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Alpha Theory Blog - News and Insights

11 posts categorized "Institutional Investor"

March 12, 2018

Capital Allocators Podcast with Ted Seides: Moneyball for Managers


Learn how to enhance your investment results in this great podcast from Ted Seides and his guests, Clare Flynn Levy from Essentia Analytics and Cameron Hight from Alpha Theory.

This conversation covers the founding of these two respective businesses, the mistakes portfolio managers commonly make, the tools they employ to help managers improve, and the challenges they face in broader adoption of these modern tools. The good news is the clients of Essentia Analytics and Alpha Theory have demonstrated improvement in their results after employing these techniques. If you ask Clare and Cameron, you may develop a whole new appreciation about the potential for active management going forward.




By creating a disciplined, real-time process based on a decision algorithm with roots in actuarial science, physics, and poker, Alpha Theory takes the guessing out of position sizing and allows managers to focus on what they do best – picking stocks.

In this podcast, you will learn how Alpha Theory allows Portfolio Managers convert their implicit assumptions into an explicit decision-making process. 


To learn how this method could be applicable to your decision-making process:





March 02, 2018

Size-Based Batting - A Different Perspective on Stock Selection


How do you determine if an investor is a good stock picker? One commonly used measure is to count the number of positions that make money (winners) divided by the total number of positions. This metric is commonly called a Batting Average, analogizing stock picking with baseball hit-rates.

The problem with Batting Average is that several inconsequential positions that lose money can really bring down the total. We saw this with our clients. They have historically outperformed other funds (every year for the past six) but have a batting average, adjusted for the move in the bench, of only 51%.

We decided to take a different approach and measure the total exposure of positions that made money versus the total gross exposure of the fund. For instance, if 60% of a fund made money on an alpha-adjusted basis and the fund was 120% gross exposed, then the fund had a Sized-Based Batting Average of 50% (60/120).

Our clients had a Sized-Based Batting Average of 54% versus the non-sized based average of 51%. That means that our clients were good at selecting investments and at sizing them, but they were harming their overall returns with small losing investments.

Alpha-Adjusted Batting Average1


Screen Shot 2018-03-02 at 10.09.00 AM


In the table above, Size-Based Batting, while not perfectly consistent, is generally better from year-to-year for our clients (exceptions being 2012 and 2015).

We’ve performed other analyses that have proved this point, specifically that our clients’ positions under 1% dramatically underperform the rest of the portfolio, but Sized-Based Batting presents a compelling way to highlight the “small position” issue (see the “Concentration Manifesto” for other issues with small positions).

In our profession, it is incredibly difficult to detangle skill from luck and, as cathartic as it would just rely on returns, returns are actually negatively correlated with next year’s returns for most funds (i.e. funds that outperform in year N have a higher likelihood underperforming in year N+1 – there are multiple research sources that analyze mean reversion in funds, here is one).

Sized-Based Batting is a nice addition to the allocator’s tool bag for finding managers with stock picking skill. In much the same way, managers should use Sized-Based Batting as a way to highlight their strengths and compare it to traditional Batting Average as a way to potentially point out weaknesses.


1 S&P 500 for US securities and MSCI WEI for non-US securities

2 Why is “All Time” so low compared to each year? Reason #1: There are many more observations in the more recent years which skew the overall results to be more similar to the more recent years. Reason #2: There were many assets that were losers over “All Time” while being winners for multiple years (small win in 2015, a small win in 2016, big loss in 2017 = 2 winning period vs 1 losing but a loser in the All-Time bucket).



February 07, 2018

Alpha Theory Case Study: Top Performing Funds of 2017

Alpha Theory’s clients have historically outperformed (see 2017 Year in Review from last month), but 2017 was special as our most active client was also the 2nd best performing equity fund. We have worked with them since their launch, and their focus on discipline and process is a testament to how to build a fund. If you would like to learn more about the client, their challenges, their solution, and the data supporting their process, check out the Case Study.





January 05, 2018

2017 Year in Review


Alpha Theory’s product helps investment managers reduce emotion and guesswork in position sizing. The result is reduced errors and improved returns. For six consecutive years, Alpha Theory clients have outperformed their peers (see table below – we use the benchmark of Major Equity Hedge Index because 86% of Alpha Theory clients are hedge funds). Our clients have consistently outperformed their competitors, more than doubling their returns over the period.



*Totals are not including 2017 data

In 2017, our average client generated 18.9% returns and, when it is released, I anticipate that we’ll beat the Hedge Index again. These results are consistent with other blog posts we’ve written highlighting our clients in 3rd party rankings: Reuters / WSJ / Novus.



This year, we expanded our analysis through a new 13-F dataset with all publicly filing funds. The upside of using this dataset is it enables us to compare results against every reporting fund in 2017. The downside is it only includes the US equity long positions. The results indicate that once again, Alpha Theory clients outperform their peers.

The average Alpha Theory client performance in 2017 (13-F data) was 27.6% vs 19.9% for all others (3013 total funds with over 20 positions). That’s almost one full standard deviation higher (8.8% standard deviation) than the mean and has a Z-Score of 2.03 (statistically significant above the 95% confidence level).

