Episode 183: Market Efficiency Myths and Misconceptions
While there is certainly room for rigorous debate regarding market efficiency versus inefficiency, there are many who dismiss Eugene Fama’s Efficient Market Hypothesis (EMH) as an incorrect model without understanding what the implications are or how to test it. In today’s episode of the Rational Reminder Podcast, we tackle some common market efficiency myths and misconceptions using Fama’s 1970 paper on EMH as well as supporting papers by Kenneth French, Lubos Pastor, José Scheinkman, and many others. You’ll also hear about behavioural finance, quantitative investing, human bias, and momentum as they relate to market efficiency before debunking some anecdotal misconceptions about EMH involving Warren Buffet and Renaissance Technologies. In addition to our fascinating main topic for today, you’ll get a glimpse into the four waves of a career in Cameron’s review of The Long Game by Dorie Clark and Benjamin shares some notes and corrections regarding the user cost model from Episode 180: Is Canada Really in a Housing Bubble? We also discuss housing as a depreciating asset, innovation stocks in deep value territory, and the size of innovation platforms relative to global market cap and what that means for investors, plus a whole lot more. Make sure not to miss this jam-packed episode for everything you need to know (and forget) about market efficiency!
Key Points From This Episode:
Kicking off with a book review of The Long Game by Dorie Clark. [0:10:53]
Four waves of a career as per Dorie Clark: learning, creation, connecting, reaping. [0:13:04]
Benjamin readdresses the user cost model from Episode 180 on the Canadian housing bubble (or lack thereof). [0:16:06]
Insights from the user cost model regarding price sensitivity and rate changes. [0:20:13]
Addressing common confusion regarding housing as a depreciating asset. [0:22:53]
Speaking of bubbles: innovation stocks in deep value territory as per Cathie Wood. [0:26:08]
ARK’s forecast for innovation platforms and the 30-40 percent compound annual rate of return their strategies could deliver in five years. [0:32:01]
What deep value looks like according to ARK; prices to book, sale, and earnings. [0:33:30]
Thoughts on the size of innovation platforms relative to global market cap. [0:34:47]
Why growth in earnings per share, not market cap, results in growth in returns. [0:36:14]
The impetus for today’s topic: Market Efficiency Myths and Misconceptions. [0:40:03]
Eugene Fama’ himself on why the market isn’t expected to be perfectly efficient. [0:41:44]
Testing market efficiency categorized by weak, semi-strong, and strong forms. [0:42:29]
Why applied micro-economist and market design specialist Eric Budish believes the market is objectively inefficient at the millisecond horizon. [0:43:35]
What EMH has to say about information markets, competition, and actual prices. [0:45:11]
Some ways to test market efficiency taking different models into consideration. [0:47:22]
Understanding what EMH does not say, including that prices are right at all times. [0:50:43]
Alternative models to EMH; behavioural finance as explained by Professor Hersh Shefrin in Episode 167. [0:53:18]
What Wes Gray says about quantitative investing and human bias in Episode 69. [0:59:09]
Market efficiency and given anomaly: seasonality, momentum, and more. [1:02:12]
Ken French on how momentum relates to market efficiency in Episode 100. [1:03:40]
Anecdotal misconceptions involving Warren Buffet and Renaissance Technologies. [1:08:54]
Whether or not people with specialized knowledge earn excess returns. [1:13:13]
Overconfidence as per Ben-David, Graham, Harvey, Scheinkman, and Xiong. [1:17:18]
Talking Cents: we share our comfortable and uncomfortable responsibilities. [1:23:53]
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 are hosted by me, Benjamin Felix, and Cameron Passmore, Portfolio Managers, at PWL Capital.
Cameron Passmore: Back in the new year, Happy New Year. So skiing is something that happened in both our households over Christmas. So I got James a set of skis for Christmas, and we got out three times. In fact, we got out this past weekend and it was just fantastic.
Ben Felix: I didn't go. It's not so easy for me to just go and rent boots because people don't rent size 17 boots, but my wife took three of the kids, for they all had lessons including my wife, which was pretty cool. So they had four days of lessons and then they loved it. Kids loved it.
Cameron Passmore: That's great. So you got equipment or you rented equipment for them?
Ben Felix: Rented.
Cameron Passmore: Just to make sure they like it?
Ben Felix: Yeah. Maybe for next year, we'll get it because I think, it's like a three-minute drive from our house. So there's no reason for us not to have that as a primary activity in the winter.
Cameron Passmore: Very cool. It was so great to get back into it. We haven't done it in over a decade and it was beautiful. Got out even nice skiing too, which was so much fun.
Ben Felix: That's awesome.
Cameron Passmore: Two very quick show shoutouts for you. If anyone's into the music industry, and I know we mentioned the show, The Defiant Ones, which talked about music producer, Jimmy Iovine, but we discovered two more similar documentaries over the holidays that were both unreal. One was on Clive Davis and the other one was David Geffen. So they're both icons clearly from the 70s, 80s, 90s into 2000, how they came to discover their careers and what talent they uncovered and developed is just jaw-dropping, unbelievable stories, both of them. Clive Davis, for example, is responsible for discovering Aretha Franklin, the Grateful Dead, Bruce Springsteen, and even up to Maroon 5, like current day bands. One of the coolest parts of the story I found was with Whitney Houston. So he discovered Whitney. And when they were doing the movie, The Bodyguard, he pushed back and said, "You're not focusing enough on her music. The movie should be better music." And they actually went back and rebuilt the movie based on his feedback. And this is what caused Whitney Houston to be such a huge star with the hit, I Will Always Love You, which I didn't know was actually written by Dolly Parton. Pretty cool. And David Geffen, he co-created Asylum Records. And then later in his career created, and I'd forgotten this, DreamWorks Studio, movie studio with Stephen Spielberg and Jeffrey Katzenberg.
So Geffen was in music as well as Broadway, as well as movies. And he, get this, he helped create Jackson Brown as well as the Eagles. The Eagles were nothing when he found them. He said, "Go back for two years and make an album." So they went away. It seems obvious now the Eagles are huge, they're unbelievable, but back then, there were nothing. He helped create them. Also bands like Joni Mitchell, Bob Dylan, Aerosmith, Nirvana. He was also very close friends with John Lennon and Yoko Ono. And in fact, he was in New York when John Lennon was shot. And he's the one that had to give the news to Yoko that John Lennon did not survive the shot at the Dakota.
Ben Felix: These are shows on Netflix or what?
Cameron Passmore: They're on Netflix. Believe they're both on Netflix. Phenomenal, both of them, if you're into the music industry.
Ben Felix: A couple quick shoutouts that we missed in our year-end show, in hindsight of course, should have given shoutouts to the community moderators. I mean, they put in a ton of work behind the scenes and not behind the scenes, making contributions to the community, but that's Alex, Dan, Marco, Bento, and Erask Eric Big, thanks and shoutout to all of our community moderators, who again put in a ton of work, both moderating, but also contributing valuable content. Can't thank them enough.
Cameron Passmore: That was our mistake not including them in the year-end show and our apologies and extreme gratitude for the great work you guys are doing. Wanted to announce something that I put out on Twitter and on LinkedIn over the holidays. So you remember Katy Milkman last year talked about how new years can be a fresh start for people and over the holidays had the free time to get through four books. So I was writing my Peloton one day and as you know, I get a lot of these crazy ideas when I'm on my Peloton. I'll often drop you a text message when I'm writing. Anyways, I came up with this idea of just thinking about the books I've read as well as the number of people that have reached out to me saying, "Oh, I wish I could read as much as you," which four years ago, as you know, I was not a reader.
I'd mentioned that before. I hardly read at all. So I was thinking on the Peloton what could I do to maybe encourage people to read more? So I came up with this idea of 22 in 22, just a simple reading challenge. So I put it out there on Twitter and LinkedIn to see if anybody would be interested, ended up getting almost 250 people saying, "Yes, I'd be interested in some sort of something." I don't know what that something was going to be anyway. So I talked to Angelica and she found a software tool that gamifies the experience of reading, which is a super cool idea. So we've signed today to get the software and we're going to on February 10th launch the 22 in 22 reading challenge. So this is not a book club. You read what you want to read. It's just a place for us to meet up, to create a community to talk about, and to share the books we're all reading with the hope of encouraging people to read their 22 books in 2022.
So it's a pretty fun site. You can share books, there're badges you earn. We can do little sub communities. So we have a lot to learn at that. So on February 10th, we will be launching that.
Ben Felix: Very cool.
Cameron Passmore: I think it's going to be very cool. I was quite taken by the interest in people. And a lot of people actually DM me on the platforms to say how much they're looking forward to it. So I think we'll get some good book ideas going, which will be fun. Had some very nice recent reviews, Gowlinaoeu in Canada says, "Yay, great financial podcast. Clear and accurate." Had a nice feedback from KennethS1987, who said that the podcast made him rearrange his personal finances, which I thought was pretty nice and took a simple approach to his portfolio, sold all my stocks and moved my mutual funds into indexes. It has helped me ignore all the mega-hype noise that dominates every platform. I now have a plan, not just for my portfolio but for everything else. An investing plan, a savings plan, a spending plan, and a living plan. I started listening to the podcast in late September from episode one, yes a lot of people go back to episode one, and just finished 181. I’ve been reading the guests’ books and they’ve also proven very helpful and informative. I recommend this to anybody.
