March Madness 2013 - Your Mom Always Wins

I first posted the blog below in 2012. As I'm preparing my brackets for 2013, I was referencing this post as a reminder of how to operate the machine I built last year. First step, download data for 2013. I typed in KenPom.com to get Winning Percentage scores to drive my March Madness Machine. KENPOM.COM WAS DOWN!!! Apparently, I'm not the only one heading to the KenPom site to get my stat fix (apparently the NCAA site was down for a little while too). So, what do I do? Be patient and wait for the mad rush to subside? No, I do a Google search for KenPom to see if I can find a backdoor. No luck, but I do come across this article that may change everything: KenPom vs Your Mom. 

The article was written by Peter Tiernan who has been using stats to analyze March Madness for 22 years (this according to his bio) and is an expert that writes for CBS during the tourney. The point of the article was to compare the new BPI system to KenPom. No need to go into results here, but basically no real difference. The more interesting section of the article was the comparison of KenPom to Your Mom. "Your Mom" is basically, just picking the higher seed in every round.

"Okay. So I did one other analysis yesterday—and I think it’s more relevant than determining round-by-round prediction accuracy. This analysis compared the accuracy of KenPom to YourMom in filling out your entire bracket and living with the consequences of lost games in previous rounds. I was able to go back nine years for this analysis. What did I find? Using KenPom efficiency data would’ve predicted 376 of the 567 tourney games played between 2004 and 2012. That’s a 66.3 percent accuracy rate. And how would’ve YourMom done? Amazingly, two games better—for a 66.7 percent accuracy rate. That’s right. Picking by seeds and margin beats out using KenPom."

Your Mom wins!!! Actually, this isn't that surprising. I've filled out my bracket using KenPom and Vegas odds for many years and I always notice that I'm pretty much picking the higher seed. Very rarely (and I'm not counting 9 vs. 8) does the underdog have a higher KenPom Winning Percentage, so I pretty much end up filling out my bracket picking the higher seed. I knew I ended up picking more favored seeds than the average bracketeer but now I have some emperical evidence to support my choice. 

Here's the rub, most NCAA pools only pay 3 places. Over the long-run, I will almost certainly be better than average, but randomness places in the the top 3 spots more often than not. For example, imagine I'm competing in a pool with 99 others. Assume all 99 are randomly generated and assuming my method is the superior statistical method, I still have a very low probability of finishing in the Top 3. This goes down even further if there are 1000 participants. 

What does all this mean? I'm sticking to small office pools or pools with Cinderella points (extra points for picking upsets) and I will not be offended when someone calls me a moron for picking every favorite.

2012 Blog Post:

AUTHOR’S NOTE: My second child was born less than 24 hours ago but I felt like I had to get this out by tip-off. Please excuse any errors.

 

I love college basketball. I’m a graduate of UNC-Chapel Hill (home of Michael Jordan) and grew up a fan living thirty minutes away. Needless to say, I spend a little too much time filling out brackets and watching hoops during business hours this time of year. And in the spirit of all things Alpha Theory, I have a systematic approach to filling out my NCAA brackets. But my system needs a little fine tuning. I’ll give a little background to set up the problem and hopefully someone will have an answer.

GENERATION ONE. Creating a systematic approach to fill out the brackets requires good input. From 2008-2011, I took Vegas odds for each team to win the national championship to serve as a proxy for team quality and strength of the path they’ll have to travel. For an example of the calculation, see the chart below. Kentucky is the favorite at 8/5 odds. If I bet $5 on Kentucky and they win, I receive $8. That assumes that 8 times out of 13 (8+5) Kentucky will win or 61.5%¹ (8/13). The next step was to calculate the percentage for every team in the tourney, sum up all the percentages, and divide the individual teams win percentage by the sum of all the percentages to get a true probability of winning the tourney². The next step was to use those probabilities to create a forecasted probability of winning for one team versus another. For example, if Kentucky (29% chance of winning it all) plays Missouri (4.6% chance) then the adjusted probability of Kentucky winning is 86% (29% / (29% + 4.6%)). At this point I could have filled out my brackets using a random generation (i.e. use a random number generator to pick a random number between 0 and 100 and if it falls above 86 then Kentucky loses, and if it falls below, they win. Or I could have just used Vegas probabilities to pick the winner which pretty much means picking the Vegas favorite in each round. But here is the problem, Vegas odds don’t give the granularity necessary to pick the early round games because they make very little differentiation between teams (see the clusters of odds below).

