I was speaking with the risk manager at a new shop when he stopped me mid-sentence and asked, “why wouldn’t I just build this myself in Excel?” Now I’m used to the question, as most of us in this profession are quantitatively savvy and have at least one person in the firm that is a power Excel user, but this was different because I could see that he was ready to engage Alpha Theory immediately or go back to his desk and start building his own version of Alpha Theory. My first reaction was to explain that when I was an analyst at a hedge fund, I built my first version of Alpha Theory in Excel. As I developed Alpha Theory I kept coming across hurdles created by the Excel limitations for which I knew a true software solution was the only answer. Below are some highlights of why Excel does not work, the complexities of building a full solution, and most importantly, the positive ROI of using Alpha Theory off the shelf:

Cost and Time to Build – Alpha Theory is the culmination of over 20 man-years and millions of dollars in design and development. For many funds, the money variable in the equation gets a small weighting, but the time is another matter. A few dedicated resources will be required to steward the process but do not forget that the portfolio manager and analysts will continuously be involved with design and testing. Their time is precious and their mental capital is better allocated on analysis, not software building.

It’s Complicated! – We all have smart people on our teams so yes you can figure out many of the challenges but there is also a possibility that some of these hurdles may render the system ineffective. Let’s go through a few challenges:

+Time Horizon. How do you deal with short dated returns versus long dated returns? You can’t use text book annualization because they produce wildly inaccurate return profiles over very short timeframes. Additionally, how do you handle losses in short time periods? Let’s ask a question, would you rather lose 20% in 2 days or 2 years. The gut reaction is 2 years, but that’s incorrect because you would rather get the loss behind you. I’ll ask it another way, would you rather have $0.80 two days from now or $0.80 two years from now. Dealing with these challenges in determining returns is complicated and a challenge that Alpha Theory has solved for you.

+Portfolio and Sector Exposure. Do you care about total portfolio gross and net exposure? How about sector exposures? If so, then let’s take a portfolio with constraints of 200% gross/40% net, global region exposure maximums of 50% gross/30% net, and industry exposure maximums of 40% gross / 20% net. Assume you have lots of good ideas and your portfolio of research exceeds many of these constraints. How do you construct a portfolio that maximizes risk-adjusted return while paying heed to each of these constraints? The answer requires an optimization function. To do this in Excel you either need to buy incremental software or create kludge Solver functions. The optimization function is inherent in Alpha Theory.

+Extreme Loss Constraints. Not all returns are created equal. The width of the distribution has a dramatic impact on the long-term geometric return of the portfolio for two assets with the same arithmetic risk-adjusted return. For example, assume you could only make one bet over and over for the rest of your career but they both had a 20% risk-adjusted return. Bet #1 has a 100% chance of going up 20% each year. Bet #2 has a 50% chance of going up 90% or 50% chance of losing 50% each year. Each bet has a 20% expected return but which one do you prefer and how should it change your position size? Well if we assume that we have $1 today and we make each bet sequentially 10 times in a row we would end up with $6.20 from choosing Bet #1 and $0.77 from choosing Bet #2. This explains the potentially damaging effects of wide-distribution returns and why Kelly Criterion is the optimal method of choosing bet size. Alpha Theory includes this dynamic in position sizing and continues to research new ways to improve the long-term geometric return of the portfolio.

+Other Complications. Alpha Theory has spent exhaustive time investigating improvements to representing research and constraints in the form of position size, including market correlation, liquidity, loss constraints, analysis confidence, market-implied expectations, differences between longs and shorts, and many more factors that go into position sizing. These are challenges that every homegrown solution will have to traverse. Any cost-benefit analysis of growing your own solution must include these intricacies.

