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Alpha Theory Blog - News and Insights

46 posts categorized "External Articles"

January 30, 2015

The Worst Year Ever for Hedge Funds (Novus Article)

Our friends at Novus put together an interesting article called “The Worst Year Ever for Hedge Funds” that used statistics to analyze which market environments are easiest for fundamental investors to generate alpha. The conclusion was that 2014 was the hardest year ever for hedge funds to generate alpha because the dispersion between winner returns and loser returns were the tightest in hedge fund history (read the article for a thorough explanation). I will not attempt to articulate any deeper, for doing so would basically require me to read you the article. What I will attempt is a few basic observations:

1)      I really hope the premise of this article is correct and 2014 marks a nadir for hedge fund outperformance. As my partner, Benn Dunn, keeps saying, “what hedge funds really need in 2015 is a good down 5% for the S&P 500.”

2)      For the hypothesis of this article to hold in practice, there is an assumption of luck. Basically, if we pick random portfolios, 2014 was the toughest year to produce outperformance. If alpha generation were pure skill, the evidence for 2014 wouldn’t stand-up because the skillful manager would buy the very high performers and short the very low performers…even though there were a limited number. The truth lies somewhere in between.

3)      If there is a prospective element (these variables are forecastable) then a manager could use them to determine leverage (i.e. when it is easy, lever up, when it is like 2014, lever down). However, I’m finding it difficult to find variables in the analysis that are forecastable or are even stable. If they were at least stable, then you can use a tight current spread to suggest lower leverage, but the spreads can change rapidly.

Overall, this is a great read and made me think critically about disaggregating alpha. If you haven’t read the author Joe Peta’s book “Trading Bases”, you should. Finally, I’ll leave you with my favorite quote from the article, an excerpt I chose for clearly selfish reasons given Alpha Theory’s focus on position sizing:

    “At Novus we’ve examined the performance of many hundreds of hedge funds and we’ve found that, by far, the most important skill to possess is the ability to size positions effectively. That’s because unlike Exposure Management, and to a lesser degree Security Selection, the ability to size positions efficiently is the most persistent and consistent alpha-generating skill that a portfolio manager can possess.”

March 21, 2014

Dynamic factor modeling reveals hidden risks

GUEST POST FROM BENN DUNN, President of Alpha Theory Advisors:

Damian Handzy, CEO of Investor Analytics, and I developed the concept of dynamic factor modeling in this latest article on Risk.net.  We argue that traditional 3rd party vendor models do not accurately reflect many firm’s investment processes and leave measurable risk hidden.  Using beta as a common language between risk and portfolio managers, we recommend leveraging the literally thousands of listed instruments and funds to ease the process of risk measurement and hedging.

Click Here to full the article on Risk.net.

November 5, 2013

Less Correlation Gives Stock Pickers Opportunity

We’d like to welcome our first blog from Benn Dunn who runs our Risk Consulting practice. I’m a little biased, but I believe that Benn is one the smartest risk minds in investing today. Check out this article on Risk.net where he is quoted on the topic of correlation in portfolio management.

While lower correlations across asset classes and within markets are generally thought of as positive for security selection, the path to lower correlations can often be confusing.  Traditional risk models deliver confusing and difficult to interpret results during these regime shifts.  Fortunately, Alpha Theory is not dependent on trailing correlations when making portfolio construction recommendations. 

Click here to read the full article on Risk.net

 

May 1, 2013

The Checklist: Evolution of an Effective Decision Process Part 2

As a continuation to The Checklist: Evolution of an Effective Decision Process Part 1 we now turn our attention to money managers and their checklists. 

Fund managers fall into similar traps of overconfidence about their ability to manage the complex system of investing. Dr. Gawande discusses investing (Chapter 8) by analyzing three public equity managers that employee an explicit checklist and a study by Geoff Smart, which analyzed 51 venture capital managers. The evidence, although not a large sample size, is compelling. The public managers attribute the checklist to being a primary component of their success and a distinct competitive advantage.

