What We’ve Learned in 2014: Desktop vs Mobile

December 8, 2014

As we leave 2014 behind and enter 2015, it is a good time to review a few analytics trends and see where the analytics are pointing us. I’m fortunate in that I get to see a cross section of websites that our company manages so I can see if a trend on one site can be found on multiple sites. I’ll be posting a few of my favorite trends throughout the month as we wind down 2014.

1. Designing for the right screen size. Comparing Desktop vs Mobile.

Traditional web design starts with the desktop design first and then works to scale it down for both tablet and mobile devices. However, the data suggests the focus needs to shift. For our clients with a physical presence in Fresno, CA, desktop was the primary method people viewed the websites in 2013. But 2014 saw mobile browsing overtake desktop browsing.
2014-2013-visits
This trend was noticeable even for blogs which don’t lend themselves to physical locations. While desktop was still the dominant method, it gave some ground to mobile, a trend we should continue to see.
2014-2013-visits-blogs
Essentially, we’ve learned that the mobile experience is taking precedence over the desktop experience. In addition to the stats listed above, mobile users typically spend less time on a site (on average, one minute less) and view few pages per visit (on average, one page fewer). This means we have to rethink how visitors are using the site. The mobile visitor is a different animal than the desktop visitor and we need to make sure that we are aware of that as we move forward. They seem to want to answer a single question as opposed to exploring a site like desktop visitors. Design and user experience need to adjust to make this more of a priority.

Track Viewport Size with Universal Google Analytics Events

November 21, 2014
track-viewport-size

Why on earth would you want to track viewport size? Google Analytics already tracks screen resolution so what more do you need to know? The short answer is: a lot. Viewport size is important because it tracks this true size people are viewing your website – not just the size of their screen. I have a large 27″ iMac monitor (2560px wide) but my browser almost never have my browser full screen (as I write this, it is 1550px). A quick poll of my friends and coworkers shows that they almost never use their browser full width either (except maybe when using a small screen laptop.

I’ve found quite a bit of code out there for tracking viewport size using Google Analytics. The only problem is, most examples use the older version of Google Analytics and are not updated for Universal Analytics. The code uses the _gaq.push method when Universal Analytics uses the ga(“send”…) method. This code originally hails from two sites: Beantin & Adrian Roselli, and I’m grateful for the starting point. Their original code has been updated for Universal Analytics.

The areas that are commented out allow you to track only height or only width, if you choose. The code pasted below, as is, will report the viewport size and looks like this under the Events tab:
universal-google-analytics-viewport-tracking

To use, paste this code just before the closing head tag:

<script type="text/javascript">
function viewport() {
  var myWidth = 0, myHeight = 0;
  if( typeof( window.innerWidth ) == 'number' ) {
  //Non-IE
  myWidth = window.innerWidth;
  myHeight = window.innerHeight;
  } else if( document.documentElement &&
 ( document.documentElement.clientWidth
 || document.documentElement.clientHeight ) ) {
  //IE 6+ in 'standards compliant mode'
  myWidth = document.documentElement.clientWidth;
  myHeight = document.documentElement.clientHeight;
  } else if( document.body &&
 ( document.body.clientWidth
 || document.body.clientHeight ) ) {
  //IE 4 compatible
  myWidth = document.body.clientWidth;
  myHeight = document.body.clientHeight;
  }
/* To Track Width x Height, leave this section uncommented */
  ga('send',
   'event',
   'Viewport',
   'Size',
   myWidth+'x'+myHeight,
   {'nonInteraction': 1});
/* To Track Width only, uncomment only this section
  ga('send',
   'event',
   'Viewport',
   'Width',
   ""+myWidth+"",
   {'nonInteraction': 1});
*/
/* To Tack Height only, uncomment only this section
  ga('send',
   'event',
   'Viewport',
   'Height',
   ""+myHeight+"",
   {'nonInteraction': 1});
*/
}
</script>
</head>

And then finally, place the viewport() in your opening body tag like this:

<body onLoad="viewport()">

It’s simple as that. Now when it loads, it will send the viewport dimensions into your Google Analytics events.

