In our second special release episode during the 2020 COVID-19 bear market we discussed a broad history of US bear markets from 1900 to 2020, the recent volatility in the bond market, bond ETF NAV spreads, a nuance in the legislation on tax-loss harvesting, and some of the tax-related changes that Canada has rolled out in light of the current situation.
Read the Transcript:
Ben Felix: This is the Rational Reminder Podcast, a weekly reality check on sensible investing and financial decision-making for Canadians. We are hosted by me, Benjamin Felix and Cameron Passmore. Welcome to episode, what we're calling 90-2, a special release episode breaking from our usual cadence.
Cameron Passmore: So how has working from home been for you?
Ben Felix: It's been fine. I had a home office work station set up quite a while ago before all this happened. So that's been fine and kids have been great.
Cameron Passmore: You're not having that experience like that guy on British TV, the BBC interview where the kids were running behind him?
Ben Felix: I mean, I've been on some calls where there were screaming kids in the background for a period of time. They have not come into the room, but I think people are pretty understanding though because everyone's working from home.
Cameron Passmore: Yeah. Me, it's been the dog and I think you'll hear Oscar in the background a few times in this discussion today. I've been doing my Peloton every day so thankfully I got that in time. And you'll be impressed, Anna, my daughter is taking up knitting.
Ben Felix: Oh, wow.
Cameron Passmore: Yeah. She's doing her school work and the restaurant laid her off unfortunately. Shout out to that restaurant, great friend and a great fan of the podcast. And looking forward to having them back up in business soon, of course. So did you know that March 9th, the ETF, the Exchange Stated Fund celebrated its 30th birthday.
Ben Felix: I did not know that.
Cameron Passmore: And the first ETF was created in Canada.
Ben Felix: I did know that. There you go.
Cameron Passmore: And the other big news, you hit 100,000 subscribers on your YouTube channel which is amazing.
Ben Felix: I figure if I make YouTube videos about the market crash, then the YouTube channel becomes a hedge against the market crashing.
Cameron Passmore: I got a kick out of some of the comments. So one of them was, "Ben, I would like to have you declared an essential service during this pandemic." Another one was this video is definitely a keeper as the video on the market crashes. Something to play every seven to 10 years, and I agree. Anyway, so this podcast, we thought we better do these regular updates, given all this happening in the world. We have a lot of interviews recorded, so we're going to keep them coming as usual, but I think we're going to keep doing these probably weekly updates.
Ben Felix: As needed. This past week was pretty spectacular, so we figured it was worth giving some more rational perspective. Maybe next week is all green and we don't do a special episode. But don't let me jinx it.
Cameron Passmore: But bottom line is that we're going to keep up the same cadence with us and then guests every other week. We have phenomenal guests lined up for the next, I think we have, I don't know six or seven, I think already done which is great. So we're going to keep them going at the regular cadence and then we will do all we can do to fill in the gaps in between. So speaking of a crazy week, the numbers this week were just unbelievable.
Ben Felix: Yep.
Cameron Passmore: A few data points here. The Canadian dollar started the week at 72.4 cents and then bottomed out Wednesday afternoon at 68. Just above 68 cents and closed the week at 69, almost 70 cents. It's amazing. You figured start of the year just under 77 cents. So it's a drop year to date of almost 10% versus the US dollar. Oil though. Man, can you believe oil this week? I mean, the West Texas Intermediate started the year at 62. Started the year at 62 and opened this week at just above 30. Hit 20, just over 20 Wednesday afternoon. It finished back up to 27 Friday afternoon. And the Western Canadian Select, it open up Monday at 13.30, hit the low of 5.43 Thursday afternoon and then almost doubled to 10.51 by Friday afternoon.
Ben Felix: I'm not super familiar with how low are those prices in the context of broader history. There have been oil price drops in the past. I'm not an expert in oil price history. Do you know?
Cameron Passmore: No. These are just observations of this kind of volatility we're seeing this week. That might be a idea for a future show. Look at the Dow this week. The Dow opened on Monday at 20,935. Hit just under 19,000 at the low on Wednesday and closed Friday at 19,173. Started the year just below 29,000. And the same thing on the bond side, the XBB, the iShares Canadian Bond ETF, bond index ETF opened the week at just under 32. So 31.73. Bottomed out at 28.65 Wednesday afternoon. That's almost a 10% drop in just under three days.
We're talking length in the episode about the difference between that and net asset value, which is super interesting. And on Friday, XBB traded 400,000 shares, which is double the normal volume. Incredible, incredible week.
Ben Felix: Yep, quite a week. Hopefully the content of this podcast helps people take a bigger picture, longer-term view of all the stuff that's going on.
Cameron Passmore: Yes. You did an amazing deep dive into past events, past drawdowns which was fascinating. The stories that go along with these drawdowns. I thought that was incredible. So we went through that slowly and carefully. So it's a good pace I think for people to take away. A little bit longer than usual, but I think that's okay.
Ben Felix: If we do an extra episode next week, maybe we'll do the same kind of analysis for Canadian bear markets because this one was just for the US data. Our guest episode coming out this week, the timing of that episode is interesting because it's all about... It's with Robinson Smith who's the son of Fraser Smith who invented, or at least named the Smith Maneuver. But we basically talk about leverage investing. We talked about the details of the Smith Maneuver, but we talked a lot about leverage investing. I think in the world we're living in right now where being a leveraged investor would be real scary, because not only are prices dropping, but there's all the talk about the economy slowing or stopping, which means incomes and the ability to service debt become questionable.
I just want to give a bit of a primer for. That is who the guest is this week and it's about leverage. That was recorded before all of this happened, but I think listening to it in the context of what's going on now, it'll be interesting.
Cameron Passmore: Well, it also gets you to think about being leveraged in times like these. So it's an interesting time. It's also interesting to contrast to our last guest, which is all about being safety first oriented in retirement income planning.
Ben Felix: Yeah. It's a big contrast. That's true.
Cameron Passmore: It's a big contrast, but the show must go on and we will look back on these times and remember this is good to have this recorded. But I think the sequence is quite interesting for sure. Anyways, I'm wearing my University of Chicago sweatshirt for good karma this week. Anything else to add?
Ben Felix: No, let's go with the episode.
Cameron Passmore: All right. Thanks for listening. So one of my favorite writers, Morgan Housel. I know you're a big fan of Morgan as well, released what I thought was an excellent piece yesterday called Common Enemies. So those who don't know Morgan Housel, he's a partner at Collaborative Fund, and a former columnist with The Motley Fool, and also the Wall Street Journal. I think you and I both agree on this, he's one of the most thoughtful writers in our business.
Ben Felix: Yeah, definitely.
Cameron Passmore: So I reached out to him a few weeks ago and we hope to have him on the podcast just as a sidebar. Sometime this summer, he has a new book coming out so we're looking forward to that very much. But this week's piece, Common Enemies, I thought was just fabulous. He kicked off the article by talking about how everybody in times like this wants a map, a simple guide to see what will happen next. He said it's very common to compare the current situation and he highlights not the market, but the situation, which I know you're going to talk about later on in this episode.
Ben Felix: We talked about that in the last episode where we talked about narratives. That's one of the biases that narratives fire up is comparing the current situation to past situations even though that might not necessarily be a good comparison or a good way to think about it.
Cameron Passmore: Yeah. So he says you can look at past crises like the 2008 financial crisis, the 9/11, the 1918 flu or the Great Depression and you're right, we talked about these in the last episode, but his argument is that none of these really fit. He said this is far worse in 2008. He said the enemy is much more visible than in 9/11 invisible.
Ben Felix: Invisible?
Cameron Passmore: Invisible to 9/11, correct. And medical knowledge exceeds 1918 and he said the policy response currently is much faster and deeper than it was in the Great Depression. So he quotes, "Complexity never repeats itself in this exact form. But as Voltaire says history never repeats itself, man always does." So he goes on to talk about how we can look at history to tell us what might happen next and we can use history as a guide to predict the kind of behaviors that people are susceptible to do when faced with a similar event.