“The checklist doesn’t tell the manager what to do. It is not a formula. But the checklist helps him be as smart as possible every step of the way, ensuring that he’s got the critical information he needs when he needs it, that he’s systematic about decision making, that he’s talked to everyone he should. With a good checklist in hand, he was convinced he and his partners could make decisions as well as human beings are able. And as a result, he was also convinced they could reliably beat the market.” “They (Checklists) improve their outcomes with no increase in skill. That’s what we are doing when we use the checklist.” “When he first introduced the checklist, he assumed it would slow his team down, increasing the time and work required for their investment decisions. He was prepared to pay that price. The benefits of making fewer mistakes seemed obvious. And in fact, using the checklist did increase the up-front work time. But to his surprise, he found they were able to evaluate many more investments in far less time overall.” 1

And even though their competitors have noticed their success and asked them for their secret, when told that the key is a checklist, they turn up their nose.

“In the money business, everyone looks for an edge. If someone is doing well, people pounce like starved hyenas to find out how. Almost every idea for making even slightly more money— investing in Internet companies, buying tranches of sliced-up mortgages, whatever— gets sucked up by the giant maw almost instantly. Every idea, that is, except one: checklists. I asked one of the equity managers how much interest others have had in what he has been doing these past two years. Zero, he said— or actually that’s not quite true. People have been intensely interested in what he’s been buying and how, but the minute the word checklist comes out of his mouth, they disappear. Even in his own firm, he’s found it a hard sell.” “I find it amazing other investors have not even bothered to try,” he said. “Some have asked. None have done it.” 1

The evidence from the VC community is even more compelling because the results are more statistically significant (51 managers) and are empirical. Mr. Gross categorized managers into five different categories by how they made decisions. One of the classifications was “Airline Captains” which meant they used checklists extensively to make decision.

“Smart next tracked the venture capitalists’ success over time. There was no question which style was most effective— and by now you should be able to guess which one. It was the Airline Captain, hands down. Those taking the checklist-driven approach had a 10 percent likelihood of later having to fire senior management for incompetence or concluding that their original evaluation was inaccurate. The others had at least a 50 percent likelihood. The results showed up in their bottom lines, too. The Airline Captains had a median 80 percent return on the investments studied, the others 35 percent or less.” 1

Smart’s study was performed over ten years ago and his findings are known by the VC community. You would think these fact-based results would change behavior, but as Gawande found when asking about Smart about behavior changes for VC managers:

“But when I asked him (Geoff Smart), now that the knowledge is out, whether the proportion of major investors taking the more orderly, checklist-driven approach has increased substantially, he could only report, “No. It’s the same.” We don’t like checklists. They can be painstaking. They’re not much fun. But I don’t think the issue here is mere laziness. There’s something deeper, more visceral going on when people walk away not only from saving lives but from making money. It somehow feels beneath us to use a checklist, an embarrassment. It runs counter to deeply held beliefs about how the truly great among us— those we aspire to be— handle situations of high stakes and complexity. The truly great are daring. They improvise. They do not have protocols and checklists.” 1

The Checklist Manifesto is well worth the day or so it takes to read. The real question, are you willing to adopt a Checklist mentality? Will you take the time to check the boxes? Are you disciplined enough to stop yourself from making decisions unless those boxes have been checked? I know that if I were back on the buyside, I would take out my mini-checklist and expand on it, formalize, and refine as we improve our process. I’ve seen firsthand the importance of making decisions explicit. But I think Dr. Gawande says it best:

“Instead they (pilots) chose to accept their fallibilities. They recognized the simplicity and power of using a checklist. And so can we. Indeed, against the complexity of the world, we must. There is no other choice. When we look closely, we recognize the same balls being dropped over and over, even by those of great ability and determination. We know the patterns. We see the costs. It’s time to try something else. TRY A CHECKLIST.” 1

 1Gawande, Atul (2009-12-15). The Checklist Manifesto: How to Get Things Right. Picador.

April 30, 2013

The Checklist: Evolution of an Effective Decision Process Part 1

Back before I started Alpha Theory, I was an analyst at a hedge fund. I spent my days trying to find good ideas in which we could invest. Along the way, I built a mini-checklist to speed up the process of evaluating potential ideas. The checklist included mostly quantitative metrics that I could build into a fancy spreadsheet like current PE compared to 10 Year Average PE / High / Low, same for EV to EBITDA and Price to Sales, leverage ratios, accounting ratios, insider holdings, short interest, etc. I could pull this all together pretty quickly thanks to a combination of links from Bloomberg, Capital IQ, and Factset and a few hours of manual due diligence.

With our quick checklist, we could plow through dozens of names and eliminate those that didn’t qualify with speed. In a world where positive expected return bets are few and far between, a streamlined evaluation process increases your odds of finding more good bets. Of course, we ended up doing customized analysis after it passed the initial test, but answering some basic universal questions was step one.

