Penny stock investing
Episode #7: Playing Defense Against Black Swans
Guest: Episode #7 has no guest, but is co-hosted by Meb’s co-worker, Jeff Remsburg.
Topics: With Brexit rattling the markets recently, it’s a good time to revisit the discussion of “black swans” (not that Brexit was a black swan, despite catching many investors off-guard). So what exactly is an investing black swan? And is there anything you can do to protect yourself from one? That leads Meb into a discussion of outliers – specifically, how your returns would look if you missed out on the 10 best market days, but also avoided the 10 worst market days. From there, we discuss a way to help protect your wealth from the biggest drawdown-days in the market. (Hint – it’s how Paul Tudor Jones avoided the ’87 crash, and something you can easily implement in your own account today.) From there we move to actionable takeaways for listeners – after an extended down-period, what markets and/or countries might be cheap and starting to enjoy an uptrend, which would make them good investments right now? And finally, you’ll hear how Meb just lost his entire Kansas wheat crop, destroyed by a fire from an exploded combine. Black swan event? Find out on Episode #7.
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Links from the Episode:
- Ahrens, Richard (2008). “Missing the Ten Best Days.” Technical Analysis of Stocks and Commodities, 26:4 (56-57).
- Aparicio, Felipe, and Javier Estrada (2001). “Empirical Distributions of Stock Returns: European Securities Markets, 1990-1995.” European Journal of Finance, 7, 1-21.
- Estrada, Javier (2007). “Black Swans and Market Timing: How Not to Generate Alpha”
- Jansen, Dennis, and Casper de Vries, (1991). “On the Frequency of Large Stock Returns: Putting Booms and Busts into Perspective.” Review of Economics and Statistics, 73, 18-24.
Running Segment: “Things I find beautiful, useful or downright magical”:
Transcript of Episode 7:
Welcome Message: Welcome to the Meb Faber Show, where the focus is on helping you grow and preserve your wealth. Join us as we discuss the craft of investing and uncover new and profitable ideas all to help you grow wealthier and wiser. Better investing starts here.
Disclaimer: Meb Faber is the co-founder and Chief Investment Officer at Cambria Investment Management. Due to industry regulations, he will not discuss any of Cambria’s funds on this podcast. All opinions expressed by podcast participants are solely their own opinions and do not reflect the opinion of Cambria Investment Management or its affiliates. For more information, visit cambriainvestments.com.
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Meb: Hello, friends. Welcome to the show. No guest today, but due to popular demand we’ve brought back Jeff as a co-host. Jeff, welcome.
Jeff: Thanks. How’s it going?
Meb: It’s going great. I’m gearing up for a little bit of travel, getting ready to head to Calgary for the rodeo and then home a bit in Colorado, and giving a talk next week, but otherwise enjoying the summer.
Jeff: Wait, what rodeo are you referencing?
Meb: It’s the Calgary Stampede, one of the biggest in the world. And I’ve been to the… What’s the one in Wyoming called? The Grand Daddy of Them All? Tim McGraw and Faith Hill way back in the day?
Jeff: Are your listeners…should they expect you to be actually participating in this rodeo?
Meb: You know, I got a lot of cowboy gear, although I don’t have any cowboy boots. I lost my cowboy boots. I left them once in a hotel and never bought them again. So maybe I’ll re-up. Anyway, off topic, so today, what are we gonna talk about? So, you know, a lot of the news media lately has been focusing on Brexit, and thank god not a single one of our clients emailed us about this. So kudos, clients, you know who you are.
However, my mom did give me a text, and she’s one of the best contrary indicators. I love you, mom. She actually listens the podcast by the way. So, you’re the best. But so we…I thought we would talk a little bit about large market events, which people often confuse with black swans. And black swan is a popular term in the media, sort of like bubbles, we’ll talk about bubbles in another podcast. Let’s talk a little bit about black swans and so get our terminology right.
Really, black swans are popularized by Nassim Taleb. I actually met Nassim almost 10 years ago. In 2007 we were speaking together at a conference in London called “The Battle of the Quants.” Super nerd fest, I can say that, because I’m an engineer and nerd, but it was a panel discussion and Nassim was the keynote speaker. Well, my flight got delayed out of JFK for an entire day, so I slept in the airport that night, arrived in London, and let’s call it 3:00 or 4:00 in the morning, already jet lagged, take a shower, drink some coffee, go straight to the conference.