We also got a nice email from Mike in the states last week, who said that he stumbled across the podcast community into work one day. And it was such a huge breath of fresh air to hear the peer review fact-driven approach that you have in finance as a whole, let alone the ability to look at a situation from two different perspectives and potentially arrive at the same or diversion conclusions while having similar end goal in mind. So he talks about how the podcast had a huge impact on his company. And he has two full-time employees and two season employees, and they've had conversations at work to get this about the market. Some of the concepts renting versus buying and personal finance has really become more commonplace in his workplace, which is a rarity in his field to say the least. So his crew has used the computer after hours to set up retirement accounts, and they've done research on index funds that get started.
Ben Felix: I believe it. You've become part of a community. I mean, The Podcast Rational Reminder Community is up to, by the time those episodes' comes out, it'll be at 6000 users in there, which is a lot of people, but you get people who are coming in and learning and educating themselves. And if you're in there, if you're in that community, it's what you chat about. It's whatever the whole thing's based around that, what Mike is saying is great, but doesn't surprise me. You get a bunch of people together with similar interests and they'll talk about their interest.
Cameron Passmore: It just shows the love to community, right? And hopefully that's what happens with the reading challenge. Upcoming guests, next week we have Robin Wigglesworth, author of Trillions, phenomenal interview. West Gray joins us the week after that to talk about the ETF marketplace, and then Andrew Hallam, who is the author of the upcoming book, Balance: How to Invest and Spend for Happiness, Health and Wealth. His book comes out January 18th. Do you want to talk about your little part of that book?
Ben Felix: Sure. Well, Andrew asked me to write the cover blurbs. So he sent me a draft copy. I don't know, it was months ago, and I read it and wrote a little blurb and my blurb exists on the cover of the book now.
Cameron Passmore: And then two weeks after that on February 17th, Ayelet Fishbach will be joining us, and she's the author of the just released book, Get it Done: Surprising Lessons from the Science of Motivation. Excellent book. I just finished it this morning. I loved it. So she's a colleague of Katy Milkman. So that's how we got the introduction.
Ben Felix: So you finished a book. I was thinking 22 in 22, you must be done your first book by now.
Cameron Passmore: I've done three. I got three done. I've got four done over the holidays. I've got three done this year.
Ben Felix: You're crazy. I'm rereading books that I read years ago and very slowly rereading them.
Cameron Passmore: I mean, we shut down over Christmas for the first time ever, which gave me lots of time. COVID gave lots of time.
Ben Felix: Right.
Cameron Passmore: And people know I get up early, so there's so many other things I just stopped doing like watching news or TV and stuff. So I just enjoy it so much. As always, connect with us on Twitter, LinkedIn, I'm on Peloton at Cp313 and #rationalreminder. And don't forget the Instagram page for the Rational Reminder Podcast. If you checked it out today, you saw my Fender Stratocaster guitar.
Ben Felix: I didn't check it out today.
Cameron Passmore: I had also, don't forget in our store, we have winter tux still available. I think there's like 40 or so still available. And there's Talking Cents Cards available. It's unbelievable how many people have ordered Talking Cents Cards.
Ben Felix: They're amazing. They really are.
Cameron Passmore: It's nuts. We have a whole new batch coming in soon. So there'll be lots of supply. Then shortly, we'll also have the 2021 guest mug available. Anything else?
Ben Felix: No, I think that's good. We can go. We've got a, I think a pretty, pretty meaty episode today. Actually, I spent a fair amount of time over the holiday, just because I enjoyed it chipping away at a lot of this content. So hopefully people like it.
Cameron Passmore: See you're working on the content. I was reading books.
Ben Felix: It's fair.
Cameron Passmore: It's not different. Cool.
Ben Felix: All right, here we go. Welcome to episode 183 of The Rational Reminder Podcast.
Cameron Passmore: Okay. Kicked out with a QuickBook review. This one was recommended to me by a previous guest, Robin Taub. So she saw that I posted online how much I enjoyed Dopamine Nation, which was Dr. Anna Lembke's book, and past guest Robin recommended I check out the book, The Long Game: How to Be a Long-Term Thinker in a Short-Term World, which was written by Dorie Clark. I loved the book. Certainly you know and everyone on our team knows and certainly my kids know, it's probably the phrase I say the most, which is, "You got to play the long game, keep a long-term focus." And certainly we've built our company to play the long game. This is also very much the spirit of the Michael Dell book that we reviewed, Play Nice But Win. And once you're clear in your values, you can then set out to build something spectacular over the long term and get away from thinking solely about short-term results.
And that's kind of the point of the book is to help you become a strategic long-term thinker and to get above the day-to-day noise and to start thinking about your life, your business goals, and then set out to create the strategy, the skills, and the tactics necessary to achieve those goals. So the book is full of ideas, full of tactics. It's extremely well written and I found it quite interesting. Maybe I was overthinking this over the holidays, but the book actually caused me to stop and think about all sorts of stuff and also made me contemplate how to value time. And I don't know what it was and maybe it was me more than the book, but somehow this thinking experience happened for me. So I started to wonder, so if I spend three hours reading this book or five hours, whatever it takes, and if it shifts me or the reader a degree or two and cause some sort of crystallization of an idea, I personally view that as time well spent.
Other people might think that's not a good use of time, but some of the ideas that really cause me to think which links to kind of the 100-year plan that we've talked about for our company, but she talked about how a career has four waves. This really stuck with me. And I've mentioned it. I know I mentioned it to you and to others over the past couple weeks. So the four waves of a career that the author talked about. Number one is learning, which is when early in your career, you finish your studies, you are becoming more knowledgeable about your field, and then it morphs into the creative or creation phase or wave, which is where you take what you know, and you start creating value both for your clients and for the enterprise, no matter what kind of enterprise you're in.
Then the next phase as you get along in your career is connecting, where you connect with others in your field, you learn from them, you contribute back to the community to make it better. And these waves aren't all mutually exclusive, of course, but they're just general waves. And the last wave is reaping, which is when you're at the top of your feel and your game and it's time to enjoy the benefits. The so-called, it took 20 years to become an overnight success. Another interesting insight from the book was, she talked about competition and the longer the game you're playing, the fewer there are competitors competing with you. That's pretty meaningful. You're willing to do the work, and this podcast is a great example. This is a lot of work week in, week out, right? So just by that amount of work, you're sifting out a lot of potential competition.
And she also talked about how, she gave the example of Daniel Pink, who we've mentioned before, the author and thinker. So you may not be Daniel Pink today, but you can be Daniel Pink of 20 years ago before he was Daniel Pink. So what do you have to do over the next 20 years to become the Daniel Pink in 20 years of who Daniel Pink is today? It was about those kind of interesting too, just showing you could be anything you want to be. Four years ago, we'd never, while we'd been in front of the microphone at the radio station, but we'd never done a podcast before, and I'm not saying this is good. I'm just saying you can recreate yourself.
Ben Felix: Right.
Cameron Passmore: In the end, the three keys that she summarizes to being a strategic long-term thinker are; number one, independence, the heart, you have to be true to yourself, you have to focus on what's important to you and develop your own long term personal strategy. If you don't enjoy it, you're not going to do the 10,000 hours to become great at it. You have to be curious why live the life that someone else may have predetermined for you. It doesn't make any sense. Be open to clues that may help set your direction in life. I mean, this has been your message all along. You don't have some master plan, you just go where there's an interest for you, right? And then lastly, resilience, just try to experiment. Who cares if it doesn't work? Who cares if this podcast didn't work?
Ben Felix: Love trying stuff, throwing stuff.
Cameron Passmore: Rejection is just another piece of information. We're totally living proof of that. We've had so many experiences where you happen to trip over some sort of lucky experience that ends up becoming some great vein of opportunity. So you have to be resilient and keep trying until those veins show up in your life.
Ben Felix: We all build on each other too though. Success is building each other.
Cameron Passmore: For sure. Anyways, highly recommend the book.
Ben Felix: So I had a couple notes on episode 180, where we talked about the lack of a Canadian Housing Bubble. I saw some interesting comments online about that episode, really meeting it with skepticism about how you could possibly say that candidates not in a housing bubble, but just to reiterate the model there is called the user cost model. And it's the model for the economic cost of living in a home. And from that perspective, Canadian home prices are not detached from fundamentals and they're easily justified without making implausible assumptions for future price appreciation, which is one definition of a bubble. That's the rob or not definition of the bubble. Now I do have to make a correction though. So I had said in that episode that capital appreciation expectations today have to be slightly elevated relative to history to make the current imputed rent. That's the rent implied by the user cost model, the ratio of that to actual rent.
