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Building An IKEA Portfolio

Originally Published on March 16, 2016 If you get someone to build an IKEA sideboard – you know, one of those flat-pack conundrums that involves trying to work out what a cartoon character is doing with a hammer, a drill and forty-three assorted metal dowels – they immediately place a higher value on it than anyone else would, even if it goes on to develop an alarming 45-degree tilt. This is the IKEA effect . It’s associated, sort of, with a more general behavior that’s been known about for years – the endowment effect – in which possession of an item immediately causes us to value it more highly. Just imagine what the impact might be if you build your own portfolio, no matter how wonky it might be. Well Endowed The endowment effect was originally demonstrated in an experiment by Daniel Kahneman, Jack Knetschand Richard Thaler , who gave half of a graduate class a college-themed mug and then invited them to trade with the other half. Little trading occurred, because the valuations set by the mug-possessors far outstripped those set by the mug-less. Somehow, mere possession of a mug was enough to endow it, in the eyes of the possessors, with a value that made no sense to an outsider. In part, this looks like status quo bias – people like to stick with what they know. In combination, it’s not hard to see how these issues could cause problems in other sorts of markets. If we overvalue items of any kind – stocks, say – merely because we possess them, then we’re likely to find it difficult to sell them whatever the circumstances. Status quo bias and the endowment effect are among the culprits proposed for loss aversion, our tendency to hold onto loser stocks regardless of their underlying worth. Building Bears There are three underlying odd behaviors associated with the endowment effect. The first is the obvious one – that sellers and buyers place radically different valuations on the same thing, an effect that holds even when we adjust for negotiation strategies (i.e., put in a low bid as an anchoring point). The second is the mere ownership effect – merely owning something is enough to increase the perceived value of the object. And the third is a reluctance to trade at any price – some people simply don’t want to be parted from their belongings, no matter how tatty or valueless they appear to be to everyone else. The IKEA effect is clearly related to these effects, but there’s also something else going on. For instance, if you expend effort at Build-A-Bear to help your child with the creation of their very own growly playmate, you don’t then expect the store to reduce the price of your ursine friend because you’ve spent your time making it. In fact, you probably pay more, and do so happily, because your added input increases your estimate of the value of the critter. Justified Prior research suggests that the more effort we put into some activity the more we value the outcome – a behavior known as effort justification . So if you’re inclined to do lots and lots of research into stocks before buying, you’re likely to end up suffering from both effort justification and the endowment effect. Now, that doesn’t automatically mean your efforts aren’t worthwhile; but it would strongly suggest that the more work you put into deciding to buy a stock, the more likely it is that you’ll end up biased towards it and against alternate views. We have perhaps all met people who know every single detail of their favorite shares but completely miss the big picture; Polaroid was a great investment all the way up to the point that digital photography took off. You could analyze the company’s numbers till the end of time, but you still wouldn’t have seen the digital cliff coming. Failed Erections However, the research into the IKEA effect adds a second factor: the research suggests that the effort expended in all this work has to result in some level of success. A failed attempt to erect a chest of drawers is more likely to cause feelings of regret than an increased level of attachment. It’s hard to be happy with yourself if your furniture keeps on collapsing around you. In ” The IKEA effect: When labor leads to love ,” the researchers Michael Norton, Daniel Mochin and Dan Airely found that: “Participants saw their amateurish creations as similar in value to experts’ creations, and expected others to share their opinions. We show that labor leads to love only when labor results in successful completion of tasks; when participants built and then destroyed their creations, or failed to complete them, the IKEA effect dissipated”. Interestingly, they then go on to show that this isn’t simply an effect experienced by novices: experienced do-it-yourselfers also got caught up in the pleasure of admiring their own creations. Effort justification appears to be behind this – the more effort that people put into their successful creations, the more in love with them they became. Overvaluation Now, because the experiment used pre-packed components from IKEA, they didn’t allow for any customization. Every creation was a clone of every other creation. Yet still, participants habitually overvalued their output apparently because of the effort they’d expended in making it. If this research translates into a more general problem, then the issue for investors is starkly obvious. Overvaluing our investments simply because of the sheer amount of effort we’ve expended in figuring out that they’re worthy of our capital would trigger confirmation bias. We’re likely to miss future changes in prospects because we’re deliriously happy that all of our research efforts have resulted in a successful investment: we’re less likely to acknowledge evidence that points to the fact that things are going wrong, because we can always summon up a battery of figures to show that critics are idiots who haven’t done the necessary detailed work. Life Choices The idea that less is sometimes more, and that if you actually have to spend weeks of your life analyzing a company in order to determine whether or not to invest in it is probably an indication that you shouldn’t, is anathema to some investors. And, to be fair, people who do this for a living should expect to do this level of research and will either be successful or be culled by the invisible hand passing their money to less gullible people. But for most of us, with limited time and resources, if we have to commit so much time to analysis that we end up suffering from the endowment effect, we’re probably looking at the wrong stocks. Building an IKEA wardrobe is fine and well, but equating its value with something created by a craftsman is stupid and biased. And, more importantly, it’s a pointless waste of a life.