Collaborative Environment – To foster an effective solution the system must encourage multiple users. Excel is a closed environment that generally allows one user at a time and in not conducive to personalized views. An analyst, a portfolio manager, and a risk manager will all look at the system differently and be tasked with different elements of maintenance. They need their own customized views that highlight the variables to which they need to manage. Alpha Theory is an open architecture which allows multiple users simultaneous access to the system at any time and any geography. Excel is not built to allow this level of collaboration and synchronization and will falter as the organization tries to create a holistic solution.

Three Dimensions – Excel is two-dimensional which makes it difficult to have much variance in research structure. For instance, one analyst wants to describe the distribution of returns in a simple Bull and Bear case, but another has a more complex representation which has a Bull and Bear case but also a possibility of Black Swan and takeout. These are real scenarios that should be encouraged in the research process. Alpha Theory’s multi-dimensional platform allows analysts the flexibility to describe their research in ways that best represent the true byproduct of their research.

History – To maintain a history of research in Excel, firms will keep snapshots of the system going back in time. This is kludge way to retrace the firm’s investment process. Alpha Theory keeps a record of change made by analysts and portfolio managers and can show a consolidated history of changes.

Maintenance – Once the first version is complete the second version should be under way. A system is a constantly evolving organism that requires constant feeding and training to keep up with the demands and challenges of the firm. There will need to be dedicated resources for the system and the portfolio manager and analysts’ time will be sucked into continuous testing and design, just as with the initial build.

Return on Investment – Alpha Theory points out inefficiencies in the portfolio where the firm’s research and constraints do not match their position sizing. For most firms this can represent the largest source of lost return and unnecessary risk. If Alpha Theory points out a couple of inefficiencies per month it will return hundreds of basis points of incremental alpha over the course of the year versus the 1 or 2 basis points of cost for a medium sized fund (even lower cost for larger funds). Even without pointing out a single mis-sized position, Alpha Theory improves the investment process by allowing analyst to describe research in the form of distributions that the portfolio manager can use to make portfolio decisions. No matter how you measure it, the ROI is wildly positive.

Continuous Innovation – Alpha Theory releases updates to the product on a quarterly basis with improvements that continually enhance a firm’s ability to manage the investment process. These enhancements come from internal development and input from partners and clients. Alpha Theory spends 100% of its time dedicated to this concept. An internally developed product would have to focus dramatic energy to keep pace with Alpha Theory’s development and industry-driven ideas.

By the way, the risk manager at the beginning of this post decided to go and build his own version of Alpha Theory (probably because I did not have this article in my hip pocket). However, after a few months, he hired us as a consultant to help him get over a few of the challenges above. Finally, after a year he decided to scrap the whole process and use Alpha Theory. Now granted, we added several features between our initial conversation and when they finally implemented Alpha Theory, but the moral is to proceed with caution if you plan to build your own Alpha Theory.

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“I must concede that my occupation, active money management, may be one of the best examples of the illusion of control in the professional world.” – Michael Mauboussin.

If ten randomly selected people play Federer in tennis, they will, with a very high probability all lose. You would get the same outcome if ten people play Kasparov in Chess. If ten randomly selected people pick a portfolio of stocks against an expert, say Warren Buffett or Seth Klarman, there is a very high probability that at least one will beat the expert. If we change the number to 100 people playing against Federer, there is still a very high probability that all will lose. With 100 random investors, you are almost guaranteed that several of the average Joe portfolios will beat the experts.

Why do experts of one field dominate, while others could lose at anytime to a random player? The answer lies in the “random” part. Think of each sport as an equation where we select the three most important variables to determine the outcome and add a random variable. For example, a random variable in tennis would be Federer breaking up with his girlfriend right before the match. We’ll say the equation for tennis is Serve + Backhand + Forehand + Random Variable = Tennis Winner. Now, add a subjective weighting of how important each variable is to success. For tennis I’ll say, 30% Serve + 20% Backhand + 40% Forehand + 10% Random Variable = Tennis Winner, for Chess I’ll say 60% Strategy + 10% Defense + 25% Board Memory + 5% Random Variable = Chess Winner. In investing, I’ll go with 30% Stock Selection + 20% Position Sizing + 20% Risk Management + 30% Random Variable = Top Portfolio.

