Juhani Linnainmaa is a finance professor who studies asset pricing, investments, and household finance. From 2006 to 2016, he was a faculty member of the University of Chicago’s Booth School of Business. He joined the Tuck School of Business faculty in 2019. Linnainmaa is a research associate at the National Bureau of Economic Research and a recipient of the Amundi Smith Breeden Prize (2018), the Marshall E. Blume Prize (2016), and the CFA Society & Hillsdale Canadian Investment Research Award (2015). As a finance professor at Tuck, he teaches an elective course in investments.
If you dive deep into financial advisor fixed effects, you’ll begin to understand that an advisor's own portfolio has a bigger impact on the portfolios of their clients than the characteristics of the clients themselves. To help us make sense of this and to further explain financial values and the cross-section of returns, we are joined by the influential and notorious Professor of Finance, Juhani Linnainmaa. Our conversation begins with a comprehensive analysis of financial values, including a comparison between the trading patterns of advisors and those of their clients, a disquisition of misguided beliefs, an examination of client characteristics, and the ins and outs of portfolio variation and customizations. Canada recently adopted regulations from the Mutual Fund Dealers Association (MFDA), and we discuss how this has affected the use of financial advice in the country before comparing the benefit of increased equity share to the cost of advice, what hiring a new advisor before a financial crisis may mean for clients, and the role of regulation in the industry. We end with the cross-section of returns by examining accounting-based anomalies pre-1963, how profitability and investment relate to data mining, why a financial firm would switch between growth and value, and finally, Professor Juhani Linnainmaa’s definition of success.
Key Points From This Episode:
(0:00:42) A very warm welcome to the influential Professor of Finance, Juhani Linnainmaa.
(0:03:52) Comparing the trading patterns of advisors to those of their clients.
(0:08:45) How regulators can go about addressing misguided beliefs.
(0:11:08) Client characteristics that advisors base portfolio customizations on.
(0:13:22) Whether the variation in a client’s portfolio can be explained by their characteristics.
(0:14:49) Explaining the remaining variation in portfolios.
(0:19:38) Other reasons for the high cost of advising, aside from portfolio customization.
(0:22:03) How the adoption of the MFDA affected the use of financial advice in Canada.
(0:26:03) Comparing the benefit of increased equity share to the cost of advice.
(0:31:45) How getting a new advisor before the financial crisis affects ongoing investments.
(0:35:46) The role of regulation.
(0:37:47) Getting into the cross-section of returns with accounting-based anomalies pre-‘63.
(0:40:51) Weather profitability and investment are data-mined factors.
(0:44:05) The optimal X-anti mix of factors in a portfolio.
(0:46:56) The mechanisms that cause firms to move between growth and value.
(0:56:31) Professor Juhani’s definition of success.
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 and Cameron Passmore, portfolio managers at PWL Capital.
Cameron Passmore: Welcome to episode 278. This week, Ben, we have a great conversation with a professor who will certainly appeal to the more nerdy of our listeners, which I know there are many.
Ben Felix: I don't mean to interrupt you, but it's both. The back half of this episode will satisfy the geekiest of geeks, but the first half, or whatever it was, I don't think was just for geeks.
Cameron Passmore: That's fair. That is fair. Anyways, we were joined by Juhani Linnainmaa, who is a Professor of Finance at Tuck School of Business at Dartmouth College, where he studies asset pricing, investments, and household finance. Before joining Tuck, he was a faculty member at the University of Chicago's Booth School of Business. He was also a research associate at the National Bureau of Economic Research and has won many awards for his research. He holds a PhD in management from UCLA Anderson School of Management, an MSC and BA in economics from Helsinki School of Economics. With that, Ben, give the back story and cue this up.
Ben Felix: I mean, listen, this is another case of a guest that I always knew we had to have on the podcast, because his research is just incredible. I had referenced it so many times in my YouTube videos and in past deep dives on this podcast as well. I don't know. It was an inevitable episode. I finally reached out to Juhani, and he was more than happy to come on the podcast, which we appreciate very much.
I think it was an awesome conversation. He's got this big body of research on financial advisors and people will be familiar with the big paper, ‘The Misguided Beliefs of Financial Advisors’, which I've been referencing since before it was even published. But he's got more on advisor fixed effects, which is basically like – an advisor's own portfolio has a bigger impact on the portfolios of their clients than the characteristics of the clients themselves.
Cameron Passmore: So interesting.
Ben Felix: Fascinating. It's like, if an advisor has a home bias in their own portfolio, their clients are more likely to have a home bias, even if there are other reasons that their clients shouldn't have a home bias. Likewise for equity, if they’re an equity-heavy advisor, their clients are more likely to be equity heavy, even if based on whatever life cycle model or something, you would expect them to have more fixed income.
If you switch advisors, like if you switch from a fixed-income-heavy advisor to an equity-heavy advisor, the portfolio becomes more equity heavy, which suggests that it's not clients choosing advisors that meet their portfolio needs, but it's the advisor fixed effects that are explained. Anyway, I'm giving away the whole episode here.
Cameron Passmore: I was going to say, you're spilling the candy here in the lobby.
Ben Felix: It's just so interesting. Advisors are not doing portfolio customization much, and they often have misguided beliefs, so why do people use them? There are a couple of really interesting papers that they've done, that Juhani’s done, using regulatory changes to look at how advisors influence clients' portfolios. Just fascinating. Then at the end, we talk about the cross-section of returns, which I'm sure that's the part that the geeks are going to like. That's more than enough for introduction, though.
Juhani is also the Co-Director of research at Kepos Capital, which is a systematic macro hedge fund founded by Mark Carhart, Bob Litterman, and Giorgio De Santis, which is a pretty serious crew to be involved with. Anyway, I think this is a great conversation. I enjoyed it.
Cameron Passmore: All right, let's go to our conversation with Professor Juhani Linnainmaa.
***
Ben Felix: Juhani Linnainmaa, welcome to the Rational Reminder Podcast.
Juhani Linnainmaa: Thank you. It's great to be here.
Ben Felix: Yeah, we're super excited to be talking to you. Juhani, I want to start with financial advisors, which you've done a lot of really interesting work on. In the sample for your paper, The Misguided Beliefs of Financial Advisors, how do the trading patterns of advisors compare with the trading patterns of their clients?
Juhani Linnainmaa: That's a good question. Other people had previously looked at the trading patterns of clients and advisor foundations, and they had found that many of the things that clients do seem to be something that academics would be advising against. Things like, they mostly invest in acclimate funds, they chase returns, they then prefer more expensive funds, and they also under-diversify in their portfolios. In our paper, we got access to data on the advisors on portfolios, and we find that they do many of the same things. Their portfolios look almost the same as those of the clients, which was a bit of a surprise.
Cameron Passmore: How do their investment returns compare with their clients?
Juhani Linnainmaa: Given that they do very much the same things in the portfolios and hold the same investments in terms of growth of fees, their performance is almost identical. Net of fees, there's a bit of a difference, because effectively, the advisors are paying some of the fees for themselves, like trading commissions, they come back to their own pocket. In terms of net returns, there would be a bit of a difference between the advisor's clients.
