investment opportunities, or is it because they have higher discount rates, but are pretty good companies?

There's a lot of noise in that. When you just look at the single-factor value sort, when you introduce profitability, it's a lot easier to find which companies have high expected returns. They've got high profitability and low prices, which is the combination that you want.

Low prices without sorting on profitability are enough on their own to have higher expected returns as a single-factor portfolio. I suspect it's something similar going on with profitability, where you really want the high profitability companies with low prices, and that's a multi-factor sort. If you just take the aggregate profitability portfolio, you're probably still picking up enough of that for there to be a premium.

That's the way I've always thought about it. Maybe there are better ways to think about it. I agree it is a less intuitive story, especially if you try and think about it from a single-factor perspective.

Dan Bortolotti: Once you layer on the factors, because again, maybe most companies will sort high or low across multiple factors, a lot of value stocks are probably high profitability as well. That's what makes them value as opposed to simply cheap. A value stock is not just one with a low price, it's a low price relative to some positive characteristics in the company earnings, assets, whatever it is.

A lot of these are going to overlap. The companies with the highest expected returns may score high on three or more of these factors.

Ben Felix: In a lot of cases, they're going to be negatively correlated. If we just take the portfolio value stocks, it's going to tend to be less profitable than the market as a whole. Similar things can happen with company size, which is why taking that multi-factor perspective ends up being so important.

Because if we just take value stocks, you end up with a basically low profitability tilt, which has a negative expected premium. But if you can take that value portfolio and bring the profitability characteristics up by also doing a profitability sort, then your expected return just goes up. This is why, as this research has come out, it's been so important for firms like Dimensional and Avantis, who we'll talk more about in a second, to incorporate that research.

You don't want to just own cheap stocks when you don't really know why they're cheap, because maybe they're crappy companies and you don't actually want to own them. I think you do get a lot of that if you just do the single factor sort. Once you add in the second factor, you see that in, especially now that they've changed the way that they're doing their weighting of the factors.

Dimensional, in the vector portfolios, which are Canadian listed, well, they have a Canadian and US funds that are pretty aggressively tilted towards size and value. They used to be pretty aggressively tilted towards size and value and then took profitability into account, but they fairly recently changed their methodology to have a much more equal emphasis on size, value, and profitability. When you look at their aggregate characteristics, it used to be that the vector portfolio would be way cheaper than the market, but also have a little bit lower profitability or similar profitability to the market, which was in itself pretty impressive.

If you just did the value sort, you'd end up with a much worse profitability characteristic. Then now with more equal emphasis, it's even more balanced.

Dan Bortolotti: You need to make some kind of active decision about how you want to weight all of these factors, because in a naive way, the more factors you layer on, especially if they're negatively correlated, at some point, you just end up with the market. Unless you plan on concentrating the portfolio quite aggressively, you just end up with a portfolio that's not dramatically different from just a boring market cap weighted one. You're going to see a lot of correlation with just a plain vanilla index and any opportunity for outperformance, but also risk of underperformance maybe gets narrowed.

Unless you've got a really compelling formula for how to combine these in the right way, that's where the skill comes into. You've got the research, okay, that's the raw material. Now, what do we do with it?

Ben Felix: I think there's skill. There's also an element of humility. The way that Dimensional, and I think Avantis is similar, the way that they do this is very diversified and it is still dominated by the market factor.

It's going to have relatively low tracking error to the market, because they are taking that super broad diversification. There are products out there on the market that are much more concentrated. They tilt much more toward the active portfolio construction by building concentrated portfolios that are still quantitatively informed.

You could still call them factor portfolios. I think a lot of the product names actually do call themselves factor portfolios, but they're much more concentrated. In that case, you're making a much bigger bet on the premiums to your point, Dan.

You're also going to have much more tracking error. You've also got much more risk of underperforming the market if the factors don't work out as expected. With something like Dimensional, which we haven't really talked about yet, although listeners are probably already familiar, you do look a whole lot like the market.

From our perspective, that's a good thing. From someone's perspective who wants really high conviction or wants to really outperform the market, they might consider that not such a good thing. We don't have so much conviction in these factors that we want to build 25 stock portfolios that are as concentrated as possible in the multi-factor sort.

I think that's right. At a certain point, you do look a lot like the market because you're broadly diversified. I think that's kind of a good thing from our perspective, but not everybody would necessarily agree with that.

Dan Bortolotti: Yeah. I think that's why the term tilt is apt because it's subtle. It's not an enormous fundamental move away from a broadly diversified portfolio.

