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The One Way To Stay ‘Long’ In A Down Market – Un-Beta Part III

No one knows if this market will continue to move ahead or stall out. But I think it bears considering a bit of perspective on what we mean when we discuss “this market.” With a capitalization-weighted index like the S&P 500, the market can consist of deceptively few issues and provide a poor benchmark against which to measure your own results. In 2015, for instance, four S&P tech stocks – Facebook (NASDAQ: FB ), Amazon (NASDAQ: AMZN ), Netflix (NASDAQ: NFLX ), and Google (NASDAQ: GOOG ) (NASDAQ: GOOGL ) (the “FANGs”) were responsible for $450 billion of growth in market cap. Pretty wonderful! You don’t remember that? You thought 2015 was a wasted year in terms of gains, with the S&P 500 finishing almost flat? You’re correct. But the four stocks above did contribute $450 billion in market cap growth. That’s because, as a group, the other 496 stocks in the S&P collectively lost even more in capitalization. If you owned just the four FANGs, you had a mighty fine year. If you owned none of them, but all the others – not so much. Let’s use AMZN as an example. Amazon’s market capitalization today is over $325 billion, larger than the combined market values of Wal-Mart (NYSE: WMT ), Target (NYSE: TGT ), and Costco (NASDAQ: COST ). These three “old economy” firms reported trailing twelve-month GAAP net income of just under $17 billion, while Amazon’s net income was… an underwhelming $328 million. Of course, I think we can all agree that we buy stocks for what we believe they will be worth in the future, and I imagine Amazon the company will show increasing revenue in the future! However… As of today, Amazon trades at 501 times earnings per share. While I believe AMZN will continue to earn more revenue every year and may translate that into earnings, if the P/E it is rewarded with for that future growth slips to only, say, 400 times earnings per share without any increase or decrease in actual earnings, that would mean a 20% drop in stock price from 626 to 501. Heaven forbid if, in a down market, it would slip to only 300 times earnings; that would take the share price in this example to 375. It gets worse. As of April 1, 2016, the aggregate price/earnings ratio for stocks in the small cap Russell 2000 index is zero; those 2000 stocks (taken as a group) effectively had no earnings over the past 12 months. On the off chance that current valuations, combined with current revenues and real earnings, might not end well, we placed roughly half our assets into the above-mentioned yield and fixed income alternatives. But of course, there is a sucker born every minute and we’ve seen greater fool markets before that lasted beyond any reasonable connection to reality. (It seems the supply of Greater Fools is bigger than anyone imagined back in, say, 1998.) So in the event this one does continue, roughly half our asset base is still long – sort of. Just as for Mr. Clinton, for whom it depended on what your definition of “is” is, our portfolio is long, depending on what your definition of “long” is. My definition consists of the following: “Flexible funds:” These are long-only funds with excellent flexibility to go to cash, select different sectors or asset classes, or choose different capitalization sizes, world markets, or cash while awaiting reasonable entry points. “Long / short funds:” These are funds whose charter allows them to go long the issues they believe offer the greatest return or defensive characteristics and simultaneously short those they believe are most vulnerable to a decline. “Liquid Alternative funds:” Liquid alternative funds come in all sizes and flavors but their basic premise and promise is that they don’t limit themselves to buying common stocks, so they are an “alternative” to the benchmark investing so in vogue these days (as it always has been after a few years of good general market appreciation.) They might invest in or short currencies, commodities, bonds, stocks, options, futures or any of a half dozen other offerings. Our favorite flexible funds are those offered by Leuthold Weeden Capital Management. These are all no-load funds, have good track records, and are helmed by managers that are both transparent and humble. By “humble” I mean lacking in hubris and quite candid about their mistakes as well as their successes. Fortunately for us, the latter have outnumbered the former. The two we have in our portfolios are Leuthold Core Investment (MUTF: LCORX ) and Leuthold Global (MUTF: GLBLX ). Here’s LCORX, in their own words, from the Leuthold Funds website: The Leuthold Core Investment Fund differs from most other mutual funds by investing in stocks, bonds, money market instruments and certain foreign securities. When appropriate, as disciplines dictate, the Core Fund may also hedge its market exposure. We adjust the proportion of each asset class to reflect our view of the potential opportunity and value offered within that sector, as well as the potential risk. Although there are no guarantees, it is our belief that successful investing demands skill both in making money and attempting to preserve any gains. Flexibility is central to the creation of a core portfolio that you can depend on in a variety of market conditions. We possess the flexibility and discipline to invest where we see value and to sell when we believe there is undue risk.” Most recently, LCORX has been 18% in various bonds, 52% in select sectors, and 17% hedged, with the rest in cash and smaller positions. In a rip-snorting bull market, I’d go more for aggressive funds like former holding Akre Fund (MUTF: AKREX ). For this market, nothing beats LCORX. Leuthold’s sister fund for global investing, GLBLX, is invested in a similar ratio, but with a global bent, holding a little less cash and a few more longs. I consider these two funds as fine bookends for a conservative defensive portfolio. Then there are funds that are long-only and equities pretty-much-only that simply refuse to buy anything unless they have great faith in the future of a particular company and can buy it at a reasonable valuation. If