I’ve spoken to hundreds of managers and I believe that most funds have an implicit checklist (one that is understood but not formalized). An analyst generally knows what questions the portfolio manager will ask and experience leads them to ask many of the other important questions. However, after reading the book, The Checklist Manifesto, by Atul Gawande, I think it would be wise to make the fund’s implicit investment checklists explicit. Alpha Theory is in the business of providing systems to help managers make their decision process more objective by making it explicit. But The Checklist Manifesto raises explicit to a whole new level. The book begins by explaining the challenges of decision making in complex systems. Complex systems are simply too grand, too multivariate, for a human to comprehend their full scale. Because of this complexity, we use heuristics (rules-of-thumb) to simplify the pieces into understandable parts. These heuristics, while handy, can leave us with blind spots. Gawande, who is a practicing surgeon, was tasked with trying to improve surgical success for the World Health Organization. His answer, a checklist for the nursing, surgical, and anesthetist teams to run through before and after surgery. After a few iterations, the success was astonishing.

“The final results showed that the rate of major complications for surgical patients in all eight hospitals fell by 36 percent after introduction of the checklist. Deaths fell 47 percent. Overall, in this group of nearly 4,000 patients, 435 would have been expected to develop serious complications based on our earlier observation data. But instead just 277 did. Using the checklist had spared more than 150 people from harm— and 27 of them from death.” “More than 250 staff members— surgeons, anesthesiologists, nurses, and others— filled out an anonymous survey after three months of using the checklist. In the beginning, most had been skeptical. But by the end, 80 percent reported that the checklist was easy to use, did not take a long time to complete, and had improved the safety of care. And 78 percent actually observed the checklist to have prevented an error in the operating room. Nonetheless, some skepticism persisted. After all, 20 percent did not find it easy to use, thought it took too long, and felt it had not improved the safety of care. Then we asked the staff one more question. “If you were having an operation,” we asked, “would you want the checklist to be used?” A full 93 percent said yes.” 1

These results are astonishing. If a drug or medical device reduced death rates by 50%, it would be a blockbuster that every doctor demanded. But even after these astounding results were published in the New England Journal of Medicine, many doctors and hospitals were reluctant to implement the checklist. Why? Perhaps inertia, dogma, ossification? Whatever the reason, it seems to be common in most professions. The mentality suggests “I’m sure it is important for others, but I don’t need it”.

Check in tomorrow for Part Two of “The Checklist: Evolution of an Effective Decision Process.” We’ll find out what Dr. Gawande discovered about money managers that use checklists.

1Gawande, Atul (2009-12-15). The Checklist Manifesto: How to Get Things Right. Picador.

 

February 1, 2013

Kelly Criterion in Practice Part 2

As a continuation to Kelly Criterion in Practice Part 1 we dive into more tests and conclusion of the evaluation of the Kelly Criterion for portfolio management.

Trial #2. The performance improvement of the portfolio made me wonder how a group of investments with similar Expected Returns (all 20%) but different payoff structures would look (see below).

Here we see that neither the Uncorrelated or Correlated portfolio includes Investment #3 because it is severely impacted by its 90% chance of complete loss even though its expected return is 20% like the others. Investment #1 is favored by a wide margin because it has the lowest probability adjusted chance for loss of 15% (50% loss * 30% probability) versus 32% for Investment #2 (40% loss * 80% probability) versus 90% for Investment #3 (100% loss * 90% probability). In this case, the Kelly bet did a very good job of constructing the portfolio. This makes sense because the problem with the Kelly Formula for portfolio management is that it looks at each bet individually. We allowed the Kelly Formula to work individually by making all of the investments have the same Expected Return of 20%.

One of the most interesting outcomes of the second portfolio trial is that the CAGR of the Correlated Portfolio is 8.0%. That is lower than the 9% attained we could get from just betting the Kelly Bet of 80% in Investment #1 and holding the other 20% in cash. In this case, the best result is not a portfolio. I believe this is an important caveat in portfolio construction and should make cash an option for any portfolio. So instead of 3 Investments, you would always have 4 because cash may be a better option in certain circumstances.