It's sort of starting from the premise that a diversified portfolio is very good and now we're just trying to tweak it around the edges.

Ben Felix: It's like Fama's famous line that you've got to talk yourself out of the market portfolio. That really comes back to how much conviction do you have in these kind of tilts. It is also worth mentioning that there's still tons of ongoing debate, both in academia and in practice, about which factors make sense and which asset pricing model we should be using.

Dimensional and Avantis as well are broadly using the Fama and French Five-Factor Model and that general approach and type of thinking, but there are tons of competing factor models. There are tons of competing products. There are companies that have their own factors.

That's not like a settled debate by any means. I'm pretty comfortable saying that when transaction costs are accounted for, which is something that has been done in academic literature that compares asset pricing models, that there's one paper that shows that when you account for transaction costs, Fama and French's Five-Factor Model is really, really strong. It's a very good foundation for thinking about portfolio construction and evaluating portfolio performance.

That transaction cost thing is important because another factor is momentum, which is very empirically strong. Fama and French have always – it's interesting reading their papers. They've always kind of struggled with it.

They say in one paper that they included momentum in an asset pricing model that they were testing, but they say that they include it reluctantly. They're very concerned about data mining and about adding factors just because, but without any sort of possible theoretical story. There's one paper that compares different asset pricing models and shows that momentum can look really good when you ignore transaction costs, but when you account for transaction costs, the Fama and French model without momentum starts to look a lot better.

Dan Bortolotti: Yeah, momentum is one of these things that's always famous. It makes theoretical sense. There's a lot of data showing, like you said, if you ignore transaction costs, it's compelling, except you can't ignore transaction costs.

Unlike these other tilted portfolios, which are pretty buy and hold, they will need reconstitution from time to time. The momentum factors are typically like one to three months trading cycles and they're like, who's implementing that? If you are, just the tax impact, brokerage fees are kind of not a thing, but certainly just the churning and the tax implications of that are likely going to eat up any premium that was there.

So hard to implement in a way that some of these other factors are not terribly difficult to implement.

Ben Felix: Relatively speaking, they're lower turnover. They're not super complicated. All the metrics are fairly straightforward, but even momentum, there are a bunch of different ways you can measure it.

You don't know which measure is the best one. We can say the same thing for value and profitability too though. Every layer of this is still hotly debated.

You could go and find five different academics who have published papers on why their measure of value or their measure of profitability is better than everybody else's. They would all have great cases and great evidence. One line that I love in this type of stuff is that for every PhD, there's an equal and opposite PhD.

You can find me someone to tell me why their asset pricing model or their factor or their product is the best and I can find somebody just as smart, just as qualified to disagree with them. One of the funny things about our space and nobody will know if they're right until 30 years from now and then we'll look back and say, oh yeah, well that guy was right, but then we won't know if they're going to be right for the next 30 years.

Dan Bortolotti: That's so true.

Ben Felix: We work in a funny space. People in finance can just say stuff. People can choose to believe them because they have a good story, but we won't know if what they're saying is right or true until many years in the future.

Everyone will have probably forgotten about the whole debate by then anyway and chased whatever new hot trend is out there. That's right. I do want to bring this back to practical relevance.

We're talking about these asset pricing factors. We're talking about the idea that they may have higher expected returns and that investors could potentially tilt toward them to increase the expected returns of their equity portfolio. Owning the market capitalization weighted market is giving you exposure to the market risk premium, but as you said, Dan, you can tilt toward other factors to potentially increase your expected returns, at least if you believe what these models suggest.

How do you do that? For an individual to do it by selecting individual stocks, it'd be probably arduous and tricky and probably not worth it, but there are fund companies that are using this research to build diversified portfolios. They're like low cost index funds that deliver exposure to more than just market risk.

They're usually not technically index funds because they don't track an index, but functionally, they're very similar, broadly diversified, low turnover, low cost, all that stuff. Dimensional fund advisors have a long history of implementing factor investing research. They started building products before any of this research came out.

I think their first product launched around the same time that the paper documenting the size anomaly came out. Factor investing wasn't a thing when Dimensional launched. They were just trying to build a small cap index basically, although it wasn't actually an index.

They were trying to build a small cap product that institutions could use to compliment their often large cap tilted portfolios. Then the academic research starts to come out and they already had connections to the academic community, so they continued to implement the academic research as it came out. They started with a size portfolio.

They eventually started using value once that research started to come out and they've continued to implement things like profitability. They do have a way of implementing momentum actually that's not super high turnover. They use it as part of their trading process.