I put down probably 15 cups of coffee and I was a little nervous. I was in my late 20s at the time. Don’t remember anything I talked about at the conference, but do remember Taleb’s keynote speech and it… The impression a lot of people get from Taleb in the media, and probably rightfully so is that he tends to be a little…what’s the right word? Confrontational? He has a little bit of hubris. He’s obviously very smart, but I love his books, particularly his first book. If you haven’t read “Fooled by Randomness,” get it, summertime reading. Read all of the rest of them.
The “Fooled by Randomness” was really one of the best books in investing. Anyway, the speech he gave was totally different, and Nassim in person was totally different than the kind of media Taleb. He loves picking fights on Twitter. Huge advice, don’t pick any fights with Taleb on Twitter, but a warm, humble, funny speaker, which I was not expecting. So I was all prepared to hate him, but I thought he was actually a really great speaker.
Anyway, so, he wrote “Fooled by Randomness,” he also wrote “The Black Swan,” “Antifragile.” But he popularized this concept of black swan, namely the occurrence of utterly unforeseeable events that are thought of was not being possible based on previous experiences. So his definition of black swan, which we’ll use, is, “An outlier, outside the realm of regular expectations, because nothing in the past can convincingly point to its occurrence.” That’s one. Two, “The event carries an extreme impact.” And three, “Explanations for the occurrence can be found after the fact, giving the impression that it can be explainable and predictable.”
So a lot of commentators have blast onto this term to describe all sorts of financial markets events. However, the existence of this large outlier events are simply known as fat-tailed distributions in the financial market world, and it’s been well documented for over 40 years. So Mandelbrot, Fama, in the 60s, were talking about this. So the media often says, “Well, you know, if the markets aren’t normally distributed,” well, no shit. Everyone’s known this for 70 years. If you don’t, you’re simply not a student of history. But everyone’s known this for a long time.
So, people have revisited this fat-tailed concept mainly because of the big two busts we’ve had in the U.S. in the past decade, internet bubble bust, the global financial crisis, ’08, ’09. It’s something that’s been around for decades, and you shouldn’t be surprised by that.
One of the biggest takeaways from a lot of things we talk about market history is normal market returns are extreme. We talked about this in the podcast the other day, where we said, “Seventy five percent of all U.S. yearly stock returns are either negative or grater than 15%.” So while you may expect a 10% return in stocks, which is unreasonable by the way right now I think, historically, that 0% to 10% range is the minority. It’s not the norm, but it averages out to that, which makes it so though for people to stick with it.
If you think about it, bear markets are common, markets can and do decline by 50% to 100%. So if you look at the return distributions, it’s similar to a fractal system that follows a parallel distribution. So stick with me for a second. All that means is, it’s kind of useful when describing events like earthquakes. So if you think of the Richter Scale, we’re based here in L.A., get earthquakes a lot, haven’t had a big one in a while, knock on wood. But, a 4.0, is 10 times bigger than a 3.0, and a 5.0 is 10 times bigger than a 4.0. So people will say, “Hey, we had a 5.0 earthquake.” And then if you talk about a 6.0, it only sounds a little bit bigger, but in reality that scale is 10 times worse.
We’ll post a chart to the show notes, but it’s all in a book called, “The Failure of Risk Management,” that kind of illustrates this inability of these Gaussian models to account for large outlier moves. So in a normal distribution world, a 5% decline in stocks should not have happened in the past 100 years, but in reality it’s happened nearly 100 times.
Jeff: Good question for you. That definition, I think you said, or Taleb said, “It has an extreme impact.” That’s a little vague to me. And given what you just said about the randomness of market moves, a lot of times they are much greater than 20%, a lot of times negative. What’s the real definition of what an extreme impact is?
Meb: Well, so then you can talk about standard deviation events, what percent of the time, and then really just becomes semantics. So it could be like, “Well, maybe we considered a 1% outlier, an extreme event, or 0.1,” but that’s where the tails really start to come into place.
There is a recent example. We have a farm land in western Kansas, had an awesome weed harvest this year, literally cutting the weed. So, this is like a year-long process, right? Planting, fertilizing, everything else going on, literally cutting the weed, so there’s nothing left to do. A combine essentially explodes, catches fire, nobody’s hurt, thank god, essentially destroys an entire crop. Is that a black swan? No, I mean, it’s foreseeable. It happens. People get insurance for that, although my insurance doesn’t cover it because it’s only a natural event. That’s not a natural event. But, it is a tail event. So it carries extreme impact, unfortunately in the bad side, and a lot of things that we talked about this with Gerard a little bit is that tail events also happen to the upside too.