Lessons Learned. So what have we learned from this analysis:

-Kelly bet size alone is not sufficient to determine position size

-Expected Return alone is not sufficient to determine position size

-Investment correlation has a large bearing on position size

-Probability Adjusted Chance for Loss has a large bearing on position size (Probability * Potential Loss)

-Cash is a legitimate option even if it has a 0% expected return

Ultimate Solution. The ultimate solution requires incorporation of research's scenarios analysis, correlation amongst assets, and picking the array of position sizes that maximizes long-term geometric expected return. The first step is to forecast thousands of arrays of returns for each investment based on its probability weighted scenario analysis resulting from your analyst's research. Next, choose the one set of arrays from the thousands generated which most closely matches the correlation statistics for each asset and the portfolio. Finally, use the best set of arrays to interpolate the bet size for each investment that maximizes portfolio return (if someone knows of a closed-form way of producing the array of position sizes please let me know).

Problems with the Ultimate Solution.

-Forecasting payout streams that have inter-correlation is very difficult. They could be calculated with historical correlations but that would suggest that future correlations will be the same as the past. The firm could forecast correlations but that is complicated and may be too much to ask from an operational perspective.

-The arrays are constructed at random which causes them to change with each iteration. This could cause unnerving degrees of variance in optimal position sizes depending on the randomized set of arrays. For instance, you could see that the optimal position size for IBM is 4.3% one minute, have nothing change, and the optimal position size move to 4.6% because the random array being selected has changed. My guess is the arrays would have to be stabilized until the addition of a new asset or research is updated. There is some work being performed by Sam Savage to standardize arrays (http://probabilitymanagement.org/index.htm).

-An optimization function is required to incorporate time horizon, liquidity, gross and net exposure, sector exposure, analysis confidence, etc. Certainly not an insurmountable hurdle, but could pose pitfalls.

Conclusion. Alpha Theory has tackled the problem in a straightforward way that accomplishes much of the task of position sizing described above. As we showed in Trial #1, Expected Return is a good predictor of position size in situations where there is variance in Expected Return. The predictive power is greatly enhanced by adjusting for the probability-weighted chance for loss (loss * probability of loss) which Alpha Theory does. The result is position sizing that is close to optimal position sizing (non-correlated). Lastly, because Alpha Theory is a linear function, it gives stable results and can scale position size to control for minimum and maximum position size, return parameters, liquidity, market correlation, time horizon, sector exposure, portfolio exposure, etc. Alpha Theory represents the best solution by providing most of the benefits of the complex ideal while still maintaining practicality for everyday use. At a bare minimum, it is light-years ahead of what most money managers are doing today!

January 31, 2013

Kelly Criterion in Practice Part 1

A friend of mine recently forwarded an article by Kyle Mowery of GrizzlyRock Capital where he discusses the Kelly Criterion and how his fund implements it for position sizing. First off, I'll say kudos to GrizzyRock Capital for a thoughtful approach to position sizing. I've written numerous times about the benefits and deficiencies of the Kelly Criterion and Mr. Mowery's article does a good job of laying out the implementation and some of the benefits and detriments of Kelly. I'd like to use Mr. Mowery's article as an opportunity to discuss some of the benefits of a disciplined approach to position sizing while discussing some of the limitations of Kelly.

Mr. Mowery correctly highlights that his fund's use of Kelly helps increase portfolio potential returns and reduce behavioral bias. The later aspect of behavioral bias is the benefit I find to be the most important attribute of adopting a process for sizing positions. Basic questions like, "how much can we make, what is the downside risk, and what are the probabilities of each", must be answered before any asset is placed in the portfolio. These questions are imperative to the fund's success and can be overlooked or poorly accounted for if not required as an input to the model. Potentially flawed position sizing derived by instinct and heuristics are highlighted by an optimal position size. Granted there may be legitimate reasons to have a position size other than the suggested optimal, but at least with a model, the difference is highlighted and justified.

Equal weighting is a model that many firms employ to counter the effects of behavioral bias. Mr. Mowery discusses the pros and cons:

Some allocators elect to equal-weight investments given uncertainty regarding which investments will perform best. This strategy creates a basket of attractive investments that should profit regardless of which investments in the basket succeed. This method benefits from simplicity and recognizes the future is inherently uncertain. Drawbacks of the strategy include underweighting exceptional investments and overweighting marginal ideas.