This is where he goes on to say that the best thing that we can compare this to in history is probably World War II. He says, "Not the battle or the geopolitics, but World War II united most of the world against a common enemy in a way that is incredibly rare. Cooperation within and between countries surged." He goes on to say, "This may be the first time that the world has united so firmly against a specific foe, since the 1940s. And the abilities, outlooks and their incentives surprised many during World War II. If history is any guide, we are about to be surprised again."
So he goes on to describe some of the decisions that governments made back in World War II, corporate retooling, new tax policies, et cetera that were enacted to support the war efforts. And this next part, he goes on, I found so fascinating. He goes on, "After the war, sociologists around the world went to work studying how the tragedy affected the human mind. One of them was an English man named Charles Fritz who spent years in America studying the psychology of disasters. His broad thesis is the opposite of what you might expect. Disasters do not make societies panic. They bring them together in calm solidarity."
"Pandemics kill people and recessions ruin people. Saying they have a silver lining is a step too far. But I wonder if the best map we have that tells us what to expect next is a kind of extreme cooperation, solidarity, and empathy we last saw in the 1940s. And I wonder if we'll look back at COVID-19 as one of the worst things to happen to us, yet triggering something positive that couldn't be achieved in any other way. History never repeats itself, but man always does."
Ben Felix: Yeah, it is interesting.
Cameron Passmore: Very, very powerful piece and very interesting perspective.
Ben Felix: Yeah. It is interesting. I don't know how much it helps us think about the current situation. He's kind of saying there's maybe a silver lining, although he explicitly said that's not what he's trying to say, but trying to say that there could be some good that comes out of the whole thing, which is important.
Cameron Passmore: But if you don't believe that there will be good, I mean you need to have that to believe that your portfolio will rebound. Our life will get back to normal. People will be okay.
Ben Felix: Right.
Cameron Passmore: Anything else to add to that?
Ben Felix: No. It's a nice story. I like the data better, personally.
Cameron Passmore: I know you do. I try to give a different perspective here. That's all. Okay. Another perspective. I received a really nice email from a client yesterday from a very thoughtful and highly respected surgeon here in Ottawa about decision-making. He sent me a note saying medicine... They often have discussions about hindsight and regret with patience, and regret is a natural emotion, but not a particularly helpful one in most situations. So he says he's not an expert in the field of decision-making, but in dealing with cancer patients, he's learned a lot about how patients make decisions.
Ben Felix: Well, and he's a surgeon, right? An expert in making decisions. Maybe not an expert in decision-making theory, but clearly a lot of thought and reflection on the decision-making process from this person.
Cameron Passmore: Right. And they make their decisions based on evidence, but how an individual reacts to that evidence can be different person to person. So the advice might be based on a population, but a single person is not a population. He talks about how when he advises people, their choices, he tells them to make the best possible decision with the information available. And once you've made that, his advice to them is don't look back. He says that unfortunately with cancer patients, often the outcomes are not what they want, but that does not mean you did not make a good decision.
So he's had several patients come back to him as they were dying, specifically, get this, specifically to thank him about that regret in hindsight discussion that he had earlier, and how it helped them and their family deal with the prospect of death due to their disease. But get this, he actually had drafted before the market correction, he drafted an email to me to sell everything based on seeing the China data and the Italian data. He was willing to miss out perhaps a possible gain to avoid a slide in the market.
He actually, part way through drafting that email got called away to the OR and never finished it, and came back, and talked himself out of selling, believing he was not being "rational". And within a few days, when the market started to go down, he really regretted it. But then the more he thought about it, the more he realized that he had made what he thought was the best decision and was very similar to what he tells his patients.
He said to me, "The decision to not sell was based on our inability to predict market changes. So while the outcome..." It sucks and we know how we feel about that word. "This decision was correct. This actually erased the regret I was feeling."
Ben Felix: It's kind of like we talked about in a past episode about counterfactual thinking. In this case, there was a lot of closeness to the alternative outcome because he had written, drafted the email. So it makes sense that you'd feel regret after that experience, but I think hearing the discussion we had on counterfactual thinking, his own experiences with decision-making. But once you start thinking about it, you realize pretty quickly that it doesn't make sense to have those feelings of regret even though they're hard to avoid.
Cameron Passmore: Yeah. You think about a lot of the comments that you've been getting on your YouTube channel about some of your fact-based presentations. When you hear all that, it's quite easy to feel good about not selling, but then you go and watch the news and they can completely spin you on your head with this whole thing and charges the emotion.
Ben Felix: One of the interesting things about... And you and I were talking about this before we started recording, but one of the common comments that I'm hearing is that the market is not pricing how bad this is properly. So as much as it's an argument about things are going to get worse, the thing that I'm hearing a lot is the market is not pricing this properly. It seems to be as much a market inefficiency argument as it is, this is going to get worse argument. Because it has to get worse than the market currently expects, and I think that's what some people, people that are talking about wanting to potentially get out are thinking. They're thinking we know this is bad, the market knows it's bad, but I think it's going to get worse than the market thinks it's going to get worse.
Cameron Passmore: And if that thesis is true, I cannot afford that downside.
Ben Felix: Right. And we're going to talk more about not being able to afford more downside. I built a market timing model, well, to model the model we're getting out after a downturn.
Cameron Passmore: Anyways, I appreciate that email and he's an avid listener so thank you very much for that perspective. So portfolio topic?
Ben Felix: Yeah, into the data.
Cameron Passmore: Your happy place again.
Ben Felix: Yeah. I looked at US bear markets and I plan to expand this into Canadian and international markets although there's not quite as much data. For Canada, I have it going back to 1951. I just didn't have time to look at the numbers and for international have it going back to 1970. Anyway could be some interesting work to look at bear markets in those regions, but also compare the timing of those bear markets to US bear markets. But anyway for now, for today, we just have US bear markets.
For my methodology, I was using monthly data. Bear markets are usually defined as a 20% peak to trough drawdown. Monthly data smooths out a bit and. So I dropped my threshold for a bear market in this analysis to 15%. And the reason was, because I was using monthly data, the smoothing eliminated a couple bear markets that I know are documented 20% slides, but they didn't show up as 20% slides in the monthly data just because of the smoothing.
Cameron Passmore: Oh, I see because the other data would be pulling mid month perhaps.
Ben Felix: Daily. If you look at daily data, there are some bear markets that show up where they don't show up in the monthly data.
Cameron Passmore: That's what I mean. So it could be intra month. Got it.
Ben Felix: Right. Yeah. So I expanded it to 15% drawdown being a bear market and that added in the bear markets that I know exist that were missed in my 20% filter, but it did not add any additional bear markets. So I think the 15% threshold using monthly data is pretty good to define a bear market. I mean, 20% being a bear market is also who decided that, but anyway that's another topic. So from 1900 through to now, I counted the decline to date in this bear market, as a bear market and I counted 27. So 27, 15% or greater drops from 1900 using US stock data through to now 2019. I found the average peak to trough drop for all of those 27 bear markets from 1900 to 2019. The average peak to trough drop has been 29%, which interestingly is very close to the current peak to trough drop.
Cameron Passmore: Okay. So just clarify for the listeners the difference between the peak to trough of 29% and the 15% bear market that you described earlier.
Ben Felix: Okay, sure. So 15%, I recorded a bear market in this data set if the drop was 15 or greater. Of all of those 15% or greater drops, the average drop was 29%.
Cameron Passmore: Got it.
Ben Felix: So of the 27, there was a 29% average drop.
Cameron Passmore: So the threshold is 15% to get counted.
Ben Felix: Correct.
Cameron Passmore: And once you recounted, the average of all those drops was 29%.
Ben Felix: That is correct, yeah. Of the full data set, it's taken 13 months on average from the peak to trough decline to occur. This is one of the things that makes the current bear market somewhat unique, and not totally unique, which I'll touch on in a second, but it's been fast. 13 months on average to get to a 29% drawdown and we're two weeks, well, I guess from peak to trough. The peak was in December to now. So I guess if you think about it that way, it is three months.
Cameron Passmore: But look at how fast that peak showed up as well on the way up.