Ben Felix: Yeah. Okay, that's super interesting. Advisors are doing the same trades and similar trades in their own accounts as they are on their client accounts. They're earning similar gross returns, but advisors are doing a bit better, because they're compensating themselves.
Juhani Linnainmaa: That's right. Of course, given they invest in more expensive funds, the advisors could also improve their own performance by holding a little bit different portfolios.
Ben Felix: The advisors invest in more expensive funds than the clients?
Juhani Linnainmaa: No, they invest in similar expensiveness, so they could also improve their own performance by investing in different ways.
Ben Felix: Right, right, right. Both the advisors and clients are investing in high fee funds.
Juhani Linnainmaa: That's right.
Ben Felix: That's incredible. How does the investing approach of advisors change after they leave the financial services industry?
Juhani Linnainmaa: That’s a great question. One of the concerns we had with the study was that if you look at the advisor's portfolios, maybe the advisors are not really doing what they truly believe in. That maybe they only do something, because they want to convince their clients to do the same. Maybe in that case, only when they're actively advising people, they invest in income, like bad ways into expensive funds, chase returns, and so forth. And so, after they leave the industry, given that they have no clients anymore, maybe they now start investing in a way that they truly believe in.
What we find in our data is that advisors keep on investing in the same way, even after they leave the industry. There would be no motivation to be like, what would be faking the portfolios, doing something that they don't truly believe in.
Ben Felix: That part of the paper is so interesting. Well, it's like what you said, advisors could be fooling their clients, or selling their clients by saying, “Look, I'm doing the same thing as you,” but then they clearly, or we're not just doing that because they keep doing the same thing after they leave.
Juhani Linnainmaa: That's right. Also, we didn't really expect to see much of a difference, because the way we got the data, it's something that the clients wouldn't have access to. It would only be that if the advisor self-disclosed their portfolios, then the clients would see it. There's not great motivation for the advisors to do something in their own portfolios that they didn't believe in, because they probably don't seem that many people are going to be having access to their holdings and trades.
Ben Felix: Yeah. No, that's interesting.
Cameron Passmore: What do your findings suggest about the cause of advisors recommending high cost mutual funds and chasing performance?
Juhani Linnainmaa: Broadly speaking, I guess, there has been one main explanation for why clients do what they do, and that has been about conflicts of interest, that people have been looking at the client's portfolios alone and they say that, well, given that they invest in very expensive funds, active funds with high fees, maybe they're doing it because advisors tell them to buy those funds because of conflicts of interest. Typically, advisors get compensated more by selling the more expensive mutual funds. It could also be, and that's the explanation that we explore, is that it could be coming from misguided beliefs, as we title the paper. That maybe the advisors are not telling the clients to do something, because of conflict's interest, that they get more compensation, but because they truly believe in that these are the right investments.
In the sense that maybe the people who end up in this industry actually believe that markets are not that efficient, that it’s pretty easy to beat the market. They are providing service to their clients, they have higher returns, and as a compensation, the service they provide, they're going to get some fee for that one. Of course, and the data doesn't work out that way. Typically, high fee funds often have lower returns, but the people who end up being advisors may be predominantly don't know that. Again, it could be a conflict of interest, but our results seem to be more consistent with misguided beliefs, given that the advisors themselves are doing the same things.
Ben Felix: Yeah, such incredible finding. I mean, we ourselves talk about that all the time, that there's so many conflicts of interest, and then particularly in Canada, where so many of the mutual funds are sold on a commission basis. Your finding suggest that maybe that's not the problem, or not the total problem.
Juhani Linnainmaa: That's right. It could also contribute to the issue. Undoubtedly, there are cases where people are so seamlessly exploiting the system that's in place, but it doesn't seem to expand the broad patterns in the data.
Ben Felix: What do you think of regulatory implications? If beliefs are such an issue, how can regulators be addressing it?
Juhani Linnainmaa: That's a really tough question. In time, it comes to regulation, there's always a great temptation to just introduce more and more guardrails, because it should be pretty easy to say what's good and bad, but it gets out of hand pretty quickly when you try to regulate anything, like we saw in the other study, there are always maybe some internal consequences. In this study, we didn't take any stance on that one.
Ben Felix: Yeah. We'll come back to that later, because your other study on that is also incredible. At the client level, how do you think investors can assess the quality of a potential advisor's beliefs?
Juhani Linnainmaa: The problem is going to be that if you – as a client, you know that's the right questions. That's already going to imply that you probably wouldn't even need a financial advisor. Of course, we want to advise people that when you look at an advisor, pay attention to fees that you're going to be paying. Don't try to invest in funds that are going to be churning around too much and things like that.
Again, if they can be asking those questions, that already implies that maybe they will know how to do it on their own. Somehow, it would have to be about education, not just about how you approach the advisors, but how you tell people about the financial markets. The issue that people have found, and this is not about our study, is that educating people about financial matters is really, really tricky.
I think there was a study by Bruce Carlin from UCLA. In a lab, they had people do experiment where they gave people different types of credit cards, different types of fees, and then they asked people to choose the best card. What would be the cheapest option for them? Of course, people made many mistakes with the choices. Then they educated people like, what would be the right choice? When they tested the people again, after the education session, they found that people make many fewer mistakes, that it's easy to teach people to do the right thing.
Then they did a follow-up where they asked the same people back to the lab a few weeks later and gave exactly the same test. They found that none of the advice had stuck, that people had made the same mistakes. We can always tell people that in terms of advisors, don't pay too much for mutual funds, don't chase returns, and probably they're going to be noting and saying that that makes perfect sense.
Then when you meet with an advisor, they're going to be very convincing when they say that, well, this can be high-fee fund, but the returns are going to be even higher. All the education that you have given is going to go out the window. Again, the main question of how do you educate people how to make the choices? That's the big question. We don't have an answer to it.
Ben Felix: Yeah, that's not an easy problem to solve. Okay, so you've got another great paper on financial advisors, ‘Does One Size Fit All?’ In the sample for that paper, for retail financial advice, does one size fit all? Which client characteristics do you see advisors basing portfolio customizations on?
Juhani Linnainmaa: In that paper, we have a pretty massive sample. In terms of statistics, plus significance, almost everything is going to be playing a role. In terms of economic attitudes, which characteristics lead people to all different portfolios, something like, of course, stated risk tolerance would be very important. People who say that I'm going to take a lot of risk, not surprisingly, they're also going to be taking much more risk in their portfolios and things like investment horizons. People who say that, “I'm going to be in this for the long run,” they're going to take more equity risk in their portfolios. Many of the characteristics also play a role. Again, statistically, that's in statistical terms. In terms of economic magnitudes, things like risk tolerance and investment horizon and age are going to be the more important ones.
Cameron Passmore: How does the evolution of advice portfolios over the client's life differ from a lifecycle fund?
Juhani Linnainmaa: That's a good question. If you were to look at only the effect of age, that you partition people based on being 20, 25, 25, 30, and so forth, and you look at the marginal effect of that one, so you're holding everything else fixed, and you plot that out, it doesn't look that different from the targeted fund, that people tend to ramp up the riskness of their portfolios when they're mid-40s, and then they take it down. That's broadly speaking, the same type of shape of the pattern that we see for the targeted funds.