 
 

 GENERATION TWO. While looking for a solution, I came across KenPom.com. This is a sophisticated site steeped in the teachings of Bill James, the pioneer that created the statistics that led to the Moneyball movement. KenPom creates an adjusted winning percentage that controls for a multitude of factors. This winning percentage allows for comparison of teams across the spectrum. Additionally, in the KenPom blog there is reference to the Log 5 method which takes two teams’ winning percentages to determine the probability of one team beating the other. This is the breakthrough I was looking for.

This allowed my NCAA bracket generator to have much more precise winning probabilities and thus a much more accurate forecasting engine. Here are the brackets based off KenPom adjusted winning percentages and Log 5-based probability of head-to-head success:

THE PROBLEM. Now here is the rub. Let’s say that I’m playing in an NCAA pool that has Cinderella points (1 point added for each point of difference in the seed). I can calculate an expected return using the probability of winning (i.e. Wichita St. (#5 seed) versus VCU (#12 seed) – KenPom is 93.5% and 80.4% respectively). Using Log 5 we calculate a 77.1% chance that Wichita St. wins the game. The expected return for Wichita St. is 77.1% * 1 pt = .8pts and the ER for VCU is 22.9% * (1pt for win + 7pts for seed) = 1.8pts. In this case you would pick VCU because they give you an expected value of 1.8 versus .8 for VCU. If the tournament were one round, this method would maximize expected return. But if I pick VCU to beat Wichita St. in the first round, then I can’t use Wichita St. in future rounds. If I wouldn’t have chosen Wichita St. or VCU to move beyond the second round, then I should definitely pick VCU in the first round because there is nothing lost for not having the right team lose in the second round. But if I would have had Wichita St. win a future round, which I would have in this case, then I’ve done myself a disservice by eliminating them in the first round to maximize first round expected return.

I’m looking for a good way to account for this dynamic. I’m assuming someone with a good grasp of Bayesian/Stochastic tools may have a solution. Anyone have a suggestion? I would be glad to send my sheet along to anyone that would like to try and take a stab at solving the problem or would just like to use the sheet. Please help me solve this March Madness Math.

¹ Clearly 61.5% is too high a chance of Kentucky winning the tournament. But if Vegas used realistic probabilities, then they wouldn’t make money from the spread. But how unrealistic are the probabilities that they use? Let’s assume that Kentucky is the best team in the country by a wide margin and figure out the odds of winning the six games necessary to win the national championship:

First game – 100%, no chance they lose. If they play 100 times, they don’t lose once.

Second game – 95%, they’ll be playing an 8/9 seed, play 100 times, they lose 5.

Third game – 80%, playing a 4/5 seed

Fourth game – 70%, playing a 2/3 seed

Fifth game – 65%, playing a 1/2 seed

Sixth game – 60%, playing a 1/2 seed

 

Multiply them all together and the probability of winning is 21% for the best team in the country. Even if I raise the winning percentage to 80% for all the rounds from the third round on, it is still only 39%. So 62% odds are off the chart and tell you how expensive it is to do prop bets in Vegas. What this also tells us is that fans really shouldn’t be upset when their team doesn’t win the national championship. The odds of the best team in the country are only slightly better than rolling a die and landing on 1. Take solace in the fact that single elimination tournaments are subject to all kinds of luck and be happy that your team is dancing.

 

² Sum is greater than 100% because Sportsbooks make their money from the spread. If the odds were reflective of the real probability, then Vegas would just breakeven…and we can’t have that can we? For example, Kentucky is forecasted by Vegas to have a 62% chance of winning it all, but adjusting by the sum total of all teams’ odds of winning, which is 215%, the true Vegas probability of Kentucky winning it all is 29% (61.5%/215%). So instead of paying $1.60 (8/5) for each dollar bet, Vegas should actually be paying $3.50 for each dollar bet. Needless to say, the futures bet on NCAA champs is a real suckers bet.

