To Buy or To Sell, That is the Question

I was reading a recent article by Bloomberg news about Todd Combs, Warren Buffett’s new right-hand man on stock picking. The article illustrates how Combs consistently buys when prices fall. This buy-low/sell-high strategy is the counter-strategy of riding winners/paring losers which I’ve seen recommended by many traders, behavioral economists, technicians, and statisticians. So where is the truth? Like most disputed questions, the answer lies somewhere in between. Technicians, traders, and statisticians cite the fact that stocks that are down have a better than average chance of going down more and that the market probably knows something that you do not. Behavioral economists cite our tendency for loss aversion which causes humans to hold onto losers too long because of the aversion to realizing those losses and our tendency to sell winners too early because of a desire to “lock-in” profit. The problem with these arguments is that they ignore the crux of any rational investment decision.  Specifically, they should simply ask, “What is the value of the company? “

As can be seen in Todd Combs’ strategy (which just so happens to be the philosophy of Buffett as well), a true sense of business value is the driver of buy and sell decisions. When a stock price falls, all else being equal, the risk-reward has become more favorable. When the stock rises, the risk-reward becomes less favorable. This reason alone should be the driving force behind buy and sell decisions for those who actually fundamentally research companies and stocks. Clearly, if the stock is down, the market could be signaling something that the analyst has missed. It serves as a notice to question one’s research, to find the devil’s advocate. But after doing so, if the analyst finds that the facts have not changed, then the improved risk-reward created by a lower price gives the value investor an opportunity that other investors are willing to let slip by.

So what makes the value investor so special? Due diligence. Investors that lack the in-depth research required to understand the company, its financials, and its valuation are subject to the pressures of the market because they do not have the anchor of their conviction. Investors that do not have a calculated potential downside risk and a calculated potential reward, do not have the triggers that allow them to buy and sell with confidence. While clearly, these price objectives are only subjective estimates, they are rooted in concrete research and serve as the critical focal point in any conversation about buying and selling. So, if we want to answer the “To Buy or To Sell” question, the first question an investor must ask is “what is this thing I’m buying or selling worth?”  Even though most investors do ask this question, very few actually answer it with a number.  Doesn’t that seem odd?

The Role of Diversity in a Better Future

The future is like a complex algorithm with virtually infinite variables. Mankind does not know the optimal inputs for the variables. Nature controls a large number of the most powerful variables, but mankind can shape many others. One way to think of the future is that mankind is in a constant search for the optimal set of inputs to determine the future. This is not a conscious goal, but if you think about it, each individual, in their own tiny part of the world, is influencing the future by making decisions every day. Each decision affects a variable in the algorithm that results in our future. To determine the optimal set of variables, mankind uses a crude genetic algorithm (of course without knowing it) to search for the optimal set of inputs.

From Wikipedia: “A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection and crossover." 

A genetic algorithm (GA) is pretty much a fine-tuned method of making educated guesses, analyzing the results, and then using that information to make more guesses until a final set of “optimal” results is found. Each variable has a range of possible inputs. The GA will randomly mutate variables to make sure it isn’t going down a sub-optimal path. Mankind is similar in that its seemingly chaotic nature allows for a wide range of inputs. This wide range (diversity) and chaos (mutations) allows for more optimal results without getting stuck in rut (local minima). With diversity and chaos, the world is able to keep variables from falling into ruts and settling on sub-optimal solutions. This is why it is important to have Type As and Type Bs, OCDs and slobs, democrats and republicans. Each play their part in making the range of inputs as wide as possible.

Without differing opinions, the algorithm has no method to optimize the final results. It takes extreme inputs with sometimes horrific results for the system to purge sub-optimal paths (slavery, eugenics). Just like it also requires extreme inputs to find sea-change pulls towards optimality (democracy, language). So next time you get frustrated by an extremist pundit you don’t agree with, realize that they serve an important purpose in society. Without them and everyone else, the future would be sub-optimal. And if you still want to call them a name, call them what they probably are, a mutation.

Capitalizing on the Random Walk

Just how volatile have the markets been the last two months? Would you be surprised to know that August and September 2011 rank amongst the top 5 most volatile periods in the last 50 years? I was. I knew things were bumpy but I didn’t realize they were Top 5 bumpy.

