5 Ways Analysts Benefit from Alpha Theory

I was having a conversation with a portfolio manager the other day who was talking about how much sense Alpha Theory made, but how he expected his analysts to hate the idea of having to build scenario analysis for all of their research. It reminded me of a piece I put together a while back when the same question came up, take a look at "5 Ways Analysts Benefit from Alpha Theory".

To Price Target or Not to Price Target…that is the question

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.

EMPIRICAL EVIDENCE: YOU MUST USE RISK-ADJUSTED RETURN TO SIZE POSITIONS

Portfolio managers often ask me for empirical proof for why they need to calculate risk-adjusted return to size positions. Because I do not have empirical evidence, my argument is that risk-adjusted return is a precise measurement of an asset's impact on the portfolio and, to maximize overall returns, you must make your best ideas your biggest positions. Their concern is that they do not have confidence in their calculated risk-adjusted return or their ability to differentiate between the quality of investments in their portfolio.

I always make a few points in return:

1) If you can differentiate between an asset that deserves to be in the portfolio and an asset that should not, then there is clearly some differentiation between quality of assets in the portfolio.

2) Whether you calculate risk-adjusted return or not, you are estimating risk-reward when making investment decisions so you might as well calculate it to cut down on mistakes.

3) Monte Carlo simulations show risk-adjusted return as a superior method of portfolio construction when compared to other position sizing methodologies.

4) Probabilistic portfolio management is the competitive advantage of some of the smartest and most successful funds that I have spoken with.

All of the reasons mentioned above are compelling but a little academic support is nice. Below are four academic papers that show why it is critical to make your best ideas your largest positions and how not calculating your best idea can put you in a 6% return hole compared to your peers.

Excerpts (Once you click on the link, push download, then select the SSRN site):

"Best Ideas" – Randy Cohen, Christopher Polk, and Bernhard Silli (2009) – 1 to 4% improvement

We examine the performance of stocks that represent managers' "Best Ideas." We find that the stock that active managers display the most conviction towards ex-ante, outperforms the market, as well as the other stocks in those managers' portfolios, by approximately one to four percent per quarter depending on the benchmark employed. The results for managers' other high-conviction investments (e.g. top five stocks) are also strong. The other stocks managers hold do not exhibit significant outperformance. This leads us to two conclusions. First, the U.S. stock market does not appear to be efficiently priced, since even the typical active mutual fund manager is able to identify stocks that outperform by economically and statistically large amounts. Second, consistent with the view of Berk and Green (2004), the organization of the money management industry appears to make it optimal for managers to introduce stocks into their portfolio that are not outperformers. We argue that investors would benefit if managers held more concentrated portfolios.

When asked to talk about his portfolio, the typical investment manager will identify a position therein and proceed to describe the opportunity and the investment thesis with tremendous conviction and enthusiasm. Frequently the listener is overwhelmed by the persuasiveness of the passionate presentation. This leads to a natural follow-up question: how many investments make up the portfolio. Informed that the answer is, e.g., 150, the questioner will often wonder how anyone could possess such depth of knowledge and passion for so many disparate companies. Pressed to answer, investment managers have been known to sheepishly confess that their portfolio contains a few core high-conviction positions, the "best ideas", and then a large number of additional positions which may have less expected excess return but which serve to "round out" the portfolio.

This paper asks a related simple question. What if each mutual fund manager had only to pick a few stocks, their best ideas? Could they outperform under those circumstances? We document strong evidence that they could, as the best ideas of active managers generate up to an order of magnitude more alpha than their portfolio as whole, depending on the performance benchmark.

We argue that this presents powerful evidence that the typical mutual fund managers can, indeed, pick stocks. The poor overall performance of mutual fund managers in the past is not due to a lack of stock-picking ability, but rather to institutional factors that encourage them to overdiversify, i.e. pick more stocks than their best alpha-generating ideas.

"The Value of Active Mutual Fund Management" – Hsiu-Lang Chen, Narasimhan Jegadeesh, Russ Wermers (1999) – 2% improvement

When we examine mutual fund trades, we find that stocks that the funds actively buy have significantly higher returns than stocks that they actively sell. This return difference is roughly two percent during the one-year holding period following the trades, adjusted for the characteristics of the stocks that are traded.