Then, if you zoom out and you're taking to account all the characteristics at the same time, there's going to be so much variation in the portfolios coming from other sources that age doesn't really mean much at all. It doesn't go up and down, it's tiny, tiny variation when it's confounded by all these other differences in demographics and the portfolios that people hold.
Ben Felix: How do the client portfolios vary with their labour income characteristics?
Juhani Linnainmaa: There wasn't that much of a difference in that one. When we do the study, we look at the multivariate regressions. We're asking, holding everything else constant, like age, wealth, education, and things like that, how much more or less do you take equity risk when income increases? In that case, I think we find that the amount risk you take goes down a little bit when you increase labour income. Economically speaking, the effect is not that meaningful.
Cameron Passmore: How much the variation in client portfolios is explained by client characteristics?
Juhani Linnainmaa: When we put everything into the regression, so that's going to be 25, 30 characteristics, and there can be some dummy variables, on top of that, I think we get an R square, something like 8%. That would be saying that there's going to be lots of variation in how much risk different people take in their portfolios. You put all these characteristics in, and it turns out that you can explain about 8% of the total variation. 92% of what's happening with portfolios remains unexplained, even though you throw in all these demographics.
Ben Felix: I want to ask what explains the rest of the variation. But before that, the characteristics that you look at in the regression, in theory, like in economic theory, would you expect them to explain most of their variation in portfolios?
Juhani Linnainmaa: That's a different and much harder question. We are not taking a stance on what portfolios people should be holding. People have been writing about papers about the life cycle problem for a long, long time, but we just don't know what talk to my portfolio would look like. How much you should be investing as a young person, as an old person? People often look at the data, they see something like, dependent VC that people closer to retirement take less risk, and so forth. Then they change the modelling assumptions to fit, to match the pattern in data.
People haven't done really starting from the first principles, make the most reasonable model and then solve that model and see what that advice would be. I think it makes sense that labour income should be important, it should be important, and so forth, but we don't exactly know what the magnitude of those effects should be.
Ben Felix: Right. Yeah. I guess, it would depend on asset class assumptions, too. Anyway, so you find about 8% of the variation is explained by client characteristics. What explains the rest of the variation in portfolios?
Juhani Linnainmaa: Yeah. That's the key point of our study. We would assume that even if we don't know how the demographics are going to be coming together to determine the portfolio, we think that when you got an advisor, they're probably going to look at you and talk to you and figure out that this is going to be exactly the best portfolio for you. In the data, it turns out that the most important determinant of what kind of portfolio you hold is, who is your advisor? Then we throw into the regression so-called advisor fixed effects. We just control that these clients have the same advisor, these clients have the same advisor, the power of the model index variation goes up significantly.
We started from 8% and throwing in the fixed effects is going to take that R-square, or the adjusted R-square up to 26%. Much, much more information coming from just from the fact of who you have as an advisor, as opposed to all these demographics.
Cameron Passmore: Hmm. How do we know that clients aren't self-selecting into advisors that match the portfolio that they're actually looking for?
Juhani Linnainmaa: That would be one of the big concerns here that maybe the advisor is already telling you something what the client looks like. Maybe all the young people are going to find a certain type of advisor. Maybe all the high-net-worth individuals are going to have different advice, and so forth. Maybe adding the advisor fixed effect is like controlling for, example latent characteristics of the clients. What we do in our studies, we look at advisors who quit the industry.
You have an advisor with 20 clients, that advisor disappears from data and all the 20 clients get transferred to different advisor. If the portfolios are tailored based on the characteristics of the clients, then we would expect that who you have as an advisor doesn't make that much of a difference. Even after you get transferred to a new advisor, the portfolios remain the same. What we find is that when you get transferred to a new advisor, all of these clients' portfolios change at the same time. It has to be coming from the advisor, not some kind of constant, latent, unobserved demographics.
Ben Felix: That's such a smart way to look at it. If the clients were self-selecting to advisors that had portfolios that they liked, if their advisor left the industry, they would go and find a new advisor that had those same fixed effects. Instead, the portfolio changes to whatever the new advisor prefers.
Juhani Linnainmaa: Yeah, that's right.
Ben Felix: Such a smart way to test it and such an interesting, but scary finding. What explains the variation in advisor recommendations?
Juhani Linnainmaa: Yeah, that brings us back to what we discussed before. When we now take these fixed effects from the regression and try to understand, well, controlling for all the demographics of the clients, why does this advisor advise all the clients to hold risky portfolios, or less risky portfolios, we can first try to understand that by the advisors on demographics. We do find that things like age is going to play a role. All the advisors tend to recommend their clients to hold risky portfolios, controlling for the client demographics.
The biggest determinant is going to be advisor's own portfolio. Again, if we find that you just happen to have an advisor who's taking lots of equity risk in his or her own portfolio, then all the clients are going to be doing the same. Again, I should be emphasizing that this completely, in terms of identification, there's going to be randomness here. It's as if you take a client from an advisor who doesn't like equities, as a consequence, the client is going to be taking very little equity risk in their portfolios. If you put them to an advisor randomly, who's taking more risk in their own portfolios, then that advisor seems to be telling the client that, well, you want to be taking lots of equity risk. That's what happens. Who you have as an advisor, just by luck, plays a tremendous role.
Ben Felix: That's crazy. I was Googling about something last night, and I came across this thing. I won't say what it was from, because I don't want to upset anybody. It was an educational seminar hosted by a respectable organization in Canada. The seminar was about why the stock market is way too risky for most people in the long run. They should instead be investing in GICs, which is like a CD in the States. I was just blown away. But you could totally see how a client going from one advisor to that advisor, the portfolio would change pretty significantly.
Between the other paper we just talked about, so we know that advisors have misguided beliefs, and we know that an advisor fixed effects are pretty important to the portfolio's clients get. I don't really have a question, but that's a disaster, isn't it?
Juhani Linnainmaa: I agree with that one. The issue is going to be that while we came in with the prior, that the advisor is going to be looking at their clients individually and customizing their portfolios, maybe the way they customize doesn't make sense that it goes against economic theory. At least, they're trying to do something different for the clients. Like you said, the fact that they just give the same portfolio, or to all the clients is sound maybe a little bit troubling.
Cameron Passmore: If not portfolio customization, what do you think explains the high cost of advising your sample?
Juhani Linnainmaa: Unfortunately, that's one of the limitations of what we can say here. We could be looking at the investment performance of the portfolios. Even though you don't customize, you give them such great portfolios that the performance model makes up for it. We do look at that one. We find that the clients’ portfolios underperformed all these other benchmarks. They don't seem to be adding value in that dimension. Then the problem is going to be, and you always run at the same issue in household economics, that there are always other things that could be going on, that maybe there's a reason why the clients are paying so much in fees, because they're getting some other services in return.
Maybe the advisor advising in taxes, maybe they're advising in how much to consume, how much to save, and questions like that. We can never quite quantify those in the data. Always when we try to account for some other value and even if we find that advisors are not adding value in this dimension, it could be something else. We pretty much throw our hands up in the air and say, it's not customization. It's not better performance. It could be something else. We just can't say.