So it’s not just always bad things, but that’s what people remember. Because the financial media doesn’t mind, doesn’t really get excited talking about extreme events to the upside because no one is complaining and freaking out, but it’s the extreme events to the downside that really cause the problems.
Unfortunately, many investors have come to the conclusion that these rare events are impossible to predict, and therefore there’s nothing you can do, other than buy and hold and sit it out, which is tough. We’ve talked about the emotions in investing. This explanation simply rids the investor or adviser of any responsibility. It’s sort of the fatalistic attitude becomes, “Hey, it’s a black swan. It’s not my fault. So we can’t do anything about it.”
However, let’s talk a little bit about market outliers in the U.S. Let’s take this all the way back to the ’20s. This is an interesting topic because you see it a lot in the financial adviser media. And one of the biggest defenses of buy and hold, and remember I have no problem in buy and hold, I think it’s perfectly fine, but one of the biggest defenses is that demonstrating the effects and missing the best 10 days in the market. And I think this is very instructive actually, and how that would affect the compound return to investors.
However, a lot of times you see it and it’s perhaps one of the most misleading stats in our profession, because…and there’s a lot of academic papers that have looked to this, and we’ll post them to the show notes, but they often don’t mention what happens if you miss the worst 10 days as well. So they say, “All right, so let’s look at outliers.” So, if you… “Let’s look at the worst and best 1% of all days.” So that equates to about two to three days per year, so normal, happens every year. Those worst days and best days are around a 4% to 5% gain and loss.
We haven’t seen many of those lately. I have to look it up on the last time. But, on average, you’ll see a few of those per year. Then, if you look at the worst 0.1% of all days, which occur on average only every few years, that’s a loss of 8% in a day, or gain of 8%, 8%, 9% roughly. But, you’ve had worse, so on the U.S., we had a 20% down day back in ’87. Most countries have seen these down 10%, 15%, 20%.
I think the highest was in Hong Kong, which lost a third in one day. I’d love to check that perform [SP] and develop. I can’t remember. But the worst days is…and then the best day we’ve ever seen in the U.S. back in the ’20s is 16%.
Jeff: Do these worst and best days tend to net each other out?
Meb: Here’s the deal. So, if you look it all days, and we’ll just do this on daily lev and we’re excluding dividends, because it doesn’t really matter for this analysis, but the dividends would accrue above. All days about… It’s 4.8% going back to 1920s return [inaudible 00:11:41]. You know, add on inflation, add on dividends, you get up to a historical 10%, but let’s call it 4.8%. If you miss the best 1% of all days, it takes you down to a minus 7% return.
I mean, think about that. That is an astonishing amount, where in the 0.1% it takes you down 3%. But the flip side is also true. If you miss the worst days, it makes your return much higher. So, you have this scenario where people only talk about these worst and best… These best days have such a major impact, and there’s so few of them, therefore your chances of predicting when they occur are so slim, you have to buy and hold, otherwise you’ll risk ruining your entire strategy.
And that’s generally true. However, that’s kind of like taking the ball down to the five in your own line [SP] and stopping there. And so, if you extend the research and say, “All right, let’s look when they occur. When do the worst days occur and when do the best days occur? Can we learn anything about that? What are people missing?”
As we know, and we’ve talked about this on the podcast, markets are a collection of people, and people being human is the collection of emotions, greed, fear, jealousy, pride, envy, which is one of the biggest, all manifest themselves to the fullest in our capital markets. So when you’re making money or thinking about a new car you’re gonna buy, how smart you are, how much smarter you are than your neighbor, the vacation you’re gonna take, second, third, fourth house you’re gonna buy, a part of the brain firing here is the same region that gets stimulated by many types of drugs.
However, losing money, you’re not opening your account statements in the mail. If you get them digitally now, you’re deleting the emails. You’re thinking about how dumb your neighbor was for recommending that stock, how you’re gonna pay over that second house. What about your kids? You just got fired. And you feel significant revulsion to even thinking about investing. And the part of the brain that processes losses, money losses, is the same region that’s stimulated by the flight response.
So, if you look at these kind of behavioral biases, and we’ll get in depth more in this in future ones, but as the great Andrew Lo professor at MIT video called “Technical Analysis and Academic Perspective,” that talks a lot about behavioral biases. But if you look at historically when they occur, you can actually predict when most of them will occur, and it’s around two-thirds to 70% of them. And the indicator that we’re gonna be talking about here is a very simple TRIN point [SP] indicator. So 200-day moving average, which simply is a way of finding the signal from the noise. It’s the most common, probably technical indicator out there, the most common TRIN point.