Why is the random variable so dominant in investing? It comes down to the uncertainty associated with stock selection. There are no 100% certainties and, quite honestly, very few 80% certainties either. So if I am presented with a bet where 50% of the time I make 100% and 50% of the time I lose 10%, I’ve been given a great opportunity to achieve a 45% expected return, however, I still may lose because the recognized possibility of failure can in fact occur.

To drive the point home, take an expert lottery player versus a novice lottery player, the equation of success has no variables except the random one (assuming the novice can fill out the lottery ticket): 100% Random Variable = Lottery Winner.

As investors, we must understand that a portion of our success or failure is out of our control. It reminds me of the serenity prayer:

God, grant me the serenity to accept the things I cannot change; Courage to change the things I can; And the wisdom to know the difference.

In the dynamic where outcomes do not effectively measure decisions you must be vigilant in evaluating your decision process and prune the inherent bias that comes from watching the daily profit and loss and associating every success with good decisions and every failure with poor decisions.

For some great writing on the topic, see “Think Twice” by Michael Mauboussin, Chapter 3 – The Expert Squeeze.

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I was working with a smart firm the other day and one of the partners was reticent to implement Alpha Theory. He believed  that it was flawed by forcing assumptions on top of assumptions. While I can understand this visceral response, the logic doesn’t hold true in complicated decisions like asset selection and portfolio management. At the end of the day you are making an economic decision about an asset, whether to pay a certain amount of dollars. This means that your “assumptions” must be expressed in economic terms to balance the decision equation.

Assigning probability is the task that creates the most angst, but look at why it is important in three ways. One, if you can look at your portfolio and say that you have more confidence in one idea versus another idea, then you have expressed probability and you can simply classify positions as either High, Medium, or Low Conviction Level and those automatically translate into probabilities. Two, let’s run through an example and say I offer you a bet to win $1 if Obama wins the 2012 election. Would you pay $.30 for that bet?, $.40?, $.50?, $.60?, $.70?. The highest amount you would be willing to pay is an expression of your subjective probability of Obama winning the 2012 election. Certainly this is subjective, but an effective expression of your assumptions is required to make effective decisions. Third, and lastly, psychologists have consistently proven that a weak model is better than strong heuristics. This blog references an article by Robyn Dawes that shows why we build some basic processes around complicated decisions (Robyn Dawes article).

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When people mention “Risk Management” in investing the traditional metrics of volatility, correlation, Value at Risk, Beta, Sharpe ratio, etc. come to mind. But for fundamental shops (stock pickers) it is difficult to utilize risk management statistics to manage a portfolio. In fact, at my old shop, we would fire up the risk management software on the 30th of every month so we could put the data in our investor letter and that was about it.

The reason is because good fundamental portfolio managers understand that risk is not volatility, it is loss potential. Loss potential is measured by their fundamental research and should be the primary risk constraint. This is a piece that I wrote a while back discussing some of the differences between fundamental and traditional risk management.

I think the concepts are more important today as the number of experts decrying the use of traditional risk metrics grows.

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Dan Goldstein of the London Business School was kind enough to contact me and show me more intellectually accurate ways of interpreting and explaining the concepts of cognitive bias after reading my blog post, “To Price Target or Not to Price Target…that is the question.” Through our conversations he forwarded along a paper written in 1979 by Robyn Dawes called the “The Robust Beauty of Improper Linear Models in Decision Making” and said, “this paper will change your life.” Now only a couple of dorks would make a statement like that about a paper on linear models, but, as my wife can attest, my dork status has been well solidified for years.

In fact, the paper really does change my life. No, I’m not going to become a monk, but it does give me renewed confidence and substantial credence to the idea that I have professed for years now…you can’t manage a portfolio in your head (read my article on the subject featured in “Institutional Investor” here).