I’ll be updating a post with my results shortly.

Warren Buffett on Investing Small Sums

October 17, 2014

Warren Buffett makes a statement in here that should excite most small investors. If he were investing the small sums of money most of us have access to, he would be able to do better, on a percentage basis, than he does now. This is for a variety of factors but it goes to show that even though we don’t have access to the billions of dollars Berkshire Hathaway does, we can still follow a certain set of investment principles to achieve good returns.

Video transcript below

Question: I read about your investment in Korean stocks for your personal portfolio. My question is, if you are managing very small amounts of money, like $3 million or $10 million, do you still look for Graham’s approach for low P/E and discounted book value? Will you still look for these type of situations if you couldn’t find any franchise stocks?

Warren Buffett: If I were working with small sums, I would certainly be much more inclined to look among what you might call classic Graham stocks. Very low P/E’s and maybe below working capital and all that. Although, I would do far better percentage-wise if I were working with small sums. They’re just way more opportunities. If you’re working with a small sum, you have thousands and thousands of potential opportunities. When we work with large sums, we have relatively few possibilities in the investment world that can make a real difference in our net worth. You have a huge advantage over me if you’re working with very little money but there are compensations to that on the other side of the equation.

The Korean stocks you mentioned that I looked at 6 or 7 years ago were companies we were going to put a small amount of money in and I was sort of reliving my youth. Somebody sent me a handbook of Korean stocks and told me the market was interesting. So one Sunday afternoon I did leaf through a couple thousand pages of Korean stocks and I was sort of reliving my youth. Other people go through old Playboy magazines or something like that but I for these old manuals of stocks and I bought a number of stocks in small amounts from companies whose name I couldn’t pronounce. But the stocks as a group were so cheap, that you had to make money on them. They were Graham-type stocks and that’s what I would be doing.

Now if I found a wonderful company that Graham wouldn’t have bought but I was really convinced about its future, I would have bought it. Incidentally, I’ve written about it in our last annual report about Geico and I bought that stock in 1951 when I had about $10,000. Geico I looked at because Benjamin Graham was the Chairman of the company but Geico was exactly the sort of stock Graham wouldn’t buy (it was selling way above book value and all that). There’s a certain irony in that. But I’m glad I did it.

How To Bet On Horse Racing Using Investing Principles

October 13, 2014
horse-race-betting

Horse racing has been around for a very long time and there are many supposedly “proven” systems you can buy that will “guarantee” you to win big in the long run. While Googling, I even found suggestions that the best way to make money is to have fun! If you like the name of the horse, bet on it. If your lucky number is 4. bet on that horse. While that method may be fun, you run the risk of following some potentially hazardous ways of thinking. There is no correlation to horse number or horse name. Intuition only runs so far.

I’m sharing my system because I am able to have fun with it. Thee’s no guarantee of wining and it certainly may not hold true in the long run. I first tried this method last year in Del Mar. I had been reading some Warren Buffet and Benjamin Graham so the concept of value investing was on my mind. Reading this particular subject must have colored my thinking during my trip to the track. I saw the opening odds for the horses and then saw how “Mr Market” influenced those odds based on the bets placed. Some horses odds improved and others decreased. I decided to apply this knowledge to see what I could come up with. My primary value takeaways and applications to horse racing were these:

  • If you value a stock at $50 and it’s trading at $30, you should probably buy
  • Mr Market and other animal spirits will drive the odds in different ways and you must focus on the big picture
  • Strong, well performing companies aren’t something you should avoid

So how do you apply these principles to horse racing?

Trading at a Discount

If  a stock is trading at a discount from where you believe it should be, you should likely purchase it. If that stock is trading at $30 but you value it at $50, other information notwithstanding, you should buy. I applied that principle to the odds in horse racing. A horse that came in 10-1 but is now 20-1 is trading at a discount relative to its value. The initial odds are set by the handicapper the day of the race or the day prior to the race. This person likely knows more about the horses than any normal bettor so his odds are usually fairly accurate.