We find that stocks that funds newly buy have significantly higher returns than stocks they newly sell. This is true for large stocks as well as small stocks, and for value stocks as well as growth stocks. The evidence that stocks actively traded by the funds outperform stocks that are passively held from prior periods suggests that mutual funds hold stocks longer than the horizon over which they can predict returns, possibly because of a preference to avoid high transaction costs or capital gains taxes.

Overall, our evidence is suggestive of the funds possessing superior stock-selection skills.

"Fund Managers Who Take Big Bets: Skilled or Overconfident" – Klass Baks, Jeffrey Busse, and Clifton Green (2006) – 4% improvement

We document a positive relation between mutual fund performance and managers' willingness to take big bets in a relatively small number of stocks. Focused managers outperform their more broadly diversified counterparts by approximately 30 basis points per month, or roughly 4% annualized. The results hold for mimicking portfolios based on fund holdings as well as when returns are measured net of expenses. Concentrated managers outperform precisely because their big bets outperform the top holdings of more diversified funds.

The findings lend support to the notion that the managers who tilt their portfolios toward their favorite stocks assess correctly the relative merits of stocks overall as well as within their portfolios. By contrast, funds whose portfolio weights more closely approximate a uniform distribution display less ability to correctly sort stocks within their portfolio according to future performance. Overall, our results suggest that concentrated fund managers do have some ability to correctly pick stocks.

Using a variety of performance measures, we find that concentrated fund managers outperform their diversified counterparts. This result lends support to the notion that the managers who are confident in their ability assess correctly the relative merits of stocks overall as well as within their portfolios. By contrast, funds whose portfolio weights more closely approximate a uniform distribution display less ability to correctly sort stocks within their portfolio according to future performance. Overall, our results suggest that focused fund managers do have some ability to correctly pick stocks.

"The Information Content of Revealed Beliefs in Portfolio Holdings"- Tyler Shumway, Maciej Szefler, and Kathy Yuan (2009) – 2 to 6% improvement

In this paper, we elicit heterogeneous fund manager beliefs on expected stock returns from funds' portfolio holdings at each quarter-end. Revealed beliefs are extracted by assuming that each fund manager aims to outperform a certain benchmark portfolio by choosing an optimal risk-return tradeoff. We then construct a measure of fund managers' forecasting ability—the belief accuracy index (BAI)—by correlating a manager's revealed beliefs on stock returns with the subsequently realized returns. We measure the differences in beliefs between funds with high BAI and all other funds, the belief difference index (BDI). Sorting stocks based on BDI, we find that the annualized return difference between the top and bottom decile is about two to six percent.

Knowing the Financial Spread - Investor Lessons from WhatIfSports.com

“Once The Star-Spangled Banner began to play, I’d tell myself, “Here you go.  Start pulling away, start computerizing.  You must think clearly and remove yourself”...It was like watching a game through a window.” – Bill Walsh, Head Coach of San Francisco 49ers and creator of the West-Coast offense

A buddy of mine who knows how much I love sports analysis, sent me a website called WhatIfSports.com that runs mock simulations of games 10,000 times to create a projected outcome. Now I have no idea about the efficacy of WhatIfSports's Monte Carlo simulation, but I love this kind of stuff as anyone that has spoken to me about the chance of the Tarheels winning the National Championship in basketball can attest (we’ll save that diatribe for another blog). So, I decided to see what the best way to profit from this simulation, assuming it was accurate. I pulled up Vegas odds and Whatif’s NFL week 8 projections to see if I could find any inconsistencies and did a quick analysis: 

Based on this, Vegas was pretty much dead on, but not perfect. How would I profit from these mis-priced games? I would definitely bet the under on the Falcons/Saints, because Vegas has the game total at 54 and WhatIfSports has the total at 45.  I would also pick the Rams getting 9.5 points over the Lions, when WhatIfSports has the Rams winning outright. I may also pick the Broncos and 49ers, but I would not be as confident and would certainly place a smaller bet on those games. This got me thinking about how this analysis applies to investing.