Ben Felix: Yeah. This is one of the things that I've heard and this is a total narrative, but I've been hearing that this drop was so fast because of how fast information travels now. When I was looking at all these different bear markets, the 1916 bear market, some people theorized that that was caused by the discovery of the Spanish Flu as the data started to merge from Spain. And now Spain was not the first country to have Spanish Flu, they were neutral in the war in World War I, so they were reporting on the data. No other countries were willing to report on the data because they didn't want to give up the information to the other countries that they were at war with, but Spain was neutral. That's why it's called Spanish Flu.
It shouldn't be because it didn't actually start there. It's just kind of funny. So October 1916 was the peak around the time the Spanish Flu was discovered and it took 13 months to drop 30%. Now, was that actually caused by the Spanish Flu? They also had rising interest rates at the time. I found one blog post from some hedge fund person saying that the market took a while to price it in that time, but it got priced in much more quickly this time. Similar data starts emerging, but it just travels so much more quickly.
Anyway, that was a bit of a digression. So 13 months from peak to trough, and now we've gotten there in three months. On average, it's taken 40 months to return to the previous peak. So from peak to trough, and then to get from trough back up to where the peak was, it's taken 40 months on average. That 40 months is skewed by 1917, which took 98 months to recover. The Great Depression which took 177 months, and that's the longest one, but we're going to talk more about that later. There's some debunking to do there. And the tech crash in 2,000 which took 78 months to recover.
So if you take those out, obviously, it gets a lot shorter. And then the other interesting thing is that post World War II, so if you look at the data after 1945, the average recovery has been a lot shorter. Now, that could be a monetary and fiscal policy thing. Governments are getting better at policing this stuff. I don't know. Maybe it could be a war thing too, I guess We had World War I ad World War II, and the depression. So all those took a long time to recover from.
Cameron Passmore: Which index are you looking at for this analysis? These are all market cap indices?
Ben Felix: Yeah. So from July 1926 to 2000, to now. And I did mid month. I did the data for march as of Friday. As of Friday, March 20th. Everything else is monthly. I just pulled that intra month data point so that we could examine it. July 1926 to now is the CRSP 110 Total Market Index and from 1900 to June 1926 is S&P 500 data from Robert Shiller. So post World War II, so starting 1945, the average recovery from the drawdown has been 26 months. So those long recoveries pre-World War II really make that average recovery longer.
Now, the current decline, I mentioned a minute ago that it's not totally unique on paper in that 1987 look very similar. Negative 30% drawdown within three months. Now, I mentioned on paper because if you look at the data, that's what exactly what this looks like although we know that most of that drawdown came in two weeks. That part is new. That's unique. The speed of this drawdown has been impressive if that's the word that we can use to describe it.
Cameron Passmore: It'd be so amazing to know the cause of the speed as opposed to all the theories. Maybe something we'll never know.
Ben Felix: I don't think you can know. I think just the idea of narratives and the speed with which information travels seems to be a plausible explanation for the speed of the drop. Like I mentioned that 1916 data point where the Spanish Flu started to emerge and that got priced in as well. The article that I read about that, and this was not an academic paper, it was a blog post, but the ideas were interesting is that the market priced in the worst of the Spanish Flu before the worst happened. So by the time the worst was over, prices rebounded extremely quickly.
So based on what people were expecting, how bad people were expecting Spanish Flu to get in 1916, the market priced that in. And once it was priced in, it didn't fall a whole lot more. And that kind of speaks to what we were talking about earlier where the only way the market keeps dropping from here is if things end up getting a lot worse than we currently expect them to, which could happen. I'm not saying it can't. That's the assumption that we're making to say that.
Okay. So another data point that I looked at just because I had this whole data set pulled together was what if you missed... And I know this one's a bit controversial and we had some comments on the Rational Reminder website about this. I looked at what if you missed the best month in the recovery? And I think it's controversial because I mean, why would you look at that? Why would you look at missing the best month when you're not also looking at missing the worst month? I think it's worth looking at the data point, and I think it's worth looking at it because the best month tends to happen after the bottom.
And nobody can time the bottom. Nobody can time the top. You can't time an exit or an entry, on average. If you think you can time the bottom, and if you think that's how you're going to win by getting out before the bottom I guess, there's a good chance you're going to miss the best month in the recovery because they tend to happen early in their recovery.
Cameron Passmore: And I would think behaviorally many people would feel better about getting back in. I've heard this from people. If we see a good turnaround, then I would get back in because I know things will be getting better, and treat that as a signal of future stability. So I guess you can rationally say that, that makes sense, but I think it'll show us that there's a cost to that.
Ben Felix: Yeah. Well, when do you get back in? When are things getting better. I built a kind of naive marketing model to look at that too. So in the full data set including pre-1945, and we talked about those having longer recoveries in general, missing the best month in the recovery resulted in needing to wait 21 additional months to reach the previous peak. So I talked about how it took 39 months to reach the previous peak after hitting the bottom. If you miss the best month in the recovery, that extends to 60 months.
Cameron Passmore: Can you imagine, that's the cost effectively of waiting for that signal to buy back in?
Ben Felix: Yeah.
Cameron Passmore: If someone is using that as a trading signal or decision signal?
Ben Felix: If you could know it was the best month and you got in after the best month, which I think is plausible because if somebody's waiting for good returns to get back in, the best month seems like a plausible re-entry point, after the best month.
Cameron Passmore: Right. And if you say the market is efficient, the market is realizing that the worst is behind us, therefore we're good to go again.
Ben Felix: A lot of that comes in a burst, a quick burst. And we've talked about I'm using monthly data. We've talked about in the past how a lot of that's probably coming within a few days of that best month. Anyway, so then post 1945, not as bad and pre-1945, one of the reasons it was so bad was because if you missed the best month, the next peak was also followed by another recession. So if you missed that next peak, you often went into a whole other recession and had to wait for the next, next peak. Anyway, so post 1945, it resulted in having to wait an extra six months to reach the previous peak.
So then I built another market time model because people also ask about, so that's just you're invested the whole time. You don't miss any of the downside, but you do miss the worst month, which basically means you got out at the worst possible time. So of course that makes things look bad. But what if you got out... So we're defining a bear market is 15%. What if you got out as soon as you realize you're in a bear market? So as soon as the drawdown hits 15%, you get out of the market at that time.
Cameron Passmore: So like a stop loss effectively?
Ben Felix: Yeah, which is that's the wording somebody used in one of the questions on the Rational Reminder side, I think. And then for the re-entry point, and this is where I just made a naive assumption that you're going to get in halfway through the recovery. You can't know when the recovery is. You can't know when we're going to reach the next peak again. But kind of like we were talking about, I just made the assumption that halfway through the recovery, people start to feel a bit better and they get back in.
Cameron Passmore: So halfway back to the peak?
Ben Felix: Yeah. And that could be like some recoveries took four months. So you missed the first two months of the recovery.
Cameron Passmore: Okay.
Ben Felix: Some recoveries are 30 months or whatever and so you miss 15 months of the recovery. How else do you define when you get back in? Practically, if someone's trying to get out, when do you get back in? So anyway, that's what I decided to define getting back in was halfway through the recovery. So I used the same data set from 1900 to 2019 and the annualized return for the full period was 8.39% So that's average annual return, geometric average return 8.39%. And then with the market timing model, so this is getting out of the market after a 15% drop and then missing the bottom, great job. And then from the bottom to the recovery, you miss half of that. So that's important, I guess. If you get out of 15%, but if the drop is more than that, which on average it is, because we talked about the average dropping 29%.
Cameron Passmore: So you're missing that 14 on average.
Ben Felix: Yep.
Cameron Passmore: And you're buying back in halfway back up.
Ben Felix: Halfway back up. So that average annual return drops to 7.93% with a market time model.
Cameron Passmore: So 46 basis points.
Ben Felix: Yeah. Which sounds kind of trivial and maybe people would say, "Well, that's not such a bad price. But 50 basis points, I mean, when you're looking at the data from 1900 to 2019, I didn't pull the growth of wealth numbers, but it's staggering. I think it was double actually. I think the ending wealth was double for the regular just staying invested versus the market timing model.