So I had said that you had to make slightly elevated capital appreciation assumptions to hold that ratio constant. And it was 0.84% above the 1% baseline expectation, which is what we said is a reasonable baseline for capital appreciation. So not a huge, not an implausible assumption for capital appreciation to justify current prices. Now, I'm going to do a white paper on this topic. And so I was just revisiting my data and going through the numbers and starting to formalize the ideas a bit more to put on in a paper. And I realized that I had incorrectly referenced a column. So I'd mixed a real and nominal variable in calculating one of the ratios. And so for one of my charts, including the one that I got the 0.84% higher price appreciation from was off.
So I corrected that. I'm laughing because people were skeptical that you needed a slightly higher, slightly elevated assumption for future growth. Correcting that, it actually completely goes away. So interestingly, when I mixed a real-real or nominal-nominal variable to create that ratio, the current ratio of imputed to actual rents is almost exactly at its historical average. Almost exactly. So I normalized it to the historical average because it's comparing average rents for two bedrooms, I think to home prices. And that ratio is not necessarily that meaningful, because why would you compare two bedroom rents to median home prices? So I normalized it based on the full average. So what ends up happening is the average is just one. And the current ratio is 1.01. So almost exactly on the average without having to make that higher assumption for capital appreciation.
So it's actually even less bubbly than I had said it was in episode 180, which I thought was kind of interesting. So as high as prices are, they're exactly where we would expect them to be in equilibrium with rates where they are and with the set of reasonable assumptions that we walk through in episode 180 for the risk premium, the property tax maintenance costs/depreciation. So I thought that was interesting. I saw one and comment online for that episode saying that, "Well, this definition of a bubble could only be called a bubble in hindsight." And I read that and it's like yes, that is what a bubble is in an efficient market. You can only call the bubble in hindsight, if prices reflect current information, you can only call it a bubble in hindsight.
So the commenter on the internet was saying that this was a negative of the model, but it's actually a feature or expectation at least.
Cameron Passmore: Well, you can see you caught Oscar's interest. See perked up here.
Ben Felix: Interested in home prices makes sense. Now, the other reason that I wanted to bring this up, so I wanted to make that correction and say that houses are even less bubbly than I had said they were. But the other thing that I wanted to mention is that doesn't mean that there's not a lot of price risk in Canadian Housing. So just because there's no bubble, there's no requirement to make implausible assumptions about future price appreciation to justify current prices. That doesn't mean there's not a lot of price risk. One of the other insights that we get from this user cost model is that prices should be very sensitive to changes in rates when rates are already low or when the user cost is low. So I'm not saying everything's going to be fine. Prices are going to stay this high forever. That's not the point of saying that it's not a bubble. And if we look at the historical correlation between home prices and real interest rates, the user cost model would predict a relationship that historical correlation is negative 0.81.
So there does appear in fact to be a relationship. Now, the sensitivity to a 1% change in rates, and this is the important part, is much greater today than it was in the past. So if rates rose 1% in the early nineties, when the user cost was around 9% to start a 1% rise in rates, we would expect prices to fall by 11%. The user cost today is closer to 3% and a 1% rise in real rates would be expected to drop prices 33%. Just because of how low rates are to start or how low the user cost is to start. And that's just a very simple mathematical insight that comes from the user cost model. So I'm saying prices are justified by that. That doesn't mean it's safe and what it means is you can't predict why it's going to pop, right? Like that commenter said, which is exactly what you'd expect if you believe the market's at least somewhat efficient.
So I'm not saying housing's a safe investment, I'd be pretty nervous buying a house if I had a five-year time horizon. The interest rate risk with home prices right now is pretty, pretty scary. So I want to make sure that was clear. I'm not saying Canadian Real Estate is not in a bubble, everything's fine. I am just saying that prices can be rationally justified, but there's still a lot of price risk because of interest rates.
Cameron Passmore: Right.
Ben Felix: And then I just had a couple more comments on housing, just from stuff that I've seen online with people talking about our episodes. Our comments has investing episodes about housing-
Common sense investing episode about housing. A common confusion, I've said that houses are depreciating assets. And a lot of people on the internet say, well, no, they're not. They appreciate. But the important distinction there is that, buildings, houses, the construction, the structure, is a depreciating asset. That's a fact. Whether you call that a maintenance cost or depreciation, it's there. And that's the one percent-ish maintenance cost that we always use in modeling this. And a side note on that. I think 1% is way too low. Oh my goodness. I haven't even lived in the house for a year, and the amount of time that I spend on house, not even the money. Not even the money. Buying house stuff is expensive. But the trips to Home Depot, my own time that I have to spend because a plumber's not going to come for this little thing. Or a carpenter's not going to come for this little thing, so you do it yourself. Anyway. I think 1%, when you factor in the time cost is way, way too low.
Cameron Passmore: I agree. And especially when you take a look at large renovations. It's so easy now to do a $50,000 bathroom or kitchen. You start amortizing that over a number of years.
Ben Felix: But the big capital costs, for sure. But maybe that gets you to 1%, right, if you amortize the 50,000 over 30 years, whatever.
Cameron Passmore: But you're talking about small stuff. There's endless small stuff.
Ben Felix: Yeah. Just the little stuff. It just chips away. It's like, okay, I've got to spend one day of my weekend doing house stuff. And that's just the thing. I knew that going into buying a house, but it just becomes more real when you live it, I guess. Anyway. So I think 1% is too low of maintenance cost. But to finish up on that, buildings depreciate. Find someone that disagrees with me on that, I'd be curious to hear their opinion. But land appreciates though. And so, in aggregate, the building plus land, you do tend to see appreciation, especially if you're keeping up with your depreciation costs, your maintenance cost.
And then the last comment I want to make on housing before we move on is that I've never really had a strong opinion on whether owning or renting is better. My position's always been that renting deserves consideration in that decision. And from a financial perspective, you can be agnostic. Or there are cases where, from a financial perspective, one option can be better than another. And therefore you should make the decision based on other factors, like your time horizon or where you want to live, or how much control you want to have over the property, all that kind of stuff. So my intended message has always been that renting deserves a place in that discussion, but not that it's better than owning. It can be better in certain cases, just as owning can be better in certain cases. And I think I've always tried to make that point, but I just want to make sure it was clear. That's all I got for the housing update.
Cameron Passmore: Speaking of bubbles, you're quite happy to know that innovation stocks are not in a bubble. In fact, they're deep value.
Ben Felix: Yeah. So this one made a lot of noise online.
Cameron Passmore: Boy, did it ever?
Ben Felix: So Cathie Wood posted on the Ark website, the article of the title that you just said, Innovation Stocks Are Not in A Bubble: We Believe They Are in Deep Value Territory. Interesting. So she laments, in the article, and that this is a quote, "Perhaps influenced by negative headlines in the media and by the inherent volatility of our strategy, some clients have sold near the bottoms of market cycles, turning what otherwise would have been temporary losses into permanent losses." And we know that. We discussed in a previous episode that the average investor return in Ark has been not so good despite the fund having positive returns because, well, people buy and sell at the wrong time. And that's nothing new. Investors tend to do that.
Something that I did, just for some additional context, was look at Cathie's past funds, so pre Ark. Pre Ark, what did Cathie do and what was her experience? It's not intended to be a knock on Cathie. But Cathie's talking about people bailing on strategies. And it sounds like, from reading the article, she's got experience with that happening. So I just wondered, has she lived this before, pre Ark? And then she has. So she managed the Alliance Bernstein Discovery Growth Fund from 2003 to early November 2009. And using return attribution, it behaved like a mostly US small and mid-cap growth fund. Over that period, the fund trailed IJT and IWP. Those are US small and mid growth ETFs. The fund returned 7.11% compound over the period versus 7.43% and 7.57% respectively for the ETFs. And the market for fund flows did not like that very much. She grabbed some inflows in 2003 when she had started managing the fund, but every other year saw big outflows, like large percentages of the total assets of the fund.
She also managed the Alliance Bernstein Sustainable Global Thematic Fund from November 2008 to late May 2013. And again, using return attribution. Wow. Because that's a rough period.
Cameron Passmore: No. Wow, because she started it or started managing it right at the tail end of the crisis.
Ben Felix: Oh yeah, yeah. So this one behaved like a US large and mid-cap growth and emerging markets fund, again, based on return attribution. I didn't have the easy ability to go back and look at the actual geographic allocation. So that's just a returns based attribution. The fund returned 10.5% per year on average. IWP returned 19.43%. VIMAX, that's the Vanguard Emerging Markets Fund, returned 15.33%. And IVW, that's iShares large growth, returned 15.73%. And so again, the market wasn't really into that. And the fund saw big outflows every year that Cathie managed it.
And then the last one that I was able to get data on, and there are a few others that were listed in other countries, and it just wasn't as easy to get the same kind of information on those ones. So she managed the Alliance Bernstein US Strategic Research Fund from the end of 2009 to June 2013. And I couldn't find details, but it might have been an incubation fund because it was never very big. It had small inflows in the single digit millions in most years. And then the fund closed down completely in 2013, liquidated in 2013. That fund returned 9.47% from inception into liquidation. And I didn't have returns attribution for that one because it was closed. So I didn't have the same access to data. But over that period, small growth returned 17.41%. Large growth, 13%. And mid growth, 15.12%.