 

Volatile markets with high correlation can be the bane of the stock picker’s existence (Correlation article) because it is difficult to monetize idiosyncratic value when everything is moving wildly in the same direction. Many of the clients I’ve spoken to over the last couple of months have reduced exposure by lowering gross and net exposure. In fact, I was recently working with a client and developed a heuristic method to suggest gross exposure based on a few general factors:

 

A portfolio manager uses this rubric by defining the minimum and maximum gross exposure for their portfolio, defines a combination of external and internal factors to determine exposure, then creates Risk-On and Risk-Off parameters for each factor. The external factors like volatility, correlation, and S&P PE Multiple help highlight when the markets are difficult for fundamental portfolio managers to navigate. The counterbalance is the internal factors (Portfolio RAR and Downside Risk) that highlight the current opportunities derived from the firm’s investment process. Is the Portfolio Risk-Adjusted Return high and is the Portfolio Downside Risk low? If so, a portfolio manager may be willing to wade into the chop of the market to harvest the opportunities.

As volatile as this market is today, it doesn’t come close to the bouncy house in a tornado we went through in Oct 08-May 09 (6.4% average intraday change versus 3.0% today). Additionally, the direction of the market was pointedly up or down during ‘08/’09 (mostly down) versus the current market which is more mean-reverting. This creates an environment for Capitalizing on the Random Walk. If you look at the oscillation in the example below, you will see that while a stock increases in value, the position size increases (because the total number of shares stayed constant) but the Risk-Adjusted Return falls. The dynamic of increasing exposure when return falls is counter to sound portfolio management. Continuing the example the stock falls to $27 then rises back to $30, no trading has occurred and the resultant trading profit is $0.

 

But for fundamental investors that have a sense of long-term value, the gyrations create opportunity. See in the example below that as the price rises, the risk-adjusted return falls, and the position is reduced. The counter occurs as the stock decreases. In both examples, the beginning and ending share count is identical but “Capitalizing on the Random Walk” below creates 50bps of additional return net of commission. This is one stock out of dozens in the portfolio that have moved like this over the past two months.

Taking advantage of market volatility certainly isn’t top of the list when describing value investors but if expected return changes then the disciplined investor should react. A firm should create a disciplined method to highlight disparities between position size and risk-adjusted return. This is critical to Capitalizing on the Random Walk.

Here are a few quotes that lend credence to the strategy:

“When the facts change, I change my mind. What do you do, sir?” – John Maynard Keynes

“Our trading models tend to buy stocks that are recently out of favor and sell those recently in favor. Thus, to some extent, our actions have the effect of dampening extreme moves in either direction, and,  as a result,  reducing volatility in those stocks.” – James Simons, Renaissance Technologies (testimony to Congress 11/13/08)

“We as a firm are always going to buy too soon and sell too soon.  And I’m very at peace with that.” – Seth Klarman, Baupost Group

“When JP Morgan was asked how he had become so rich?  He replied, “I sold too early.” – JP Morgan, famous financier

“The riskiest moment is when you’re right.  That’s when you’re in the most trouble, because you tend to overstay the good decisions.” – Peter Bernstein, legendary investor

“Make a rule:  Whenever an investment doubles in price, find out who has the most negative view of it and give this devil’s advocate a full hearing.” – Jason Zweig, author of Your Money and Your Brain

Multiple’s Personality Disorder

The pervasive use of PE multiples (Price-to-Earnings) to value stocks has always been perplexing to me. What do we use Multiples for? To help us make a more informed investment decision. Well let’s put that to the test and say that I’m evaluating Google. I determine that the PE multiple is 20x Earnings Per Share (EPS). What does that really tell me? It tells me that if Google’s EPS remains flat in the future, I will double my money in 20 years. Sure, that’s informative, but wouldn’t a better perspective be to say that Google has an earnings yield of 5%? I know it is just the reciprocal (1 / 20) but it seems so much more informative. Just like 60 miles per hour is more informative than 0.02 hours per mile.

If I were running a fund, I would require my analyst to speak in terms of yield instead of multiples. When you tell me that Google is trading at 20x and Citigroup is trading at 10x, I understand that Citi is cheaper on a pure multiple basis. But it seems much more informative to know that Google is yielding 5% while Citigroup is yielding 10%. This makes the appropriate asset class comparison more obvious. I can get a money market yielding 1%, a treasury yielding 2.5%, a muni yielding 4% or Google yielding 5%. This seems like a more proper framing of the equation.