 

 

 

The Real Cost in Cost to Borrow

Alpha Theory has always incorporated the Cost to Borrow in the Risk-Adjusted Return calculation of shorts but recently we’ve improved our calculations and I’ve really been pounding the table to get clients to incorporate Cost to Borrow more formally in their decision process. For those unaware, the “Cost to Borrow” is the annualized expense charged to a manager for borrowing the shares of another investor to short in their own account. For many stocks the cost is fairly low (0-5%) but for other “Hard to Borrow” stocks, the cost can be 15-50%. “Hard to Borrow” stocks are expensive because lots of investors are suspect of the company’s prospects and want to short the shares. It is simple supply and demand.

What this means is that a 25% Cost to Borrow short would have to be down at least 25% by the end of the year to breakeven. But what if the investor believes the payback will be three months? Or, to complicate things, that there is an 80% chance the payback will be in three months and a 20% chance that the company will be able to work things out by the end of the year? Our new Cost to Borrow calculation adjusts returns by the duration and probabilities associated with the investment thesis. The concept is simple but not generally implemented. This is surprising since effectively accounting for Cost to Borrow is critical to running a good short book.

Ever since we began making a concerted effort to capture clients’ Cost to Borrow data I’ve seen how often it is being under-represented in client return expectations. Many times a client’s best short idea has a high Cost to Borrow because it is an obvious dud that others would like to short. Because of the manager’s high-conviction that the company will fail, he puts on large positions. The problem is that there may be other short ideas with slightly lower certainty or downside but also dramatically lower Costs to Borrow which make their effective return better than the high-conviction short. Now that Alpha Theory incorporates the Cost to Borrow into Risk-Adjusted Returns, clients are able to compare their short ideas on an apples-to-apples basis. Would you rather have a 40% Risk-Adjusted Return with a 30% Cost to Borrow (10% net Risk-Adjusted Return) or a 20% Risk-Adjusted Return with a 3% Cost to Borrow (17% net Risk-Adjusted Return). Of course this seems completely obvious but until it is spelled out and placed in front of the manager it is easy to let the conviction and absolute Risk/Reward sway them into oversizing the position. The message is straightforward, anyone that shorts Hard to Borrow equities should calculate a Risk-Adjusted Return adjusted for Cost to Borrow before making portfolio decisions.

What’s Up with Up/Down Ratio?

I had a couple of meetings yesterday and in each of them, the concept of Up/Down Ratio (Risk/Reward) popped up. Both portfolio managers calculated the ratio by taking the amount of potential upside for an investment and dividing it by their potential downside. For instance, let's say they were considering an investment in IBM. They would do research on IBM and determine their upside was 30% if they sold a few extra Watsons and would be down 10% if they didn't. The ratio is straightforward, 30%/10% or 3x. The portfolio managers in both meetings said they were looking for Up/Down of 2x or greater and would size positions accordingly to the size of the Up/Down. Sound familiar? Yes, it does sound like Alpha Theory.

Now let me start by saying that by calculating an Up/Down Ratio, these firms are asking important questions about risk and reward and giving the portfolio manager a powerful tool to more efficiently size positions. I can promise most firms are not this disciplined. But Up/Down is so close to great but suffers from two fundamental flaws: 1) Up/Down has scale issues and 2) it gives equal probability of risk and reward. To explain the scale issues, let's compare two other companies to IBM's 3x using the same measure. Homerun Company has an upside of 90% if their new drug is approved and down 30% if it isn't – Up/Down Ratio = 3x. On the other hand, Dull Company, is worth 9% more if it gets a new contract and 3% less if it doesn't – Up/Down Ratio = 3x. All three companies have an Up/Down Ratio of 3x but they're not created equal and they'll affect the portfolio differently. If we assume the upside has the same probability (50%) as downside (50%) then Homerun, IBM, and Dull have expected returns of 30%, 10%, and 1.5% respectively. That's not even close to being equal.