So if it was any of those, which I imagine it probably was, based on Cathie's general strategy, it was a pretty significant under-performer. And again, it's not a knock on Cathie. That's not the intention. But it's just interesting to see that she's been in a similar situation where she's had negative returns or benchmark trailing returns followed by negative flows.
Cameron Passmore: But nothing compared to the scale of the Ark funds.
Ben Felix: Nothing at that scale. And it's different this time because Cathie owns the company now, right? She bought it out, or whatever happened, last year. And it's possible that in the past situations, I mean, what happens to a fund manager who's managing a fund with big outflows? They probably get reassigned to a different fund, and they bring a new manager in to freshen things up. So maybe that happened. And maybe now it's different because Cathie can actually stick with the strategy that she wants to execute on. So all that remains to be seen, of course.
Now, the article that was posted on the Ark site that we're talking about it here, it is suggesting that innovation stocks are in deep value territory because of the recent decline in their prices. And that other companies in the benchmarks, the non innovation companies are at risk of being disrupted. And that based on that disruption risk, their current valuations are far too high. And that's been the Ark narrative forever. So they say that, based on their forecasts for innovation platforms, their strategies today could deliver a 30% to 40%, and this is the headline grabber, 30% to 40% compound annual rate of return during the next five years. That's a forecast, if I've ever seen one. Now, I wondered what is their definition of deep value? And I know it's different from ours. Because they're saying deep value relative to their projections, which are their models.
Cameron Passmore: Exactly.
Ben Felix: And we're saying that the market has the right information. They're basically saying the market doesn't have the right information.
Cameron Passmore: I think they were saying is, the deepest value they've seen by their metrics since 2018, I believe.
Ben Felix: Which is an interesting comment because it depends on their cashflow expectations. Change a decimal point or change things. DCF models are crazy. I remember when I was doing my MBA, I was on the student investment fund, that we had to do DCF models to value companies and decide whether the fund should allocate to them. It was striking to me that literally changing a basis point. Well, of course, this makes sense when you think about it. But when I was sitting down doing the modeling, it was just striking to me. You change a basis point in your discount rate, for example, and a stock goes from a buy to a sell. Or you change a basis point in your growth expectations, same thing. So it's funny to hear it from Ark, who's relying on their models. Okay. So what does a deep value look like? Ark, A-R-K-K, their flagship innovation fund has a price to book of 5.19. I know a lot of people don't like price to book. So it's got a price to sales of 11.48, and a price to earnings of 41.51 as of December. Now I compared that to the iShares Mid-cap Value ETF, and I chose Mid-cap because it had the closest market cap, average market cap, to the innovation fund. It's got a price book of 2.45, price sales of 1.69, and price earnings of 11.73. So for the iShares Mid-cap Value ETF, it's valuation is 47% the valuation of Ark by price to book, 15% by price to sales, and 43% by price to earnings.
So, I mean, it takes some gymnastics to make Ark look deep value. And the iShares Mid-cap Value is not even supposed to be like deep value. It's just value. So as expected, little different definition of what constitutes deep value. But interesting, nonetheless. Now reading through the letter, the part that I found, I think most interesting, and I'll do my best to explain why. So they start projecting the size of the innovation platforms relative to global market cap. Based on their research, the opportunities will scale from 10 to 12 trillion, or roughly 10% of global market cap today, to 200 trillion plus in the next 10 years. Okay. Now, remember they said that they've got a 30 to 40%, five year compound return projection. I saw the 10 to 12 trillion to 200 trillion growth in the next 10 years. And I just wondered, what is the compound return to get there? Well, it's 39.5%. I was like, huh, that's interesting.
So then I start wondering, is the 10 to 200 trillion growth projection, is that supporting the 30 to 40%, five year compound return projection? And I don't know. It's not explicit that it is supporting it, but it's certainly implied. And the problem with that, if that is what they're saying, is that we know, from financial economics research, that growth and market cap is not the same thing as growth in returns for investors.
Cameron Passmore: Unless no new entrants in the marketplace.
Ben Felix: Unless there's no new share issuance or new companies issuing stock.
Cameron Passmore: Exactly.
Ben Felix: So another way to say that is that it's growth in earnings per share that results in growth in returns, not growth in market cap. Market cap can grow while earnings per share decreases, if a whole bunch of new competition raises capital or stays the same, at least, maybe not necessarily decrease. Now, in a faster growing sector or country, which is what Ark is explicitly investing in, that gap between capitalization growth and returns is going to be larger than a more stable sector because of the new equity issuance and existing companies raising capital to fund their growth. So those are both activities that grow the size of the market, but they don't increase the earnings per share. And who knows, you could even see earnings per share decrease. So it's not obvious to me that saying, well, this sector's going to blow up, and therefore returns are going to follow. That's not how it works.
Cameron Passmore: Isn't that also what you would expect from innovation?
Ben Felix: The new equity issuance and all that stuff?
Cameron Passmore: Yeah, like new innovators coming to market to innovate?
Ben Felix: Yeah. Yeah. So this is one of the problems, one of the many problems, with investing in technological revolutions, is all of the new equity issuance dilutes earnings per share. Plus all the other stuff, like, what's the other problem? Well, one of the other problems is that investors will often overpay for growth, which results in lower realized returns even if the market capitalization does increase a lot. And that's not even to mention the Lubos Pastor Jensen's inequality idea where uncertainty about future profitability causes prices to be high, all else equal. That's the rational story for high prices and technological revolutions. But take all that together, and the thing that, it just struck me is, huh, interesting. They're saying that the sector's going to grow by 40%, 39.5% over 10 years. And they're saying that their returns are going to be 30% to 40%. I don't know. Didn't quite add up. And neither did the deep value comment.
So Cathie, I don't know if Cathie wrote the whole post. She definitely wrote the introduction. But at the end of the post, it says, "In our view, these Pavlovian responses, responses to flee from their types of strategies, will prove just as wrong as those in the early days of the coronavirus crisis. They are backward looking and do not recognize that companies investing aggressively today are sacrificing short term profitability for an important reason, to capitalize on an innovation age, the likes of which the world has never witnessed. We will not let benchmarks and tracking errors hold our strategies hostage to the existing world order." What a statement.
Cameron Passmore: And we all know what you're thinking.
Ben Felix: So my take on that was okay, so-
Cameron Passmore: Oh, we know your take-
Ben Felix: Basically small growth, low profitability, which we know is the worst performing segment of stocks. Small growth, low profitability is different this time, is basically what they're saying. And economic growth is the same thing as investment returns. Those are a couple of pretty big leaps of faith, at least relative to history. But history is backward looking. So maybe we should ignore it.
Cameron Passmore: I'm sure Mike and his coworkers in the states recently listened to that episode, or the couple episodes on technological revolutions and know exactly what you were thinking. It was fascinating. So the big topic for today, this is something you've been thinking about for a while.
Ben Felix: Yeah. So where this came from, you always ask me where I got the idea or whatever from. In my efforts to learn more about Bitcoin and cryptocurrency and all that stuff, I was listening to a podcast where somebody who believes markets are efficient, and it was not a good podcast. I wouldn't really recommend it to anybody. But I'm trying to understand the culture of, what are the people that believe, really strongly in Bitcoin, what are their beliefs? What do they think? And this podcast was great for that. But for other things, I wouldn't really recommend it.
Anyway. So they had somebody who believes markets are efficient on the podcast. And they were all debating with that person why markets are inefficient, and how they've made so much money in Bitcoin by being smarter than the market. And the efficient market side of the debate was, I think, well represented. It was pretty good. And the inefficient market side was, I'd call it egregious, but that's just my opinion, I guess. But there were so many common misconceptions in that podcast that it jumped out at me as an opportunity to not respond to that podcast that I listened to, because I added a bunch more stuff and took some stuff out. But just to do an episode on market efficiency myths and misconceptions. Because I think there are a lot of people out there that just dismiss market efficiency as an incorrect model, without understanding what the implications of market efficiency are, or how you test it.
So the first one, is the market perfectly efficient? It's not. And if you go back to Fama, so Fama 1970 says that a market in which prices always fully reflect available information is called efficient. That's Fama's original definition of market efficiency. But he also says, "Though, we shall argue that the model stands up rather well to the data, it is obviously an extreme null hypothesis. And like any other extreme null hypothesis, we do not expect it to be literally true." So there it is, from Fama himself. We don't expect the market to be efficient. It's just a model.
Cameron Passmore: Just a model.
Ben Felix: And one of the ways that they broke that down is that Fama categorizes tests for efficient markets into the weak, semi-strong and strong form. And that you can test each of those forms of market efficiency to see where, on that spectrum, are real markets. So weak form market efficiency means information in historical prices isn't prices. So in other words, technical analysis of past data does not result in trading profits. Semi-strong form means public information isn't prices. So public information can't be used to earn profits if the market is semi-strong form efficient. Strong form means no investors have higher expected trading profits because they have monopolistic access to some information. If the markets are not strong form efficient, it means that some investors do have monopolies over some types of information, which they can use to earn trading profits.