In addition, a small change in low multiple stocks can have a large impact in yield but have the opposite impact on higher multiple stocks. In the chart below, a multiple expansion from 6.5x to 10x lowers the yield from 15% to 10%. Clearly an investor would prefer to pay 6.5x instead of 10x, but understanding the yield has decreased to 10% makes decision making more exact. For a more profound reason to use Yield, see the top end of the scale (Assets #1-#5). A large multiple shift from 50x to 75x barely nudges the earnings yield.

A savvy investor will quickly point out that all of these arguments are flawed because we’re not including growth. I agree. I may be willing to buy stock trading at 75x if it’s growing 75% a year. The problem is that both multiples and yields are point in time snapshots. The PE-to-Growth (PEG) multiple is an attempt to account for growth, but it doesn’t do a great job (see the chart below). If we divide PE by growth, we get a series of 1.0x PEG assets. I would argue that their identical PEG does not equal identical investments. Assuming you would rather have a dollar today than tomorrow, you would prefer Asset #13 with a 100% yield and 1% growth more than Asset #1 with a 1% yield and 100% growth, but the PEG doesn’t differentiate between the two. A better method is to use a Forward Yield (Current Yield * Growth) which gives an advantage to current dollars versus future dollars.

The idea of favoring current dollars (higher yield) rings empirically true when the analysis is stretched beyond one year to look at the cumulative yield over 5 and 10 years. However, growth starts to catch up with yield quickly in the 10 year analysis. Asset #1 had the lowest Forward Yield above (2%), but after 5 years, its Forward Yield (31%) is better than Asset #2 (27%) and #3 (26%). After 10 years, Asset #1 (1023%) is better than all assets except Asset #13 (1046%). Of course, this assumes that Asset #1 can actually grow earnings by 100% a year for 10 years, but the conundrum is still apparent. How do I use Multiple or Yield to measure the investment qualities of an asset?

The answer is that you cannot use point in time valuations like Multiple or Yield to correctly assess an assets value. They are good heuristics when doing a superficial initial analysis, but fall short when compared to a discounted cash flow analysis (DCF). A DCF allows for varying degrees of growth, expenses, and time value. All of which are necessary to properly characterize the investment value of an asset. So in the pecking order of valuation methods, the Multiple gets bottom billing but, for some reason, is still the most used metric in the industry. Going forward, remember this simple equation: DCF > Yield > Multiple. It’ll help prevent you from Multiple’s Personality Disorder.

Modern Portfolio Theory Stacked the Deck

I have often made the case to clients that diversification and volatility are portfolio management distractions. Not because they are uniformly irrelevant, but because industry dogma gives them a status well above their merit. Our industry uses diversification and volatility as yardsticks of comparison, so funds are naturally incentivized to alter their behavior to maximize their performance based on these measures. If a potential investor gauges a fund’s performance using return per unit of volatility, Value-at-Risk, Beta, tracking error, and diversification – guess what happens? You get lots of fund managers building portfolios with too many positions and avoiding volatility. Not surprising then that our industry has been increasingly dominated by high diversity / low volatility funds since the advent of Modern Portfolio Theory (average fund has 140 positions - study by Pollet and Wilson).

Scott Vincent of Green River Asset Management recently published an article titled “Is Portfolio Theory Harming Your Portfolio?” In it, he describes how Modern Portfolio Theory (Efficient Frontier – Markowitz, CAPM – Sharpe, and Efficient Market Hypothesis – Fama) has changed the shape of the investment industry from stock picking funds to super-diversified quantitative or quasi-quantitative funds. Volatility gained acceptance as the standard measure of risk for one primary reason, it was measurable (see answers to questions 2 and 10 in “Great Investor Mentality Quiz”). But being measurable doesn’t make it right. In “Is Portfolio Theory Harming Your Portfolio?”, Vincent explains:

Amazingly enough, there’s not much empirical “proof” as to why we should use variance as a measure of risk, yet it plays a critical role in almost all large financial transactions. It seems that academicians needed a way to quantify risk to fit mathematical models and they grabbed variance, not because it described risk very well, but because it was the best quantitative option available. But just because it is convenient, and it carries a certain intuitive appeal, doesn’t make it right.