I would add that equal weighting suffers also from the cliff effect and static rebalancing. The cliff effect is simply that the best idea and the 20th best idea all get 5% exposure then the 21st best gets 0%. That drop off the cliff is clearly suboptimal. Second, equal weighting is static in that it either rebalances positions back to equal weight as prices change or it lets them ride. Either way, the impact of falling risk/reward as prices rise is not accounted for until the position goes from equal weight to 0%. Trading around positions is a huge benefit of a position sizing model that can add large amounts of alpha. Equal weighting simply misses much of the trading benefit.

Mr. Mowery goes on to discuss allocating capital to ideas with the most potential:

Another strategy is to allocate large amounts of capital to the investment ideas with the most potential. This methodology suggests investors should invest proportionally according to their ex-ante return expectations. The advantage of this methodology is matching prospective return to investment size. However, this strategy breaks down when allocators are incorrect about future investment return or risk prospects.

I'm not sure here if Mr. Mowery is talking about the return to the upside case or an expected return which is probability-weighted and includes downside. Either way, the argument against this method, "this strategy breaks down when allocators are incorrect about future investment return or risk prospects" isn't a successful counterpoint for why Kelly is better because Kelly will also be wrong if the inputs are wrong.

Kelly Formula Based Position Sizing. The Kelly Formula is great, but it is my belief that the Kelly Formula is sub-optimal to expected return-based sizing for portfolio management because it assumes that 100% of the bankroll can be bet on any one investment and it requires bimodal inputs (upside and downside only). Kelly's base assumption that 100% of capital can be allocated to a single bet necessitates that the formula is naturally cautious when sizing a position that has potential loss. It is my belief that expected return based position sizing (controlled for distribution width) is superior to Kelly.

I recently ran a Monte Carlo simulation comparing the Alpha Theory position sizing technique to a myriad of common position sizing methodologies including Kelly Criterion (Optimal F), Up / Down Ratio, Equal Weighting (and by proxy 14 Markowitz Mean-Variance Modern Portfolio Theory systems - Two studies of Markowitz Mean-Variance systems show that mean-variance maximization does not beat Equal Weighting (DeMiguel et al (2006) / Jobson-Korkie)).  Alpha Theory measured success by measuring the amount of Portfolio Expected Return added per 1% of portfolio exposure.  Alpha Theory beat the closest methodology, Kelly Criterion, by 18%, Up / Down Ratio by 52%, Equal Weighting by 48%. 

Kelly Criterion is the superior method for generating the maximum long-term geometric expected return when the whole portfolio can be wagered on a single investment.  However, portfolios are comprised of multiple investments and thus Kelly Criterion under bets good expected returns because it's trying to protect against complete loss of capital and over bets poor expected returns with very high probability of success.  Because portfolio investing has inherent capital protectors by limiting position size maximums, Kelly Criterion breaks down. 

-To prove this out I performed a Monte Carlo simulation which randomly created 10,000 portfolios of 50 stocks

-Randomly assumed that analyst's upside, downside, and probability estimates were up to 50% inaccurate

-Random variables included: assets, scenarios, success/failure of analysis, and position size and expected return parameters

-Alpha Theory (Expected Return adjusted for distribution width) created the optimal portfolio 7,074 times out of 10,000 (71%)

-Alpha Theory was 53% better than the next best method, Kelly Criterion

Kelly Maximization of Long-Term Geometric Expected Return. I have seen several workarounds that use the Kelly Formula to construct a portfolio but most focus too heavily on the bet size of each individual investment. If John Kelly were alive today, I imagine he would probably tell us that the formula is a shortcut and the more important concept is finding the portfolio that maximizes long-term geometric expected return. That was the assumption that I made when I constructed my own Kelly calculator. The first step was scrapping the Kelly Formula and coming up with a way to account for investments with multiple scenarios and loss less than 100%. I could not figure out a way to make a closed-form solution, which is one of the best attributes of the Kelly Formula. I had to create an open-form calculator that used an iterative formula using the Solver function in Excel (there is a similar calculator at http://www.albionresearch.com/kelly/). With my new calculator I could create any investment with various economic outcomes and probabilities and derive the bet size that would give me the maximum expected return over the long-term (geometric). I made an assumption that I could not bet more than 100% (-100% for shorts). In reality, a fund could leverage investments and receive higher returns but for this portfolio example I assumed no leverage.

Trial #1. I plotted out the Kelly bet for a bunch of random investments and noticed the Kelly bet did not match up with the position size I would have expected for the portfolio. This is because the Kelly bet was not considering the portfolio. However, I did find that expected return was a good predictor of portfolio position size (example below).