We also use what we call as the monthly equivalent, the 10-month simple moving average, and it’s a way of just establishing signal from noise. So if a price is above the 200-day moving average, you’re in an up-trend, the price is below the 200-day moving average you’re in the down-trend.
Now, Charles Dow [SP] talking about this has been around for 100 years. Charles Dow was talking about similar ideas in the early 20th century. Many, many people were talking about momentum in trend in the 1940s and ’50s. And so, there was actually a great book Tony Robbins put out called “Money: Master the Game,” where he interviewed a lot of famous investors and one of the top traders of all time, Paul Tudor Jones, who teaches a class at the University of Virginia, my alma mater, and he’s interviewed him in the book, and we’ll read two quotes real quick from him. So Tony says, “Okay, are there any specific strategies for protecting your portfolio?”
And he says, “I teach a class at UVA and I tell my students, ‘I’m gonna save you from going to business school. Here, you’re getting $100,000 class, and I’m gonna give it to you in two thoughts, okay? You don’t need to go to business school. You’ve only got to remember two things. The first, you always wanna be with whatever the predominant trend is.’”
And so Tony says, “Okay, well, how do you determine the trend?” He says, “My metric for everything I look at is the 200-day moving average of closing prices. I’ve seen too many things go to zero, stocks and commodities. The whole trick in investing is, ‘How do I keep from losing everything?’ If you use the 200-day moving average rule then you get out. You play defense, and you get out.”
And Tony says, “That was also considered one of the top three trades of all time in history, the 1987 crash,” which Paul Tudor Jones was famously missed, and got along bonds after. He says, “Did your theory about the 200-day moving average alert you of that one?” He says, “You’ve got it. It had gone under the 200-day moving target. At the top of the crash I was flat.”
If you look at the 200-day moving average, one of the reasons why it works, you know, most people seem to think market timing is this magic elixir that is gonna tell you when to get in at market bottoms and when to get out at market tops. And you’re gonna magically have a vastly bigger return, then buy and hold. And that’s not the way it works. Historically market timing through TRIN point gives you a similar return as buy and hold. Sometimes it’s quite a bit higher, but in general expect similar return, but it vastly reduces the volatility in draw-downs.
Why does it do that? Well, it does that because when markets are down-trending, they’re much more volatile than when they’re up-trending. If you look at where the worst 1% and 0.1% of days occur, 70% of them occur…60% to 70%, it varies by the market, 60% to 70% of them occur after the market is already down-trending. Now the great irony is 70% of the best days also occur when the market is already down-trending. And the reason being is that if you think of a rubber band, it’s simply that markets are much more volatile when markets are down-trending, but that makes sense.
People are freaking out, they don’t know what to do, the future’s uncertain, they just lost 10%, 20%, 30%, 50% of their portfolio. There’s a lot of uncertainty. So simply it’s volatility expansion. And so these days occur, we call it volatility clustering. So not to go down a mathematics rabbit hole but, if you avoid high volatility, your geometric return can be higher because you’re sitting on the sidelines avoiding, we call these volatility gremlins, the difference between arithmetic and geometric returns, you end up with a higher return by missing both. So you actually want to miss the worst and best days.
And so Mandelbrot actually has a great quote in his book “The Misbehavior of Markets,” hugely big recommendation, wonderful book, where he says, “What matters is the particular, not the average.” Some of the most successful investors are those who did in fact get the timing right. If you look at someone in the U.S., as with anything in investing, if it doesn’t work elsewhere you have to question, “Is it just data mining in one market?”
And so we’ve actually looked at this in an old paper called “Where the Black Swans Hide,” where we looked at it in all sorts of foreign markets, I think it’s about 20 of them, and it turns out that we find similar results as the U.S., namely a small amount of outliers have massive impact on performance, and the best and worst outliers tend to cluster when the market is already declining. However, if you miss the best and worst days, in every case your compound return is higher than buy and hold.
Jeff: Sounds like if we use the moving average to try to protect us here, we’re gonna do far better. But one question is, you know, by definition these black swans are unpredictable, something hits and you’re already gonna be entering at a larger loss. You can’t just immediately say, “All right, we get to net out all the worst days.” You’re gonna suffer some of the worst days, right, because you’re getting triggered…
Meb: Well, so it depends. When we’re talking about black swans and market outliers, one, realize that market outliers are normal, not a normal distribution, but normal part of this type of distribution for financial markets. That’s the beauty of TRIN point, is that you don’t have to necessarily predict the black swans, the upside and downside, but rather just realize most of the large volatility occurs when markets are already declining. Now this doesn’t mean TRIN point is a magic elixir again, it’s not some magic holy grail, and this is why so many people struggle with it.