If volatility is not a very good proxy for risk then are our historical judgments of active managers wrong? Yes. Do we need to change the way that we judge managers? Yes. In fact, there are half a dozen “risks” that are more important than volatility. I’m often surprised by investors that care more about volatility than leverage. I certainly believe the latter is more indicative of potential risk (i.e. Asian Financial Crisis, Mexican Financial Crisis, Russian Financial Crisis, S&L, Junk Bond, Sub-Prime Mortgage, et. al. – see article comparing Sub-prime and Junk Bond). Volatility can be tough to stomach, but potential downside loss is true risk. As Vincent says (concept described in “Eight Mistakes Money Managers Make” presentation):

Risk is often in the eye of the beholder. While “quants” (who rely heavily on MPT) might view a stock that has fallen in value by 50 percent over a short period of time as quite risky (i.e. it has a high beta), others might view the investment as extremely safe, offering an almost guaranteed return. Perhaps the stock trades well below the cash on its books and the company is likely to generate cash going forward. This latter group of investors might even view volatility as a positive; not something that they need to be paid more to accept.

Recognize that there is more than one measure of risk and that volatility is not a synonym for risk. Risk is a combination of downside potential, liquidity, time horizon, sector exposure, leverage, market correlation, and volatility (and probably several more). Just like a pilot cannot look at one gauge to fly the plane, a portfolio manager cannot look at one measure of risk to manage a portfolio.

Another major point of “Is Portfolio Theory Harming….” is that diversification is not only over-rated, but it becomes corrosive at a certain point:

The appeal to diversification, according to quantitative finance, is the idea that it allows us to enjoy the average of all the returns from the assets in a portfolio, while lowering our risk to a level below the average of the combined volatilities. But since we can’t call volatility risk and we can’t reliably predict volatilities or correlations, then how can we compile diversified portfolios and claim they are on some sort of efficient frontier? These super-diversified portfolios may be inefficient -- it may be possible to earn higher rates of return with less risk. It may be that by combining a group of securities hand-selected for their limited downside and high potential return, the skilled active manager with a relatively concentrated portfolio has greater potential to offer lower risk and higher returns than a fully diversified portfolio.

Even if we were to make volatility reduction paramount, the case for extreme diversification does not hold true. A study by Fisher and Lorie concludes that, “Roughly 40 percent of achievable reduction is obtained by holding two stocks; 80 percent, by holding eight stocks; 90 percent by holding 16 stocks.” Other studies by authors such as William F. Sharpe, Henry A. Latane' and Donald L. Tuttle make similar statements.* Needless to say, it is hard to argue that 100 positions is necessary for volatility reduction.

But honestly, the more damning case against super-diversification is time:

A fund manager’s job is to identify assets that are priced “inefficiently,” where the market has ostensibly made an error and a stock is available at a level that allows for relatively little risk versus expected return. But finding inefficiencies and maintaining a portfolio is difficult work and requires resources (a manager’s time and brain power, among the most important of these). Resources are not unlimited (most importantly a manager’s time). Therefore, the amount of resources devoted to each specific investment varies inversely with the amount of investments owned in the portfolio. The more positions added to the portfolio, the less likely a manager is to capture these difficult-to-find inefficiencies because he/she has less time and other resources available to do so.

I have used the concept of “mental capital” for years with clients. I ask the client how many hours a month it takes an analyst to cover an investment. For example, let’s say 10 hours. Then we’ll also assume that the analyst has other ideas that are being considered for the portfolio and for each existing investment, they spend another 5 hours working on new ideas. That works out to 15 hours for each portfolio position. If we assume each analyst works about 150 hours a month (excludes time staring at the P&L and filling out March Madness pools), that means each analyst can cover about 10 names with 10 on the watchlist. That means a fund with a team of four can reasonably cover 40 names. But a majority of funds end up with 80 positions meaning that something is being sacrificed for the sake of diversification. More than likely, the portfolio ends up with a mix of insignificant positions that take just as much time as the “core” positions, but have very little impact on the portfolio’s returns. Very rarely will the 50bps position have a large impact on portfolio returns. If it does not matter, get rid of it because it is a drain on mental capital.