We have 3 potential investments with which to build our portfolio. If I look simply at the Kelly Bet, I would maximize Investment #1 and #2 because they are 100% versus 80% for Investment #3. But the Expected Return for Investment #3 is higher than #1 and #2. This is the point where I hypothesized that I could compute the Maximum CAGR (Compound Annual Growth Rate) by investing the Kelly Bet of each investment, calculating the CAGR (14%,15%,16%), and then use the CAGR as a way to determine the correct position size. This certainly seemed to point in the right direction but it still did not feel right to have them so closely sized. I decided that the ultimate method would be to skip the calculation of individual bets and calculate which bets would maximize the expected return of the portfolio (Uncorrelated Portfolio bet size in chart). As you can see, to maximize the portfolio's return, the best allocation was to bet 43% on Investment #1, 6% on Investment #2, and 51% on Investment #3. This array of bets is how I came to the conclusion that the original Expected Return was a great predictor of portfolio position size.

Correlation in Trial #1. But then I thought about the Central Limit Theorem and I realized that diversification makes a difference when assets are uncorrelated. But what if they are correlated? The benefit surely must be reduced. I subsequently built a string of payoffs where the gains and losses of Investment #1 and #3 occur in the same period (#2 doesn't matter because it always goes up 15%). When I recalculated the Correlated Portfolio position sizes, I got 0%, 48%, 52%. No exposure to Investment #1 in the Correlated Portfolio when the Uncorrelated Portfolio suggested a 43% position size.

This tells me that the correlation inside the array of outcomes has a large bearing on position size. What I needed to do is ensure that each array properly matches the inter-correlation amongst assets in the portfolio. At this point, I'm still working on that issue but maybe a starting point is the historical correlation and beta of each asset to the portfolio and other assets. Next, build thousands of hypothetical arrays of returns for each asset based on the scenario analysis. Finally, pick the set of hypothetical arrays that is most closely aligned with the inter-correlation of assets. From there we can iterate position sizes or use an optimization function that finds the portfolio with the maximum CAGR.

Speaking of maximum CAGR, see how both portfolios have higher Portfolio CAGR (24.1% and 21.6%) than any of the individual investments (14%, 15%, 16%)? This is the benefit of portfolio construction, which in this case is 5 to 10% of return.

Check back tomorrow for the 2nd Trial and conclusion.

November 29, 2012

Moneyball for Money Managers

Baseball is the birthplace of “Moneyball”. Other sports soon followed once the concept of Moneyball was proven and made public. General managers from basketball to football to soccer to hockey now employ statisticians in their front office. Drug and energy exploration companies have been playing their own form of Moneyball for years before baseball caught on. Now politics is in the game (see full LA Times article here). This recent LA Times article gives a glimpse of how the Obama campaign used their own brand of Moneyball to help win the election which will change campaign strategy for evermore.

“… the goal was to rank individual voters in the swing states based on their likelihood of voting for the president or of being persuaded to vote for him, to volunteer for his campaign and to vote early. The Obama campaign developed separate models for each.”

What strikes me about this article isn’t that politics is using Decision Theory to win elections, it’s that money management, in general, still does not use it. In baseball, Moneyball showed how picking players with the highest on-base percentage improved team success. In money management, a fund must ensure their best ideas are their largest positions to improve success. But most firms don’t effectively measure idea quality. They don’t “rank individual voters.” They don’t compare the “on-base percentage.” If they did, they would have a spreadsheet that had every investment idea ranked by Expected Return and scored by other qualitative and quantitative factors. But instead, most firms just use instincts to manage the portfolio. In fact, most firms don’t have a systematic way to size positions. I can hear the drug and energy geeks now, “and they get paid the big bucks.”

Alpha Theory is “Moneyball” for asset managers. Alpha Theory’s software captures a firm’s price targets and probabilities, then highlights the position sizes that are over or under-weighted based on those targets. It factors in liquidity, volatility, time horizon, sector exposure, etc. to give the manager a repeatable process for sizing positions. This saves the portfolio manager’s time, reduces emotional decision making, and helps you stay on top of what your analysts are thinking. Just ask the simple question: What is your 6th largest position? Is it your 6th best idea? What is your upside reward and downside risk? If you don’t know, a little Moneyball could go a long way.

September 30, 2012

The End of the World as We Know It?