Basic tenet of RBILMDM: hundreds of studies have proven that Proper Linear Models (regressions, etc.) are better at predicting dependent variables from independent variables than intuitively predicting the dependent variable. This paper shows that even improper linear models (experts define variables as positive or negative and then a simple linear function is built without being regressed) beat experts studying the independent variables and forecasting the outcome (heuristics).

One of the more explanatory studies was a group of doctors that analyzed the biopsies of 193 Hodgkin’s disease patients. They asked the doctors to predict the survival time of each patient. Their correlation with actual survival times were effectively 0, meaning the doctors’ forecasts had no predictive power. However, if you construct a linear model using the variables the doctors labeled as important on the biopsy, then you can accurately predict survival time. The point is that experts can intuitively determine the relationship of variables to outcome but do a poor job of synthesizing multiple variables to forecast an outcome.

What does this mean for investors? If we consider ourselves proficient in portfolio management then we could well define the variables that have a positive or negative influence on position size. For instance, I could quickly tell you if there is a positive or negative correlation between risk-adjusted return, liquidity, downside risk, and conviction level to position size. However, what the myriad of studies suggest is that I would not do a good job of accurately sizing the position given access to all of this data. This puts intuitive portfolio management at a significant deficit when compared to a basic linear model. If we know that the average expert (doctors in our previous example) are not able to make predictions without a model, why would we as investors try and make portfolio decisions without a simple linear model? In fact, I’m guessing that constructing a basic linear model would take a user no more than a few hours and would dramatically improve position sizing. Additionally, it would allow the portfolio management process to be dynamic and refined over time so that the model’s predictive power evolves.

The author says it best, “…paramorphic representations (improper linear models) consistently do better than the judges from which were derived” – Robyn Dawes, “The Robust Beauty of Improper Linear Models in Decision Making” (1979). I may not be an expert but I’m pretty sure I know the correlation between using a model to manage the portfolio and fund success and this would be my improper linear model: Stock Selection + Position Sizing + Risk Controls = Fund Success.

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I recently finished Michael Mauboussin’s new book entitled “Think Twice” which expands on many of the topics of “More Than You Know” but is geared a bit more towards a general audience and less directly at institutional investors. Mauboussin’s goals are not dissimilar from those of Alpha Theory. We both strive to help people appreciate the common and avoidable mistakes of the decision making process, although he explains the pitfalls much more eloquently than I.

Now I read a lot of books on Behavioral Finance and Neuroeconomics, so the studies he reference in “Think Twice” are old hat, but Mauboussin does a great job of teasing out new insights. Check out the first chapter for a taste of how Mauboussin takes an interesting subject – Big Brown in the Kentucky Derby, makes it relevant for decision making – odds were obviously incorrect because of emotional bias, and creates a repeatable way to enact it – every chapter has a summary of how to incorporate each lesson into your decision process.

Additionally, I ran across a recent interview with Mauboussin discussing “Think Twice” and it highlights a few of the key tenets of his new book. Take a look (video here) and if you liked “More Than You Know” you should definitely check out “Think Twice”.

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The other day, I was doing what I spend much of my days doing – talking to a portfolio manager about Alpha Theory. He told me that Alpha Theory makes terrific sense for firms that calculate price targets, but that he didn’t believe in price targets. When I asked him why, he responded that there is a lot of instinct that price targets do not capture and it is his instinct that makes him successful. I explained that instinct and price targets are not mutually exclusive because price targets are estimates. Instead of estimating whether to buy or sell (pure instinct), you’re estimating reward and risk (price targets). To drive the point home, I asked him, “What are the 5 best ideas in your portfolio? Are they your 5 biggest positions?” He did not know. Is there any more proof needed?

