Tag Archives: etfs

Upgrade Your Investment Approach And Put Some Fears To Rest

Despite the pleas of many consultants and wealth managers for investors to ignore tumult in the markets, the fact is that oftentimes such fears are warranted. Although long term investors should not impulsively react to small market moves, they should be alert to signs that things are “not right”. The mean-variance approach to investing is a very common one, but time has revealed a great number of weaknesses that unnecessarily expose its adherents to risk. The Kelly criterion is a very useful approach to investing that also corresponds more closely to the way markets actually work. The investment services industry as a whole has been slow to disseminate improvements in investment theory and practice. We are born with some pretty good warning mechanisms and most people are pretty good at sensing when things are not right. Martin J. Dougherty makes exactly this point in his book Special Forces Unarmed Combat Guide : “Victims of assault often say afterwards that they could see it coming.” He continues, “The problem, then, is not being able to spot danger but being willing to act on this information and avoid it.” While this is just one manifestation of our defense network, it does highlight our natural ability to “spot danger”. It also highlights the imperative of being able to act on useful warnings. Given that volatility and risk are endemic to the exercise of investing, there is no particular reason why most market behavior should cause undue duress for a well-informed investor. And yet times of unsettled markets and high volatility can keep a lot of investors awake at night, including seasoned investment professionals. Oftentimes, concerns revolve around a sense of uncertainty – a sense that something isn’t quite right or that something is being missed. Sometimes it comes from an uneasy feeling that a prescribed course just doesn’t seem right. Indeed, it may just be that one’s approach to investing is the source of discomfort as much or more than market moves. Two common approaches to investing vary substantially in their assumptions and in the logic of how they aim to get you from point A to point B. If you are feeling uneasy, it may be a good time to make sure that your investment approach will allow you act so as to avoid danger. One approach focuses on the importance of diversification and uses statistical analysis to design portfolios that maximize returns for a given level of risk. It is well entrenched in investment theory and practice. This approach is characterized by graphs that show the upper and lower bounds of growth in assets and gives assurance that if you just stick to the plan, you will have an extremely high chance of meeting your investment goals. It makes a lot of sense and is hard to refute. Another approach is described by William Poundstone in Fortune’s Formula as being one that “offers the highest compound return consistent with no risk of going broke.” It is well recognized in investment theory, though probably less so in practice. It can certainly be characterized by wide swings, but gives the assurance that if you just stick to the plan, you will maximize your wealth over your investment horizon. It makes a lot of sense and is hard to refute. This juxtaposition of strategies highlights a common investment challenge: how can you tell which one is better and/or which one is more appropriate for you? Do you know which one your financial planner or wealth manager or consultant uses? These are exactly the types of fundamental questions that are so critical to long term investment success but are so rarely discussed thoroughly. The fact is that both approaches have merit to them, but both also rely on important assumptions. The first approach is referred to as the mean-variance framework and is a part of a body of thinking called “modern portfolio theory”. While the mean-variance approach correctly highlights the importance of diversification, it does so at the expense of some serious structural shortcomings (For an excellent, though technical discussion, see Michael Mauboussin’s interview with the physicist Ole Peters here ) . One of the flaws of the approach is that it models returns using only mean and variance. Unfortunately, the reality is that return distributions have other dimensions that are extremely important to investors. Considering only mean and variance is akin to describing a three dimensional object with only two dimensions. The description will be at best incomplete and at worst, wholly unrepresentative. The implication is that all of those great graphs of wealth accumulation are at best possibilities and at worst complete fantasy. Another important flaw of the mean-variance framework is that it relies on expectation values. In theory, according to Ole Peters, expectation values represent an “ensemble of imagined parallel universes” and can potentially serve as the “basis for sensible behavior”. In practice, however, most firms simply apply averages from the past, but these past actualities fall well short of representing all imaginable future possibilities. In other words, since (arguably) most firms do not populate the model with the right information, one cannot expect it to produce useful results. Garbage in, garbage out. This common deficiency almost completely undermines the case for using mean-variance as an investment strategy. The second approach is referred to as the Kelly criterion and gained notoriety as a betting system. Michael Mauboussin gives a nice overview in “Size Matters” here : “Based on information theory, the Kelly Criterion says an investor should choose the investment(s) with the highest geometric mean return. This strategy is distinct from those based on