The odds change because horse betting is based on a pari-mutuel system – mutual betting. As bettors bet on the horses, the odds change based on the activity of the bettors. This can drive odds up or down for any of the horses leading up to the start.

raceboard

Mr Market

The market drives the odds from those initially set by the handicapper. But why? Do the market participants (bettors) have some knowledge that the handicapper didn’t? The answer is likely to be no. Most are simply betting on their favorite horse name or lucky number but these bets influence the odds of other horses. We can take advantage of these shifts.

Good Horses are Like Good Companies

Well performing companies are what you look for when you aim to put stocks in your portfolio. You wouldn’t avoid them simply because they make solid, regular gains. The same is true of horses. A horse with 5-4 odds is a pretty strong candidate to perform well. The payout may be lower for him but he stands a good chance of competing well against the field.

Betting on the long shot stock often makes for a good story if  it pans out well, just like in horse racing. But the reality is you are more likely to go broke, or at the very least pass on other,better candidates, as you wait for your long shot to pay off.  The moral of the story is that you should incorporate the strong performers into your portfolio.

My Example

I first employed this for the final race at Del Mar one year ago. I managed to win $80 on a $8 bet. I wanted to try it again but didn’t have a shot until this year (I’m really not much of a gambler). This trip, I focused on the Exacta Box bet which requires you to pick two horses that can finish either first or second. You can apply this formula to many types of bets but this is the primary method I used .

Race 4

We didn’t arrive until just before Race 4. I wanted to hurry up and put in a bet before the race began. It was the first time I employed my system and it really wasn’t in the most optimal conditions – limited horses and not much difference between the early odds and the current odds.

Result: no winners.

Race 5

Race 5 was finally a good opportunity. The second-favored horse, Horse 4, had moved from 2-1 to 10-1. The favorite, Horse 2, stayed at 7-5. This was a perfect opportunity – exacta box for 2 and 4. I also liked the discount Horse 4 has so I put a Place bet on Horse 4.

Result: 1st- Horse 4, 2nd- Horse 2.

finish

I ended up with a handsome payout on my bet. Celebratory beer!

race5

Race 6

Race 6 was not one I was feeling too confident about. There were 2 scratches and none of the horses were really trading at a discount. They were all either flat or improving. Not what I want! I still made a bet but wasn’t feeling too great about it. That is also the benefit of the system. You may not feel great about it but you have to apply it consistently in order to win in the long run. This is true of many things – investing, working out, studying – unless you apply it all the time, it isn’t a good system.

I went with an exacta box for Horse 3 and 7.

Result: Horse 3 finished 1st and Horse 7 was somewhere near the back. All losers.

Race 7

This was to be our final race of the day. When I stepped up to the betting machine, I loved what I saw. The favorite was even more the favorite and there were several that were steeply discounted. I elected to go with an exacta box bet on Horse 2 and 6. Because Horse 6 and 8 were also trading at such steep discounts, I placed a bet on each to Show (finish in the Top 3).

Result: 1st – Horse 6, 2nd – Horse 2, 3rd – Horse 8.

Boom! I ended up hitting a pretty good one here.

race7

Conclusion

Like stocks, there is certainly no single proven method for making money.  The method I’ve outlined above is no guarantee but I feel that it gives the best probability of payout by taking advantage of the behavior of bettors. I’ve eliminated my “hunches” which is exactly what I intended to do.

Odds

racing odds

Want to share some horse betting strategies? Tweet me: @keeganlarson

Warren Buffett Interview on How to Read Stocks

September 30, 2014

Warren Buffett drops a few information nuggets for new investors in this interview. I particularly like his discussion on valuing the business before looking at price. I often find that looking at price first influences your evaluation, almost like giving you a target to hit. He stops before getting into his “trick” of analyzing what makes a good investment, but still provides some solid foundational level insight for value investors.