Ben Felix: Is that a revealed preference type thing? We know that people are paying these fees, so there must be something which you don't know what it is?
Juhani Linnainmaa: Yeah. That would be the argument that if we don't find it, then people are going to say, that well, you are just missing it. There's something more. Because obviously, the clients are getting some value. They wouldn't be paying 2% per year, or 3% per year if they didn't feel like it.
Ben Felix: Incredible. Between those last two papers that we just talked about, misguided beliefs, and the lack of customization and portfolios, what do you think advisors and clients who are listening to this should do with the information?
Juhani Linnainmaa: That's again, the hard question, the one about how you educate people. Something that might be useful would be some kind of more transparency, not in the sense of having more disclosure of something that people are never going to be reading, but it would be nice for the clients to know what type of portfolios other clients seem that demographics are holding, just to see if the portfolio that they're getting recommended is going to be very, very different than what other people have been recommended. That wouldn't be solving the issue, because again, we don't know what the right portfolio would be for the person, but at this time, I'm going to benchmark that am I holding really expensive things? Am I under-diversifying compared to other people, and so forth?
If your portfolio looks weird compared to the rest of the population based on match demographics, then maybe you want to be asking more questions. Again, more broadly, there's no perfect solution to it, but maybe transparency would be better than just regulation.
Ben Felix: Okay. I want to move on to regulation. How did adoption of the Mutual Fund Dealers Association, or the MFDA in Canada affect the use of financial advice in Canada?
Juhani Linnainmaa: The reason for the MFDA, the stated reason for the regulation was to after the great recession, to restore the faith in the financial system. That maybe people were afraid to participate in the financial markets, because they didn't understand those markets and they were concerned that there was rent and fraud going on. Maybe if we have more rules, people have more trust in the system and they're going to be more willing to use advisors.
What happened in terms of this regulation was that when it was introduced, according to industry reports, you had lots of consolidation. The competition went down massively. Effectively, the supply of advice went down. When we look at people in our sample, the use of advisors in those regions that were affected by the regulation first went down significantly.
Cameron Passmore: How did it affect equity market participation?
Juhani Linnainmaa: Yeah. Advisors have a huge effect on equity market participation. Maybe not surprisingly, that if you take away some of the advisors, you decrease the supply, people are not going to have the advisors. If they don't have the advisors, they're not going to be participating in the markets. It's interesting like this. Clearly, an intended consequence of the regulation, because the whole point was to restore the faith, get people to use more advisors and by using more advisors, participate more in the markets. But because of the decrease in the competition, decrease in supply of advice, it turns out that the participation went down, because of the regulation.
Ben Felix: Crazy. How important is the role of financial advisors in getting households to take equity risk based on your findings?
Juhani Linnainmaa: We were lucky that the MFDA regulation was rolled out in different province at different times. We can use the staggered introduction of the regulation to try to estimate the causal effect of having an advisor on stock, on participation. Again, the concern would be that maybe the decision to participate and the decision to have an advisor are going to be correlated, in which case the correlation might be overstating the impact of advisors on participation.
In our case, again, we can try to identify what is the causal effect of advisors. Whether you don't participate, or participate in the markets and then you have the advisor, or don't have an advisor, having an advisor is going to increase the probability of participating between 54 and 90 percentage points. There's a massive difference coming from that one. Advisor, they're not going to be participating. If you have an advisor for extra instance reasons, you're going to be much more likely to participate.
In terms of how much you invest in the market, do they put 30% of your money in the equities? That increases by about 30 percentage points in the sample. You would go from 30 percentage points without an advisor to 60 with an advisor. They are economically huge effects.
Ben Felix: That is wild.
Juhani Linnainmaa: There's interesting thing over here. I mentioned before that there's a concern about the correlation that maybe if people who would be participating in the markets anyway, they are more likely to get advisors. Then if you just look at the – if you have an advisor, don't have an advisor and you measure how likely to participate, that might just be overstating the effect. In the data, it turns out that the bias goes the other way around. It's telling us that the people who are less likely to participate when left there on their own are more likely to seek financial advice and then participate.
When you cut down the supply, you're really like – if we only care about equity market participation, you're hurting the neediest people the most, the ones who are not going to be participating without advisors.
Ben Felix: That is crazy. I just want to recap and collect my thoughts for a second. The MFDA is created to increase trust in the financial system, which increases regulation on financial advisors. That's rolled out to provinces in Canada at different times, so you can test the effect of what happened. It reduces the supply of advice, which causes people who would have been more likely to seek advice to have a much lower equity share in their portfolios, because they needed the advice to get invested in the equities.
Juhani Linnainmaa: Yeah, that's right.
Ben Felix: Wow. That's crazy.
Cameron Passmore: How does the benefit of increased equity share compare to the cost of advice?
Juhani Linnainmaa: Now, they're the same points. Before when we spoke about, well, it's some customization, a bit of performance, well, what's going on here? One could say that, well, if advisors help get people to stock market, there's going to be some benefit from that one, because that's going to be someone able to risk premium, maybe 5%, 6% in terms of long-term estimates. If you're going to be investing 30% more in equities, if you're an advisor and that's the right thing to do, then you're going to be getting more return on your portfolio. If you are 30% and you have 5% equity premium, that would be a sizable increase in the performance of portfolio.
But that wouldn't be enough to offset the cost of these advisors in our sample alone, because the cost is going to be 2 to 3 percentage points. It has to be something more than sustainable market participation that's going on here. Differently, having advisors to just put them in the stock market to give people high returns, the return is not going to be high enough to cover the cost of advice. Again, it could be some other benefits that people get from having advisors. In terms of direction, having people participate in equity markets is a good thing, setting aside the effects of how risky the portfolio is going to be.
Ben Felix: Do you have any empirical research on what the other services that clients may be getting from advisors would be? Is it increasing their saving behaviour? Do you have anything on that?
Juhani Linnainmaa: We don't have something where we could compare it to not advice on advice people. We see in the sample of advice people, we see the extent of which they use some automatic savings plans, which might be a good thing, but we don't have the same information for the unadvised people, so we haven't been able to look at it.
Ben Felix: Okay. Now, the MFDA sample in Canada, we're very familiar with this, because we're in Canada. It's notorious for mostly using high-fee commission-based funds, where the complex ventures that we talked about are a problem. Based on your findings in this paper, is it reasonable to assume that financial advice that's using lower fee products is net beneficial by increasing equity exposure and whatever else, but not having the 3% fee?
Juhani Linnainmaa: Yeah. I think that sounds broadly speaking, correct, in the sense that if you think about just investing in any mutual fund and you randomly pick one, if you pick one with the low fee, that's never going to be a terrible investment. Just like, it's really hard to beat the market in terms of gross returns. It's very difficult to systematically lose to the marketing in gross returns. If the fee is tiny, it's not a terrible mistake to make.
Giving those mutual funds that people would be a pretty good idea. That being said, it brings us back to the problem that we don't really know what's the right thing for people to do, how much risk you want to be taking in portfolios. But if we fix that one and we say that, well, you want to be allocating 6% of your money into equities, and then you just randomly pick some low fee funds into it, that's going to be a good thing.