They're very connected to the academic roots of this idea. Eugene Fama, one of the paper that we're talking about, his coauthors, was a founding director and remains on the board today. Ken French, the other coauthor, has long standing connections to Dimensional.

Dimensional's track record in the long run has been pretty good. They've struggled a bit recently in the US in particular because large cap growth has just done so well there and Dimensional tilts away from that. But in international markets and even in Canada, they've done pretty well.

One catch is that Dimensional used to be only available through financial advisors for many, many years. They're now available as ETFs in the US market, not in Canada though. But what happened more recently is that Avantis Investors, which is a Dimensional competitor, basically, some folks who had previously been at Dimensional for many years left and started this competitor called Avantis.

They launched similar products. They launched ETFs in the US before Dimensional did. They have now just launched, a few weeks ago when this episode comes out, ETFs in Canada in partnership with CIBC.

We did have the CIO of Avantis, Eduardo Repetto, on this podcast back in March talking in lots of entertaining detail about those Canadian products. I think we probably will do an episode at some point doing a deeper dive on their Canadian products. I do want to mention real quick, PWL, we do use Dimensional funds pretty extensively.

We're not paid by Dimensional or Avantis. We're not being paid to mention them in this episode. We don't do that kind of thing.

What does this all mean for you, the investor? Evidence suggests that long-term expected returns are driven by specific systematic exposures, exposures that we know about today due to decades of academic research, implying that investors may achieve higher expected returns by tilting toward certain types of stocks. Which stocks and knowing how to do it effectively and efficiently at a low cost is a whole other kettle of fish, but there are fund companies like Dimensional Fund Advisors and Avantis Investors that do that.

They create low cost, broadly diversified investment portfolios that are specifically built to take the academic theory that we've been talking about, multi-factor asset pricing, and apply it to investment portfolios. Like I said at the beginning of this episode, this seminal paper from Fama and French really forms the foundation for a big part of how I think about investing. As I mentioned earlier, it's part of how I arrived at PWL.

It's also part of the reason that PWL Capital has been using Dimensional since we helped bring them to Canada back in 2003. That's a whole other story. That was like Cameron and a few other folks came across Larry Swedroe's writing on this stuff and thought that was pretty cool.

It ended up getting connected with Dimensional and ended up, I think there's a funny story in there somewhere about David Booth came to Canada to talk to PWL. I think they almost shut us down because we had a research department. Then they assumed that that meant that it was like individual security research and like, no, no, no, we don't want to partner with you guys.

Then they figured out that we were using index funds and it wasn't that kind of research that we were doing.

Dan Bortolotti: Back then, I mean, PWL was not that big. Today, it wouldn't surprise me if a big fund company said, okay, we're going to listen to what you have to say. Maybe if this is a big US company, maybe bring the product to Canada.

In 2003, it was a pretty small company. PWL transitioned from being stock pickers to traditional index fund investing before that. Even in 2003, there wasn't a whole heck of a lot of product out there for people who wanted to do even the most basic index fund investing in Canada.

That was really early days. It would have been fun to have been in those rooms and heard those discussions.

Ben Felix: Oh, I'm sure there are some cool stories. That's something I've never really chatted to Cameron in too much detail about. I know the kind of the broad strokes of the story, but yeah, I'm sure there's some neat stories in there.

You're right. I haven't seen the models that they were using back then, but I know they were using some US listed small cap value ETFs to get some factor exposure. They were using some index funds, probably some index mutual funds, I would imagine.

I think those were a little bit more common.

Dan Bortolotti: Was it iShares at that point? I guess it was.

Ben Felix: Jeez, yeah.

Dan Bortolotti: Barclays, that was the original Canadian. In the late 90s, yeah, there was some ETF availability in Canada, but the pickings were pretty slim. You didn't have a whole lot of flexibility with what you were buying.

Ben Felix: If listeners want to learn more about how we apply this thinking to our clients' portfolios at PWL or not, which we did an episode on discussing how Dan and I think about that a little bit differently, you can use the link in the episode description to book a time to chat with one of our folks here at PWL. All right, that concludes our discussion on the finance paper that changed everything. Let's go to the after show.

I think we just have one review in there today. It's a long one though. Do you want to read it, Dan?

I did a lot of talking today.

Dan Bortolotti: All right. Do we need to read this disclaimer first?

Ben Felix: Oh, true, true, true. I'll do the disclaimer. You can do the review

Dan Bortolotti: That sounds good.