Cameron Passmore: Yeah. It doesn't sound like a lot and people might say, "I'd be quite happy with that," but how would you know when you're halfway back either, right? This is just a model. You'd have to somehow pull this off perfectly, but it's almost impossible to pull off.
Ben Felix: Yeah. And you could build a back-tested model that looks better than this that maybe looks like it adds value, but again, how do you actually execute it? Now, one point is when I was going through the data, it was a bit of a manual process. I didn't have a computer program to run the simulation, but I spent a lot of time with the data anyway, so running this additional piece wasn't too much of an issue. It's nice to get familiar with the data for one of the reasons I'm about to mention. So after 1929, I'm running this for each bear market period, after 1929, the market time model looked fantastic. I'm thinking like, "Oh, wow. Should we be timing the market?"
Then you look at the data and it's obvious because in 1929, in September or in October, things tanked and they kept tanking and there were recessions within the recession and market drops within the Great Depression. People often talk about from 1929 to 1941 being one big bear market, but there were probably three bear markets within that period. So with the market time model down 15%, you get out, great. You missed an 85% drawdown. And within that, another couple of 30 or more percent drawdowns.
So after that whole timeframe, so looking at like 1940, the market time model looked... You look like an absolute genius, because you're sitting in cashflow while everything's tanking. But then throughout the rest of history, so excluding that, some people may be thinking, "Well, this current situation is going to be the next great depression. I mean the only answer I can give is maybe. Maybe it is. There's no way to know for sure. But of all of the 27 bear markets that we looked at, that was the only one where market timing seemed like a good idea.
Cameron Passmore: So it's skewing the data basically.
Ben Felix: Yeah. I didn't do this, but if we took that out, the market time model would look a lot worse.
Cameron Passmore: Exactly. Fascinating.
Ben Felix: The other thing that I did which was a bit time consuming, but also pretty interesting was I looked at all of these bear markets that were in the data set and I tried to pull what were the primary narratives at the time.
Cameron Passmore: How did you do this?
Ben Felix: Googling market crash of 1902, market crash of 1907.
Cameron Passmore: This was like hours of Googling?
Ben Felix: Yeah. It was. I think it was worthwhile. We have a table that we'll try and publish on the rationalreminder.ca site so that people can look at it too.
Cameron Passmore: Let's go through some of this.
Ben Felix: Sure. So let's just go through the ones that are sort of really interesting, because a lot of them are monetary fiscal policy decisions. Not a lot of them, some of them. So 1902, which was that was the peak. This was called the panic of 1901 is when it started and that was really price manipulation by wealthy investors. So this is people that were trying to corner the market in different industries by buying up all the stock of companies.
This happened a couple times where people tried to do this, tried to corner the market and they do it with leverage. So they bid up the prices of securities with a ton of leverage trying to corner the market to make a bunch of money and they can't do this anymore. This is price manipulation, but what would happen is they would... If their play didn't end up working out as expected, they were massively leveraged, which caused this whole rippling effect through the financial system. So panic in 1901, that's what it was.
The market peak in August 1906, the following crash. And this one was like you read it and it's like, "Man, this sounds worse than today. They had an economic recession. They had bank runs with banks closing and people could not get their money out of banks. The New York Stock Exchange almost collapsed and JP Morgan had to shovel a bunch of money into it, to make sure it wouldn't collapse. To keep it open, they were going to close the exchange because they didn't have the liquidity that they needed, and JP Morgan said, "We cannot close the exchange. This will be a complete disaster, loss of confidence with investors. We have to keep it open." But he had to use his personal cash to keep it open. And during this time, they had the San Francisco earthquake which was massively destructive.
Cameron Passmore: Can you imagine?
Ben Felix: Well, yeah. I know. This is why I was so interested to go through these. The peak in August 1912, the following crash, there was the start of World War I. Global stock markets closed for the longest period in history and there was a massive liquidity crunch. So kind of like we've seen recently people selling bonds, driving bond prices down. The peak in 1916, and we mentioned this one earlier, the Spanish Flu had been discovered or made public and governments were also raising interest rates. In the peak in 1919, the following crash... And these are all... Nobody can say for sure why the crashes happen. These are all, I guess, theories or opinions on why this may have happened.
June 1919 was the peak in the crash that followed. There were World War I veterans returning to the US. There's a massive influx of labor supply. There was an economic recession possibly caused by that and the government was raising interest rates. 1929, the big one that everybody knows about and talks about, and again nobody knows for sure, but there was a ton of speculation, stock prices from '21 when they had a recession to '29 increased spectacularly. I didn't pull the annualized rate of return over that period, but presumably it was nuts, and a lot of it was driven by leverage. And this is where the line about the shoeshine person giving you stock tips, that's when it's time to get out.
Everyone was investing. Kind of like the 2000 tech bubble maybe. Everyone is investing in stocks. Everyone is using leverage, and then they're hit with this economic depression. Everyone is de-leveraging and just destroying stock prices. And then within the Great Depression, I mentioned there were a couple of sort of depressions within the depression. So those happened in the peaks were August 1932, August 1933, December 1933 and March 1937. Other than being within the Great Depression, the 1937 one is the only one I could find an independent reason for and that was tightening monetary and fiscal policy. Things were starting to get a bit better by 1937 and then the government decisions, allegedly, I guess, we don't know for sure, but that seems to have caused another recession.
Cameron Passmore: So these are all the episodes, just to go back to your original point of drawdowns. A lot of these drawdowns are 1949, 28, 83% drawdowns.
Ben Felix: So from September 1929, going forward, people talk about that as one bear market. And that peak to trough bear market was -83% which is a lot. But within that, so the peak in August 1932, if you invested then... So forget about starting in September 29, if you invested in August 1932, which was kind of close actually to the bottom. I think the bottom was in June for the September 29th crash, June 1932. if you invested in August 1932, you had a 29% drawdown. Over the following six months. If you invested in August 1933, 18% drawdown. But '37 too, if you invested in March 1937 over 12 months, you had a 49% drawdown.
Cameron Passmore: After the prior three.
Ben Felix: Yeah. Well, it's ugly.
Cameron Passmore: Talk about being beat up. Man, hit the '40s.
Ben Felix: Yeah. So September 1939, there was a 28% drawdown. I mean just think about being being alive and being an investor at this time. So there's a 28% drawdown and it coincides with Hitler invading France and japan attacking Pearl Harbor. That would be scary, intimidating.
Cameron Passmore: Beyond.
Ben Felix: In '57, there was a bit of a bear market and people call this, I saw it written about, is the teddy bear, because I guess it wasn't that bad. And this is one of the ones where I had to adjust my threshold. So this was a 15% bear market in the monthly data. In the daily data, it was closer to 20%. But at that time, they had the launch of Sputnik by the soviets, the soviet invasion of Hungary and the Suez Canal Crisis. It's a lot of war related stuff. Other interesting ones, 1968. So that was the peak and the bottom was in the 1970s. November 1968, and then 19 months down to the bottom. I'd never heard of this before. Cameron, maybe you had. It was the 1970 tech stock crash.
Cameron Passmore: Okay. I'm not that old.
Ben Felix: I didn't know there was a tech bubble in 1970. There was a tech bubble. Before the 2000 tech bubble, there was a tech bubble. There's all these computer and data companies. There's an article that I linked in this table. So if we put it up, people can click on it. Nasdaq wrote a whole article about this 1970 tech stock crash. That was a 34% drawdown. 1972, 46% drawdown and that was collapse of Bretton Woods coincided with that. 1973 oil crisis and the Nixon shock which was a bunch of policy decisions by Nixon, I believe. May 1981, 17% drawdown. Cold War going on. There was a military draft in the US and there was a sharp rise in interest rates.
Cameron Passmore: There's also a big recession. I remember that time so well.
Ben Felix: Oh, there we go. Now, we're in the history of your memory.
Cameron Passmore: That's a little modern era now. So I remember my father lost his job and I can remember we'd be at home, and he'd be sending out... He used to type out these on an old typewriter, applications for jobs, and the the response from companies would come in, so my brother and I would rush home get the mail. We'd actually steam open the envelopes and open to see if he got accepted. We were so petrified of losing everything.