All of these facts lead to the question, how do low diversity / high volatility portfolios perform? In fact, fairly well, granted that we do not have a good way to “risk adjust” portfolio returns given that we are no longer using volatility. However, Vincent highlights, “Multiple studies indicate that funds which are more actively managed, or more concentrated, outperform indexes and do so with persistence (Kacperczyk, Sialm and Zheng (2005), Cohen, Polk, Silli (2010), Bakks, Busse, and Greene (2006), Wermers (2003), and Brands, Brown, Gallagher (2003), Cremers and Petajisto (2007)). While we need to acknowledge that because we can’t measure risk, these studies, like any empirical work, need to be taken with a grain of salt. It is nonetheless interesting that if we compare the studies that focus on teasing apart the influence of more active, concentrated management, to the broad all-inclusive studies, there’s a large change in the signal received.”

Funds with the highest Active Share [most active management] outperform their benchmarks both before and after expenses, while funds with the lowest Active Share underperform after expenses …. The best performers are concentrated stock pickers ….We also find strong evidence for performance persistence for the funds with the highest Active Share, even after controlling for momentum. From an investor’s point of view, funds with the highest Active Share, smallest assets, and best one-year performance seem very attractive, outperforming their benchmarks by 6.5% per year net of fees and expenses. – Cremers and Petajisto (2007)

Basically, volatility is a distraction, diversification is a drag, and active concentrated management is a superior method of investing. That is music to the ears of Graham & Dodd’er out there. In a world where the dogma is against you, hold fast that the truth (i.e. common sense) is on your side.

Finally, I have saved my favorite quote of Mr. Vincent’s for last because it describes Alpha Theory perfectly, “The degree of concentration in a fund should reflect the confidence a manager has in the inefficiencies found, and the weight of those investments should reflect the probability of success as well as the level of asymmetry present in the prospective return profiles of the assets.” Right on Mr. Vincent, write on.

 

*If volatility reduction was the game, then holding 8 positions would get you almost home. But that would mean that the average position size would be 12.5%. I believe that diversification can be approached from another angle that involves downside tolerance. Start by asking, what is the maximum position size I am willing to take? Let’s say it is 6% of fund value. And if the minimum position size is 1% and position sizes are scaled linearly then a 100% gross exposure fund would have about 29 positions (6% max position size - 1% min position size = 5% / 2 = 2.5% midpoint + 1% min position size = 3.5% average position size – 100% gross exposure / 3.5% average position size ≈ 29 positions).

The Marshmallow Experiment

In 1972, Dr. Walter Mischel performed an experiment in which he presented kids with a marshmallow sitting on a table. The kids were told that if they can wait until some later time, they would receive a second marshmallow for their patience. Of course, there were kids that could hold out for the extra marshmallow and others that ate it right away. The kids with the ability to wait were said to have higher “impulse control.” Delayed gratification studies had been performed previously, but this was one of the first to follow up on the subjects over subsequent years. Over the years, they tested how kids with higher or lower impulse control performed in life (follow up study with results, Shoda and Mishcel, 1990). The results were convincing, people that displayed impulse control at an early age had higher coping and cognitive competence, higher aptitude scores, and general self-control later in life. A number of similar studies were launched that showed a causal effect between low impulse control and obesity, drug addiction, and criminal activity (The New Yorker article, DON’T!). Needless to say, the implications of the Marshmallow Experiment are powerful.

Now what does this mean for investors? We can assume that most investors have reasonable impulse control or they would have never made into or through college (personally, my impulse control went on sabbatical a few times during college). But there are still situations where the Id has to be tamed and the elephant kept in check. So what is an investor’s marshmallow moment? How about when an investment’s value increases beyond our expectations and our emotion tells us that “this thing has legs.” Our impulse is to scoop that marshmallow up and enjoy the ride as our investment trades even higher. The more difficult decision is to sell because the asset has met expectations. How about putting an idea, that a buddy told us about, in the portfolio before we do our full due diligence? How about holding onto that 40 basis point position, even though we know it has very little impact on portfolio performance and is a distraction from other research efforts?

Impulses and delayed gratification come in various forms. When it comes to fatty foods and narcotics, we may be rock solid, but when it comes to financial decisions, make sure you aren’t scooping up one marshmallow today at the expense of two tomorrow.

Why Hedge Funds Benefit from the Asymmetry of Returns

I wrote an article last month about why 50% of upside is not as good as 50% of downside is bad (see article here– Recap: A $100 million fund that rises 50% then falls 50% the following year will be left with $75 million. This asymmetry highlights the critical importance of understanding downside in portfolio management). I subsequently went out to lunch with a friend who is the marketing person from a fundamental long/short fund. We were discussing how some investors ding them for underperforming the market when the market rises and fail to give them credit for their positive relative performance in down-markets because they still lost money. This made me think of our previous example of the fund that started with $100 million and ended with $75 million. What if I just said that hedge funds only participated in 80% of that move, or 60% of the move, how would that change the results?