mean/variance efficiency.” In general, Mauboussin continues, “The Kelly Criterion works well when you parlay your bets, face repeated opportunities, and know what the underlying distribution looks like.” Poundstone adds, “The Kelly criterion is meaningful only when gambling profits are reinvested. A practical theory of investment must largely be a theory of reinvestment.” This is a key point: most people do think of and act on investments as discrete opportunities that change over time and not as a singular procedure that operates like a reliable machine. In this way, the Kelly approach seems to correspond with the way many people actually invest. According to Poundstone, “They [most people] buy stocks and bonds and hang on to them until they have a strong reason to sell. Market bets ride by default.” It is also natural to recognize the importance of reinvestment: One good investment does not a retirement make. You need to keep it up. Poundstone clarifies the strategy: “The Kelly formula says that you should wager this fraction of your bankroll on a favorable bet: edge/odds. The edge is how much you expect to win, on the average, assuming you could make this wager over and over with the same probabilities. It is a fraction because the profit is always in proportion to how much you wager.” As Mauboussin puts it, “As an investor, maximizing wealth over time requires you to do two things: find situations where you have an analytical edge; and allocate the appropriate amount of capital when you do have an edge.” An important condition for the Kelly approach is that the system only works as long as the investor “stays in the game long enough for the law of large numbers to work.” Further, it is also natural to think of calibrating the magnitude of investments according to their attractiveness. While the Kelly approach does require one to have an edge in order to make an investment, it doesn’t require one to invest when no edge exists. This all makes common sense – which ought to make it easier to adhere to even in tough times. Conversely, investors may have trouble adhering to a mean-variance approach because it isn’t that hard to perceive problems with its assumptions and logical consistency. For one, it’s not an inherently bad idea to look to past returns for an indication of what future returns might be, but why should that be the only input? Other things matter a lot such as valuations and your starting point. Likewise with assessing diversification benefits. It’s not bad to look at past cross correlations for starters, but why not also consider the potential for increased global interconnectedness to increase correlations and reduce diversification benefits in the future? Arguably the biggest issue with the mean-variance approach, however, is that it understates risk. It would make sense that unprecedented levels of central bank intervention the last seven years is a factor that ought to be incorporated into one’s investment approach, and yet mean-variance ignores it. It is also true that sometimes bad things do happen and it makes sense to try to avoid them. The mean-variance approach is very weak at adapting to change: it essentially says that since the vast majority of the time you don’t get attacked in dark alleys, you shouldn’t worry about dark alleys. Thus, although this approach is an industry standard and used by countless wealth managers, financial planners, consultants, and other industry participants, it actually serves as a very weak foundation upon which to base one’s investments. It treats the market as a utility, reliably cranking out returns, but that isn’t how the market actually works – as anyone who follows it knows all too well. As a result, it may well be that much of the anxiety investors feel in regards to unsettled markets has a lot to do with the discord that they feel in regards to the mean-variance approach. To be fair, it is not like the mean-variance framework is an obviously bad idea that never should have taken hold. The theory is over fifty years old though and a great deal has been learned during that time to improve and refine investment approaches. As one example among many, advances in behavioral economics have been a major development. Indeed it is one of the weaknesses of the investment services industry that it has been slow to disseminate many of the useful advances in investment theory and practice nearly as quickly as markets have evolved. The Kelly approach isn’t the end of the line either, but it does represent progress. Just like walking alone down a dark alley at night can intuitively seem like a bad idea, so can navigating through markets with an investment strategy that you don’t really trust. Neither may seem incredibly risky at the time and you might even be able to get by unscathed a few times. Don’t let anyone convince you that such actions are a good idea though. People are usually pretty good at spotting danger; make sure you are just as good at responding to it. If you don’t have a good idea of where to go, ask for help. (click to enlarge)

A Volatile, Illiquid Paradise