The same reason they struggle with buy and hold is that there’s many periods where it doesn’t work, so no investing strategy is gonna work all the time. And so, there’s a…in case of TRIN point, there’s two main drawbacks, one, a TRIN-less market, but it kind of whipsaw side to side. You know, so you buy into a market, they’re starting to go up, and it goes back down, and you sell, and then it goes back up and you buy. And so back and forth, so it creates a lot of this losses. So a lot of people struggle with a system that doesn’t have a really high percentage of winning trades.
Jeff: I think your model uses one month in terms of looking at when to be in or out based upon the 200-day moving average, is that right?
Meb: Yeah. I mean, we publish because you can get data that goes back a lot farther with monthly data than daily data. So, I think it’s meaningless which one. I think the vast majority, I don’t think it matters if you use 50-day, 200-day, 40-week, 10-month.
Jeff: In a market that is whipsawing, is there not an optimal time?
Meb: Well, to say again, the problem with the whipsaw question is, you have to be able to predict when whipsaws will occur. So it’s easy to look back and say, “Oh, the last few years were TRIN-less in whatever market may be,” or, “Wow, TRIN point.” I mean, there’s been a bull market since ’09, or, “Hey, TRIN point worked great in 2008 and 2000, 2003.” But that’s backwards looking. So will there be a whipsaw market in the next five years? Who knows? Even a market could decline 80%, it could go up 80%. So that’s kind of the beauty of the indicators. You don’t know what the future’s gonna hold.
Jeff: I think we kind of discussed this the other day. Is there any other indicator you might suggest that might give somebody an idea of whether or not are we’re entering a whipsaw market or just a down market that’s gonna sustain for a while?
Meb: Real quick before that question is that the other problem with TRIN point is that it doesn’t guarantee you you’re gonna miss a big down move. So the 200-day moving average is an interesting example because had you used the 200-day moving average in 1987, you would have been out during the crash, like Paul Tudor said. If you used the 10-month or anything longer than the 200, you would have been in during the crash.
And so people that would look back at that, if you’d started writing about this or managed your money in ’86, that is a very binary outcome. So the guy that was out now manages billions and the guy that was in probably lost all of his clients. And so you have to have a long term perspective, and the beauty of being a quant is that this averages out over…you wanna use as many markets as possible. Most CTAs trade 50 markets. It’s not always work through every market cycles, so we’re talking 5, 10 years in any one market, but it works in most of them most of the time. So if you look at gold, you look at commodities, you look at interest rates, you look at stocks, foreign stocks, TRIN point works in almost all of them, like over, over time.
Jeff: If you have the patience.
Meb: If you have the patience. And so a lot of people look at TRIN point over the past number of years. Hey, it’s done an awesome job in some markets. I mean, commodities, phenomenal job, missing the commodities debacle over the last few years, where they’ve just had massive bear markets. Ironically a lot of those signals are hitting bias right now. Commodities, commodities are not a strong up-trend this year, gold, a lot of other commodities as well.
Same thing with emerging. Emerging after this long down draft is finally entering into bias signals. U.S. stocks has been particularly challenging, because in a bull market there’s not much that can perform, outperform a bull market then just long only. Because every small dip is a chance for it to go back up. So the only thing that beats an up-trendable market for U.S. stocks, of course, is to have leverage and own more.
Kind of wrapping this up and thinking about it, kind of the main summary without going down the TRIN point rabbit hole, which we’d love to, maybe in another podcast, is that stock market in general goes up about two-thirds of the time. Almost all the stock market returns occur when the market is already up-trending.
A great indicator of when to be out is a TRIN point type of indicator. The volatility is much higher when the market is declining. I think it usually is about 30% higher per market, usually on average. And so most of the best and worst days occur when the market is already declining, and the reason being is simply just because the volatility is higher, so you just stretch all the returns a little bit. And of course, the market is much riskier than models that assume a normal distribution predict, but that’s been well-known for 70 years. You got anything else, Jeff, before we wrap it up?
Jeff: Well, I’m kinda curious. On the clustering of the worst-down days were stop days, was there any study about the linked up time or the timing involved in those clusters? My brother question is, all right, let’s say I’m getting greedy. The market starts going down, hits some really bad down-days. If I was somehow able to look at timing involved in the clustering rather than wait for the 200-day to move above its average, when I’m looking…when the rise is happening, maybe I get back in earlier and can capture more of the gains. Is that just being to greedy?