If I am evaluating a basket of stocks for potential investment, the Vegas Odds are the current stock price because they indicate what I can “buy” the bet for today and the WhatIfSports analysis is my proprietary research. I want to find the assets with the biggest differentials, Falcons/Saints under and Rams and make big bets on them. If I find other stocks with a reasonable difference between the market price and my calculation of value then I will place a bet on them as well, but not to the same degree as the large spreads.

If I’m an investor, how can I determine which assets should go in my portfolio and how to size them without calculating the risk-adjusted return of every investment? I must measure the difference between the market price and what I think the value is to determine the attractiveness of the bet. This concept seems so straightforward, yet most investors are willing to allow their mental calculator to be the final arbiter of portfolio inclusion and position size. That’s just like looking down the list of Vegas Odds and saying, “hmmm, I know the Saints score a lot and 45 isn’t that high, I think I’ll take the over.” First off, our brains are not very well designed to make those kinds of decisions, just read any book on behavioral finance or neureconomics. Second, even if you are right in your assessment that it is a good bet, how do you know exactly how good it is. Is it pretty good, really good, or freakin’ fantastic? Those differences affect how the position should be sized.

No doubt, calculating risk-adjusted return is harder than not calculating risk-adjusted return. But honestly, there are millions/billions of dollars at stake. How do you know what to bet if you don’t know your own spread?

So, wish me luck this Sunday and GO RAMS!!!

The Probability Problem

“The fundamental law of investing is the uncertainty of the future.” – Peter Bernstein, famed investor

 

I am offered two bets. In bet number one, I am paid $150 for every heads and pay $100 for every tails. My risk-adjusted return is 25%. In bet number two, I’m presented with a bag of poker chips that are only black or white. I’m paid $150 for each white chip I pull out and I have to pay $100 for every black chip I pull out. I don’t know the distribution of colors, so my probability assumption would be 50/50. Drawing poker chips also has a 25% risk-adjusted return. Would I be equally likely to make both bets? No, I prefer the coin-flip bet because I am more certain about the distribution of probabilities.

 

To try and balance this issue, let’s assume that we could, with reasonable certainty say the range with which our poker chip probabilities would fall. In this example we’ll assume that white chips are somewhere between 30% and 70% of the contents of the bag. This widened distribution takes into account my uncertainty regarding my probabilities. Unfortunately, if I plot out every payout between 30% and 70% probability of success, I get an average of 25%. I’m back at square one.

 

What about betting systems that constrain loss? If I use Optimal-F (Kelly) suggested bet size, I get 17% bet for the coin-flip, which is the same as the average of all of the Optimal-F bets between 30% and 70% probability. Alpha Theory optimal position sizes suffer the same issue with a position size equal for both coin-flips and poker chips.

 

Here is my simple solution until I understand a better Bayesian solution. I have a somewhat arbitrary Analysis Confidence rating. Let’s name them High, Medium, and Low. The coin-flip is definitely “High Confidence” because I am certain about my coin-flip probabilities. The poker chips are “Low Confidence” because I know nothing about their true distribution. But my knowledge about the poker chips is not static. The probabilities are epistemic because, as I draw more poker chips, my knowledge of the distribution of chips will improve. I will adjust my probabilities as I draw chips and change my Analysis Confidence from Low, to Medium, and eventually to High when I have a better grasp on the distribution of chips in the bag. To account for uncertainty, I’m going to cut my bets. If I have Low Analysis Confidence, I cut my suggested bet in half, if I have Medium I cut it by 25%, if it is High, I don’t cut my bet at all. This is certainly imperfect, but it does create the effect we are shooting for, less exposure when we have less certainty in our assumptions.

 

This, of course, applies to equity investing. You may have high certainty in your probabilities for one investment and only low certainty in another. They both may have the same Risk-Adjusted Return, but you are not willing to invest in them equally. Use the same Analysis Confidence constraint to adjust position size and apply a heuristic-based cut since probability theory does not have a better answer. Alpha Theory provides an Analysis Confidence setting for precisely this purpose to better refine position sizes beyond Risk-Adjusted Return.