This example shows that, at 80% capture of upside and downside, the loss is decreased to 16%. At 60%, this example shows a 9% loss. But this is an extreme example where loss is equal to gain, what if we dampened the loss to 30%?

This next example is interesting because we now have positive overall returns and we find that the best Capture (you can think of the Capture as amount of bankroll bet because betting a percentage equal to the capture would create the same return) is somewhere around 60%. Actually, the optimal bet is at 67% which I explain how to derive in my previous article on the Kelly Criterion. My friend also told me that his fund captures 63% of market upside and only 23% of market downside and that the Credit Suisse Long/Short Equity Index captures 62% of upside and 37% of downside. I thought it would be interesting to take market (S&P 500) historical returns and see how they would compare to just being long the S&P 500 given the favorable upside/downside capture of the Credit Suisse Long/Short Equity Index. This analysis does not use actual hedge funds results, but instead implied returns using the capture rates compared to the S&P 500. The results are interesting:

Over the past 15 years, hedge funds have outperformed the S&P 500 due to the simple fact that they have smaller drawdowns. This leaves more capital to benefit in up markets even if the upswings are to a lesser degree. Even over longer periods of time where the S&P outperforms hedge funds, the hedge fund returns are subject to a lower standard deviation.

Hedge funds are generally considered risky investments. But I believe the opposite is actually true for fundamental long/short funds that do not use excessive leverage. As the results bear out, hedge funds are better protectors of capital and are not damaged to the same degree as long-only strategies during down markets. The asymmetry of returns in compounding investments is a true "feather in the cap" for hedge fund investments and does not get the credit it deserves from some hedge fund investors. In fact, I believe the up 50%/down 50% example should be a component of every hedge fund manager's marketing documents. It highlights the true benefits of capital preservation for compounding investments inherent in hedge funds. If you would like Alpha Theory to help your fund analyze the impact of up-down capture in your portfolio and help you customize your presentation to investors, please contact us at info@alphatheory.com.

Which Way Is Up? Why six of one is not always worth a half dozen of another.

Rule No.1: Never lose money. Rule No.2: Never forget rule No.1. – Warren Buffett

If a $100 million dollar fund is up 50% one year and down 50% the next, do you still have a $100 million dollar fund? No, the fund has been reduced to $75 million or a 25% loss. I use this question in almost every conversation with an investment manager to highlight the importance of downside.

And the order of the sequence doesn't matter. We could have lost 50% first and then gained 50% and the ultimate result would be identical. So, why does loss have a disproportionate impact? This simple illustration highlights the asymmetry of returns in compounding portfolios. This means that returns come in sequence not simultaneously. So any loss creates a smaller bankroll with which to make subsequent bets. Gains on the other hand, although they do increase your bet potential, lack the impact of a commensurate amount of loss (this is why the Kelly Criterion makes sense). The reason is simple. In our original example, the $50 million gain from a 50% profit is only 33% of the overall $150 million in the fund. But, the $75 million loss associated with going down 50% represents a full 50% of the $150 million fund total. The 25% loss associated with this example is the empirical proof of Buffett's very famous quote that served as the prelude to this article.

So if loss and gain are not created equal, then what is more important to define in portfolio construction? How much you can make or lose? Clearly, understanding loss is the foundation of all sound portfolio management. Just ask any manager fighting to get back to their high-water mark (a manager down 25% in '08 has to have returns of 33% to get back to pre-2008 levels).

The message is short and sweet. Spend the time to estimate an explicit downside before an asset is allowed into the portfolio because downside risk is the true swing factor in portfolio management. Additionally, if a firm only calculates a single value based on the thesis coming to fruition there is an implicit assumption of a 100% probability of that thesis coming true. Finally, mandating a discrete downside calculation allows the research team to expand their investment mind and encourages the search for the devil's advocate. This subtle shift moves a firm away from a process where the focus is typically finding evidence to support their thesis (http://en.wikipedia.org/wiki/Confirmation_bias) to a process that searches for complete information.