Video transcript below

Interviewer: A lot of people interested in your purchase and subsequent sale of PetroChina. This is a controversial stock for our viewers who aren’t familiar with it because the parent company, China National Petroleum, had some dealings with the Sudanese government that of course the Bush administration has said is responsible in part of the genocide in Darfur.

I watched in May at your annual meeting where shareholder came up – some of them very unhappy – and wanted you to divest. You disagreed, took a shareholder vote and most agreed with you, and you did not divest. That was May. Now since September, you’ve sold your entire stake of PetroChina.

Warren Buffett: We sold it, but we sold it based on price. I think around the annual meeting it’s price was around $110 and I think it’s more than doubled since then. Unfortunately, I sold a little too soon. But it was 100% a decision based on valuation. When we bought PetroChina, he whole company was selling for $35 billion. If you take the number of shares and multiply by the price (market capitalization). When we sold it, it was at about eight times that – wait. I’m sorry, the original price was about $20 in terms of U.S. dollars and we sold it from about $160 to $200, or something similar. We made about $3.5 billion on a $500 million investment. But after we sold it, that’s when it really decided to go up. We left a lot of money on the table.

Interviewer: But for the average person buying a stock, what’s your advice about that? You can constantly sit there and think it’s going to go higher.

Buffett: We don’t think about that. What we think about is – how much is it selling for and how much do we think its worth. When we bought it at $35 billion, effectively, I felt that it was worth $100 billion.

Interview: How did it come to your attention? How do you find a stock like PetroChina?

Buffett: I sat there in my office and I read an annual report, which fortunately was in English, and it described a very good company. It told about the oil reserves, it told about the refining, about the chemicals, and everything else. I sat there and read it and thought to myself, this company is worth about $100 billion. Now I didn’t look at the price first. I look at the business first and try to figure out what it’s worth because if I look at the price first, I’ll get influenced. So I look at the company first, I try to value it, and then I look at the price. If the price is less than I value it at, I’m going to buy it.

Interviewer: How do you value it?

Buffett: Well… That’s the trick [laughs]. But it’s essentially what I would pay for the whole business if I could buy it. When we sold the stock, it was valued at $250 – $275 billion and we thought that was a fair price. Oil had gone up from $35/barrel to $75/barrel, and now it’s up even further. But today, I believe PetroChina is the second most highly valued company in the world. Higher than GE and next to ExxonMobil.

Interviewer: Would you ever buy or sell a stock based on political pressure?

Buffett: No.

Interviewer: How did you find PetroChina?

Buffett: I’ve read a lot of reports. Most people read Playboy, I read annual reports. But that’s my job, to allocate capital, and the way I allocate capital is by looking at the opportunities to use capital.

From One, All – Don’t Make a Bad Leap in Judgement

May 7, 2014
from-one-all

There is a behavior common among people. Maybe we think we are smarter than we are. Maybe we’re just lazy. But the tendency of taking a sample of one and making sweeping generalizations about the population can be tremendously misleading and can lead to poor decision making.

Example

Just this morning I was working with a client on a proposal they were submitting for a design job. I had a question about a statement in their proposal and here is a little of how it went:

Me: “Why are we saying X here? Does that need to be stated?”
Reply: “Yes, because no one else does this. Everyone else’s is generic, our’s is specific.”
Me: “How do we know that no one else does this? Who is everyone?”
Reply: “Company Z gives a generic list.”
Me: “Is that the only example we have?”
Reply: “Yes. That’s because it’s from Company Z and they’re the biggest.”

Analysis

Sweeping generalizations from a single sample of the population. Granted, this company was larger and more well-known, but does that automatically mean every single firm in that industry follows the same practice? There are roughly 25 firms that could provide a comparable product/proposal in that geographic region. We can’t say that everyone does this because we only know of one specific example.

It makes decision making easy to assume that the entire population has a certain quality because one individual possess it. Our brain creates these representations because it is lazy. It looks for patterns so that way we don’t have to think hard. This is what Daniel Kahneman would call System 1 thinking and it allows us to flip on the auto-pilot and cruise through the day so we can focus on other things and not “waste” brain power.