Estimating Future Stock Returns, Follow-Up

Click to enlarge Idea Credit: Philosophical Economics Blog My most recent post, Estimating Future Stock Returns was well-received. I expected as much. I presented it as part of a larger presentation to a session at the Society of Actuaries 2015 Investment Symposium, and a recent meeting of the Baltimore Chapter of the AAII. Both groups found it to be one of the interesting aspects of my presentation. This post is meant to answer three reasonable questions that got posed: How do you estimate the model? How do we understand what it is forecasting given multiple forecast horizons seemingly implied by the model? Why didn’t the model forecast how badly the market would do in 2001 and 2008? And I will add 1973-4 for good measure. Ready? Let’s go! How to Estimate In his original piece , @Jesse_Livermore freely gave the data and equation out that he used. I will do that as well. About a year before I wrote this, I corresponded with him by email, asking if he had noticed that the Fed changed some of the data in the series that his variable used retroactively. That was interesting, and a harbinger for what would follow. (Strange things happen when you rely on government data. They don’t care what others use it for.) In 2015, the Fed discontinued one of the series that was used in the original calculation. I noticed that when the latest Z.1 report came out, and I tried to estimate it the old way. That threw me for a loop, and so I tried to re-estimate the relationship using what data was there. That led me to do the following: I tried to get all of them from one source, and could not figure out how to do it. The Z.1 report has all four variables in it, but somehow, the Fed’s Data Download Program, which one of my friends at a small hedge fund charitably referred to as “finicky”, did not have that series, and somehow FRED did. (I don’t get that, but then there are a lot of things that I don’t get. This is not one of those times when I say, “Actually, I do get it; I just don’t like it.” That said, like that great moral philosopher Lucy van Pelt, I haven’t ruled out stupidity yet. To which I add, including my stupidity.) The variable is calculated like this: (A + D)/(A + B + C + D) Not too hard, huh? The R-squared is just a touch lower from estimating it the old way… but the difference is not statistically significant. The estimation is just a simple ordinary least squares regression using that single variable as the independent variable, and the dependent variable being the total return on the S&P 500. As an aside, I tested the variable over other forecast horizons, and it worked best over 10-11 years. On individual years, the model is most powerful at predicting the next year (surprise!), and gets progressively weaker with each successive individual year. To make it concrete: you can use this model to forecast the expected returns for 2016, 2017, 2018, etc. It won’t be very accurate, but you can do it. The model gets more accurate forecasting over a longer period of time, because the vagaries of individual years average out. After 10-11 years, the variable is useless, so if I were put in charge of setting stock market earnings assumptions for a pension plan, I would do it as a step function, 6% for the next 10 years, and 9.5% per year thereafter… or in place of 9.5% whatever your estimate is for what the market should return normally. On Multiple Forecast Horizons One reader commented: I would like to make a small observation if I may. If the 16% per annum from Mar 2009 is correct we still have a 40%+ move to make over the next three years. 670 (SPX March 09) growing at 16% per year yields 2900 +/- in 2019. With the SPX at 2050 we have a way to go. If the 2019 prediction is correct, then the returns after 2019 are going to be abysmal. The first answer would be that you have to net dividends out. In March of 2009, the S&P 500 had a dividend yield of around 4%, which quickly fell as the market rose and dividends fell for about one year. Taking the dividends into account, we only need to get to 2,270 or so by the March of 2019, works out to 3.1% per year. Then add back a dividend yield of about 2.2%, and you are at a more reasonable 5.3%/year. That said, I would encourage you to keep your eye on the bouncing ball ( and sing along with Mitch … does that date me…?). Always look at the new forecast. Old forecasts aren’t magic – they’re just the best estimate of a single point in time. That estimate becomes obsolete as conditions change, and people adjust their portfolio holdings to hold proportionately more or less stocks. The seven-year-old forecast may get to its spot in three years, or it may not – no model is perfect, but this one does pretty well. What of 2001 and 2008? (And 1973-4?) Another reader wrote: Interesting post and impressive fit for the 10-year expected returns. What I noticed in the last graph (total return) is, that the drawdowns from 2001 and 2008 were not forecasted at all. They look quite small on the log-scale and in the long run but cause lot of pain in the short run. Markets have noise, particularly during bear markets. The market goes up like an escalator, and goes down like an elevator. What happens in the last year of a ten-year forecast is a more severe version of what the prior questioner asked about the 2009 forecast of 2019. As such, you can’t expect miracles. The thing that is notable is how well this model did versus alternatives, and you need to look at the graph in this article to see it (which was at the top of the last piece). (The logarithmic graph is meant for a different purpose.) Looking at 1973-4, 2001-2 and 2008-9, the model missed by 3-5%/year each time at the lows for the bear market. That is a big miss, but it’s a lot smaller than other models missed by, if starting 10 years earlier. That said, this model would have told you prior to each bear market that future rewards seemed low – at 5%, -2%, and 5%, respectively, for the next ten years. Conclusion No model is perfect. All models have limitations. That said, this one is pretty useful if you know what it is good for, and its limitations. Disclosure: None