Also, if you have the low fees and you have the advisor, they are going to be helping with the level of participation, and that might be a good thing. Again, with the caveat that we don't know what the right number would be. Do people on average in the 30% of risk in equities, or 90%? What's the right number?
Cameron Passmore: How does the duration of the relationship with a client affect the equity participation and equity share?
Juhani Linnainmaa: We looked at that in a different paper. The motivation for that study was this already classic famous study by Gennaioli, Shleifer, and Vishny, Money Doctors. The point of that paper, the theory paper is simple. They view advisors being like doctors in the money space. The idea being that many people don't understand the stock market. They're fearful to invest in the stock market, because it's unknown. If you have these advisors who people can contract and the advisor is going to be holding the client's hands, now they're going to be more willing to participate in the stock market.
When they participate in the stock market, they're going to be earning a higher return. Part of the return that they get, they can hand over the advisor as a conversation. Both parties are going to be better off when they have these money doctors. An important element of the story would be the trust that you are fearful of the market on your own. If you have the advisor who you trust, then you're more willing to take the equity risk.
In our study that you are referencing, we were looking at the building of trust over time with the assumption that if you have an advisor for a week, you're not quite sure what to make of this advisor. But if you have been with the advisor for 10 years, that at least signals that somehow, you're probably more trusting of this advisor. What we find in the study is that the longer the relationship between the advisor and the client, the more equity risk on average the clients are taking their portfolios.
Ben Felix: How big is the effect?
Juhani Linnainmaa: We looked at this in terms of things like, the financial crisis. We looked at people who go into the financial crisis with the new advisor, that all the old advisor had just left the industry and developed with the new one, come to the people who had had the advisors for a long time. We looked at that, what's the probability that you stay invested in the equities throughout the crisis? We found that there was a 7% difference in the sense that if you go to the crisis with the person who you don't really seem to be trusting, it's very likely that when things go south, you're going to be selling your portfolio and just giving up on equity markets. If you have an advisor who you have known for years, you're much more likely to just come out from the other end.
Ben Felix: How does the duration of the relationship with a client affect the client's equity participation and equity share?
Juhani Linnainmaa: The effect is going to be positive, that the more time you have known your advisor, the more you're going to be investing in equities. For example, when we partition people based on how much they invest in equities at the time they first found the advisor, and you look at people who had somewhere between 0% and 20%, that's going to be running up over 10 years. At the end of 10 years, those people are going to be having about 30% of these points more invested in equities at that time. It's a pretty massive effect in terms of equity magnitudes, going from 0 to 20, to having 30 percentage points more at the end. Of course, that's over 10 years.
Ben Felix: That's still a huge effect, though.
Cameron Passmore: How did getting a new advisor before the financial crisis affect investors' ability to remain invested?
Juhani Linnainmaa: Okay. There are two different effects that we were looking at. One of them would be exactly the taken and the mean effect. The longer you know how much more risk you're going to be taking equities. The other one was about the sensitivity to bad returns. We know from the mutual fund literature, we know from other studies that people, of course, become very fearful and call it quits when they have bad performance in portfolios. We wanted to understand if knowing somebody, trusting somebody is going to be attenuating the return investment sensitivity, in the sense that if you had known somebody for a long time, maybe you are willing to go through these rough times without liquidity in portfolio.
Going to your question, when we look at the financial crisis, when we look at people who enter the crisis with a brand-new advisor, because they're all advisor with the industry and we look at people who go into the crisis with an advisor they have known for a long time, there's an 8 percentage points difference in coming out from the other side with the portfolio intact. The people who have a new advisor are much more likely to just give up on the stock market during some financial crisis than those who have the trusted advisor.
Ben Felix: That's 8 percentage points measuring the number of people that stuck with it?
Juhani Linnainmaa: Yes, that's right. The probability of quitting is let's say, 10 percentage points for the people with a long running advisor. It would be 18 percentage points, probably of quitting for the people who have a brand new advisor.
Ben Felix: That's amazing. There's two effects. They're having more exposure to stocks in their portfolio and also are less likely to capitulate when the market crashes?
Juhani Linnainmaa: Yeah, that's right. Both of those effects would be consistent with the money doctors type of story, that again, these advisors are providing a valuable service, that there's somebody who's holding your hand and telling you that, okay, sometimes stocks go down, you don't want to give up on the stock market when the returns are poor, that in the long run, it's probably going to be a good investment.
Ben Felix: Yeah, super interesting. Okay, so what do you think that those findings tell us about the role of financial advisors?
Juhani Linnainmaa: They tell us that financial advisors are really important in the markets, that they do seem to be serving a valuable role. The different question would be like, well, what do we do with this information? I guess, the key lesson would be that the market for advice is just like any other market, and so we want to think carefully about the regulation of the market. We could be pretty heavy-handed. Like we discussed before, you could always say that there's going to be bad behaviour, good behaviour. We have all this guardrails in place to ensure that people get the best possible service, so they get the benefits and the downsides.
Now, we're again back at the issue that it's really hard to say ahead of time what proper level and type of regulation would be, because there's always going to be the unintended consequences. It could be cutting down the competition as we discussed before. That could be having effects that you didn't anticipate. Again, this study is highlighting one of the benefits of having an advisor. You were asking before, well, if it's not about the customization, it's not about the performance of the portfolio compared to benchmark index, why do you use an advisor? This would be a partial answer to that one.
It would be saying that you have these people who are reluctant to invest in the stock market and left on their own, they are much more likely to be willing to take risk when they have an advisor. That is a valuable service, something that we didn't quantify in the other papers.
Ben Felix: Interesting. Really interesting. The duration of the relationship matters. Is there any lesson, or idea in there about clients not jumping around between advisors?
Juhani Linnainmaa: That's a good question. It would be interesting to look more deeply in that one. Then you would have to have data on that quantity of exactly why people are jumping around. It goes back to your revealed preference thing, that if we just see people jumping around a lot, maybe those people and the advisors they have been with are very non-random. There's some selection going on. But the question you're asking is an interesting one.
Ben Felix: Right. You're using duration of the relationship as a proxy for trust. If a client doesn't trust an advisor, even if they've been with them for a long time, then that makes the effect go away.
Juhani Linnainmaa: Yeah. It would be ideal to have some object initial trust besides the length of the relationship.
Cameron Passmore: I think you touched on this already, but I want to put a finer point on it. What do these findings tell us about the role of regulation?
Juhani Linnainmaa: I was afraid that someone’s going to be listening who's very anti, or pro-regulation. My first thing is just to say that we don't really know. What we do know from the other studies that we have done is that, again, regulation can have these bad outcomes that people don't anticipate. Even if you are really well-meaning and you think that, well, there's nothing bad in saying that we want to ban this and this and this behaviour and you're going to be charging more than this in fees and so forth, it might seem like a perfect thing to do. It's very sensible.
Then, people can be changing their behaviour, people can be change the recommendations ,or people can be quitting the industry and all kinds of less than ideal outcomes may happen. I guess, the point is to think about this more as a market and think about how you would be directly in the stock market. You don't want to just ban good and bad stuff in one dimension and assume that that's going to be helping the outcomes. Yeah, that's just a long way of saying that we just don't know. Let's just be careful.