Ben Felix: Wow.
Cameron Passmore: Yeah, it's crazy So then we'd steam it back and iron the envelope shut so he wouldn't know we opened it.
Ben Felix: Wow.
Cameron Passmore: It took a few years before he found another job. We grew up very modest in this pretty scary time.
Ben Felix: Wow. 1987 that's largely attributed to algorithms. That was the portfolio insurance crash where there was a cascading effect from computer algorithms that people were using. And there's now stuff in place like the circuit breakers that we've seen being triggered recently that are in place to stop that from happening again.
Cameron Passmore: I remember that day in October '87. I was in university that day in French class actually when I dropped. I remember exactly where I was.
Ben Felix: Wow. May 1990, they had the Gulf War going on and interest rates were rising. That was a 17% drawdown. June '98, again, 17%. Not a huge drawdown, but it was Russia defaulting on their government bonds. So again, think about being in that situation and how scary that might seem from a global perspective, a global financial markets perspective. I would be nervous and the market was too dropping 17%. And the more recent ones, people are more familiar with, but in 2000, we had the dot-com bubble. That was a 45%. I don't know. What did that one feel like?
Cameron Passmore: I can remember my son was a year old. I remember taking a videotape on his birthday and filming him. I know it was kind of weird, but lying on the table, I took a zoom on the newspaper to get the price of the Nasdaq and it was completely crazy leading up to it though It was unlike today. It was absolutely insane. Everyone was buying everything in sight, mortgaging their house to double down on JDS was big in town here as well as Nortel. It was completely insane time.
Ben Felix: Wow. Is that how it felt in the crash? Was it we should have expected this or how did it feel? Was it the world ending?
Cameron Passmore: It felt like part of the world was ending. Certainly in Ottawa it was. I mean, we had diversity portfolios so people would say like, "Why would I want to invest with you? You're only making, whatever, 15 or 20%," whatever it was at the time. But I can go and buy this other stuff and double my money, it seemed like every few months. So we were losing business to people going out and doing this on their own. I remember Nortel was paying huge bonuses to recruit people so people were leaving the government and their silly pensions. Why would I have a pendulum and get stock options at Nortel. It was a completely crazy time and we just stuck to our guns and kept diversity portfolios. I mean, we didn't grow the business as much.
Ben Felix: But after that, when the bubble popped... And that's one of the rare cases where I'm okay using the word bubble. When the bubble popped, like right now everyone's worried about how bad things are going to get and how all of the major issues and how this is a health crisis. What was the dialogue like when everything crashed in the tech bubble?
Cameron Passmore: Well, I mean it's very Ottawa centric of course because so many tech companies were here that basically blew up horribly including like Nortel is gone. A lot of the other companies here in town are gone. A lot of people went back to government. People had a lot more respect for pensions and the amount of liability that is there. I think there was a whole level of conservatism that that set in from that point forward.
Ben Felix: So we have October '07, the great financial crisis. I mean, that's a whole interesting discussion to have one day, but there was all sorts of basically fraud going on that drove up real estate prices and people were selling securitized mortgages that were not backed by good credit.
Cameron Passmore: And relying on the credit agencies and pensions around the world use. That as their seal of approval and we know how that played out.
Ben Felix: But again, so think about what was the narrative at the time or how are people feeling at the time? You know better than me because I was still in high school. But did it feel like the world was ending then?
Cameron Passmore: It was pretty scary that one. When Lehman went down in the fall, it was pretty scary. And as I told the story many times in the office, recently, we didn't have smartphones then. We were on vacation at the time and we used to rush back to the hotel at night, line up in the business center to get onto the computer and go to the Yahoo to find out what the markets had done that day. And you just pray that they would be not down a lot. Every day it felt like hundreds of points and the market was a much lower level then. It was pretty terrifying.
I remember being in Florida the year before and getting these mailers. We were staying at a friend's house, getting these mailers of insane ways of these balloon mortgages. I don't remember all the details, but I remember saying that this makes no sense. You can borrow all this money, pay interest only, and make your balloon payment three to five years. But by then you will have flipped the house taking your profit and moved on. I remember thinking this makes absolutely no sense that we were in one of the wealthiest areas in Florida at the time. I'm thinking this is going on here in these multi-million dollar beachfront houses. Obviously, I wish I'd taken more action because this was a year before the crisis, but I remember all these flyers everywhere and people talking about doing this. It was crazy, crazy.
Ben Felix: You just missed it. The Big Short could have been about you. I could have been The Big Short.
Cameron Passmore: Yeah.
Ben Felix: April 2011. Now, we're getting into where I was actually working in the securities industry. April 2011, S&O downgraded the US sovereign debt that caused an 18% drawdown with a fairly quick recovery. I mean 10 months from bottom back up. We had the Trump tariffs in 2018. Everybody remembers that. And December 2019 was the peak and now we're in... Who knows if we're in the trough yet, but we're in this downturn caused by the Coronavirus.
Cameron Passmore: The Trump tariffs is an interesting one because that one did happen quickly, but then kind of just went away and everyone sort of forgot about it and carried on.
Ben Felix: Yeah. It went away very quickly.
Cameron Passmore: And last year was a phenomenal year, 2019.
Ben Felix: Yeah. One point just on the recovery data point, I just want to clarify. The recovery is not the time from the bottom to the back of the peak, it's from the first peak back to that same level. So we're talking about months to recover.
Cameron Passmore: Includes the slide then.
Ben Felix: Yep. So it's from the market peak, down to the bottom and then back up. So I guess it's the total duration of the bear market more so than it is the months to recover.
Cameron Passmore: Okay. So it's the event duration.
Ben Felix: Yeah. The other thing that I started thinking about, and I had the data for this, but I didn't have time to do the analysis, but I did find a paper that looked at it. How have factors performed in bear markets? So there's a 2018 paper from the Zurich insurance company titled Tail Behavior and Portfolio Optimization for equity style factors. Pretty good title.
Cameron Passmore: This is pretty, pretty catchy.
Ben Felix: From the Zurich insurance company, no less. So they found that in terms of drawdown, value has actually been a buffer against market drops and they found the same for profitability and the same for investment. There's another paper that I referenced for recessions that found similar data. Recessions and bear markets are not necessarily the same thing although they're often coincident. This one was looking at bear markets not recessions. They've looked at the 10 worst us drawdown since 1927 and found that the average annual market premium in these 10 worst drawdowns was negative 29% which matches up with our data. The size premium in those events was negative 5.7%. Value was plus 8.2%, plus 8.2%, and momentum was 15%.
Cameron Passmore: Interesting. Any idea why the momentum would be so positive?
Ben Felix: I don't know why, but I do know that's one of the features of momentum. The problem with momentum is that you get these whip saws. Anyway, that's another discussion. Whipsaws is like when momentum gives you the signal to buy and you follow it. You can get completely crushed and that happens sometimes with momentum called a whip saw, which is one of the reasons we don't target it primarily or Dimensional doesn't target it primarily in portfolios.
The 10 worst US drawdown since 1963 and the relevance of 1963 is that's when the Fama-French five-factor data becomes available. They found the average. So the 10 worst US drawdowns since 1963. The average drawdown was negative 21.9%. The size premium during the drawdowns was negative 0.1%. Value was plus 10%. Profitability was plus 8.6% and investment was plus 9.8%. Momentum was plus 12.6%. So you see these massive premiums in drawdowns for value profitability investment and momentum. And they looked at global data starting in 1990, again, based on where the Fama-French datasets start. They found an average drawdown of 10.5%. Size had a negative 0.7% premium and value is plus 6.8. Profitability also plus 6.8. Investment plus 8.2 and momentum plus 8.5.
Cameron Passmore: That's really interesting, very interesting.
Ben Felix: Which also means that it's uncharacteristic for value. I guess this incident is not over yet. So value may come out looking pretty good, who knows. As of now, it hasn't. Small value had a really good day last week, but that's one day. I was still cheering it on though.
Cameron Passmore: These data points are during the drawdowns, not the recovery?
Ben Felix: Yeah. That's what it looks like in the way that they described it.