So Fama finds, back then in 1970, that there's no important evidence against the hypothesis in the weak and semi-strong form tests, and only limited evidence against the hypothesis in the strong form tests. Now, an interesting point that came up for me while I was working on this, we have Eric Budish from the University of Chicago coming up later this year as a guest. And he's done a bunch of work on high frequency trading and market structure. And he actually argues that the market is objectively testably inefficient at the millisecond horizon by design of the market.
So Eric Budish specializes in market design, which is just a fascinating concept to think about. And his argument is that the market, by design, is inefficient at the millisecond horizon, which is why HFTs, high frequency trading firms, make their money. And it's like a constant tax on market function.
Cameron Passmore: Fascinating.
Ben Felix: And his whole thing, we'll have him on as a guest later to hear about his work. But his whole thing is that this could be solved with market design by moving to discrete time trading with batch auctions. Anyway, that's way beyond.
Cameron Passmore: So it's that moment between information becoming public and the market reflecting that information, that millisecond between those two points in time. Is that what he's referring to?
Ben Felix: The continuous time trading structure of, I'd have to go back through his paper. It's not a-
Cameron Passmore: But the millisecond that is inefficient is that moment in time, I'm guessing.
Ben Felix: Yeah. And you can solve that with discrete time trading with batch auctions. That would be, people could randomly bid on the batch auctions. It would take away the inefficiency. Anyway, way beyond the scope of this. But I thought it was worth pointing out just because it's super interesting.
Cameron Passmore: That's very cool.
Ben Felix: Okay. So what does the EMH actually say? What does the efficient market hypothesis actually say? People often say markets are efficient. Okay. What does it actually say? What does it mean to say markets are efficient, before we dismiss it? So one of Fama's big insights is that markets are information markets. Markets are informationally efficient if prices reflect all currently available information about expected cash flows and risk. Efficiency comes from competition, low barriers to entry and in low information costs. If there's new information about future values that is not yet in prices, competitive traders will act on it, injecting their information into prices until the price fully reflects available information. EMH also says that, on average, competition will cause the full effect of new information on intrinsic value to be reflected instantaneously.
Instantaneously, and that's in quotation marks, instantaneously in quotation marks, in actual prices. I'll explain the quotation marks. Due to vagueness and uncertainty and new information, actual prices will initially over adjust to changes in intrinsic values as often as they will under adjust, and the lag in the complete adjustment of actual prices to new intrinsic values will also be an independent, random variable, with the adjustment of actual prices sometimes preceding the occurrence of the event.
For example, the event is anticipated by the market before it actually occurs and sometimes following. We'll dig more into that a little later, but that came from Fama's 1965 paper on random walk theory. That instantaneous reflection of information and prices does not mean prices are always right. It means prices can be wrong, but the extent to which prices are wrong is itself a random walk around actual prices. Prices aren't necessarily always right, but they're wrong randomly, not consistently.
One of the problems that I see with a lot of the criticisms of efficient market hypothesis, is that they're anecdotal. What about Game Stop? Or they're just not testable. There are lots of ways to test market efficiency and I thought it'd be useful to talk about some of those.
Now, it is important to point out that any test of market efficiency is jointly a test of the efficient market hypothesis and of the model used for asset pricing in the test. If we're running a test of efficient markets using the Fama/French 5 factor model as the asset pricing model for which the efficient market is pricing assets, a failed test, so if we find that markets are inefficient, that could mean that the market is inefficient or that the model is wrong.
Now random walks, does stocks actually follow a random walk like we would expect them to in an efficient market? We can maybe in the notes for the show in the community and on YouTube and stuff, we can link these papers, but there are tons of papers going back to 1900 with Bachelier, but it's stocks mostly follow all random walk, especially in the short run. There are stuff like momentum and reversals over longer periods of time, but in the short run, except the very short run, at the millisecond horizon. Yeah.
EMH implies that simple trading rules are technical analysis, this is weak form, should not result in excess returns beyond what would be expected by luck. And so for papers on that, Fama 1965, Fama & Blume 1966, they found that testable hypothesis to be true.
EMH implies that fundamental analysis of individual security is done by skilled managers, should not result in excess risk adjusted returns beyond what would be expected by luck. Lots of literature on that, but Carhart '97, Fama and French 2010, are two of the big ones. I think Berk and Green's theory paper on that one is really good too. That's the one that talks about the ability to express the manager skilled diminishes with increasing assets in a fund, so if there is a skilled manager, investors will identify them and shovel assets in there until there's no more alpha and the investors are just running returns commensurate with the risk that they're taking. Lots of literature on that though.
EMH implies market prices should respond quickly to new information. And again, Fama, Fisher, Jensen, Roll had a big paper in 1969 on that and there's a lot more, just grab the Fama papers. It implies that the expected returns from any investment will be consistent with the risk of the investment. Of course, Fama/French '93 and 2015, found that to be true. And then from that, we know that there are differences in expected returns, which as many of our listeners know, investors can use to their advantage.
There's a bunch of tests for EMH. Do stocks follow random walk? Does technical analysis work? Does security analysis work? Do prices respond quickly to new information? Those are called event study tests. And are returns related to risk? Of course, inconclusive because the joint hypothesis problem, but it's plausible at least. Those are some testable hypotheses.
Now, the other question is, what does this EMH not say? I think there are lots of misconceptions there too. It does not say that prices are right at all times. I think that's a big, common misconception. Even, yeah. What it implies is that errors in market prices are unbiased. Prices can be greater or less than true value at any point in time, as long as the deviations are not predictable.
Cameron Passmore: That to me is the key.
Ben Felix: Yeah. So interesting.
Cameron Passmore: Errors in market prices are unbiased.
Ben Felix: Yeah.
Cameron Passmore: It's a very powerful statement.
Ben Felix: Oh yeah. Even if you can identify inefficiencies at a point in time, it doesn't mean that you're going to be able to.
Cameron Passmore: And it's just a model. It's not reality. It's just a model.
Ben Felix: It's a pretty good model though. The random walk around actual values in an efficient market, means that there's an equal chance that a stock is under or overvalued at any point in time. If those deviations from market price are random, you would not expect any group of investors to be able to consistently find missed price securities and earn access returns. There was the empirical work that we mentioned a minute ago, finding that's generally true. Efficient markets does not say that no investor will beat the market. Doesn't say that at all. If it's random, it says roughly half of all investors will beat the market before costs and less than half after cost, that's the arithmetic of active management. And dollar weighted, I mean even less after the skewness in stock returns.
Cameron Passmore: It doesn't mean you won't have stocks that have unbelievable returns.
Ben Felix: Yeah. Well, it's the skewness, for sure.
Cameron Passmore: Like Apple or Amazon, the skewness will exist.
Ben Felix: The Hendrik Bessembinder papers were, and there's a write up about this on Financial Times that I saw when I was researching our conversation with Robin Wigglesworth coming up next week. There was an article saying that Hendrik Bessembinder's work basically proves that people like Kathy Wood should happen sometimes.
Cameron Passmore: Yeah.
Ben Felix: Pretty interesting take.
Cameron Passmore: Speaking of which, did you see the data on the amount of market cap that Apple has increased by since Tim Cook took over a decade ago?
Ben Felix: No.
Cameron Passmore: Increase in market cap, $700 million dollars per day for a decade.
Ben Felix: Wow, no bad. Not too bad.
Cameron Passmore: Per day.
Ben Felix: Yeah, that's crazy.
Cameron Passmore: As it goes through $3 trillion dollars in total value. Anyways, a little sidebar.
Ben Felix: That's a lot.
Cameron Passmore: Talking about skewness.
Ben Felix: Yeah. Well, that's ... Apple would be in there, in those papers, for the most contribution to wealth creation. Definitely.
What is the alternative model to efficient markets? I don't think there's a lot of disagreement on the empirical aspect of stock returns. You can't really disagree with that, just observations, but there's a lot of disagreement on what's driving prices, risk or behavior. In the behavioral school of thought, asset prices are not based on rational assessments of expected returns, but on predictable behavioral biases.
Now, to be fair to behavioral finance, it does have a deep and continually growing literature on how human behavior errors can explain asset pricing anomalies. And like for every rational asset pricing anomaly, there's a behavioral explanation for it, at least it seems that way. I think there is a strong case that EMH is not the only model for markets, but what does that mean practically?
It's like okay, behavioralists have won the debate. We agree asset pricing is irrational. What does that actually mean for people who are investing money? I thought we'd play a clip from our episode last year with Professor Hersh Shefrin. He's one of the first earliest and most prominent voices in behavioral finance. We heard from him on what the behavioral finance evidence means for most investors. Like, should you behave differently, if we agree that markets are not efficient?
Cameron Passmore: I'd like to go back to the theme of market efficiency and you talk about how markets can become or go out of kilter. I'm wondering based on that, should investors still own index funds, which makes so much sense in the framework of market efficiency?
Hersh Shefrin: Yes. And so at the end of Beyond Greed and Fear, I talk about in the lessons the takeaways. The lessons for most investors, not all investors, but for most investors, is from a financial wealth generation perspective, invest as if markets are efficient, even though they're not. If you're in it for the long term, you want to be careful not to outsmart yourself because there are two counterbalancing forces at work because of behavioral issues.