I've had hundreds of conversations with fund managers, analysts, traders, etc. about the warts of their investment process. And, as simple as it is, if given the ability to make only one change inside of a fund, calculating a downside would be it (try out our calculator to measure the impact of downside).

Untangling Skill and Luck – Mauboussin

"Separating skill and luck encourages better thinking about outcomes and allows for sharply improved decision making." – Michael Mauboussin, Legg Mason

Michael Mauboussin, a personal hero of mine, was kind enough to reach out to me after reading my blog, "The Investor's Serenity Prayer." Many of the ideas for the blog post were inspired by Mr. Mauboussin's book, "Think Twice." Mauboussin has expanded on some of the topics touched on in "Think Twice" with a new article titled "Untangling Skill and Luck" which can be found in the differentiated thinking portion of Legg Mason's website.

The article goes through numerous examples of how different professions/games (baseball, football, chess, business, investing, etc.) are part skill and part luck, each with varying degrees. To see the proofs, please read "Untangling Skill and Luck". For me, one example that stood out was that in major league baseball the worst team will beat the best team in a best-of-five series about 15 percent of the time. That is more frequent than flipping a coin and landing on heads three times in a row. For teams where skill is even closer, the percentage goes up dramatically. As a point of comparison, if a great chess player played a mediocre chess player 1,000 times, the great chess player would probably win all 1,000. So next time your favorite sports team is a 3-to-1 favorite and they lose, do not shoot the coach. Ask if the opponent just flipped a coin and got heads a couple of times (25% chance or same as 3-to-1 odds).

When thinking of investing positions on the skill/luck continuum, I'm reminded of a conversation with my office-mate from my first job at CIBC Oppenheimer. He told me that investing frustrates him because unlike many other professions he cannot just work harder and make more money. The guy mowing lawns can work more hours, mow more lawns, and make more money. The salesperson can make more cold calls, land more customers, and make more money. Both professions have a high correlation between effort and reward. But the investor can work 24/7 and potentially not make any more money. Clearly there is the chance that working harder will improve returns, but the correlation between effort and success is not nearly as high as a driven person like him would have liked. I believe this fact is another proof of investing's skew towards the luck side of the skill/luck continuum.

So understanding that luck plays a part is goal number one and then trying to understand how much luck is goal number two. Based on Mauboussin's estimates, investing is larger part luck than skill. This is discouraging for us that devote our life to honing our investing skill, but we should not despair that our efforts are in vain. The fact that investing is not pure luck means that skill matters! But as Mauboussin says, "The key is to focus feedback on improving skill. This is a very difficult task in activities where luck plays a big role. For example, this means that in evaluating an analyst or portfolio manager, it is much less important to see how she has done recently (whether her picks did well or her portfolio beat the benchmark) than it is to assess the process by which she did her job. Embracing and implementing this point of view is demanding. And make no mistake about it: the reason to emphasize process is that a good process provides the best chance for agreeable long-term outcomes."

Even though the heart of the article was the in-depth studies of skill versus luck, my favorite part was Mauboussin's thoughts on portfolio management:

"Finding gaps between fundamentals and expectations is only part of the analytical task. The second challenge is to properly build portfolios to take advantage of the opportunities. There are two common mistakes in sizing positions within a portfolio. One is a failure to adjust position sizes for the attractiveness of the opportunity. In theory, the positions in more attractive risk-adjusted opportunities should be more prominent in the portfolio than less attractive opportunities. In some activities, mathematical formulas can help work out precisely how much you should bet given your perceived edge. While this is difficult in practice for most money managers, the main idea remains: the best ideas deserve the most capital. The weighting in many portfolios fails to distinguish sufficiently between the quality of the ideas. The analytical part of a good process requires both disciplined unearthing of edge and intelligent position sizing aimed at maximizing long-term risk-adjusted returns." (By the way, the second mistake mentioned was overbetting through leverage.)

This is Alpha Theory in a nutshell, so it clearly warms my heart to see Mauboussin spell it out so explicitly.

Correlation and Macro Investing

Since the beginning of the financial crisis in '08, portfolio managers have repeatedly told me how difficult it has become to be a "stock-picker." Although fundamentals matter over the long-term, the short term has made the path to realizing that value unbearable and I have seen many fundamental investors partially or whole-heartedly change their stripes to being more macro-focused. I believe this is a bad idea. This is not to say that macro investing is a bad idea, but that it is probably not a particularly strong skill-set of the manager whom has been focused on fundamental investing for their whole career (previous post on macro investing, excerpt below*).