But easy isn’t better or right. Lazy thinking yields potentially hazardous results. The example above is a relatively innocuous example without harmful implications. But it is very easy to draw the line from that pattern of thinking to much more impactful ones.

Conclusion

We cannot derive assumptions of the group from a single member of the group. This type of thinking occurs in all sorts of decision we make on a day-to-day basis.

Its like going on a bad date and assuming “all guys are jerks” or “all QBs from Univ. of Wisconsin are great” because Russell Wilson won the Super Bowl. You simply can’t make that leap in judgement from single observations.

Realize that this sort of approach is not the smartest approach you can make. Challenge the basis of you assumptions; flip them on their head and do as Charlie Munger says: “invert, always invert.”

Nate Silver and the Science of Prediction

May 1, 2014

This was a great little interview of Nate Silver done by Nesta. Silver briefly gives his views on the science of prediction and forecasting. This is a rare opprtunity as Nate Silver is usually a little longer winded! I have to say I agree with a couple things he said: first, that we are really not getting better at prediction despite advances and second, large amounts of data make us lazy.

Most of our data analysis techniques give us great measures of frequency but are terrible at measuring the magnitude of events. We can generally answer “yes or no” to the question of “will it rain” but we have tremendous difficulty in predicting exactly how much it will rain. As for the data, I have experienced several important decision makers rely on spurious correlations to justify business decisions. Without sound analysis and good theory to back it up, we can’t simply rely on correlation analysis.

Video transcript below:

Nesta: What is the Science of Prediction?
Nate Silver: Well, I prefer to use the term forecasting instead of prediction. When the word forecasting came into our language, it meant “planning under conditions of uncertainty.” So it’s as much about knowing what we don’t know as what we do know.

Nesta: Are we getting better at forecasting the future?
Silver: So is society getting better at prediction? I think, as a whole, not by a lot. Maybe a little bit at a time. I mean, technology gradually improves in fits and starts and scientific knowledge becomes more complete. But it’s still very incomplete relative to the whole scope of questions that we have. Also, some things in human endeavors, society becomes more complex and so you might have better methods, arguable, or more data but you also are running against a moving target where the system itself becomes more complicated.

Nesta: Is forecasting an art or a science?
Silver: It ought to be a science, but it requires judgement, I think. Judgement is not the same as gut instinct. It requires what Daniel Kahneman might say is “thinking slowly.” Our gut instincts are often rather poor when it comes to thinking in terms of probabilities. We have a lot of biases that get in the way. So it’s a science but it’s a difficult science.

In theory, if you get more data, then it should make prediction better, other things being equal. But in fact, people can get very distracted by having a large volume of data. They can cherry pick their evidence. They can do what’s called “over fit” a model so you describe the past very accurately but won’t hold up very well with making predictions. Experiments aren’t always validated. You have false positive results. So it’s a risk as well as an opportunity.

Nesta: Will we ever be able to accurately predict the weather?
Silver: There’s actually been a great deal of success. We probably won’t have exact predictions where we say “100%” or something, but if you look at hurricane forecasts, or just temperature forecasts or rainfall forecasts, they’ve gotten much better over the last 25 years.

Nate Silver – Baseball and Politics are Data Driven

April 24, 2014

This is a quick highlight from a discussion Nate Silver gave at the USC Bedrosian Center of Governance. I really like a few of the things he touches on because he discusses the rule that correlation is not causation and you are likely to get burned assuming correlations have meaning without an underlying theory to support it.

Video transcript below:

Silver: What happens when you take a society and infuse a lot more information into it? Is that information used to benefit society or is it misconstrued sometimes, is there more noise than signal?

As we’re looking at data, what we really want to see is not just statistics for its own sake but I want to see relationships and connections that allow us to make predictions, make better decisions going forward. As you get more data, you have more truth so to speak but you also have a wider increase in false positives and statistical correlations that don’t have any meaning.