Ben Felix: We have a review from Apple Podcast to read under SEC regulations. We are required to disclose whether a review, which may be interpreted as a testimonial, was left by a client, whether any direct or indirect compensation was paid for the review, or whether there are any conflicts of interest related to the review, as reviews are generally anonymous, including this one.

Dan Bortolotti: Yep.

Ben Felix: Dave Down Under. That is anonymous indeed.

Dan Bortolotti: Could be his real name.

Ben Felix: It could be. We are unable to identify if the reviewer is a client or disclose any such conflicts of interest.

They're also in Australia, so I highly doubt. I think we would know.

Dan Bortolotti: Unlikely to be our clients. Yes. All right, here's the review.

It says, investing has been solved. How this podcast changed my money mindset. This podcast has completely changed how I think about investing, money, and many other things.

Having been told that investing is always gambling growing up, this podcast has shown me that this does not need to be the case by providing evidence-based, level-headed, and entertaining content. While some episodes are more Canada focused than others, the principles can be applied by any retail investor around the world. I especially like when Ben, his co-hosts, and guests show over and over again that investing has basically been solved and most people simply ask the wrong questions.

I do hold a globally diversified market cap weighted index fund portfolio with a slight factor tilt, but more importantly, I've created my own financial plan. Admittedly, a pretty straightforward situation. Starting with defining goals, over creating an investment philosophy that I can stick with, to eventually modeling outcomes to figure out the required savings rate, the risks I'm able, willing, and need to take along the way to meet my future spending goals has made me very confident in my decision making.

Software engineering background helps, haha. As a renter who has not missed a single DCA contribution since I started investing four and a half years ago, and in fact has managed to increase contributions consistently instead of letting lifestyle creep win, I also love the episodes about the rent versus buy decision. Not that it has convinced any of my friends that I'm not a complete idiot by buying a property, but hey, you can't win them all.

Thank you for all your work and all best wishes from the land down under, from Dave Down Understand from Australia on iTunes.

Ben Felix: Very nice review. It seems like Dave has been paying attention.

Dan Bortolotti: It sounds like he's maybe one of these people who have gone back and listened to all 400 episodes over the course of a few weeks. We've been talking or hearing about a few people who have done that.

That is quite a marathon.

Ben Felix: Yeah, it is. It is wild. We never said the name of the paper that we talked about in this episode.

We'll put it in the thumbnail or something, but it's Common Risk Factors in the Returns on Stocks and Bonds.

Dan Bortolotti: It changed the world and now you know what it's called.

Ben Felix: There we go. All right. Anything else, Dan?

Dan Bortolotti: No, we're good.

Ben Felix: All right. Thanks everyone for listening.

Dan Bortolotti: See you next time.

Disclaimer:

Portfolio management and brokerage services in Canada are offered exclusively by PWL Capital, Inc. (“PWL Capital”) which is regulated by the Canadian Investment Regulatory Organization (CIRO) and is a member of the Canadian Investor Protection Fund (CIPF). Investment advisory services in the United States of America are offered exclusively by OneDigital Investment Advisors LLC (“OneDigital”). OneDigital and PWL Capital are affiliated entities, and they mostly get on really well with each other. However, each company has financial responsibility for only its own products and services.

Nothing herein constitutes an offer or solicitation to buy or sell any security. Occasionally we tell you not to buy crappy investments in the first place, but that’s not the same thing as telling you to sell them.

This communication is distributed for informational purposes only; the information contained herein has been derived from sources believed to be “truthy,” but not necessarily accurate. We really do try, but we can’t make any guarantees. Even if nothing we say is fundamentally wrong, it might not be the whole story.

Furthermore, nothing herein should be construed as investment, tax or legal advice. Even though we call the podcast “your weekly reality check on sensible investing and financial decision making,” you should not rely on us when making actual decisions, only hypothetical ones.

Different types of investments and investment strategies have varying degrees of risk and are not suitable for all investors. You should consult with a professional adviser to see how the information contained herein may apply to your individual circumstances. It might not apply at all. Honestly, you can probably ignore most of it.

All market indices discussed are unmanaged, do not incur management fees, and cannot be invested in directly. Which is a shame, because it would be awesome if you could.

All investing involves risk of loss: including loss of money, loss of sleep, loss of hair, and loss of reputation. Nothing herein should be construed as a guarantee of any specific outcome or profit.

Past performance is not indicative of or a guarantee of future results. If it were, it would be much easier to be a Leafs fan.

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