Smart Beta ETFs Not So Smart?

Smart beta ETFs that were on fire for quite some time now appear to be losing some momentum. Smart beta strategy helps to exploit market anomalies by adding extra selection criteria to the market cap or rules-based indices. These include among other strategies value – stocks trading cheap but performing better than stocks trading at a higher value, momentum – based on ongoing trend, dividend – stocks paying high dividend perform better in the long run and volatility – stable stocks perform better any day (read: How to Play the Choppy Market with Cheap Smart Beta ETFs ). In fact, the popularity of smart beta has soared to such a point, where a Create-Research survey has found that smart beta ETFs make up for around 18% of the U.S. ETF market. The U.S. markets are experiencing extreme volatility and the factors responsible for it are global growth concerns, escalating geopolitical tensions, a surge in the U.S. dollar and uncertainty over the timing of the next interest rate hike. Against this backdrop, investors look for smart stock-selection strategies to alleviate market risks. But nothing works forever, not even smart strategies. This is as true for smart beta ETFs as for market anomalies. Per a report by Research Affiliates’ analysts, one of the primary reasons why smart beta strategies have been performing well is because of their growing popularity, which led to higher valuations rather than structural alpha. The latter is the quality of the strategy and its potential to beat the benchmark on a sustainable and repeatable basis. This does not mean that one should reject smart beta ETFs altogether. If any inefficiency is spotted in the market, smart beta ETFs enable investors to exploit it at a cheap cost. However, it should be noted that not all smart beta ETFs have fulfilled their promise of delivering market-beating returns (read: Smart Beta ETFs That Stood Out Amid Market Volatility ). Below we have highlighted a few ‘Smart Beta’ options that underperformed the broader U.S. market ETF SPDR S&P 500 ETF (NYSEARCA: SPY ), which has gained about 1.6% so far this year (as of March 30, 2016) First Trust Dorsey Wright Focus 5 ETF (NASDAQ: FV ) This ETF tracks the Dorsey Wright Focus Five Index, which provides targeted exposure to the five First Trust sector and industry-based ETFs that Dorsey, Wright & Associates (DWA) believes have the highest potential to outperform other ETFs in the selection universe. It is a popular ETF with AUM of $4.6 billion and trades in solid volumes of around 2.2 million shares a day on average. The fund charges a higher 89 bps in fees. The ETF has lost 8.2% in the year-to-date period (as of March 30). Guggenheim S&P SmallCap 600 Pure Growth ETF (NYSEARCA: RZG ) This fund tracks the S&P SmallCap 600 Pure Growth Index. The product has a wide exposure across 146 stocks with each holding less than 2% share while healthcare and financials are the top two sectors accounting for over 20% share each. The ETF has AUM of $192 million but trades in light volume of about 28,000 shares a day on average. It charges 35 bps in annual fees and fell 2.4% in the year-to-date period. SPDR Russell 1000 Momentum Focus ETF (NYSEARCA: ONEO ) The fund tracks the Russell 1000 Momentum Focused Factor Index and holds a broad basket of 903 securities that are widely diversified with none holding more than 0.82% of assets. ONEO has accumulated $340.2 million in its asset base. It charges a lower fee of 20 bps per year and trades in solid volume of around 137,000 shares. The ETF fell 0.5% in the year-to-date period (read: 5 Very Successful ETF Launches of 2015 ). Original Post