The first is markets are not fully efficient, and so there are theoretical profit opportunities. And the second is, you can be the victim of your own behavioral biases and if the biases are stronger than the potential for alpha, you'll be sorry in the long run. Most people will be sorry. That's why most investors earn less from a return perspective than the overall markets, because their biases get in the way. It's not as if there isn't theoretical alpha out there waiting to be exploited. Alpha exists as a potential. You have to be pretty close to neoclassical in your behavior to actually exploit it over the long run and you have to be willing to take a risk.
The message at the end of the book is to understand the following. Number one, most investors should invest as if the efficient market school prescription is right. Don't try and beat the market. Just put together a long term sensible investing strategy and stick with it along the roller coaster.
Second, what behavioral forces will do, especially heuristics. Heuristic driven bias, will inject volatility into the market, over and above fundamental volatility, the volatility created by fundamentals. Because of that, you have sentiment based risk, risk associated with changes and sentiment. Sentiment being psychological forces at work that, things that impact us because of heuristic driven bias in particular, but also framing effects, which I can talk about later. But because of that, it's a wild ride. And the best thing is, for most investors, from a financial wealth perspective, be willing to experience sentiment based risk. There's really not much you can do about it. It's simply the cost you bear for getting a decent return in the long run. If you can accept it, then you can sort of have peace of mind over the long term, in terms of your investment.
Ben Felix: I think that's pretty big to hear from one of the original guys in behavioral finance. He very explicitly believes. I mean, that's one of his big pillars of belief, that markets are not efficient. And he spent his career testing that hypothesis, the anti-EMH hypothesis. But his conclusion, is that investors should act as if the market is efficient, for most people, because our own human biases are going to outweigh the alpha opportunities most of the time. I don't know.
Another way to think about that, is that while there are investment strategies that could theoretically exploit the behavioral errors of other investors, investor failures in applying those philosophies to earn alpha can be attributed to probably the same set of behavioral errors that cause the anomalies to exist in the first place.
Cameron Passmore: Fascinating.
Ben Felix: It's a funny thing to think about. And therefore, it's like the random walk around actual prices. We know prices can be wrong, but it's going to be a random walk. We know there can be behavioral errors, but whether you can actually exploit that or not is probably going to be a random walk too, so therefore behave as if markets are efficient.
I also wanted to note, just as I was preparing this, it made me think back to Wes Gray, which is an older episode, but Wes decided to become a quant investor after getting his butt kicked as a fundamental stock picker. And so again, I just thought it's interesting to hear somebody who's gone through that transition of let's behave as if the markets are inefficient. I know Wes doesn't necessarily believe that the markets are efficient now, but he sure invests in a way that could be justified on the basis of behavior or risk. I thought we could hear from him quickly too.
Can you talk a little bit more about the importance of biases when you made that switch from being a human stock picker, obviously with biases, to quant? How did you realize that you had to eliminate the biases?
Wes Gray: Again, this is a personal thing because there definitely are stock pickers, and I know a few, who they're just machines or robots and for whatever reason, they have way better capability to harness and manage these problems. I used to think I could do that too, but for me, it was just eating like tons of humble pie. I kind of did, if you guys know the Warren Buffet story. There's stories about him putting 70% of his capital in Geico and obviously, that ended up being a good thing and he's a multi-billionaire.
I ended up doing similar thing, where I had this super concentrated portfolio, these uber small net liquidation deals, and on one of them in particular, which ended up being a fraud and a total bust, I think I got it up to 60% of the capital there, because I'd done so much work. When you do so much analysis, what happens ... And now, of course, you read all the psych studies that say I was just succumbing all the problems, but as you read more and more information, your confidence goes up, but a lot of times your forecasting ability of predicting doesn't really move.
You need the first few pieces of information to get you in the right ballpark of reality, but then what happens is a stock picker like me, you get so obsessed and you read everything, you do all this work, you spend all this time, and you just keep getting more and more confident in your decision, and you end up doing stupid things like I did, where I had like 60% of my portfolio in one penny stock. Which after the fact, obviously seems stupid, but I actually did that. I think it was just because I was falling prey to all the standard problems. I realized, through experience and after getting my ass handed to me, that I just needed rules, man because there's no amount of convincing yourself when you're in the fight that you're not suffering from overconfidence, you're not believing your own story. You just need rules, at least I did. For me, that's very important, but other people are different.
Ben Felix: Okay. Just a neat story from Wes and fun to resurface something from a while back in the podcast.
One of the other ones that comes up, is what about given anomaly? Seasonality is a big one. Momentum is another big one. Those, it's hard to explain seasonality by risk. It's hard to explain momentum using risk. If a market inefficiency can persist or a market in inefficiency can persist, if it's too expensive to trade it away. For seasonality, there's a good paper by Novy-Marx and Velikov from 2015, and they find that the net alpha after transaction costs for trading on seasonality is negative in microcaps, small caps, and large caps, with micro caps being by far the most negative, due to the highest transaction costs.
There's a limit to arbitrage, where even if seasonality does exist and it is a market inefficiency, you can't really exploit it. That's why that's why it still shows up in the data, but if you go and try and trade on it, it'll go away or you won't capture it after costs.
Now, momentum is harder to explain, and Ken French will tell us the same thing in a second, but it is resilient to transaction costs. It does take a hit. Transaction cost momentum are higher than other strategies or some other strategies. It doesn't disprove EMH necessarily, keeping in mind EMH is just a model. It is a challenge to efficient markets, for sure.
Now, it's a hard topic to talk through and we asked Ken French about this back in Episode 100, so I thought we could just hear from him, instead of me trying to stumble through, hear from Ken French on how momentum relates to market efficiency.
We've talked quite a bit about the risk based framework for asset pricing. One of the things that's been observed empirically, pretty persistently, is the momentum effect, which does not fit out at all I don't think, well anyway into the risk based framework for asset pricing. How do you think investors should approach momentum, which again is empirically robust? How should investors approach implementing that in their portfolios?
Ken French: Well, first I'm with you. It's really hard for me to reconcile momentum with a risk based story. I never argue prices. All prices are right. I think there probably are mistakes in prices. My trouble is, I don't know which ones are too high and which ones are too low.
If I'm thinking about momentum, what I basically want to do, is look at the last year. It's not quite the last year. If I look at last month, there are reversals. The companies that did really well last month, tend to do poorly. And the companies that did really poorly last month, tend to do well. Momentum's the reverse. Momentum says, okay, stocks that did really well last year, they tend to continue to do well for the next few months. Stocks that did poorly for the last year, they tend to continue to do poorly.
And so you can see if I've got this reversal going on last month, I can strengthen the momentum effect just by holding last month out. That's what we do. We look from minus two to minus 12 and that's where we see persistence.
What dimensional does is say, look, we don't really understand what's driving momentum, but it's such a robust characteristic in the data. Let's not ignore it. On the other hand, we know it's a high turnover strategy. If I really wanted to pursue an active momentum strategy, you're going to be turning your portfolio over a lot. That's going to cause transactions costs. We're careful. We don't want to impose transaction costs on our clients, so what we say is, let's not chase momentum, but let's use momentum if we were going to trade anyway. What I mean by that is, suppose we have a stock we'd like to sell. It was a small stock portfolio. The stock is now big. It doesn't fit in the portfolio. We'd like to sell it. Before we sell it, we look to see what happened from month minus two to minus 12. If this stock was a great performer, which you might expect because it used to be small and it's not anymore, you'd say, well, this stock went up a lot from minus two to minus 12. Momentum predicts it's probably going to continue to do well for the next few months. We don't sell it.
For individual investors, there's no reason they can't do the same thing if they are trying to manage their own portfolios in this fashion, so they are trying to pick winners, okay use momentum in that same fashion that says, okay, if you're going to trade anyway, then pay attention to what's happened in the prior year. If you were going to buy something and it's underperformed recently, you probably want to wait. If you were going to sell something and it's over performed recently, you probably want to wait. Otherwise, do what you would've done anyway.
Now, I'm not advocating anybody should be out there actually doing this sort of trading. But if you're going to, that's how you might want to take advantage of momentum.
Ben Felix: Okay. Seasonality is strongest in microcaps and doesn't hold up to transaction, which is why we still see it in the data. Momentum is there in the data, even after transaction costs, but it's a high turnover strategy to pursue on its own. And so many investors, including clients of Dimensional, like us, may not want to pursue momentum directly. And interestingly, if momentum does persist, the fact that Dimensional is not trying to chase momentum, is potentially a reason why it would persist. There is a bit of a limit to arbitrage or a limit of arbitrage argument in there. If a huge asset manager like Dimensional decides we're going full in on momentum and building a momentum strategy, that could make momentum go away. But because Dimensional's not sure if it's going to persist or not, the fact that they won't pursue it, means that it might persist.
Cameron Passmore: Hadn't thought of that. Interesting.
Ben Felix: Yeah. Those are both limit to arbitrage arguments, that there's an on paper arbitrage opportunity, but not everyone's willing or able to exploit it, so it persists in the data. But as Hersh Shefrin told us, an arbitrage based on investor behavior is never really an arbitrage because an arbitrage would be a risk free profit because when behavior's involved, there's also sentiment risk, which can move against you. Nothing's free and I don't think that disproves efficient markets. Pricing anomalies don't disprove efficient markets, keeping in mind it's just a model.