Institutional Investor Magazine article: A Plea to Put Down the Mental Calculator

I recently wrote an article for Institutional Investor magazine (www.iimagazine.com) called "Capturing the Benefits of Risk-Adjusted Return." It was a plea to put down the mental calculator. You can read the article here.

Here is an excerpt from the article:

Hedge funds throw away half of their potential returns by not explicitly calculating risk-adjusted return. After working for a fund and having numerous conversations with hedge and mutual fund managers over the past decade, it is obvious that an overwhelming majority of funds’ mistakes come from poor estimation of risk-reward. 

In fact, most funds have not explicitly defined an upside price target, downside risk target and conviction level for each investment in their portfolio. This is because most fund managers trust that they can manage the portfolio in their head. They analyze and discuss the upside, downside and conviction level for every investment so they assume these factors’ influence is carefully measured into every decision. But I would posit that there is a distinct difference between factoring in upside, downside and conviction level through mental calculation and measuring it with risk-adjusted return. 

Why would you trust your mental calculator for such an important decision? Could you imagine a bungee jumper that knows the height of a bridge, tension of the bungee cord and weight of the jumper but just estimates the correct length of the bungee cord? Absolutely not. For every jump, a calculation is performed to make sure that easily avoidable risk is eliminated.  Investors all too often skip the “bungee cord” calculation of risk-adjusted return and end up assuming undue risk. 

What is your investment’s Risk-Adjusted Return? The Alpha Theory Calculator will tell you.

Alpha Theory is exposing its pioneering Risk-Adjusted Return Calculator to the public at www.AlphaTheory.com/Calculator. This calculator lays the groundwork for every important portfolio decision an investment firm will make and calculates a first-ever Estimated Risk-Adjusted Return. Try it out by entering any stock, seeing its Estimated Risk-Adjusted Return, and then customizing your own Risk-Adjusted Return. Enjoy the calculator and please share it with others who may find it worthwhile.

 

The Risk-Adjusted Return calculation is the most effective way to measure investment quality. All research can be distilled down into the elements of potential profit, downside risk, and probability of each coming true. This holistic framework results in a quantitative measure that can be used to make the critical portfolio decisions of whether or not to make an investment, how to size the position, and when to trade. The use of Risk-Adjusted Return in portfolio construction reduces risk by decreasing position size when an asset has greater downside and increasing return by maximizing the portfolio’s overall Risk-Adjusted Return.

 

The Alpha Theory Risk-Adjusted Return (RAR) Calculator begins by giving you an Estimated Risk-Adjusted Return using market metrics. Enter one of your investments and see if the Estimated Risk-Adjusted Return is positive or negative. This Estimated RAR starts by deriving an Upside and Downside Price Target using an average 52-week high and low and 1-year annualized volatility implied high and low. Then, the Calculator derives probabilities by determining the Option-Market Implied Probability of the Upside and Downside targets being achieved. The Calculator then averages the Option Probabilities with Normal Distribution Implied Probabilities of Upside and Downside. The Alpha Theory Estimated RAR should be a part of every investment process.

 

The next step is to customize with your own research. Alpha Theory allows you to override the estimates with your own assumptions to truly appreciate the stock’s impact on your portfolio. Risk-Adjusted Return is the foundation of every investment decision and is imperative in ensuring that an asset’s position size is in-line with your fundamental research.

Why do you buy an asset?

"We construct portfolios the way theory says one should, which is different from the way many, if not most, construct their portfolios.  We do it on a risk-adjusted rate of return.” – Bill Miller, legendary investor

Why do you buy an asset? Because you believe that it is worth more than what you are paying for it.

Assume you can buy two different assets for $20 dollars. Stock #1 is worth $35 and Stock #2 is worth $30, which one would you buy more of?

Of course, Stock #1 with a value of $35, because it is worth more. Unfortunately in investing, assets have risk. So, unless there is a 100% probability of the stock going from $20 to $35, you have to compare its upside potential to its downside risk to better understand how much return you are being paid for the risk you are taking on.

Assume we calculate the downside using net cash per share. Stock #1 has more upside to $35 but only $5 in net cash per share ($15 of upside and $15 of downside) and Stock #2 has a lower upside of $30 but more net cash at $15 per share ($10 of upside and $5 of downside). Now, which one would you take a bigger position in?