Cameron Passmore: I just want to highlight that.
Ben Felix: Yeah. It's true. I mean we talked about in the last episode in the first bad month. Value had an 8% premium. I didn't go through and manually look at all the other worst, those first months in the drawdown, but using that one data point, we can see that it matches up with the broader data set that these guys looked at in the paper. Then there's another perspective that I looked at on factor performance, how the performance of factors affects the outcome and this was a 2017 paper in the journal of portfolio management called A Wealth Management Perspective on Factor Premia and the Value of Downside Protection. Another creative title.
They found that adding in factors to the market portfolio reduced the average drawdowns except for the size premium, which they comment tends to be pro-cyclical, but all the other factors reduce drawdowns, which is similar to the data we just looked at in the other paper. They also found that the distribution of terminal wealth for a retirement portfolio is improved on average by adding in factor exposure. So I thought both of those papers were pretty interesting.
The other thing that I've been thinking a lot about is I need to build a bootstrap model to look at this myself because I couldn't find a paper that looks at it explicitly. We're talking a lot about drawdowns as risk. We want to reduce drawdowns and look how bad this drawdown was like how long it took to recover from the drawdown. Small value has had bigger drawdowns in the market and bigger drawdowns than value, but it's also had substantially higher returns, risk adjusted returns. I noticed this when I was looking through the data on bear markets.
I had the index value for value market and small value. And even though small value was taking bigger hits, at any point in time, the small value portfolio was worth so much more than the market portfolio and the value portfolio just because the returns had been so much higher despite the volatility. Even though it was having bigger drawdowns, the percentage impact on your wealth was way bigger, but the actual amount of wealth that you had as a small value investor was way higher, even after the biggest drawdowns, just because it had compounded so much more quickly based on the higher returns. Now, we haven't seen that. Recently, we haven't seen those high returns, but historically that's what it's looked like.
Cameron Passmore: Is that relevant though a fixed allocation to small value in your portfolio? Will you be trimming it all the way along anyways?
Ben Felix: That's a good question. This is why I want to build a bootstrap model to look at it. Anyway, so I noticed that in the data that the value of the small value portfolio was so much higher than say the market portfolio that even in the drawdown, when you lose 89% as opposed to 82%, the value of the portfolio is still orders of magnitude greater just because the compounding up to that point was so much more significant.
Cameron Passmore: That'll be so interesting because a small value may have been a creator of extra value for the rest of the portfolio if you kept trimming it and adding it back to the other parts of the portfolio. It'd be really cool to see the impact overall in dollar terms.
Ben Felix: Yeah. Super interesting. That's another thing that I wanted to get to for this, but didn't get to. I'm going to build just a very simple bootstrap model and pull data from the full return, monthly return distribution from 1926 up to 2019 that we were talking about and I'll build a withdrawal strategy like a 4% withdrawal or something like that and run 10,000 simulations with bootstrap in the market portfolio and then with maybe a small value and maybe a market with small value. Anyway, that's an idea for future research.
So the thing that triggered me to think more about this, "Well, I guess seeing the data was what triggered me to think more about it, but that reminded me that William Bengen, the guy that created the 4% rule, he came out more recently. I don't know. But after he wrote his 1994 paper, he came out and said, "I'm actually changing this. I'm updating it to the 4.5% rule because I'm now including an allocation to small cap value stocks in my model portfolio. So he found the same thing. He found you could spend more by allocating to small cap value stocks. So they have bigger drawdowns. They're pro cyclical, but the expected returns are so much higher that in the data you can spend more.
I thought that was worth pointing out. Now, the last piece might be the most interesting piece of the whole discussion. It debunks some, what I would call common myths. If I asked when was the worst time to retire in this data set, and actually that's not true. But not this whole data set. I didn't look at pre-1926. This is just 1926 onwards. Not 1900 to 1926. I didn't look at that that portion. I actually have a feeling the worst time probably would have been within that, but anyway that that doesn't affect the point we're making here. It was not 1929. So the worst time to retire from 1926 to now was not 1929, it was 1966.
Cameron Passmore: Which for the record was the best year to be born.
Ben Felix: Oh, yeah?
Cameron Passmore: Yeah.
Ben Felix: Best year to be born from what perspective?
Cameron Passmore: Because I was born in 1966.
Ben Felix: Ah. I thought there was some interesting data point that you had. I guess that is an interesting data point.
Cameron Passmore: Thank you. I'm looking for that shout-out.
Ben Felix: Pretty good shout-out. Your birthday it's an interesting data point. No one hears about this, which is what I find interesting. No one hears about how bad 1966 was. That's because it wasn't that bad, except that it was. So from 1966 to the end of 1979, you lost money in US stocks after inflation. That's longer than the lost decade. People talk about the lost decade from 2000 to 2010 where you didn't make money in US stocks after inflation. Well, this is longer. So 1966 to 1979, you actually lost money in US stocks after inflation. And that is because while the market averaged 5.62% geometric average, the consumer price index increased at 6.49% per year on average.
Cameron Passmore: Sidebar, in case people are wondering, I don't have a cold. That is my dog kind of acting up in the background. So it's a little growly back here.
Ben Felix: I didn't hear it.
Cameron Passmore: That's our first bark in the podcast, I think.
Ben Felix: Yeah. So 1966 worst year to retire. Now, that raises the point of all the numbers we've talked about and I think it's the right way to talk about them for bear markets just because you live through... You feel the pain of nominal returns. You don't really feel a pain of real after inflation returns. Although, I guess if you're going to grocery store and stuff doubling in price, you'd feel that pain. But when you're looking at your investment accounts, you feel the nominal pain, so that's why I think it makes sense to talk about nominal returns and recoveries and stuff like that to frame the current situation. How does this look relative to the past?
But if we apply that thinking to the just the addition of inflation, looking at real returns, if we apply that to 1929, they had massive deflation over that time period. So I've heard this data point quoted a few times during this little bear market that we're in now. People is saying that the great depression took 25 years to recover from. And I think that's flawed heavily. The 25-year figure, I think comes from using a price only index. So strike one. Now, the data we looked at, I found 177 months to fully recover from the 1929 crash.
Cameron Passmore: So when you factor in dividends, it's closer to 14 years.
Ben Felix: Yeah.
Cameron Passmore: Still not great, but ...
Ben Felix: Still not great. Here's the big thing. When we factor in deflation in this case, you actually recovered from The Great Depression in terms of your purchasing power of your portfolio. If you'd invested in September 1929, you were back to that level in purchasing power terms in seven years.
Cameron Passmore: Wow.
Ben Felix: Which is still long. We found 40 months on average from the peak to peak. But that makes that look a whole lot less terrifying.
Cameron Passmore: But it shows you the risk of taking data points in isolation.
Ben Felix: Yeah.
Cameron Passmore: That's incredible.
Ben Felix: Another thing that I picked up just from combing through. I probably spent more time than I should have to pull out the data points we've talked about. Although, I guess that's how stuff like this works, but I spent a lot of time just staring at this big data set of... I had consumer price index, market returns, small value and value returns, and drawdowns. Well, I had to model drawdowns myself, but just combing through that data to try and figure interesting stuff to talk about. That inflation piece or deflation piece, and how The Great Depression was still bad. Don't get me wrong here.
But to say it took 25 years to recover, I think that's borderline irresponsible because that sounds terrifying. And even 14 years, when you factor in dividends that's still scary. And seven years is scary too, but not nearly as scary as 25. I've heard that data point thrown a lot a few times, so if people hear that, they now have the tools to debunk it.
Cameron Passmore: Sounds great. I'm glad we went through that slowly and carefully. Great information.
Ben Felix: We have another interesting topic to get through too.
Cameron Passmore: So good to go on that one?
Ben Felix: Let's do it.
Cameron Passmore: This one has been pretty hot in the world of trading and ETFs, fixed income in general. So the question is what is going on with a net asset value and fixed income? So this past week was pretty extreme. We saw a huge increase in volatility in bond, prices and bond ETF prices going on as investors demanded liquidity in different parts of the market. And arguably one theory is that people are leaving a lot of fixed income instruments to go to US treasuries, which is apparently the safest asset on the planet.