Using price targets is not about being precise; it is about being directionally accurate. Price targets define why you are making the decisions you are making and do not require that you strip away the instinct that may be a primary component of your abilities. In fact, it is quite the opposite.  Because price targets are part science and part art, instinct plays a critical and indispensable role. This is especially true if you use probability weighted price targets because the art-to-science ratio is even higher. If you are already good at estimating price targets and probabilities, you will create a far superior portfolio if you discipline yourself to write them down. If you are not good at estimating price targets, well … you probably would not be successful anyway.

The only way to justifiably choose against the use of price targets is to take the position that instinctual decision making is not detrimentally affected by cognitive biases.  Before taking this position and relying solely on your instinct, however, it is an enlightening exercise to review a list of Cognitive Biases and consider whether any of them affect your decision making. Believers in the instinct assume (implicitly or explicitly) that instinct reflects logic. This assumption is compellingly supported by the studies of people like Gerd Gigerenzer, Daniel Goldstein, and Malcolm Gladwell.  Unfortunately, however, these studies become much less compelling when they are applied to investing. In this area, there is much more support for non-instinct based decision making. Behavioral Finance and Neuroeconomics research shows how logic based decision process is critical in achieving successful long-term results (see the work of, for example, Amos Tversky, Daniel Kahneman, Michael Mauboussin, Ron Howard, Jason Zweig, James Montier, and Matthew Lieberman).

To illustrate why price targets are critical, ask yourself this simple question, “Why did you buy this stock?” Your answer is probably some version of “I believe I can sell it for a higher price down the road.” If your decision is only about that one stock, that’s a great answer and you can responsibly stop the analysis right there. If, however, you have many stocks to choose from and you have capital that must be efficiently allocated between too much risk and too little return, then you have to consider each asset’s impact on the overall portfolio. To responsibly measure this impact, you must quantify the potential reward and its probability as well as the risk you are taking on and its probability, the combination of which is a risk-adjusted return. Instinct can, and perhaps, should be a primary component of these estimates, but it cannot responsibly stand alone.  Repeatable success requires disciplined price targets that explain the fitness of a decision within your portfolio.

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“…we try to exert a Ted Williams kind of discipline. In his book The Science of Hitting, Ted explains that he carved the strike zone into 77 cells, each the size of a baseball. Swinging only at balls in his “best” cell, he knew, would allow him to bat .400; reaching for balls in his “worst” spot, the low outside corner of the strike zone, would reduce him to .230. In other words, waiting for the fat pitch would mean a trip to the Hall of Fame; swinging indiscriminately would mean a ticket to the minors.” Warren Buffett, 1997 Berkshire Hathaway Letter to Shareholders

I recently ran across an Investopedia article called “Think Like Warren Buffett” that reminds me of the foundation of Alpha Theory thinking.  The article analyzes 8 investment tenets that Warren Buffett employs (which are more fully discussed in Robert G. Hagstrom’s 1999 book “The Warren Buffett Portfolio“) and is worth a read.  Here’s a synopsis.

1. Think of Stocks as a Business – this is a way to force yourself to think less about the market’s influence and more about the cash flow generating potential of the “business.” You also redirect your attention towards aggregate value (enterprise value – including the capital structure of the business) and away from share price.

2. Increase the Size of Your Investment – there are several great academic studies (Cohen, Polk, and Silli (2009) and Baks, Busse, and Green (2006) ) that point to the benefits of concentration. My personal observation is that it is very difficult to find good ideas, so when you find one, you should bet accordingly.

3. Reduce Portfolio Turnover – I’m not so convinced that I subscribe to this theory. Buffett has to be less active because he is moving in such large amounts and his moves are seen to have profound effect on a stock. If, like the rest of us, you have flexibility, then you should constantly ensure that position size is well-aligned with the risk-reward balance of each investment.  This means trading whenever there is an imbalance.

4. Develop Alternative Benchmarks – This is great for firms with multiple analysts. Measuring performance based on stock movement (especially in the short-term) is futile. Determining ways to measure based on value creation (book value growth, ROIC, etc.) will redirect the conversation away from ephemeral stock prices and towards more permanent value.