	Stock #1	Stock #2
Upside	$35	$30
Current Price	$20	$20
Downside	$5	$15
Upside / Downside	$15 Upside / -$15 Downside	$10 Upside / -$5 Downside

More than likely you would have a greater exposure to Stock #2 because it has a better risk-reward. But this still misses a critical component of the analysis, conviction level. What if I’m extremely confident, say 80%, in Stock #1 achieving $35. For Stock #2, it is a coin-flip whether it will reach $30 or fall to $15. If I multiply each stocks’ Upside times the Probability of Upside and add it to the Downside times the Probability of Downside, I get a Risk-Adjusted Value of $29 for Stock #1 and $22.50 for Stock #2. The Risk-Adjusted Value is truly representative of the full qualities of this asset and should be the basis from which portfolio level decisions are made.

	Stock #1	Stock #2
Upside	$35 * 80%	$30 * 50%
Current Price	$20	$20
Downside	$5 * 20%	$15 * 50%
Risk-Adjusted Value / Risk-Adjusted Return	$29.00 / 45%	$22.50 / 12.5%

If you were to invest in Stock #1 10 times, you would make $15 eight times and lose $15 twice for a total gain of $90. If you were to invest in Stock #2 10 times, you would win $10 five times and lose $5 five times for a total gain of $25. Now, which asset would receive greater exposure?

Every investment decision should be framed by Risk-Adjusted Return. This allows an investor to properly size positions and quickly adjust exposure as the underlying price of the asset changes and as new fundamental information is received. Although the concept seems simple, it is rarely implemented. To see how Alpha Theory puts this concept into practice, view our demo (www.AlphaTheory.com/demo).

Good Sight, Good Insight: Risk Management by Ed Seykota

I was discussing portfolio management strategy with a client who runs a long/short equity hedge fund. In his former life, he worked for a large Fund of Funds where he evaluated long/short equity mangers. In that role, he frequently referenced an article on Risk Management by Ed Seykota. He had increasingly become frustrated with traditional manager measurement techniques like VaR, Sharpe Ratio, alpha, etc. He found the article's concepts to be central to evaluating the portfolio management prowess of fund managers.

Fortunately for us, the tenets of Ed Seykota's article are embodied by the Alpha Theory Portfolio Management Platform:

1. Risk is the possibility of loss (not volatility).
2. Hunch-centric betting is certainly popular and likely accounts for an enormous proportion of actual real world betting.
3. Despite almost universal agreement that a system offers clear advantages over hunches, very few risk managers actually have a definition of their own risk management systems that is clear enough to allow a computer to back-test it.
4. To maximize returns, position sizing should be based on a measurement of potential profit, potential loss, and probability of each.
5. Kelly may be sub-optimal for portfolio management because of the diversification effect.
6. Diversification relies on the average security having a profitable expected value.
7. In times of stress, investors and managers access their primal gut feelings (when they should go back to discipline).
8. In actual practice, the most important psychological consideration is the ability to stick to the system. To achieve this, it is important (1) to fully understand the system rules, (2) to know how the system behaves and (3) to have clear and supportive agreements between all parties that support sticking to the system.
9. Profits and losses do not likely alternate with smooth regularity; they appear, typically, as winning and losing streaks. When the entire investor-manager team realizes this as natural, it is more likely to stay the course during drawdowns, and also to stay appropriately modest during winning streaks.

To see the full article, Risk Management by Ed Seykota. To view how Alpha Theory help create a investment process discipline, visit www.AlphaTheory.com.

Michael Lewis: Moneyball - Basketball Style (Why investors should heed the lessons of probability)

As a North Carolina Tarheels basketball fan, it pains me to say, “I like Shane Battier.”  After reading another great article by Michael Lewis in the New York Times titled “The No Stats All-Star” I recognize his skills of making his team better and it helps explains some of the losses he handed my beloved Heels during his reign at Dook.  However the greatest take away from the article, just like Michael Lewis renowned book Moneyball, is you better get smart if you don’t want to get left behind.