So if managers wanted to make a huge improvement on their Up/Down Ratios, they could just assume equal probabilities of Up and Down and calculate an expected return like I did. 30%, 10%, and 1.5% is more predictive and differentiating than 3x, 3x, and 3x. But they can do even more. By assuming 50/50 for Upside and Downside, they run into the next problem of Up/Down Ratios. Analysts have greater confidence in some bets than they do in others. That difference in confidence should be expressed in how positions are sized. For instance, if the analyst has high confidence in Dull Co., medium confidence in IBM, and low confidence in Homerun Co. we could express that conviction as 90/10, 70/30, and 50/50. This would change our expected returns to 7.8% (Dull Co.), 18% (IBM), and 30% (Homerun Co.) These are the measures that should be used to size positions because they express the research and conviction level while measuring the assets' expected impact on the portfolio (i.e. if I were to invest in each of these assets 100 times I would actually expect to get returns similar to the calculation).

With very little change these firms that are using Up/Down Ratios can convert to an expected return. At a minimum, firms with Up/Down can multiply their Up and Down by 50% to get over the scaling issue.  Ideally, firms can take it to the next level by using probabilities to factor in conviction level.

Improve Your Chances to Raise Capital

How do you improve your chances to raise capital? Tell a good story. Yeah, it sounds simple, but you have to tell a story that investors will remember. A recent article written by  James Armstrong in “Traders Magazine” cites Bruce Frumerman, who heads the consulting firm of Frumerman & Nemeth, which helps start-up firms better position themselves to attract capital. While the article is not related to Alpha Theory, I believe there are several points made by Mr. Furman that explain why our clients have leveraged their use of Alpha Theory to help them raise capital. According to Frumerman, “the number one cause of money management firms closing their doors is not because their trading strategies blew up. Rather, it’s because they couldn’t get enough people to understand and buy into how they run their portfolios.”

Mr. Frumerman goes on to say, “in addition to having all of the right systems in place, such as trusted service providers and a solid legal structure, a firm needs to know how to properly tell its story, to fully relate how it does its research, risk management and decision making. One complaint institutions have had is that while post-crash there is more data transparency, more numbers don't reveal what the underlying investment beliefs thinking and investment process was. They are looking to determine whether portfolio managers are doing something on an ongoing repeatable basis, or if it was just serendipity that they happened to get the returns they did.”

I believe funds must tell a story that investors will be able to recall two weeks or two months down the road. A story they will be able to relate to their investment committee. This means the pitch must be concise and tangible. Concise means you go for 10 slides not 100. Tangible means you give examples and structure. Alpha Theory makes your investment process tangible.

Mr. Frumerman goes on to explain, “in a post-crash environment, investors are looking for transparency, and that means something different today than it did before 2008. It used to be investors wanted to know what all of a fund’s holdings were---now they want to know that and the logic behind all of those holdings.”

Alpha Theory provides a central system to answer the “logic behind all of the holdings” like, “Why did we invest in every stock in our portfolio? / Why did we choose the position sizes we currently have? / At what price will we add or trim our positions? / What were we thinking about company X on January 1^st and were we right?” These are basic portfolio management questions, but portfolio managers commonly give unmemorable answers like, “we constantly monitor the portfolio to make sure that our best ideas are our largest positions and that we’re reducing our exposure to the weaker ideas.” That answer isn’t tangible. A screen shot of Alpha Theory is something that any potential investor is going to remember when deciding whether to take your fund to the investment committee. Provide something tangible, something memorable, and you’ll definitely improve your chances to raise capital…and maybe your process too.

Mental Capital – Pulling the reins on your brain

Funds have limited financial capital that they can deploy based on their assets under management. Portfolio managers work within those constraints to maximize return. Just like financial capital, the human brain has limited capital that can be allocated. But many portfolio managers don't approach the Mental Capital constraints with the same rigor they employ to financial capital restrictions. This causes funds to expect too much from analysts with a resultant degradation in research quality.