The Federal Reserve now tracks 148,000 economic variables. But if you’re looking at the data, you have a huge volume of things to sort through. You have 10.9 billion correlations to evaluate. But of those 10.9 billion, there are also a lot of spurious correlations.

One infamous one involves the National Football League where, for a number of years, you had a streak where if the NFC team won the Super Bowl, the stock market would have a good year. If the AFC team won instead, the stock market would have a bad year of be flat.

If you’re betting on a correlation that doesn’t have any real causal link, I guess no real reason why investors should be concerned about which football team wins, then you’re likely to get burned sooner or later. Again, this is the problem with assuming volume conquers everything. You have to apply a filter, you have to have some theory apply some scrutiny. There are terrific insights but not without some human intelligence at the console to kinda tell the signal from the noise.

What is a ‘Black Swan’? Nassim Nicholas Taleb Explains

April 16, 2014

Nassim Taleb explains what a Black Swan is in February 2008 at a talk in San Francisco. This is particularly interesting because in just a few short months, his prediction would come true. An event similar to the ones he describes occurred and we had one of the most severe financial crashes since the Great Depression. Take a look at the way he talks about black swans and try to understand the methodologies he applies to the concept of statistical “outliers.”

Video excerpt below:

What is a Black Swan?
Before the discovery of Australia, we had no reason to believe swans could be any other color but white. Affectionately, there was an expression in medieval England, “you’d sooner see a black swan than…” for example. It was like saying when pigs fly or when… when George Bush does something intelligent or something.

So there was an expression until we saw Australia and effectively, with one sighting of a single bird, destroyed millennia of confirmation. So it was posed as a logical problem: make sure there’s no reason you cannot rule out black swans because you haven’t seen any. So my problem is not a logical question. My black swan is an event, not a bird.

It’s an event that has three properties.
1. It is hard to predict
It is hard to predict based on information before its occurrence (based on historical information). You have here a sample of black swans. The most interesting one is the tie. If someone was going to forecast the future, they’d have to forecast that human beings two thousand years away would constrict their blood supply with this device, for example. So that’s very difficult to predict. The computer was a black swan. It changed the world and nobody thought the computer would do anything. You know, it was initially used for common storage. I mean Watson from IBM did not that this tool could have any use.

black-swan-type-events

The rise of religions, black swans, totally unpredictable. Harry Potter is a black swan. A lot of cultural phenomenon are black swans.

To me the most significant black swan, and the one I’m going to focus on for the next few minutes is the First War. The first war we had after Napoleon, we thought for about 100 years that the world had become civilized and that, you know, people became conscious of the need for peace. And you had this devastating war, the biggest war, something that destroyed a great deal.

Of course it came in two volumes, you had volume one and then you had a sequel. So here we have black swans. Events of low predictability and high consequence.

But the most vicious part is the following one: that before the fact they’re extremely predictable but after the fact, we saw them coming. So we have retrospective distortion – these events are prospectively unpredictable, retrospectively predictable.

We even have disciplines to give us the illusion of understanding the world. We have disciplines that make us misunderstand the world by giving us this illusion of predictability. History, for example, economics, other such things.

Robert Shiller on Market Bubbles and Busts

April 4, 2014

David Wessel from WSJ Money interviews Robert Shiller about market bubbles and busts – and even where the term “irrational exuberance” came from. I generally enjoy Wessel and this interview was an insightful look into Shiller’s thinking about bubbles. Yes, they should be preventable but the reality is we don’t have an effective way to do it without disrupting the economy and our spirit. I also had no idea Shiller may have been the one to put the term “irrational exuberance” into Alan Greenspan’s ear! That makes for an interesting story.