I happened to read two articles in the same week talking about the end of investing as we know it. The articles, “Why It is No Longer a Good Idea to Be in The Investment Industry” and “What Business Is Wall Street In?” were written by two authors, Nassim Taleb and Mark Cuban respectively, whom most would call “love him or hate him” kind of guys. While they are known for stirring up controversy, some of their points seem reasonable.

In the “Why It is No Longer a Good Idea to Be in The Investment Industry” by Nassim Taleb, he explains the phenomenon that as the number of participants in investing has increased, the number of “lucky fools” increases making it harder for “skilled investors” to outperform the sheer number of “lucky fools” because there are only so many possible allocations. Additionally, he shows empirically (although a bit over my mathematical head) that in fat tail distributions (like the financial market) the phenomenon is amplified making it even more difficult for “skilled investors” to outperform the “lucky fools.”  There are assumptions that may nullify the proof in real life like allocators decision making processes and the assumption that most investors are random returners and not negative returners (worse than random) which opens the door wider for skilled investors. But no matter how you cut it, his overall point that as the number of investors increases, the “lucky fools” will grow in number and make it harder for allocators to look past to the truly skilled players.

The “What Business Is Wall Street In?” article by Cuban describes some of the reasons why investing has moved away from its roots of capital creation and into a market of quick-reflex participants (computers) looking for patterns and temporary arbitrage in the transfer of capital during the capital creation process. This story isn’t new and I’ve heard it repeated by almost every manager I work with, but I do like Cuban’s analogy of Traders and Hackers. The similarities are striking, especially their rationalization of why they do what they do. Also, Cuban has a prescription for getting parts of the market back under control. It sounds interesting, but policy certainly isn’t my forte.

So I come away from both articles agreeing there are distinct challenges for skilled investors from “lucky fools” and “hackers.” But I’m interested in knowing your thoughts. Does the sheer number of investors make it impossible for the skilled investor to outperform the lucky fools and subsequently get funding? Is there a benefit to the system from high frequency trading? If there is no benefit, is it ok to regulate or curtail it? If so, is a tax on high frequency the right call or is there some other prescription? This is a healthy conversation for those that like to think of themselves as “skilled investors.” It seems like something has to give and I really don’t want it to be a sacrifice of those that care about cash flow for the sake of those that care about quote flow.

July 19, 2012

Investing on Empty

How do we make portfolio decisions? How do you ultimately make the decision to buy IBM and not Dell? To make Google a 4% position and Microsoft a 2% position? To add to Apple and trim some Oracle? Could we write down or explain to someone exactly how you made these decisions? Most likely not because they required a good dose of experience-based intuition. Well a good article titled ‘Tired Investing’ by our friends at Cabot Research, walks through how our decisions are influenced by our state of mind.

Excerpt from ‘Tired Investing’ by Cabot Research:

    While your intuition and judgment may be spectacular they rely on a limited and easily depleted reservoir of psychic energy and this presents serious risk that most managers do not factor into their decision making. Baumeister brings this point home with the following: "The ease with which we have been able to produce ego depletion using small laboratory manipulations suggests that the extent of the resource is quite limited, which implies that it would be seriously inadequate for directing all of a person's behavior, so conscious, free choice must remain at best restricted to a very small proportion of human behavior."

    For portfolio managers the lesson is clear: Your best may be terrific but you can't count on being your best at every junction without help. And the support top managers employ is their investment process. Knowing when to slow down, think twice, benchmark to outside views and seek independent input are some of the process elements that guard against ego depletion, as well as a host of other emotional and cognitive biases.

    Investment decisions can pop up at any time throughout the day. What precedes them, we now better understand, can dramatically alter the choices made and their impact on performance.

    Experience enables managers to develop judgment and self awareness that can help counterbalance the effects of ego depletion. Adherence to a well calibrated investment process, however, can guide frenetic inter-day decisions towards choices that more often reflect intention, consistency and quality. The alternative may lead you towards tired performance.

Interestingly the best method to counteract the effects of poor decisions made from mental exhaustion is to create a process for decision making. This is Alpha Theory’s bread-and-butter. “Why did I decide to buy IBM and not Dell?” – well it is right there in Alpha Theory. “Why Is Google a 4% position and Microsoft a 2%?” – that’s also right there in Alpha Theory. Process isn’t a panacea for making the right decision but it sure can help avoid the wrong decision.