Meb: Well, here’s the challenge, is that U.S. stock market has declined over 80%. You have a Greece right now down 90% plus, and so this is the challenge of catching the proverbial falling knife.
Meb: And afterwards you can look back and say, you know, the cliche works either way, where immediately you look back and anoint some trader a genius because he predicted, he went all in at the bottom, in the bottom in ’09, right? Had that market continued to go down 80% then that guy would have been out of business, and a lot of value stock managers got carried out in body bags in ’08, ’09.
Some of these guys started buying financials way too early and then proceeded to lose 70%. We’ve talked about this happening in a number of other places. So predicting bottoms is really tough. We talk a lot about this on valuation though. We say, “When markets get cheap you can start buying into them.” But that’s a totally different philosophy where you say, “All right, Russia is cheap. I’m gonna buy it, ut I’m only gonna updated this or this is gonna be a 2, 5, 10-year hold, and maybe then I’ll have a value portfolio that we run and only updates once a year.”
Now my favorite combination, of course, is the intersection of value and trend. So buying a cheap market that’s entering an up-trend. So right now you have that with Russia, you have that with Brazil, you don’t have it with Europe. I mean, my god, we’re just waiting so long for Europe to get their act together on equity markets because they’re almost universally cheap but they’re not entering up-trends. Russia and Brazil are. So that’s really the best overlap. When you can find that, that’s when I think you can get the really big returns.
Jeff: I guess if we’re looking for actionable takeaways for listeners right now, sounds like you mentioned commodities might be getting signaled as a good investment right now, you just mentioned Russia.
Meb: Well, there’s a lot lining up for emerging markets and commodities. One, they’ve been down multiple years in a row. So three years down in a row, we talked about that in a prior podcast, usually lays the ground work for big returns in the next two years. Two, they’re cheap. Emerging markets are certainly cheap. Some countries are cheaper than others. Commodity is a little hard to value as far as traditional value.
Indicators, but a lot of other shops, like a QR have done research that shows, you can simply use a three, five-year trailing return on a lot of commodities as a way to look at value. A lot of these commodities have just been getting absolutely destroyed over the past five years. Also, no one wants them. So we think those are probably great opportunities. You’re starting to see them show up in a lot of the road of strength.
Real estate has been on fire for a while. That’s been in an up-trend and outperforming. And of course, bonds, one of the most hated asset classes out there consistently for a long time, has had monster returns. I was actually…and we’ll probably talk this in a follow-up podcast, but they’ve been actually having wonderful returns, too.
So, let’s wrap it up before this one gets too long. We end each episode with the things I find beautiful, useful or downright magical. Last time Jeff, I remember, said some kind of shady email service. What do you have for us this time?
Jeff: Well, I wasn’t really prepared very well this time, so I asked someone here in our office, and she swears by a site called ooVoo. That’s O-O-V-O-O.com. And apparently what this is is it’s video conferencing. Think of like FaceTime. It’s video conferencing that you can do at the same time with up to 12 friends, and it’s free. So…
Meb: Also sounds kind of shady.
Jeff: Sounds like my nightmare.
Meb: Well, it’d be good for families right across the country. You could bring in…you know, it’s interesting because every time we’ve had…have a dozen guests on this point, the episodes, I haven’t heard of a single thing anyone has suggested, not a single one of them. I mean, certainly not Patrick’s ex. All right, so mine…what’s the website again?
Jeff: ooVoo, O-O-V-O-O.come
Meb: Mine is completely different. It is a Korean chili sauce created by David Chang, who’s the chef at Momofuku. And you can buy this off their website, and it is kind of this funky, thick ketchup, but it’s like a dark magenta color. I put it on absolutely everything.
Jeff: Sounds questionable.
Meb: It’s really good. But I like sauces. I’m a huge Lizano fan from Costa Rica, Texas Pete from hometown North Carolina, that’s a good one anyway.
Jeff: When are you gonna give us some more of your personal recipes?
Meb: Yeah. Well, that wasn’t one of my personal recipes. The Nancy Silverton, that’s hers. I don’t have any. I outsource mine, believe me. I have no interest in coming up with any of my own. All right, so wind it down look. Thanks for taking the time to listen again today. We always welcome feedback and questions for the mailbag, which we’ll start doing in upcoming episodes. That’s [email protected]
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– Penny stock investing