Summary Two characteristics of today’s market – volatility and illiquidity – are in focus for many investors. What many small investors fail to realize is that straightforward Graham-style investing isn’t the only way to profit from volatility. This market is paradise for the small, self-directed value investor with a willingness to take on insurance liabilities. There is a lot of confusion about volatility . Some people think that volatility is the square root of the variance in a price series. They would be correct, except when they’re not. Others think that volatility is whatever the CBOE’s VIX metric says. This is also true, but limiting. Similarly, still others would argue that volatility is whatever the derivatives market implies that volatility is. Most will agree, however, that volatility is bad . We say “most,” but Seeking Alpha really isn’t “most” people. Any investor with even a cursory understanding of Graham-style investing knows the metaphor of Mr. Market, the moody, irrational purveyor of market prices. If we are patient with him, we can take advantage of his irrationality, which is what we ought to do as investors. In this understanding, volatility is simply noise , and it certainly isn’t a bad thing. As value-driven investors, we encourage this latter mentality, but we wonder if “volatility-as-noise” cuts the conversation too short. We see more opportunity here than the traditional Graham paradigm suggests. Taking advantage of more When we take advantage of what we estimate to be mispriced securities, we are directly using the volatility of the market to our advantage. The idea is that our counterparties (sellers or buyers) are simply lacking in time, cash, or information (or perhaps they are limited by fiduciary obligations), and when we trade shares, their loss is our gain. What if we take this one step further? If we are comfortable taking advantage of others’ value miscalculations by buying or selling a stock at a certain price, why would we not also be comfortable taking advantage of our counterparties’ miscalculations (or irrational obsession) with volatility itself? Return to the popular impressions of volatility. Each has profound limitations. When we take the square root of a variance , we are more often than not simply using a security’s end-of-day closing prices. This ignores daily ranges, which can be quite significant. When we refer only to the VIX , we correlate volatility almost exclusively with indices’ downside and thereby mistake “volatility” for “fear” (thank the financial media for this). When we rely on the implied volatility of derivatives, we assume a standard deviation of returns in a stock, largely ignoring the possibility of gapping and skewed returns. Assessing risk with any one of these volatility measures is a fool’s errand — and there are plenty of fools in the market. The illiquidity trap When we view volatility as baseless noise rather than risk , a whole world of opportunity presents itself. I.e., if we think that Mr. Market’s irrationality presents us with opportunity, then others’ “risk” can be our reward. If you were afraid of volatility (here meaning simply variation in a price series), as many portfolio managers are (think pensions), you would be eager to hedge against it. This has always been the case, though as we gaze into the maw of a potential bear market, survival instinct makes portfolio insurance more appealing than ever. As a corollary, selling insurance (puts) in periods of (VIX-style) volatility can be quite profitable. August 24th demonstrated, however, that this is not “normal” volatility. In the last few years, the Wild West of HFT penny-spread market-making turned the average transaction size into a tiny fraction of what it used to be, and largely pushed other market-makers out of the game. When the exchanges then gradually disincentivized even HFT market-making (thank you Michael Lewis ), no one was left to provide liquidity — especially in times of uncertainty (see 2010 Flash Crash ). What this means for the aforementioned portfolio managers is that the exchanges are not friendly places to do business in volume. For large orders, crossing networks and dark pools are preferred. The problem with these venues is that your counterparty is typically as well-informed as you are (i.e., they won’t be buyers when things are hairy). With nobody to sell to, paying a premium for a put option (and guaranteeing yourself a customer at a pre-determined price) becomes even more appealing. Selling insurance Value investors have beliefs about the intrinsic value of companies . Whether by virtue of cash-flow growth, “real options,” management savvy, or relative undervaluation, we can determine a range of prices at which we would be happy to own any publicly traded stock. Sometimes those ranges are small and confident; sometimes they are wide and uncertain; sometimes they converge at $0.00. Regardless, we have a basis for investment and a preferred entry point. The upshot to this assumption is that by selling insurance to portfolio managers in the form of put options, we can have our cake and eat it too. By selling a put at a strike price within our target range, we can not only provide ourselves the opportunity to buy into a stock at a favorable price, but also collect premium for our trouble (regardless). Furthermore, since brokers tend to be generous in their risk calculations for put-sellers (thank the Black-Scholes-Merton equation for conventional risk-assessment), we can get our fingers into all sorts of opportunities at relatively low cost, spread risk across multiple sectors, and collect premium while we wait. To most speculators, “risk of assignment” in the case of a decline in price would be detrimental. To a value investor, “risk of assignment” at a favorable price doesn’t sound much like risk at all. This is the strength of being a value-oriented investor. Ignoring a high-volatility, high-premium market environment is a missed opportunity. To some readers, this will already seem mind-numbingly obvious. Indeed, some contributors on Seeking Alpha are already practitioners of this philosophy (though they are not usually the most visible). Others, however, may not have seen an opportunity for this approach in the frothier, low-implied-volatility markets of the past few years, and may have discarded the idea out of hand. Now – in a high-volatility, high-uncertainty, and low-liquidity market – is the time to reconsider.