Ben Felix: Yeah, yeah. Super interesting. I think Berk and van Binsbergen had a paper on regulating charlatans, or something like that. It was like an equilibrium, and they found that even if there are charlatans, if you increase regulation, it can be a net negative to consumers.
Juhani Linnainmaa: Yeah, yeah. That's right. It was an argument about the competition. Even if you had these bad people, if you put them in the market, they are going to be competing with the good people and the good people have to be responding by lowering their fees, which are going to be good outcome for the markets. If you restrict the market to be only the good people, then it's going to be less competition and they can charge higher fees. It's interesting to take on it that charlatans can also have a positive effect, because of the increased competition.
Ben Felix: Yeah, it's crazy. Counterintuitive. Okay, that segment on financial values was incredible. Your research on that is just awesome. I'm glad we spent some time on that. But I want to move on to another area that you've done a lot of really interesting work, which is the cross-section of returns. You got this paper, ‘The History of the Cross Section of Stock Returns’, which is really cool. What did you find when you looked at accounting-based anomalies, like profitability and investment in the pre-1963 period?
Juhani Linnainmaa: Yeah, as a background, the question, when you specify the 1963, people might wonder, why that number? Why not 62, 64? That's really the key point of the paper. That when we look at any accounting-based anomalies, like value or profitability, we often start the sample in ’63, because S&P founded the Compustat in 1962. After that one, we can have comprehensive, non-selected accounting information for companies. That's when we can construct these training rules and we can see what the performance looks like. In this study with Michael Roberts from Wharton, we got access to Moody's manuals going back to early 20th century. Now, we can be looking at the performance of the same rules before 1963.
Roughly speaking, when we look at alphas, information ratios, things like that, we find that the efficacy of these accounting-based anomalies is about 50% of what they are in the in sample period, the period that the original study is looking at. They're far less powerful in the history than what they are in the in sample period.
Ben Felix: I brought this paper up in a discussion recently and a comment that I got was, “Well, we can't trust the quality of the data in that pre-sample period.” But I think you touched on this in the paper. How did the data quality compare?
Juhani Linnainmaa: We look at that extensively and we looked at other papers that had been using historical data for other purposes, and they identify different points in regulation that should guarantee that this data is going to be of high quality. Whether we go all the way back to 1920s, or whether we start in 1940s, the conclusion is going to be the same. We are pretty convinced in the paper that this is not an issue with data quality.
Cameron Passmore: What do these findings suggest about the anomalies you looked at?
Juhani Linnainmaa: Yeah. The motivation for this study was to try to disentangle two effects. That when you look at something, like, let's say, the value premium. If you think that that's coming from mispricing, you might imagine that as soon as the thing has been discovered and people start trading on it, it's going to make markets more efficient and the value premium is going to go away.
There's also the other possibility that maybe the effect wasn't real in the beginning, and so, people have been data mining, they have found something in sample, and when you look at performances anomaly in the other sample period, it's going to be weaker. Two potential effects are going on. Maybe it wasn't real to begin with. It was data mining. This was stronger, because of data mining, or it could be about the actions of arbitrageurs, that something was real, it gets discovered and people trade against that one.
If you only look at the original sample and you look at the sample that accrues afterwards, so the outer sample period, you don't know really which one is going on. There's a great paper by McLean and Pontiff that tries to separate those effects. What we do in this study is we try to look at the other end of the sample. We are saying that if people didn't know about this in the original sample period, then they most certainly didn't know about that when you go back to 1920s and 1930s. That's going to be a clean test of the data mining hypothesis. Given that we find that the decline in the performance of these factors, when you go from the in sample, either to the post sample, or the pre-sample is about the same, 50%. That is telling us that most of the attenuation that we see in the performance of these factors is coming from the fact that they were data mined to begin with. Not so much that they were discovered and then arbitrageurs traded against them.
Ben Felix: That's crazy. You're saying that profitability and investment are data-mined factors?
Juhani Linnainmaa: For any one factor, it's hard to say that, well, this is a real one, this isn't the real one. If you take value and gave as an example, value performed really well up until something like the 2007-2008, but it has been in the drawdowns since then. Given how noisy the returns are, even today, you're going to be rejecting that the mean returns since 2007 has been equal to the average mean return before 2007, just because there's so much variation in returns.
The benefit of our papers is that when we look at a large number of anomalies, you're going to have much more power to try to see if it has been breaking the performance. For any one thing, we cannot say that this was about data mining. We can just say about the averages that either 50% of the things that people have discovered are not real, or maybe all of them are real, but they're just inflated by a factor of a 100%, in the sense that the performance decrease by 50% when you go out of sample.
Ben Felix: Yeah, that's crazy stuff. That made me think of the Fama and French's paper on The Value Premium, but I think that's what you're referring to with value. If you look at their post-out-of-sample period, post-sample, they found that the value premium was gone in the post-sample period, but they couldn't statistically prove that it was different from the in-sample period, something like that.
Juhani Linnainmaa: That's right. They had a paper about that. I think you had the title right, ‘The Value Premium.’
Ben Felix: Yeah. Okay. How would the post-1963, so we take that sample that everybody's familiar with, how would the mean variance efficient portfolio using the size value, the market size value, profitability investment factor, the Fama-French five factors, how would that mean variance efficient portfolio have performed if we ported it back and invested it in the pre-1963 sample?
Juhani Linnainmaa: Okay. There are two things that we were trying to look at here. If you have some sample, like 1963 to 2020, and we see in that data that you have some factors that seem to be working, then the question would be, well, how do you optimally invest in these factors? Oftentimes, admittedly, people are pretty careless about how they already fit the data and how much they think that people could not have known ahead of time. Oftentimes, people construct exposed optimal portfolios. They look at what would have been the optimal investments in the market size, value, profitability investment, the maximum shop ratio in the in-sample period. Then they're going to assume that people would have known to make that investment ahead of time.
Even though, of course, in 1963, they wouldn't even know about the profitability, let alone know how exactly what the conferences are, great open portfolio. In the computation that you're referring to, we say, well, let's train this model of investment using all the post-1963 sample, and then let's pour it to the pre-63 sample and see what the performance looks like. Obviously, by construction in the modern data, after ‘63, this would be a great investment. The Sharpe ratio in that portfolio would be about one, which far exceeds the Sharpe for the market of 0.4, or the same time period.
If you do exactly the same trading rule in the pre sample, the performance is about the same as that of the market. Having these additional factors with those weights are not really adding any value.
Ben Felix: Yeah, that's crazy. You talked about investment fees earlier. If you were paying whatever, more for the mean variance optimal portfolio, you're that much worse off than the market.
Juhani Linnainmaa: Yeah.
Ben Felix: Okay. It's hard to form optimal anti portfolios using X-post data. Do you have any thoughts on how to think about the optimal X-anti mix of factors in a portfolio?
Juhani Linnainmaa: I think people just seem to be much more careful about the aspects of data mining and overfitting. You definitely don't want to be creating any portfolio based on the full sample and assume that that's going to be the experience that you're going to be getting. What we highlight with the computation would be the data mining aspect, that if you look at any set of factors, you probably want to be assuming that the alpha set of samples can be about 50% of what you see in sample period.