Ben Felix: It was two things like you said, right? It was bond prices tanked and bond ETF spreads. The spread between the market price and the NAV, also exploded. So it's two scary things that made bond ETFs look real bad.
Cameron Passmore: Exactly. And we'll go through this carefully to make sure everybody understands, but here's some examples. On Monday, it's the last Monday the 16th of March, the $72 billion iShares Core US Aggregate Bond ETF, AGG is the symbol. And the $51 billion Vanguard Total Bond ETF BND traded at discounts to net asset value. Get this, a 4.43% and 6.17% respectively. Now, the normal spread to NAV is 10 basis points. Here we're talking about 440 and 617 basis point spreads, which is unbelievable.
Ben Felix: Do you want to explain what NAV is?
Cameron Passmore: We will. We're going to get there. I got a couple more examples for you. So in Canada, this past week, take XBB which is the iShares Broad Bond Index, it opened the week at 31.73 a share, hit 28.65 Wednesday afternoon in the extreme stress, and then closed Friday at 30.71. So the price at the close on Friday was 30.71, but the net asset value was 31.91. So that market price was 4% below NAV. Again, we'll get to what NAV is but that's a huge spread. The Vanguard VSC, Vanguard Short-Term Corporate Bond ETF opened the week at 22.77, hit 20.88 on Wednesday. That's almost a $2 drop. Almost a 10% drop. Closed Friday at 21.93.
It was trading at 8% below the net asset value. So the question that's been... There's been all kinds of discussions and articles written about this in the past few days, why? Why is this spread happening? So we did a little bit of homework on this and let's go back to the basics to start with it. So what is net asset value. So net asset value is exactly what it says, so it's that what is that fund or ETF truly worth? The net asset value is calculated daily. So when you buy a mutual fund, you're getting the net asset value on the day of your trade.
So if you're buying a mutual fund that owns fixed income today, you'll get net asset value at night. Same as if you're selling. You get the net asset value that night. But as you know, ETFs trade live through the day. The question becomes who calculates this NAV? And this is where I both learned a ton this week about how that net asset value is calculated. So the manufacturers of products, be it ETFs or mutual funds, they have third party providers who calculate net asset value for the manufacturer.
It's usually done on a daily basis and normally they have more than one providers. They take some sort of average of the various providers to calculate what net asset value is. And these NAV providers use, what I understand as very complicated models to calculate this NAV, but it's responsibility of the product provider to ensure that the custodian who has hired the NAV pricing company, calculates NAV fairly. So you get the product provider, get the custodian of the assets and you have the company that specializes in calculating NAV for the custodian.
The interesting thing though about these companies that calculate the NAV is that they have to come up with fair value for example in VSC of a portfolio of corporate bonds, but a lot of these bonds don't trade. So how bonds trade is so different than equities. Equities trade every day all day, so it's easy to find out net asset value is basically what the most recent trade was. But take a look at bonds. There's 42,000 individual corporate bonds in the market in the US. And in 2018, the SEC found that only 20% of corporate bonds trade every day.
So in calculating NAV, every night, they have to come up with some sort of... They describe it as a matrix, some sort of calculation, take a look at what the market is doing and to come up with a fair net asset value. And that's why they use multiple providers to come up with that NAV and normally those providers are within a couple of basis points of each other. But when there's extreme events like this, that's what caused the dispersion to net asset value, and this is so interesting to me.
So one viewpoint is that the price of the ETF, if it is discounted to NAV shows what is really going on the market. If somebody wants to get out of the market, they are going to be paying that spread to get out. They can be giving up that spread to get out of the market. So if you're willing to sell and you want out, and you're driving it way below NAV, that's the premium. You're willing to let go to get liquidity, but things may normalize afterwards. So what is fair price. So the argument is that discount, the NAV is what the fair price was in an extreme event, but once things revert to normal, it'll go back to what the more normal NAV was. This is something Dave Nadig talked about with us back in episode 71 how ETFs are so good, the market is so good that pricing ETFs to reflect accurately, how different people are thinking about the market.
Ben Felix: Yeah. He gave a fascinating example when he was on the podcast. He talked about how... I can't remember which municipality, but a municipality in the US went bankrupt.
Cameron Passmore: Somewhere in California.
Ben Felix: Orange County, I think was what he said. Orange county is going bankrupt. You could not trade their bonds. The actual bonds totally locked up in trading, but the bond ETF that owned their uni bonds was still trading. It was still totally liquid and it was getting smoked in price. So Dave says that people use that as an example of the ETF structure not working because the bonds were trading. Their value hadn't changed, but the ETF is meanwhile getting smoked in price. But he's saying, the term that he used to describe it was that the ETF added another price vector, I think is what he said.
So it's another mechanism or another facility where the price of the underlying assets can be priced. Because the ETF continued to be liquid, while the underlying bonds weren't and then he said, once the bond market started trading again and once their muni bonds started trading again, the ETF price ended up matching up pretty well with what the actual bond prices were. So it's all about liquidity and pricing, but you can't always get that. You can't always get liquidity and pricing in the bonds themselves, which is why the NAV is often in quotation marks wrong, but the ETF price, you could argue is a better reflection of the actual value.
Cameron Passmore: Yes. And it is assumed that some of these NAV price providers use ETF prices in their algorithm to calculate NAV because there is information in those prices.
Ben Felix: One of the things that Dave wrote because I read about this a lot last week as you did, and we had calls with Vanguard Dimensional to hear more about it too, but I found a piece from Dave Nadig where he talked about how with ETF spreads, if the NAV and the market price deviate, the authorized participant, that's one of the places they make their profits because they can go on arbitrage. So Dave is saying that with the fixed income, the fact that that spread remains is showing us that the authorized participant does not believe that there's an arbitrage opportunity there, which is another way of saying that the market price is a better reflection of the actual value than the NAV.
Cameron Passmore: Exactly. A discount to NAV does not necessarily mean that prices are coming down, it just means it's a liquidity premium somebody wants out is willing to give up.
Ben Felix: Or it means that the NAV is just straight up wrong, right? It's the whole discussion about why are... Let's back up. Why are bond prices dropping? I mean, again, we don't know for sure, because you can't know anything for sure in this field, but it seems like people want liquidity. Maybe they're going to flight to safety like to US treasuries like you said. Maybe they're wanting to get cash out, so lots of people are willing to sell maybe at a discount. And you'd expect that to drive bond prices down. And we've seen that.
Bond prices have gone down and they've gone down a lot quickly, but the NAV is not always going to reflect that because not all of those bonds, corporate bonds like you said with all those different issues, they're not going to trade every day. So even though bond prices are dropping, not all bonds are going to reflect that, which means that it makes sense for the NAV to diverge from the market price of the ETF. But we can't say for sure whether the market price wrong or are you paying a premium or is the NAV wrong.
Cameron Passmore: With a discounted NAV, all I'm saying it does not necessarily mean that prices are coming down. You don't know that for sure.
Ben Felix: They're not going to converge, you mean?
Cameron Passmore: No. I'm saying that just because there's a big... Right now, there's a big discount to NAV as of close on Friday. That does not necessarily mean the NAV has to come down. Prices could normalize and come back up because fewer people next week could be demanding liquidity. It's possible.
Ben Felix: I mean, bond prices could come back up is another way to say that.
Cameron Passmore: Well, not so much the price. I'm saying the demand characteristics on the ETFs, that spread could narrow it. We've had double the trading volume on a lot of bond ETFs last week than normal.
Ben Felix: Yeah. It's two ways to describe... There are two ways to describe the outcome. We could just as well say that the NAV is wrong today and the ETF price is about a reflection of the actual value, but then next week maybe the value of the bonds converges toward the NAV. So you can think about it as a liquidity premium or you can think about it as just straight up the difference between the actual bond prices and the ETF market price.
Cameron Passmore: But here's a question for you. So you have to spread on ETFs to NAV. Think about owning bonds. If you're a seller of a big bond fund, a bond mutual fund in extreme events like this, you're getting out at NAV, but the market truly is worth less than NAV. Does that mean that a mutual fund seller is getting a better deal? During extreme events, which are rare, but it's an extreme event.