Buy The Fourth Quarter Of The Third Year Of The Presidential Cycle

The best time to buy the Presidential Election Cycle is from September of the second year to April of the third year. Nevertheless, the fourth quarter of the third year is strong, particularly after a weak third quarter. In the past, it was better to buy near the end of October than at the end of September. How does the fourth quarter do in the third year of the Presidential election cycle? ‘Everyone knows’ that the third year of the Presidential cycle is incredibly reliable, and has returns that far exceed the other three years. Even Grantham has touted it, which I thought must be tongue-in-cheek, because he is a macro-guy. So I decided to go back and check, and found his letter written at the end of the third quarter in 2014 for GMO. It turns out he was quite serious. Regular readers know the score: +2.5% a month for the seven months from October 1 to April 30, in year three on average since 1932 (a total of +17%). This is now the 21st cycle. The odds of drawing 20 random 7-month returns this strong are just over 1 in 200 according to our 10 million trials. But 17 of the actual 20 historical experiences were up, and the worst of the 3 downs was only -6.4%, so the odds of this consistency plus the high return would be much smaller. The remaining 5 months of the Presidential year have a good but not remarkable record, over .75% per month, but the killer here is that the remaining 36 months since 1932 averaged a measly +0.2% a month!” Reference to the remaining 5 months means that Grantham views the third year of the Presidential cycle as running from September to September. More importantly, we have missed the key months from September 30 to April 30. From 2014 to 2015, that time span had the S&P 500 rising by 11.39%, which is not too shabby given what the market has done since. Yahoo Finance only had S&P 500 data as far back as 1950. So my analysis is for the 16 third years since then (see the table below). We have completed 17 years from his September to April time frame, however, and I calculated an average 19.72% return for those time periods, with a median return of 19.49%. There was only one decline of -.76% in 1978-79. But dividends have not been included. So every period actually had a positive total return. For the full calendar third year, the average return was 17.12%, with a median return of 18.08%. That’s very good also, but not as good, and that is a 12-month return versus Grantham’s 7-month return. For all years since 1950, the average calendar year gain was 9.18%. Therefore, the average gain in the other 3 years of the Presidential cycle works out to 5.69%. Out of the 16 third years, 15 were up, and one was unchanged (2011). With stocks down YTD, the odds would appear to be good that we will get a nice rally over the last three months. I say ‘appear to be good’, because statistically we can’t calculate the odds. This is a small sample. It is not a random sample. And there is no solid theory to support why the pattern of the recent past should hold in the future. Let’s see how the last three months of the third year have done since 1950. From 9/30 to the end of the year, the average gain in the S&P has been 3.04%, with a median return of 4.39%. The mean is lower because of the skew created by 1987. Third Year Pres. Cycle %ch. Oct. 31 to end of yr % ch. Sept. prev. yr to April 3rd yr % ch. Full 3rd year % ch. April to Sept. 3rd yr % ch. 9/30 to end of 3rd yr % ch. Sept. low to end 3rd yr % ch. Oct. low to end 3rd yr % ch. Sept. 30 to Oct. low % ch. Sept. low to Oct. low 1951 3.62 15.32 16.35 3.7 2.19 2.19 4.9 -2.58 -2.58 1955 7.42 17.49 26.40 15.04 4.14 6.74 11.47 -6.57 -4.25 1959 4.12 15.04 8.48 -1.23 5.29 8.61 6.95 -1.55 1.56 1963 1.36 24.04 18.89 2.72 4.63 4.63 4.38 .24 .24 1967 3.40 22.79 20.09 2.87 -.25 2.98 3.4 -3.53 -.41 1971 8.34 23.31 10.79 -5.4 3.81 4.58 8.85 -4.63 -3.92 1975 1.29 37.39 31.55 -3.93 7.54 9.86 8.75 -1.12 1.02 1979 5.91 -.76 12.2 7.43 -1.35 1.35 7.84 -8.53 -6.02 1983 .84 36.55 17.27 1.00 -.69 .43 .95 -1.63 -.52 1987 -1.87 24.66 2.03 11.61 -23.2 -20.4 9.89 -30.1 -27.6 1991 6.28 22.64 26.31 3.31 7.56 8.73 10.69 -2.83 -1.77 1995 5.92 11.24 34.11 13.54 5.39 8.28 6.65 -1.18 1.53 1999 7.8 31.28 19.53 -3.93 14.54 15.84 17.78 -2.75 -1.65 2003 5.83 12.47 26.38 8.62 11.64 11.64 9.20 2.23 2.23 2007 -5.23 10.97 3.53 2.99 -3.82 1.15 -2.15 -1.71 3.37 2011 0.34 19.49 -.003 -17.0 11.15 11.34 14.41 -2.5 -2.69 2015 11.39 -7.93 Mean 3.46 19.72 17.12 1.96 3.04 4.87 7.75 -4.32 -2.59 Med. 3.87 19.49 18.08 2.87 4.39 5.68 8.30 -2.67 -1.09 (The median date of the September low is the 21st. The median date for the October low is the 17th.) The average fourth quarter gain for all years since 1950 is 4.06% with a median of 4.92%. So the third year of the Presidential cycle has a lower average using both measures. The much lower mean is probably because of 1987, but clearly the fourth quarter of the third year is actually not as good as other years. There were 5 down quarters out of 16. They were 1967, 1979, 1983, 1987 and 2007. But all 5 years that declined from April to September 30 (1959, 1971, 1975, 1999, and 2011) had good gains in the fourth quarter . This augurs well for 2015, but 5 out of 5 does not mean we have to get 6 out of 6. The average gain for the two months following October 31 was 3.46% with a median of 3.87%. I don’t know what the comparable percentages are for all years. Two years had declines – 1987 and 2007. So the return is better for the last two months than the last three months. This should not be a surprise. I compared the October lows with the September lows, and found that on average (in the third year), the October low was 2.59% lower than the September low (see the table). October had a lower low in 10 out of 16 years. If you can identify the October low, then the average gain from there to the end of the year was 7.75% with a median of 8.30%. 2007 was the only down year with a loss of -2.15%. Locating the vicinity of the October low is not as stupid as it sounds. The median low date was October 17th. Unfortunately, the 1987 crash was on the 17th, 18th and 19th with the huge losses on the 19th (I remember it well. I was 100% invested and canoeing a river in Missouri.). Eight of the 16 lows were on the 19th or later. Three of the lows were on the second to last or last day. So if you buy at the close on the third to last day, you should be able to beat that average return dated from the end of October. The last two days in October are pretty good on average. I will buy stocks when Financial Select Sector SPDR ETF (NYSEARCA: XLF ) hits a twenty-day high (adjusted for dividend payments). The levels are posted in my Instablog. I actually buy small caps when XLF hits a twenty-day high. I compared the Russell 2000’s performance in the fourth quarter of the third year with the S&P 500 since 1987, and found that on average the S&P did slightly better. The R2000 is more volatile. In strong fourth quarters, it beat the S&P. In weak fourth quarters, it underperformed badly; e.g. 1987. I’m pretty optimistic about the last two months of the year. There is a strong possibility that October will be bad, because of all the negative macro- indicators. Risky high-yield investments like MLPs, mREITs, and junk bonds have been hammered. Sentiment is very negative as indicated by Investors Intelligence, Hulbert’s sentiment measures, Rydex, and Citigroup’s Euphoria/Panic model. I think sentiment follows the market. If October brings further drops in stock prices, then these measures will become even more negative, but that will set us up for a bigger bounce into the end of the year.