Some of the other big one that come up are, well what about Warren Buffett?
Cameron Passmore: How many what abouts you heard like this?
Ben Felix: Well, this whole episode is what abouts.
Cameron Passmore: How many what abouts have you heard like this?
Ben Felix: Well, this whole episode's what abouts, isn't it? What about Buffett and what about Renaissance Technologies? So I wanted to give some commentary on that. I actually think Renaissance Technologies is proof that markets are mostly efficient, and I'll explain why. Their main fund with the ridiculous performance that was documented by Greg Zuckerman.
Cameron Passmore: Zuckerman.
Ben Felix: We had him on our podcast talking about that. So that ridiculous performance record, that fund has been closed to outside investors since 2003 and is capped at $10 billion and all of the profits are distributed each year.
Cameron Passmore: Yep.
Ben Felix: If markets were mostly inefficient rather than mostly efficient, Renaissance Technologies would not have to cap their fund size and distribute all the profits and close the fund to new investors. If they let their fund compound, they would run into the problem that all big active managers have, which is dealing with scale. Scale in fund management does not scale in a way that benefits investors. It scales in a way that benefits fund managers. So there's an efficient market for manager scale, but if the markets were infinitely inefficient, then scale wouldn't be a problem.
Cameron Passmore: That's the point that Cliff made when he was on. So if you can get into RenTech, go ahead, get in. You can't get in.
Ben Felix: Yeah. He says that the difference between us and RenTech is that we'll take your money or something like that. Yeah. So that's empirically well-known, that scale reduces alpha, that funds are able to generate. So if there is someone that has some ability to exploit market inefficiencies, that their ability to do that is limited because markets are largely efficient, even if not perfectly, which was never the hypothesis anyway, right? It's the hypothesis. It was never the expectation anyway, that markets would be perfectly efficient. So that's Renaissance Technologies. I think it's actually evidence that markets are mostly efficient as opposed to the other way around. What about Buffett? So there's, of course, the 2018 paper, Buffett's Alpha, and the authors of that paper find that accounting for exposure to market beta size, value, momentum, betting against beta quality and leverage, which Buffett obtained implicitly through the float in his insurance business. Those factors explain most of Buffett's past performance and they actually make his Alpha, his excess risk adjusted performance, statistically insignificant when you account for the factors that he was exposed to.
So in that model, they found that Buffett's success was not due to his superior stock picking abilities, but he was implementing a systematic investment strategy that could have been reasonably implemented by somebody else in an efficient market. I guess, momentum being the exception there, because that one doesn't fit into the mold of efficient markets, but Buffett's stuff could have been recreated systematically. Then I also thought it was interesting, I don't know if this proves or disproves anything about efficient markets, I just thought it was interesting on Buffett. Going back to 2002 through the end of 2021, Berkshire Hathaway has trailed the US market by an annualized 80 basis points.
Cameron Passmore: It's a long time.
Ben Felix: You might say, "Well that's because value's done terribly," which is true. But Berkshire has only beaten the DFA US large value three portfolio, which is a systematic value fund by an annualized two basis points over the same period. So I mean, that's anecdotal, but it would appear as if Buffett's investors are earning returns, commensurate with the risk they're taking, which is what you would expect because of the scale problems that Berkshire Hathaway's had just as the fund has, or the company I guess has grown. Okay. Good for one more section?
Cameron Passmore: Sure.
Ben Felix: Okay. This one was fun to learn about. I spent the better part of a day reading papers on do people with special knowledge earn excess returns? This is a pretty interesting question. Again, this came from the Bitcoin podcast that inspired me to do this topic. All these guys were talking about, " Well, we understood the technology better than everybody else and that's why we were able to earn excess returns." Their efficient market counterpart in that discussion was saying, "No, you just got lucky." So it's interesting though, do people with specialized knowledge earn excess returns? So let's talk about it, in an efficient market, of course. Experts in general are pretty bad at making predictions. They're pretty good at assessing base rates, but that's very different from making a prediction like a base rate is 90% of active fund managers underperform the index over 10 years. That's a base rate. A prediction is Ark is going to outperform over the next five years. Arc is going to earn 30 to 40% returns over the next five years. So experts are really bad at making those types of predictions. Better at assessing base rates.
I think using specialized knowledge to earn returns in excessive risk taking is inherently a game of predictions because you're inherently projecting cash flows. There's an NPR episode that their Planet Money Summer School won. This is just a fun one, but it's worth mentioning. Planet Money Summer School won the stock market. They give the Francis Galton at a county fair example that people have probably heard about where he discovers the wisdom of crowds and their ability to guess on average, the correct weight for a bull that nobody knew the actual weight of. So NPR recreated this experiment at some kind of fair or something.
They found the same result where the average guess was within 5% of the bull's actual weight. They also separated guesses made by experts and non-experts. So they had 3000 people who had identified themselves as experts having worked with cattle and they talked about in the episode, how they actually followed up with those people, or at least with a cross section of those people, to determine whether they were actually experts and where their expertise came from. They said that everyone they talked to had real experience working with cattle. So they could genuinely be considered experts. They hadn't just self-identified.
Cameron Passmore: We can see where this is going, but, okay.
Ben Felix: So the average guess of the experts was further from the actual weight than the average guess of the non-experts. It's crazy, right? There's a 2004 paper, Was There a NASDAQ Bubble in the Late 1990s, Lubos Pastor and Pietro Veronesi. We've talked about this paper before. They show mathematically the higher uncertainty about the average profitability of the company's creating new technologies leads to higher prices, all else equal. So when an exciting new technology company is being tested, there's a huge range of potential outcomes. Maybe it's the next Microsoft, but maybe not. There's the special case in mathematics, I made a note about it earlier, for convex functions called Jensen's inequality. So for the Gordon growth model for stock valuation, it implies that when dividend growth rates are modeled as uncertain, the expected growth rate required to explain a given price drops. The larger uncertainty about the dividend growth rate, the larger the drop in the expected growth rate required to justify a given price.
So the important takeaway for bringing that one up in this section is that uncertainty itself can result in higher prices, but when prices are high due to uncertainty, there's an increased risk in betting on a certain outcome, like saying with your expert knowledge, you know this thing is going to happen. Well, if there's a lot of uncertainty about that from all other investors, even if you have specialized knowledge, you'll be paying prices that make it pretty risky to be sure about yourself. Okay, now this one was new for me. Those first two, I'd heard before, but I found another one that's really, really interesting. So thinking about over confidence, and this first one I'm going to mention is not the one I was excited about, but this one was good too. Overconfidence. So a 2010 paper from Cam Harvey, John Graham, and Itzhak Ben David, they looked at 13,300 expected stock market return probability distributions from CFOs. So they get CFOs to say, "Give us your 80% confidence interval for future market returns."
But when they go and test those expectations, only 36% of the realized market returns fell within the confidence interval of the CFOs, or I guess the average confidence interval of the CFOs. CFOs reduced the lower bound of their forecast confidence interval when there are times of high market uncertainty, but their expost miscalibration are worse during periods of high uncertainty. So they lower the lower bound, but they actually do worse overall in terms of how poorly they predict...
Cameron Passmore: Crazy.
Ben Felix: ... the market. Yep. So it's evidence of overconfidence because they're saying, "I'm 80% sure this is going to happen," and it doesn't. Then of course, it's well-documented in other fields as well that people overestimate the precision of their knowledge in some circumstances. That's especially true for challenging judgment tasks. Even experts are not great at making predictions, like I mentioned earlier. So this is the part that I was excited about when I found it and read through it. There's theory and evidence suggesting that bubbles associated with new technology are related to overconfidence of some investors who think that they have special knowledge. So I found this paper specifically about this topic. It's by Jose Scheinkman and Wei Xiong. So it's a widely cited 2003 paper. I'm actually surprised that I hadn't come across it until now, called Overconfidence in Speculative Bubbles. Jose Scheinkman's done a bunch of talks on the paper as well that I listened to.
So they've got a model where speculators' overconfidence is a source of heterogeneous beliefs and arbitrage is limited. The model's able to reproduce the observed correlation between trading activity and asset prices that we've seen in historical bubbles. Optimistic beliefs may be the result of simple extrapolated schemes, imitation, or randomness, but also result from the actions of interested experts that wish to signal their familiarity with new technologies and have a tendency to exaggerate their value, generating over-optimism among naive investors. So those are the suggestions of the sources of these heterogeneous beliefs. So I got a quick quote from the lecture that I listened to from Professor Scheinkman. Fluctuating heterogeneous beliefs among investors and the existence of an asymmetry between the cost acquiring an asset and the cost of shorting that same asset, heterogeneous beliefs make possible the coexistence of optimists and pessimists in a market.
The cost, and this is the important part, the cost asymmetry between going long and going short on an asset implies that optimist views are expressed more fully than pessimist views in the market. Thus, even when opinions are on average, unbiased, prices are biased upwards.
Cameron Passmore: Wow.