Then when you compute optimal portfolio, that's going to be a whole different discussion, because anytime you bring in the covariance matrix, you're making lots of assumptions about how the factors are going to be offsetting each other, how much benefits you get from that one. You want to be pretty cautious. That being said, we are definitely not negative on the factor investing, because we do find that there's going to be lots of alpha remaining in the factor investing. It's us that you want to be quite cautious about this one.
I believe that in the same Fama French paper that you mentioned, they also point out that factors are really, really noisy in the sense that we tend to think about the US stock market going up, up, and up. If you look at the volatility of the stock market, it's not outside the realm of possibility that you would see a 20-year period in which you're going to be losing money by investing stocks in nominal terms, just because it's very easy to get bad enough troughs just by luck that offset the mean effect in data.
The same is going to be true for all these factors. Even if the value premium is still there, it would have still been easy to see a draw down like we have seen after the financial crisis. Again, we are not condemning on saying that factor investing doesn't make sense. We're just saying that you want to be careful about it. You don't want to look at the in-sample evidence and say that this is the greatest thing ever. I want to put 500% of my portfolio with leverage into factors. You want to construct smart portfolios, and you have to have some patience with it.
Ben Felix: Yeah, interesting. You're not negative on factors. You're more negative on over-optimizing. You wouldn't want to build a portfolio that's heavily tilted toward, I don't know, the investment factor, because that one did well in the sample that we have. Is that the idea?
Juhani Linnainmaa: Yeah, that's right.
Ben Felix: Yeah, that's interesting. We use factor investing for client portfolios. We use funds from Dimensional Fund Advisors and they tilt toward all five of the factors we're talking about. Is there any way to think about the optimal mix of how much value and how far do you push on profitability and all that stuff? Or is it just a naive, you should have a bit of exposure to factors and that's good enough?
Juhani Linnainmaa: I think that brings us into the realm of statistical learning, or machine learning, where you want to be careful about how you train the models on investment portfolios. That if you do something like – in the in-sample period, you want to be spitting that in different folds and trying to see how much I want to be tilting in different directions, not to over-fit to the sample. There are methods for doing it, but people want to be careful about it.
Ben Felix: Okay. I want to move on to a little bit more on the value premium. You've got an incredible paper on this, too. What mechanisms can cause firms to move between growth and value?
Juhani Linnainmaa: In the first part of this paper, ‘Decomposing Value’ paper, we complete the statistical approach to asking where the value premium is coming from. Like you're saying, well, why is something a value firm? Why is something a growth firm? I guess, there would be three answers to it. You might be a value firm, because you have always been a value firm, or you might become a value firm because your book value of equity went up a lot and your market value of equity didn't change. Or it could be that your book value of equity remained the same and your market value of equity fail.
Again, we are not taking any stance here in terms of economics. We're just saying that just mechanically, statistically, it has to be one of those things that tells you that you’re a value firm today, or growth firm today. Change the book value of equity, or the change in market value of equity with some fixed characteristic of the company that you're looking at.
Ben Felix: How does the value frame decompose into firms that changed in size and firms that changed in book value?
Juhani Linnainmaa: When we isolate that in industry components, that you have always been a value firm, or you become a value firm because you retain more earnings, or you become a value firm because the market value of equity goes down, it's really the last component that drives all of the value premium. That instead of looking at the total book to market ratio of firms, if you only look at the change in market equity component of that ratio, you're going to be getting more of the premium with less risk.
Ben Felix: Firms whose market value of equity declined and that's how they became value firms, that's where most of value premium is coming from. Is that right?
Juhani Linnainmaa: That's right. It's not that surprising afterwards. If you think about the sequence of events and the history of the literature, there was the DeBondt and Thaler paper in the mid-1980s about the long-term reversals. That effect was discovered before the value premium. The idea that if you look at stock returns, or the prior five years, maybe skipping over the most recent year, because of momentum, you get seen even returns on a strategy that buys losers and sells winners. That's a long-term reversals.
Fama and French came up with their value premium, or the HML in the early 1990s. They found that this correlation between these two things, that the reason you get the long-term reversals is that it's also value strategy. We are saying something a little bit different, because we are not really measuring the stock returns. We are looking at the market cap changes and the market cap, of course, can be changed for many reasons. It's going to depend on the dividends. It's going to depend on mergers and so forth. That's going to affect the total market value of the company. When you put all that stuff together, it's really the change in the market value component that tries to value premium. Note that book value of equity component doesn't really play a role.
Cameron Passmore: What does this finding mean for investors who are pursuing the value premium?
Juhani Linnainmaa: Again, I want to be cautious, because just like we discussed before, now that would be a great temptation that instead of investing with this rule, I'm going to invest a 100% of my money with this rule. Being the most cautious, you could just say that simply, statistically, if you have any predictor that seems to be predicting returns and you believe in it, and you can decompose and predict on different ways. Like in our case, there would be three components, the historical, or the long-term average to market, the change induced by the book value of equity, and the change induced by the market value of equity. If you use that as one predictor, it's probably going to be less powerful than if you decompose the predictor. Assuming that you have enough data to do so, you can definitely get some value out of that.
Our results that are specific to the US, they seem to be telling us, again, you would have gotten more premium with less risk by using the market value of equity component than you would get by using the total book to market. I'm not making guarantees that that finding is going to go well in all instances and all companies and so forth.
Ben Felix: Yeah. Yeah. That's the thing with financial economics, I think. There aren't many guarantees. What do your findings mean for the theoretical explanation of the value premium?
Juhani Linnainmaa: What we did after this point was look at the things just statistically and try to learn from the data if these three different components behave differently in terms of predicting returns. The interesting question is the one that you are asking, well, what's the explanation for what gives you the value premium? Our answer is going to be negative on that one. After the Fama-French paper, there was this cottage industry that started that was churning out explanations of the value premium. All kinds of different papers that were looking at different components of consumption and things like that to understand, well, what is the risk that people see in value stocks to explain the high premium?
The way these things work is that you have some theory. The theory is suggesting that there's some empirical factor that you can be constructing and then that factor is going to be covering positively with the value factor. That would be explained that in this dimension, value is more risky. Surprisingly, many of these theories in the work. Even though they have very different explanations, they all seem to be explaining the riskiness of value. But if you think about what we are doing, we are splitting the value factor in two components. There's a good part, the one that gets the premium and there's a bad part that doesn't get the premium.
It turns out that these models, the factors that they suggest, they do cover it with the value factor, but they cover it with the bad part of value factor, the one about the book value of equity, or the persistent value component. They don't really cover it with that component that gives the value premium. They cannot be explanations for why value stocks outperform growth stocks. They're driving on to the wrong component of the predictor.
Ben Felix: Hmm. Really, really interesting. We've got one more question for you just to finish off on a topic. The geekiest segment of our podcast community was very excited about when your papers on this came out. I hope they'll be happy that I'm asking you about it. What are your findings on factor momentum suggest about pursuing momentum as a strategy?
Juhani Linnainmaa: That's one of the key ideas of that paper. If you're doing back to momentum, people often say that the momentum has to be behavioural effect, that there's no good reason why something like this would exist in the rational world. Of course, we can have theories for momentum that would make risk and so forth, but people said that that seems, at least some segment of the economic population said that that seems implausible.