Ben Felix: It seems plausible.
Cameron Passmore: I'm just wondering here. So if you're rebalancing selling bonds in extreme event to buy equities and you always get equities at NAV, it seems like there's a bit of a... Not that you can arbitrage, but there's a bit of a free lunch going on in there.
Ben Felix: Relative to ETFs for bond mutual funds?
Cameron Passmore: Not only relative ETFs, just relative to the market if the price the ETF is accurate, that discount to NAV is accurate. You're basically getting out at a 4% premium to market if the ETF price is market. That's all I'm saying.
Ben Felix: Right. In a mutual fund though because the ETF, you're-
Cameron Passmore: In a mutual fund.
Ben Felix: ... eating the spread in an ETF. With a mutual fund, you get NAV.
Cameron Passmore: Absolutely.
Ben Felix: Yeah.
Cameron Passmore: It's a good takeaway I think for anyone trading to be aware of this. If you're trading on your own, it just shows you how irrelevant NAV usually is to be normally a few basis points apart. But when you start getting spreads like this, you have to be aware. But we can't say definitively if the price the ETF is right or the NAV is right.
Ben Felix: That's the thing. If you owned the underlying bonds. Now nobody is going to own all of the bonds that are in BSC because nobody would have enough capital or trading ability to own them all, but if you did own them and you actually went to market and tried to sell them, I bet you that the price that you could sell those bonds for would end up equating to the market price of the ETF not the quoted NAV. I think that's what's going on. I think it's the actual liquidity of the underlying bonds that is causing that discount and the NAV is not reflecting.
Cameron Passmore: He who wants liquidity, pays a premium.
Ben Felix: Yeah. Just on this topic, I had a question from somebody last week about bonds in general just because bond prices have dropped and people are... I mean, it was pretty staggering to see ZAG, BMO's total bond market ETF. It was pretty staggering to see it drop 10% basically in a day. And I think a lot of people were kind of like, "Whoa, I didn't expect that to happen because bonds are supposed to be safe. Maybe some of that is this NAV issue that we're talking about. Maybe some of it is the underlying bond. Well, I guess it's the same issue, the underlying bond prices.
I don't think this means that bonds are not safe. I think for a long-term investor, this is not a big deal. Maybe it gets worse. I don't know. I've heard people talking about a corporate debt bubble. Who knows? But I don't think that bonds are all of a sudden a bad investment or we need to get out of the bond market. It was a bad day. I believe, Cameron, there were some days like that in '08, is that correct where bonds got hammered pretty hard?
Cameron Passmore: Oh, absolutely. I don't remember the quantum, but yes.
Ben Felix: I don't think this is something to worry about. I mean you can worry about it, but I don't think long term it's something to be... It's not a reason to ditch bonds. Put it that way.
Cameron Passmore: On to the couple planning topics we have?
Ben Felix: Yep.
Cameron Passmore: We'll try to get through them quickly.
Ben Felix: Yep.
Cameron Passmore: Do you want to kick it off with some commentary on tax loss selling?
Ben Felix: Yeah. So we talked about tax law selling in our last special episode and there are a couple of new things that have come to life for us. As we started to really look into it, let's dig into doing some tax loss selling. The legislation, so if we take it right back to the core of what allows this to happen, the intent of the legislation is to avoid people doing transactions for tax motivated purposes. So we talked about identical properties and how the CRA came out with a bulletin in 2006 saying that two ETFs from the same provider that track different indexes, even if they're slightly different in terms of their underlying holdings, the track different indexes are not identical properties.
So that's a CRA bulletin. That's not law. The actual legislation would suggest that selling for that reason alone to capture the loss and only to capture the loss, not for an investment reason would go against the legislation. So there's some, I think, audit risk to tax loss selling, which makes the points we made last time about being careful about when you do it. So being in a high current tax bracket in the year where you're triggering the loss, having an actual gain to offset. So you're getting an immediate benefit and not carrying it forward.
Maybe expecting to have a lower tax bracket in the future because as soon as you do the tax loss sale, you are decreasing your adjusted cost base, which means you can have more tax to pay later. So hopefully you're in a lower tax bracket. But if you meet all those criteria, so high current tax bracket, current capital gain, expected lower future tax bracket, then maybe it's worth the hassle and the risk. But the audit risk piece is the piece that I don't think we have in our toolbox last time we talked about this. It is not a risk-free transaction because it directly goes against the intent of the legislation.
Now, I haven't heard of anybody getting in trouble for this. I haven't heard of any audit problems with this, but the point that I'm making is that it's not risk-free. So when you take into account the portfolio implications where you have to buy a non-identical property, which might introduce some tracking error. That's one downside. If you're using a balanced fund like if you're using the Vanguard or iShares balanced ETFs, we talked about how you could feasibly replace that with the underlying components for example, which based on CRA's bulletin, those are not identical properties, but based on the legislation piece, there's clearly intent to make that transaction happen for tax reasons only. There's no portfolio reason to break up VBAL into its underlying components. You could maybe make that art.
Cameron Passmore: Plus you have to be able to rebalance on the other side too, which is not always good.
Ben Felix: So it's a hassle and it's not risk-free. So as much as tax loss selling can be useful, and like I said we are still looking at it, I do not think it's a risk-free transaction. It can be a hassle and I don't think it's risk-free.
Cameron Passmore: The point you said earlier, you've lowered your cost base going forwards. This is not a total free lunch.
Ben Felix: It's not a free lunch.
Cameron Passmore: So it's the time value of the tax refund now over time. That's what you're talking about.
Ben Felix: Yeah.
Cameron Passmore: Which can be significant, but the more significant it is, perhaps the more it could be scrutinized.
Ben Felix: Yeah, this is the thing. But the legislation piece scares me and full credit goes to Carol on our team who's a CPA. She went and did the research on what does the legislation actually say. And obviously that is the law. So CRA's bulletin about index funds tracking different indexes not being identical properties, that's great. But identical properties is only one piece of this. The other piece is the intent of the legislation. If you're going against the intent, the loss could still feasibly be denied. Although, like I said before, I haven't actually seen that happen. I just think it's important for people to know that there is some audit risk there.
Cameron Passmore: Clear. Okay so let's rifle through some other things that have come up this past week. CRA reduced the RRIF minimum payment amounts that you have to make. So they've reduced the minimum payments by 25% reduction across the board. So if you turn 71 this year, your minimum payout rate has dropped from 5.28% to 3.96%. The entire schedule has been dropped by 25%. Something to keep in mind. It's a temporary measure. I don't know how long it'll be in place for, but certainly in place for this year.
Also, the CRA tax filing deadlines have been pushed back. So for individuals, the deadline to file your income tax return is June 1st and the deadline to pay me balance due has been extended from April 30th to August 31st. For trusts, the filing due date has been pushed by for March 31st, have been deferred to May 1st, 2020. And for corps, the deadline for business to pay in income tax amounts that become owing after March 18th and before September 1st have all been extended back to August 31st, 2020. The bottom line is all the dates been pushed back. Check what the dates are in your situation. Check with your tax advice people.
Ben Felix: Yep. Canada released a bunch of stimulus too to boost child benefit and some additions to EI. I think CMHC did something about deferring mortgage payments. The banks are doing some... It seems like case by case deferral of mortgage payments. The fiscal policy response from Canada, I'm not an economist, but it seems to have been... They're doing something, which seems better than doing nothing.
Cameron Passmore: Yeah. And we'll see how easy it is for people to access those benefits from their banks, since it is on a case-by-case basis, but I look forward to hearing good news about that. Another thing I heard this week is if you're applying for life insurance, it's put on hold if you have large policies that require any sort of medical fluid taking. So that's all been deferred. Another idea for people is that if you're working at home, now is a good time to get all your documents in order, and watch out for scams. I had another email this morning talking about how the scammers and phishers are just out in full force now. So be super careful on that.
Ben Felix: Cool.
Cameron Passmore: Anything else to add?
Ben Felix: Nope. I think we're good.
Cameron Passmore: All right. Thanks for listening.
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