Ben Felix: Isn't that cool?
Cameron Passmore: No kidding.
Ben Felix: So because there's a cost to shorting, more optimistic beliefs will be more pronounced in prices than more pessimistic beliefs.
Cameron Passmore: Wow.
Ben Felix: Yeah, super cool. Finally, fluctuating beliefs give even the most optimistic the hope that in the future, an even more optimistic buyer may appear, thus a buyer would be willing to pay more than the discounted value she attributes to an asset's future payoffs because the ownership of the asset gives her the option to resell the asset to a future optimist. Basically, greater fool, greater fool concept there. Basically, when short selling is expensive, the most optimistic market participants price the market, or have more influence over the price of the market, which can result in a bubble when overconfident investors are willing to pay prices that exceed their own valuation of future dividends. Because they believe it in the future, they'll find a buyer are willing to pay even more. Now, that's a tricky game to play.
Cameron Passmore: Is it the power of storytelling?
Ben Felix: Yeah.
Cameron Passmore: Or wherever the confidence come from. Isn't that an interesting dynamic?
Ben Felix: I love that their whole model, and Scheinkman's stuff is pretty heavy on the mathematics. He's actually done papers in mathematics too, so not a light read, but yeah, that was the main concept, that over overconfident investors have more influence on prices and when there's a new technology, those overconfident, in his model, which maps pretty well to realize experience and helps to explain the high turnover around bubbles, which some other models have had difficulty explaining why that happens. His model does explain that, but it's rooted in overconfidence, which I just find fascinating. It fits so well with the other stuff we've done on technological revolutions. I hadn't come across this one. Disagreement is not necessarily a source of predictable market inefficiency because we can't identify false positives, ex-ante. So as Fama says, it's only a bubble in hindsight, which ties into the stuff we talked about on real estate too, I guess. Yeah. So that's it. Even in an efficient market, there can be overconfident people who think that they have better information than the market and they can create price bubbles, but it's not necessarily proof that the markets are inefficient.
It's just evidence of disagreement. Because this was inspired by Bitcoin, I just Google searched Jose Scheinkman Bitcoin. He participates in the University of Chicago Initiative on Global Markets, which is like a survey that they do of a whole bunch of prominent economists on various questions. He'd answered two questions on Bitcoin and he made comments very similar to his past work on overconfidence and speculative bubbles, basically saying that because it's hard to short, you would expect the more optimistic people to have much bigger influence on the price and that's probably why we're seeing the prices that we're seeing. It's just interesting to see it apply to Bitcoin directly.
Cameron Passmore: Fascinating.
Ben Felix: All right. That's all I got.
Cameron Passmore: So you're good to move on to Talking Cents quickly?
Ben Felix: Yep. Let's go.
Cameron Passmore: So these are from the University of Chicago Financial Education Initiative. Maybe this week, we'll just do one. I actually have to go through and make sure we haven't done them before. So here it is. I'll go first, if you like. What is a responsibility you are comfortable with and what is one that makes you nervous? So I'm comfortable with the responsibility we have in terms of operating our company and our enterprise, which we're now over 60 people on our team, and it's certainly a growing enterprise. I'm comfortable with that. One that makes me nervous, and you mentioned, I'm trying to think what makes me nervous in my life today that I'm responsible for. I feel responsibility to understand more about crypto and Bitcoin and everything else. That does make me nervous. It's just such a mind-bending world and you've done a big dive over the past month or so into this. It's just such a different framework. It's different everything. It's a whole new world. I'm finally -
Ben Felix: It's not, it's not, it's not, it's not. It's positioned to the whole new world.
Cameron Passmore: Okay. There you go. Maybe it is that. It still makes me nervous. This whole rebranding is Web 3.0.
Ben Felix: Yeah.
Cameron Passmore: I just don't understand the technology well enough and the language is so different. I read Cam Harvey's book and it's just like the terms are different, the frame of reference of how he'd been reading is different. What it can do is just so mind-exploding. Then you take with that this insatiable demand from the marketplace and so many people are saying, "You got to do it. If your advisor's not doing this, you've got to fire your advisor."
Ben Felix: Well, that comes from someone who left, I think sold out a financial, a traditional financial services practice and is now trying to make it in crypto, that quote that you just said. I saw that on Twitter too. So a lot of the pro-crypto stuff comes from very, very biased sources. Anyway, hopefully we can make you feel more comfortable with crypto in the next few months. Not that we're going to talk about it on Rational Reminder, because we've gotten the feedback that people don't love that.
Cameron Passmore: So what's the responsibility that you're comfortable with?
Ben Felix: A responsibility that I am comfortable with, like you said, I think that that managing the PWL Enterprise and managing the assets on behalf of our clients, I'm comfortable with that. We've, like you said, built up a pretty substantial team and we've got, in my opinion, some of the best financial planners anywhere, anywhere, period.
Cameron Passmore: Unequivocally.
Ben Felix: With Canadian specific knowledge, sure, but anywhere. So that's definitely something that I'm comfortable with. It doesn't all rely on me or you...
Cameron Passmore: No.
Ben Felix: ... day to day. I think we've got pretty good systems, so comfortable with that. I'm comfortable with responsibility of raising my kids. That's one that... It wasn't that long ago, I didn't have kids and don't know how comfortable I felt with the idea of having kids at the time that we had our first one. I remember my wife and I were in the hospital, leaving the elevator when they discharged us. We looked at each other like, "Are they really letting us leave with the baby?"
Cameron Passmore: But we all go through that, right?
Ben Felix: Yeah, yeah, yeah.
Cameron Passmore: You come home and it's like, "Okay, now what do you do?"
Ben Felix: Yeah. One that I'm not comfortable with that makes me nervous is the house.
Cameron Passmore: Wow.
Ben Felix: I don't like having the house. I like living here where we live, But man, I heard a joke or saw a joke on, I don't know, Reddit or maybe Twitter about how people who own homes, when they hear a noise, they hope it's a ghost.
Cameron Passmore: Not a raccoon in attic.
Ben Felix: Because you don't have to fix something. Oh yeah, yeah. Home ownership is not... It's not a walk in the park. It's a real thing. Yeah, it's something that like I've got no choice at this point. I made the decision based on a 30-year time horizon, but it's definitely a source of...
Cameron Passmore: It's funny, you mentioned that you may know my little atomic habit that I've done, which is I have to do something every day, no matter how small it is, in the house.
Ben Felix: Yeah, yeah.
Cameron Passmore: It's every day you have to be going, you have to be pushing that rock ahead because if you don't, you just get so behind so fast on anything, everything.
Ben Felix: Yep. So I look back to my days renting and it's not always good because landlords differ, and sometimes it's very hard to get service or to get stuff fixed or whatever. But the last house that we lived in, they were great. It was like, "Hey, this thing broke," and in that case, the property manager was a man. He had his wife worked with him and his two or three brothers also worked with him and they did the labor. So it was like, you call him up and one of the three brothers is available anytime to come and fix whatever problem it is.
Cameron Passmore: Yeah.
Ben Felix: But now, to get labor as an owner and I mean, maybe that's something you build up over time, like a network kind of thing. I'm not sure, but it's not easy to call a trades person, especially for small stuff. I don't know. I don't know. I'd prefer to spend time focusing on this stuff. Yep. So there it is. The house makes me nervous.
Cameron Passmore: All right. Anything else this week?
Ben Felix: No, I think that's good. Hopefully people enjoy the episode.
Cameron Passmore: Excellent. Last week was Mac McQuown, and next week is the perfect bookend to that with Robin Wigglesworth. So as always, thanks for listening.
Book From Today’s Episode:
The Long Game — https://amzn.to/3GnbSoX
Links From Today’s Episode:
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Benjamin on Twitter — https://twitter.com/benjaminwfelix
Cameron on Twitter — https://twitter.com/CameronPassmore
'Innovation Stocks Are Not in A Bubble: We Believe They Are in Deep Value Territory' — https://ark-invest.com/articles/market-commentary/innovation-stocks-are-not-in-a-bubble/
'Efficient Capital Markets: A Review of Theory and Empirical Work' — https://www.jstor.org/stable/2325486
'Random Walks in Stock Market Prices' — https://www.jstor.org/stable/4469865
'On Persistence in Mutual Fund Performance' — https://www.jstor.org/stable/2329556
'Luck Versus Skill in the Cross Section of Mutual Fund Returns' — https://www.jstor.org/stable/40864991
'Mutual Fund Flows and Performance in Rational Markets' — https://www.jstor.org/stable/10.1086/424739
'The Adjustment of Stock Prices to New Information' — https://www.jstor.org/stable/2525569
Planet Money Summer School 1: The Stock Market (NPR) — https://www.npr.org/2021/07/28/1021770148/planet-money-summer-school-1-the-stock-market
'Was There A Nasdaq Bubble in the Late 1990s?' — https://www.researchgate.net/publication/4756122_Was_There_A_Nasdaq_Bubble_in_the_Late_1990s
'Managerial Miscalibration' — https://www.jstor.org/stable/26372532
'Overconfidence and Speculative Bubbles' — https://www.princeton.edu/~wxiong/papers/bubble.pdf