5. Learn to Think in Probabilities – Can we say this any louder, “LEARN TO THINK IN PROBABILITIES?”  If there is one overarching theme that we find with great investors it’s their tendency to approach investing with a probabilistic framework. It is not enough to say, “I’m pretty confident this stock is going to $40.” You must force yourself to describe how confident you are in probabilistic terms and, more importantly, describe what the risk (downside) is if you are wrong.

6. Recognize the Psychological Aspects of Investing – If we have learned anything over the past 40 years of Behavioral Finance / Neuroeconomic research, it is that humans are poorly designed to make financial decisions. Because of that we must protect ourselves from ourselves. We are our own worst enemies. In fact, Charlie Munger goes through many of these cognitive biases in his speech “Art of Stock Picking.” Review this list of cognitive biases and you’ll likely recognize many of them as errors you make in your daily investment process.

7. Ignore Market Forecasts – This reminds me of my previous post about Bill Ackman-Style Investing: 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.

8. Wait for the Fat Pitch – Have you ever watched poker on TV?  They show one hour of poker that actually took 10 hours to play.  How is that possible?  It is simple: great poker players fold A LOT and there is no need to show folds on TV.  There is a 1 in 10 chance that a player’s hand will maintain positive expected return all the way to the river (last card dealt). A good poker player, therefore, will fold 9 out of 10 hands – which is exactly how you eliminate 9 out of 10 hours of poker coverage.  In investing, as in poker, you are constantly searching for positive expected return.  Of course, so is everyone else, and the more people that are looking for it, the harder it is to find.

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“A reasonable probability is the only certainty.” – Edgar Watson Howe

In Einstein’s Theory of Relativity, he postulates that space and time are relative to the person observing them. That a set of twins, one standing here on Earth and the other shot at the speed of light to the edge of the universe and back, will be significantly different in age when the twin returns to Earth, even though neither one of them noticed a difference in how time passed. In fact, if I take off on a cross country flight and my wife stays at home, I will be slightly younger than her when I arrive on the West Coast. In these examples, time and space are not continuums rather they are experiences. Careers are devoted to understanding Einstein’s theory, so we will not go into the science here, but understanding relativity is important for us as investors.

Knowledge itself is relative. I do not know if a company I’m invested in will beat earnings but the CFO surely does. In this case, uncertainty becomes relative and dependent on our differing levels of knowledge. If I, the investor, am assigning a probability of the company beating earnings, I will base it on my compiled knowledge of the company. As my knowledge changes, I will change my probability of success. The CFO will do the same thing, but his base of knowledge is different. This is described in statistical parlance as epistemic probability. Epistemic is the antagonist of aleatory probability (i.e. coin-flips) which is described by statisticians as an uncertainty due to randomness. No matter how much knowledge I gain, I will never know the outcome of a coin-flip, only the probability of its outcome.

Investing is not like coin-flips, blackjack, or poker in our ability to define aleatory probability. But that does not mean that we should give up on estimating an epistemic probability. In fact, it should be the foundation of our investment process. Gene Gigerenzer describes Degrees of Belief in his book “Calculated Risk”, “The point here is that investors can translate even onetime events into probabilities provided they satisfy the laws of probability – the exhaustive and exclusive set of alternatives adds up to one.  Also, investors can frequently update probabilities based on degrees of belief when new, relevant information becomes available.”

In investing, you are forced to invest with the knowledge you have today. There are no certainties and, as a result, we must accept that every investment thesis is based on a probability (degree of belief) of an outcome. For examples sake, let’s say that our degree of belief is 80%. This creates a vacuum that can only be filled by describing outcomes that make up the other 20%. In this vacuum, lies the elegance of probabilistic investing. It is an imperative calculation for every investment because it requires you to consider all the possibilities and it provides the flexibility to incorporate ever-changing research.

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