At the same time, people were pointing out that if you look at the data, momentum doesn't seem to be a free lunch. If you look at the stocks that are winners, those winners tend to be covering with other winners and losers tend to be covering with other losers. There has to be some commonality to those. But people couldn't find the one factor that would be explaining that.
What we are saying in the paper is that the momentum actually resides in the factors themselves. You have a large number of factors, like you have the Fama-French five-factor model, but you have many, many, many factors beyond that. Some of the factors may be useless as unconditional investments, but when they have good performance, they keep on having good performance and they have bad performance, they keep on having bad performance.
The mechanism that we have here is that if you have momentum in the underlying factors and you have all these different stocks that load on these factors, then the momentum from the factors is going to be transmitting into the investor stocks. When you look at the winning stock, it has to be setting aside the idiosyncratic component. It has to be that it's loading positively on the factors with good past performance and it has to be loading negatively on factors with bad past performance. You’re trading momentum, but when you trade the momentum in the investor stocks, you're indirectly trading the momentum of the factors.
That brings up the issue of why momentum is risky and what factor momentum tells us about momentum. Every time that you trade momentum, you're effectively making a different bet on the underlying factors in the economy. When you buy the winners, you're going to be buying a certain configuration of tandem of factors. When you sell the losers, they're going to be having opposite loadings on those factors and you're going to be making bet on those factors. Obviously, if you buy those winners, it's not surprising that all of them tend to become moving going forwards in time and all the losers are going to become moving going forwards in time, because they are the similar factor loadings.
It's just that when you now go forward one year and you rebalance those portfolios, the loadings are going to be very different, because different factors have different past performance and so forth. Underneath it all, there's going to be this factor structure that's driving the returns on the stocks and the stock momentum, and it's also driving the differences and covering structures of the stocks that we are trading.
Ben Felix: Is momentum a separate factor then? Is it a standalone factor?
Juhani Linnainmaa: In our study, when we look at something like the UMD, that's going to be the complete momentum factor using all the equities in the US, and then you try to explain or span that exact momentum constructed from the US equity factors, you find that there's no incremental value in the momentum. That you can capture all of the profits of the momentum factor by trading the momentum in the factors themselves. Our results indicate that there wouldn't be a super momentum factor.
To some extent, it's something that's hard to prove. We are lucky that in our sample, the factor momentum that we get from known factors is powerful enough to spend the momentum. But you could imagine that if we didn't know about all these factors that we were back in the 1970s and we had only one or two factors, the momentum in known factors wouldn't be probably enough to spend the momentum that we see in stock returns. Because when you buy winners, you are buying factor momentum in all the factors that are out there, the known and unknown. It's not easy to show that factor momentum spans momentum, because we can only construct factor momentum in the factors that we know.
Ben Felix: Yeah. That's super interesting. What are the investment? What are the portfolio management implications of factor momentum spanning momentum?
Juhani Linnainmaa: At least it's something about rethinking about how you trade momentum, that you can always trade the momentum in the underlying assets. If you think that that's going to be a profitable net of trading costs, it should also be strictly better to do it in the factor space, because there might be better ways of trading, like portfolio stocks than trading the individual stocks and trading factors. It should be helping you construct other portfolios, if you are leaning towards momentum.
Ben Felix: Super interesting. Awesome.
Cameron Passmore: Good for our last question, Ben?
Ben Felix: Yup.
Cameron Passmore: All right, professor, how do you define success in your life?
Juhani Linnainmaa: I was thinking about that one. I think I only have platitudes for you, in the sense that for me has been most about minimizing regret, that it's hard to know ahead of time what do you want to be doing and where you might find success and what you can be good at. I always think about, I want to be doing more stuff. I want to try all kinds of new things, because that's the only way that you can actually find something that's going to be interesting. You cannot really maximize your success at a time, because you don't know what's out there. But if you grab it in every opportunity and try to do all kinds of things, it's going to be more fulfilling in the sense that I don't think we are ever going to be regretting things that we tried and it didn't work out for us. But we are going to be regretting the 10 years ago, we didn't do something. We had the opportunity to do something, but we didn't take the opportunity. Then we think that, well, what might have happened?
As I get older and older, I'm painfully aware of that one, that I always think about. I must try to do more and more, both in work and life, so that I must not grow up with the regrets that, well, I should have done that thing over here. Also, I guess, specific to academia, that there are many of these people who have been super successful, they have done some really important work. But as I get older, they tend to be doing what comes ease of them. They keep on doing variations of the research that they did before. Then they are 60s, 70s, 80s, and they do the same stuff. That might make them happy, but I'm fearful that that's where I'm going to be ending up. That's why every single day I try to do something different.
Cameron Passmore: Wow. It's really interesting.
Ben Felix: Yeah. Really cool answer. All right. Juhani, this has been an excellent conversation. We really appreciate you joining us on the podcast.
Juhani Linnainmaa: Oh, thank you. This was great. Thanks so much for having me.
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Books From Today’s Episode:
‘The Misguided Beliefs of Financial Advisors’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3101426
‘Taking the Battle for Financial Literacy to Where the Eyeballs Are’ — https://anderson-review.ucla.edu/millenial-learning/
‘Retail Financial Advice: Does One Size Fit All?’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2522934
‘Money Doctors’ — https://scholar.harvard.edu/files/shleifer/files/moneydoctors_journaloffinance.pdf
‘Regulation of Charlatans in High-Skill Professions’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2979134
‘The History of the Cross Section of Stock Returns’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2897719
‘Does Academic Research Destroy Stock Return Predictability?’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2156623
‘The Value Premium’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3525096
Dimensional Fund Advisors — https://www.dimensional.com/
‘Decomposing Value’ — https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2083166
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Rational Reminder on iTunes — https://itunes.apple.com/ca/podcast/the-rational-reminder-podcast/id1426530582.
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Rational Reminder Email — info@rationalreminder.ca
Benjamin Felix — https://www.pwlcapital.com/author/benjamin-felix/
Benjamin on X — https://twitter.com/benjaminwfelix
Benjamin on LinkedIn — https://www.linkedin.com/in/benjaminwfelix/
Cameron Passmore — https://www.pwlcapital.com/profile/cameron-passmore/
Cameron on X — https://twitter.com/CameronPassmore
Cameron on LinkedIn — https://www.linkedin.com/in/cameronpassmore/
Juhani Linnainmaa — http://jlinnainmaa.com/
Juhani Linnainmaa on LinkedIn — https://www.linkedin.com/in/juhani-linnainmaa-832134194/
Juhani Linnainmaa on Facebook — https://www.facebook.com/juhani.linnainmaa/
Tuck School of Business — https://www.tuck.dartmouth.edu/
Kepos Capital — https://www.keposcapital.com/
Chicago Booth School of Business — https://www.chicagobooth.edu/
National Bureau of Economic Research — https://www.nber.org/
UCLA Anderson School of Management — https://www.anderson.ucla.edu/
Aalto University — https://www.aalto.fi/en
Mutual Fund Dealers Association — https://mfda.ca/
Michael Roberts on LinkedIn — https://www.linkedin.com/in/prof-michael-r-roberts/