We can approach Mental Capital from the bottom-up. Let's say a firm has a portfolio manager and four analysts. All five of these investment professionals (IP) have limitations on the amount of time they can dedicate to research, their Mental Capital. If we assume that each IP can perform research for 40 hours a week (excludes non-productive time like staring at the P&L) and they work 50 weeks a year for a total of 2,000 hours per IP per year that results in 10,000 hours for a five IP fund. Now let's assume that each analyst needs to perform 100 hours per year of research per name (about 8 hours per month per stock). That means a total of 100 stocks can be effectively covered or 20 stocks per IP.

20 stocks per IP or 100 positions for the fund seems like a reasonable figure, but remember this includes ideas, as well as active names in the portfolio. If we assume that half of analysts' time is spent working on new ideas that would cut the number of active names per analyst in half to 10. This means a fund with a team of five can reasonably cover 50 active positions. But a majority of funds end up with 100+ 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 but it can be a big expense of precious Mental Capital.

To cut down on pointless wastes of Mental Capital, it is important to have a checklist of requirements before spending time on research. Does this stock meet our liquidity requirements? Is this stock is in a sector we know, or will we have to spend time on exhaustive background work? Do we have an edge? Perform a quick valuation analysis and if it doesn't look sufficiently cheap (or sufficiently expensive) then move on. Purge names from the existing portfolio that also are no longer sufficiently cheap or expensive. Mental Capital is a finite resource that is to be spent with caution. Treat it with the same care as financial capital and you'll end up with a better portfolio.

March Madness Math

AUTHOR’S NOTE: My second child was born less than 24 hours ago but I felt like I had to get this out by tip-off. Please excuse any errors.

 

I love college basketball. I’m a graduate of UNC-Chapel Hill (home of Michael Jordan) and grew up a fan living thirty minutes away. Needless to say, I spend a little too much time filling out brackets and watching hoops during business hours this time of year. And in the spirit of all things Alpha Theory, I have a systematic approach to filling out my NCAA brackets. But my system needs a little fine tuning. I’ll give a little background to set up the problem and hopefully someone will have an answer.

GENERATION ONE. Creating a systematic approach to fill out the brackets requires good input. From 2008-2011, I took Vegas odds for each team to win the national championship to serve as a proxy for team quality and strength of the path they’ll have to travel. For an example of the calculation, see the chart below. Kentucky is the favorite at 8/5 odds. If I bet $5 on Kentucky and they win, I receive $8. That assumes that 8 times out of 13 (8+5) Kentucky will win or 61.5%¹ (8/13). The next step was to calculate the percentage for every team in the tourney, sum up all the percentages, and divide the individual teams win percentage by the sum of all the percentages to get a true probability of winning the tourney². The next step was to use those probabilities to create a forecasted probability of winning for one team versus another. For example, if Kentucky (29% chance of winning it all) plays Missouri (4.6% chance) then the adjusted probability of Kentucky winning is 86% (29% / (29% + 4.6%)). At this point I could have filled out my brackets using a random generation (i.e. use a random number generator to pick a random number between 0 and 100 and if it falls above 86 then Kentucky loses, and if it falls below, they win. Or I could have just used Vegas probabilities to pick the winner which pretty much means picking the Vegas favorite in each round. But here is the problem, Vegas odds don’t give the granularity necessary to pick the early round games because they make very little differentiation between teams (see the clusters of odds below).


 

GENERATION TWO. While looking for a solution, I came across KenPom.com. This is a sophisticated site steeped in the teachings of Bill James, the pioneer that created the statistics that led to the Moneyball movement. KenPom creates an adjusted winning percentage that controls for a multitude of factors. This winning percentage allows for comparison of teams across the spectrum. Additionally, in the KenPom blog there is reference to the Log 5 method which takes two teams’ winning percentages to determine the probability of one team beating the other. This is the breakthrough I was looking for.