At first I focused on the VIX as a proxy for the level of difficulty for stock-pickers. Certainly there was some strong interdependence between the two in late '08 / early '09, but stock-picking has continued to be challenging even though the VIX has subsided. And as Seth Klarman said when discussing value investing, "think about volatility in markets as being in your favor rather than as a problem." So the VIX wasn't the proxy for difficult stock-picking, it was just a good high negative beta bet against the market. This makes sense once you think about how the VIX is calculated. The VIX is measured by taking the implied volatility of a chain of S&P 500 Index Options. As the price of options rises, so does the implied volatility given that the rest of the Black-Sholes pricing inputs are held constant. So in a falling market, investors tend to buy puts as protection which increases the price of puts but also increases the price of calls due to put-call parity. So, as the price of options increases so does the implied volatility. On the other hand, in times of rising markets, investors do not tend to purchase calls on the same order as puts are purchased on the downside, meaning that options prices do not increase as much during rising markets as they do in falling markets which makes the VIX highly negatively correlated with the markets. And also makes it a poor proxy for stock-pickers.

But recently I have seen references to a gauge that I didn't know was available, the CBOE Implied Correlation Index (JCJ Index on Bloomberg). The Correlation Index measures the correlation of stocks in the S&P 500 against each other, meaning that it suggests how much of the movement of stocks is related to the movement of other stocks. Now this makes logical sense as a good proxy for the level of difficulty for stock-pickers because it shows the ease or difficulty in realizing idiosyncratic moves versus systematic moves in a stock. Right now the JCJ is trading at 65, which means that 65% of the movement of the average stock in the S&P 500 is explained by the movement of other stocks in the S&P 500. No wonder fundamental investors are considering following a macro strategy. Needless to say it is difficult to tease out the value when correlation is so high. "High levels of correlation can create a serious challenge for long-short managers. While a long-short portfolio may yield up to twice as much as a long-only portfolio in a low-correlation environment, its performance may converge towards zero as correlation reaches extreme levels," Marko Kolanovic, JP Morgan Securities. Please check out JP Morgan's great report on the subject – "Why We Have a Correlation Bubble" by Marko Kolanovic.

 

Historical correlation for the stocks in the S&P 500 is a little less than 30%. That means we are substantially above norm. Check out the breakdown by industry compared to historical levels:

I am not sure exactly how this should change a fundamental investor's strategy, but I think at a bare minimum it should be tracked as a measure of solace that the stock-picking difficulty you are experiencing is in a historically unsustainable state. For those that would want to use the Correlation measure more aggressively, they could decrease their gross exposure in environments of high correlation as it represents a period where their skill is not translated into an outcome as effectively.

Alpha Theory is currently building an index-adjusted risk-adjusted return so that users that do not want to trade around the volatility will get a risk-adjusted return which is muted by the movement of the underlying index. For instance, if a user has a 30% risk-adjusted return for IBM and the S&P 500 and IBM go up by 10%, the risk-adjusted return of IBM would stay constant at 30%. This method is not the best way to "Capitalize on the Random Walk" (Click on #8 - Capitalize on the Random Walk) of the market but it will help lessen some of the non-idiosyncratic buy and sell recommendations coming out of Alpha Theory.

 

*Top-down can be a great way to focus on regions, sectors, and industries that you believe will outperform and then find good bargains in those regions or themes. Additionally, it makes sense to protect against macro-factors which you believe have a reasonable probability of occurrence and would adversely affect your portfolio like the fear of inflation or dollar devaluation. But that does not mean that stock pickers should all of a sudden be betting on the direction of indexes, currency, or sovereign debt.

As Bill Ackman said, "We spend little time trying to outguess market prognosticators about the short-term future of the markets or the economy for the purpose of deciding whether or not to invest.  Since we believe that short-term market and economic prognostication is largely a fool's errand, we invest according to a strategy that makes the need to rely on short-term market or economic assessments largely irrelevant."

Market and economic direction are multi-variable equations with thousands of inputs.  You can find two Nobel Laureate economists with well-defended theses for divergent directions of the US economy.  If they cannot figure it out, why should you try?  Mental capacity is a precious commodity and should be focused on reasonable prognostication, not on knowing the unknowable.