they can’t, they stick to cash and cash equivalents. The best example of such a fund today is the Intrepid Endurance Fund (MUTF: ICMAX ) which is currently holding a whopping 67% in cash. They’ve experienced significant outflows, of course, because too many investors who claim they are in it for the long haul really aren’t. Indeed, today’s typical investor, institutional and individual alike, just want to beat the S&P – basically every month and certainly every quarter. The best way to do that is to not beat the S&P at all, but to at least equal it. That’s why so many gurus advise that you just buy an index fund and never sell it until you retire, at which time you can sell part of it to buy bonds. I say phooey to that hooey. Cap-weighted index funds like the S&P 500 are, by their very nature and composition, avenues to buy a chunk of whatever’s been working most recently, regardless of valuation or quality. The highest-capitalization stocks get the most money newly devoted to purchases and lower capitalization stocks, regardless of their investment value, get the least. Then we have most “professional” investors, a term that merely means that they do it from 9 to 5 every day, not necessarily that they do it more professionally or better. Their livelihood, vacations, mortgage, and children’s higher education depends on them never under-performing the index their mutual fund, pension fund or whatever is benchmarked to. See even most allegedly “active” managers never stray too far from the benchmark. As we get closer to a real bear market bottom, I’m guessing it is the managers of funds like ICMAX that we’ll be suggesting for your due diligence – because that’s when they will be buying at (finally!) reasonable valuations. Moving on to long/short funds, let me remind you about Boston Partners (Robeco) Global Long-Short ( BGLSX Institutional/ BGRSX Retail.) Since the fund publishes its largest holdings monthly (in an age of advanced information technology, why don’t more funds do this???), I can see that as of 31 March, their biggest longs were GOOG, BRK.B , AAPL , OTCQX:IMBBY and PHG . Their biggest shorts were on TSLA , CAT , BLL , OTCPK:GEAGY and NFLX . In uncertain times, I like this kind of flexibility. We also own the Boston Partners Long/Short Research fund ( BPRRX Investor class/ BPIRX Institutional class.) As of 31 March, their biggest long holdings are PE , ORCL , MSFT , XOM and JPM . The largest short positions? ITRI , NATI , TXRH , EQIX and WIT . And finally, we own another long/short fund, the AQR Long/Short Equity (MUTF: QLEIX ) This one has beaten all the benchmarks this year so far but be aware (!) of this caveat about this fund family: all classes of all their funds I own have a $1 million or $5 million minimum purchase, depending upon class of issue, unless you buy through an RIA or financial advisor with an agreement with the fund company. Fortunately, our firm has such an agreement so we can buy in quantities as low as $10,000. See if your broker or advisor can do the same. It’s important to gain this edge because my best choice in the liquid alternatives area is the “managed futures” fund called AQR Managed Futures Fund ( AQMIX institutional/ AQMNX investor.) This fund provides a liquid alternative to solely relying on US stocks for your returns. It does so by investing in a combination of stocks, bonds, commodities and currencies across a spectrum of different time frames. It provides virtually zero correlation with the S&P 500, yet it gives us the ability to profit from global macro investing trends in over 100 markets. (Indeed, this fund alone has proven so popular to our readers that we recently lowered our assets under management minimum from our standard $500,000 to $100,000, as long as we manage only mutual funds and ETFs!) Finally, we can offer, for those who prefer ETFs to funds, one long we own, the QuantShares US Market Neutral Anti-Beta ETF (NYSEARCA: BTAL ). It is basically a hedge fund, packaged as an ETF, that places the bet that boring predictable value will outperform the S&P in any down market. BTAL shorts the highest-Beta stocks (those that move in concert with or at a greater rate than the benchmark) and buys the stocks least sensitive to the benchmark move (the lowest-Beta stocks.) BTAL’s aim is to mute market moves over time and protect capital far better than simply buying an index fund. BTAL actually has a lower correlation to the S&P 500 than other typical hedges than either gold or utility stocks. You can see why I placed the word “Long” in my headline in parentheses. We’re still long. We are long some things and short others but on balance long. And we may not be in equities, but we’re still long other assets. Finally, we’re long – but not too much! And we are invested with managers we know and trust and have given them free rein to rebalance the percentage long and percentage short. This may give them some sleepless nights. Us? Between our munis, preferreds, REITs, flexible funds, long/short funds and liquid alternatives, we sleep very soundly, thank you! Disclaimer: As a Registered Investment Advisor, I believe it is essential to advise that I do not know your personal financial situation, so the information contained in this communiqué represents the opinions of the staff of Stanford Wealth Management, and should not be construed as “personalized” investment advice . Past performance is no guarantee of future results, rather an obvious statement but clearly too often unheeded judging by the number of investors who buy the current #1 mutual fund one year only to watch it plummet the following year. I encourage you to do your own due diligence on issues I discuss to see if they might be of value in your own investing. I take my responsibility to offer intelligent commentary seriously, but it should not be assumed that investing in any securities my clients or family are investing in will always be profitable. I do our best to get it right, and our firm “eats our own cooking,” but I could be wrong, hence my full disclosure as to whether we or our clients own or are buying the investments we write about.

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