Video transcript of the interview below:

David Wessel: One theme that runs through your work is that people tend to make mistakes and they seem to make mistakes over and over again. Now that’s quite different than what I learned when I took economics in college – that people are rational. How did you get to this thought? How did you get to the point of thinking about people’s mistakes when they aren’t necessarily rational?
Robert Shiller: Well I think when you went to college, the economics profession had reached an unnatural state. Academics are a bit faddish like everyone else and the efficient market hypothesis was elevated. Mathematical models of rational behavior became the rage, and it was just an abnormal time. I think that I was reading more widely and wanting to come back to reality.

Wessel: Was there something in reality that you knew economic models couldn’t explain that led you to this?
Shiller: Well, bubbles [laughs]. The story about bubbles was that the markets appear random but that’s only because new information is unpredictable; they respond only to new information. I was thinking – new information about what? But it seemed to be almost like a mythology to me. The suggestion was it’s new information about fundamentally important real things and I just didn’t believe it. The idea that people are so optimizing and so calculating and ready to update their information; that’s true of a tiny fraction of 1% of people. It’s not going to explain the whole market.

Wessel: The story you told about the housing market in advance, famously, was that we were in a bubble. That you were one of the people, perhaps one of the most prominent people, to identify this. But, you know, one prediction doesn’t make a track record. Do you think it’s possible in stock markets and housing bubbles to know in advance that this is a bubble? Or is it a gut – sometimes you’re right and sometimes you’re wrong?
Shiller: Well you’ll be wrong sometimes, certainly. There might be a story, a real reason. That would be the efficient market’s theory that it appears mysterious but that’s mysterious only because it’s not concrete yet and people are sensing something. The efficient market hypothesis is not totally wrong, in fact it’s something that I teach and it’s an important insight. It’s just that it’s been carried too far.

Wessel: I remember in the late 1990’s when the stock market was looking sort of frothy, you went down to the federal reserve and made a presentation and I remember your wife wrote a Christmas letter which she said that you had expressed concern that you were the one who planted the idea of irrational exuberance in Alan Greenspan’s head and contributed to his use of that phrase. Then the market tanked. So, is that a true story?
Shiller: What you said was absolutely right. Greenspan said the word “irrational exuberance” in an evening speech in Washington and the Tokyo market immediately – which was still open at the time – crashed (by 4%). Then it spread over the whole world as the markets opened. And that’s why the words “irrational exuberance” are famous. Greenspan didn’t coin it, I didn’t coin it. It’s an old phrase. He just happened to use that phrase and the market immediately crashed.

Wessel: Ddi you really feel responsible?
Shiller: I came home and told my wife I may have started a worldwide stock market crash. I said it sort of jokingly but I have believed it. And she said “your ego is getting way out of control.” There was an article about me on the front page of the Wall Street Journal a couple days later about just the same issue. They didn’t say whether I did or didn’t and my wife said “ok ok I apologize.” The thing is, the markets are crazy. The power isn’t in me, it’s in Alan Greenspan. But I had his ear. I spoke before the Board and then I had lunch with him so it’s possible. This is a method of thinking about the economy that’s very different. It makes the economy as unstable and affected by individual people.

Wessel: Do you think that bubbles are always a bad thing? Or do you think they have some good effects?
Shiller: Well, first of all, it’s a free world and people can do what they want. So I’m not proposing that we put the straight jacket on these things. The other thing about it is that human nature needs stimulation and people have to have some sense of opportunity and excitement – it motivates them to action. I think profits are an important motivator. In the long run it’s hard to say that bubbles are really bad. Take the internet bubble of the 1990’s. That generated a lot of start ups, some of them foolish (and failed) and others survived. Is it a bad thing? I find it hard to think of what the alternative would be. We could have had a Fed that tried to lean against the stock market bubble (of the 1990s) or against the later real estate bubble (in the 2000s) and I think that would be a good thing. The problem is, we don’t have any… – ultimately our policies in economics are somewhat intuitive. Our models are not accurate enough to tell us what the right policy is. So I’m thinking that probably we would’ve been better off if we had tamed these bubbles but there’s no good way to be sure. And we certainly don’t want to do draconian things that would upset the whole system just to prevent bubbles.