This allowed my NCAA bracket generator to have much more precise winning probabilities and thus a much more accurate forecasting engine. Here are the brackets based off KenPom adjusted winning percentages and Log 5-based probability of head-to-head success:

THE PROBLEM. Now here is the rub. Let’s say that I’m playing in an NCAA pool that has Cinderella points (1 point added for each point of difference in the seed). I can calculate an expected return using the probability of winning (i.e. Wichita St. (#5 seed) versus VCU (#12 seed) – KenPom is 93.5% and 80.4% respectively). Using Log 5 we calculate a 77.1% chance that Wichita St. wins the game. The expected return for Wichita St. is 77.1% * 1 pt = .8pts and the ER for VCU is 22.9% * (1pt for win + 7pts for seed) = 1.8pts. In this case you would pick VCU because they give you an expected value of 1.8 versus .8 for VCU. If the tournament were one round, this method would maximize expected return. But if I pick VCU to beat Wichita St. in the first round, then I can’t use Wichita St. in future rounds. If I wouldn’t have chosen Wichita St. or VCU to move beyond the second round, then I should definitely pick VCU in the first round because there is nothing lost for not having the right team lose in the second round. But if I would have had Wichita St. win a future round, which I would have in this case, then I’ve done myself a disservice by eliminating them in the first round to maximize first round expected return.

I’m looking for a good way to account for this dynamic. I’m assuming someone with a good grasp of Bayesian/Stochastic tools may have a solution. Anyone have a suggestion? I would be glad to send my sheet along to anyone that would like to try and take a stab at solving the problem or would just like to use the sheet. Please help me solve this March Madness Math.

¹ Clearly 61.5% is too high a chance of Kentucky winning the tournament. But if Vegas used realistic probabilities, then they wouldn’t make money from the spread. But how unrealistic are the probabilities that they use? Let’s assume that Kentucky is the best team in the country by a wide margin and figure out the odds of winning the six games necessary to win the national championship:

First game – 100%, no chance they lose. If they play 100 times, they don’t lose once.

Second game – 95%, they’ll be playing an 8/9 seed, play 100 times, they lose 5.

Third game – 80%, playing a 4/5 seed

Fourth game – 70%, playing a 2/3 seed

Fifth game – 65%, playing a 1/2 seed

Sixth game – 60%, playing a 1/2 seed

 

Multiply them all together and the probability of winning is 21% for the best team in the country. Even if I raise the winning percentage to 80% for all the rounds from the third round on, it is still only 39%. So 62% odds are off the chart and tell you how expensive it is to do prop bets in Vegas. What this also tells us is that fans really shouldn’t be upset when their team doesn’t win the national championship. The odds of the best team in the country are only slightly better than rolling a die and landing on 1. Take solace in the fact that single elimination tournaments are subject to all kinds of luck and be happy that your team is dancing.

 

² Sum is greater than 100% because Sportsbooks make their money from the spread. If the odds were reflective of the real probability, then Vegas would just breakeven…and we can’t have that can we? For example, Kentucky is forecasted by Vegas to have a 62% chance of winning it all, but adjusting by the sum total of all teams’ odds of winning, which is 215%, the true Vegas probability of Kentucky winning it all is 29% (61.5%/215%). So instead of paying $1.60 (8/5) for each dollar bet, Vegas should actually be paying $3.50 for each dollar bet. Needless to say, the futures bet on NCAA champs is a real suckers bet.

 

 

I Fell In a 10 Foot Hole…How Far Do I Have To Climb to Get Out?

If you fall into a 10 foot hole, you have to climb 10 feet to get out. That simple physical rule doesn't work for portfolios. I have written a few times about the asymmetry of loss and gain in portfolios (Asymmetry, Which Way is Up). The basic concept is that risk is not equal to a commensurate amount of reward. For example, if I lose 25% of my $100 million fund, then I will have to be up by 33% the following year to be back to break even. Because of this asymmetry, it is critical to calculate risk for every investment and avoid potential loss that does not give a more than adequate level of reward.

Because I spend so much time talking about this concept (and I'm not good with math in my head), I was looking for a quick way to calculate the reward I need to break even after a loss. After writing out the formula and then refining it, I came up with a simple formula:

 

For example:

As you can see, the sum of each fraction is 100%, so it is very easy to compose the formula. The only problem is I still have to do math in my head. So I then tried to create a ratio, but I realized after plotting it out, that it is logarithmic (see chart below) and above my pay grade.

Although the BreakEvenReturn formula definitely makes the math easier, does anyone know a simple way to perform the calculation? Or at least point me to a quick way to turn fractions into percentages in my head.

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