Even more interesting was the individual performance results of our clients, one Alpha Theory client was the 2nd best performing fund in 2017 (this client thanked us more than once for our contribution to their success) and four clients landed in the top 40 performers.  We also had six of the top 100, and 10 of the top 200. Statistically, we’d anticipate less than 1% in all categories because Alpha Theory clients are less than 1% of all funds. Instead, as in previous periods, there is a concentration of Alpha Theory clients amongst the top performers.


Simply put, Alpha Theory clients outperform their peers. The traits these firms share are discipline, intellectual honesty, and process focused. They gravitate to Alpha Theory because it is their tool kit to implement and measure that process.



Alpha Theory clients use process to reduce the impacts from emotion and guesswork as they make position sizing decisions. Alpha Theory highlights when good ideas coincide with largest position sizes in the portfolio. This rules engine codifies a discipline that:

1. Centralizes price targets and archives them in a database

2. Provides notifications of price target updates and anomalies

3. Calculates probability-weighted returns (PWR) for assets and the portfolio as a whole.

4. Enhances returns

5. Mitigates portfolio risk 

6. Saves time

7. Adds precision and rigor to sizing process

8. Real time incorporation of market and individual asset moves into sizing decisions.


Alpha Theory’s research not only suggests that adoption of the application by itself leads to improved performance, but actual usage intensity further enhances results.

Usage intensity is determined by:

1. Percent of Positions with Research

2. Correlation with Optimal Position Size

3. Login Frequency



1.Measured as the annualized ROIC where data was available, for a sample of 48 clients, 12 for each quartile



Comparing clients’ actual versus optimal returns shows:

ROIC is 4.5% higher.

Batting Average is 8% higher. Explanation: many of the assets that don’t have price targets or have negative PWRs are held by the fund but recommended as 0% positions by AT. Those positions underperform and allow AT’s batting average to prevail.


1.Measured as the average full year return for clients where full year data was available, adjusted for differences in exposure, net of trading costs

2.Before trading costs


Alpha Theory clients have outperformed Major Equity Hedge Indices every year since Alpha Theory started collecting historical data. While our clients are a self-selecting cohort who believe in process and discipline; process orientation goes hand-in-hand with Alpha Theory software that serves as a disciplining mechanism to align best risk/reward ideas with rankings in the portfolio.



Alpha Theory has further found that ROIC for assets with price targets is 5.6% higher than for those without price targets. Some investors chafe at price targets because they smack of “false precision.” These investors are missing the point because the key to price targets is not their absolute validity but their explicit nature which allows for objective conversation of the assumptions that went into them.  Said another way, the requirements of calculating a price target and the questions that targets foster are central to any good process.

Graph6*Long-only as many short positions are hedges and have no price targets


December 15, 2017

Superforecasting for Investors: Part 2

Alpha Theory hosted a book club on December 6th with portfolio managers, analysts, and allocators coming together to discuss “Superforecasting” by Phil Tetlock. We were lucky enough to have a Superforecaster, Warren Hatch, moderate and perform forecasting exercises with the group. We spent 2 hours together and only scratched the surface on applying Superforecasting to investing.


Here are a few key takeaways:


INTELLIGENCE: Above average but genius isn’t required

QUANTITATIVE: Not only understand math but apply it to everyday life

FOXES, NOT HEDGEHOGS: Speak in terms of possibilities, not absolutes

INTELLECTUALLY HUMBLE: Understand the limits of their knowledge

SYSTEM 2 DRIVEN: Use the logic-driven instead of instinct-driven portion of their brain

DO NOT BELIEVE IN FATALISM: Life is not preordained

CONSTANTLY REFINE: Make frequent small updates to their forecast based on new information (but not afraid to make big changes when warranted)

COUNTERFACTUALS: Believe that history is one of many possible paths that could have occurred

OUTSIDE VIEW: Incorporate the internal and external views



2. IDENTIFYING TALENT: There are identifiable attributes that can be used in hiring and have a profound impact on forecasting skill


Active Open Mindedness*

   image from alphatheory.typepad.com

Fluid Intelligence*

image from alphatheory.typepad.com


* At a prior book club, we measured participants and the results showed they had the attributes of Superforecasters with high Active Open-Mindedness (3.99 out of 5) and high Fluid Intelligence (8 out of 10 – this is the highest score that the Good Judgment  folks have seen).

Active Open Mindedness (i) and Fluid Intelligence (a) are two measurable traits that managers can use to select talent. In the chart below, the improvement impact of the definable attributes equates to about 40% of their forecasting skill over standard forecasts.

image from alphatheory.typepad.com

3. DEVIL’S ADVOCATE: Firms should appoint a Devil’s Advocate for each investment to expand critical thinking (someone to ask the question, “I see your downside is $40. How is that if the 52-Week Low is $22 and the trough multiple would put it at $25?”)


4. OUTSIDE VIEW: Firms should require an Outside View for every investment idea (“While everyone I’ve spoken to says this deal will close, only 20% of deals with one party under SEC investigation close.”)


5. REFINEMENT: New information should always be incorporated in forecast (think Bayesian).


6. TEAMS MAKE BETTER FORECASTS: Team dialog generally improves forecasting accuracy.


7. FORECAST CULTURE: Firms should embrace “forecast” as part of their vernacular and conversations should revolve around how information impacts the forecast.


8. MEASURE TO BE BETTER: We all forecast, but we rarely measure. That fact needs to change if we really want to improve.


9. CLUSTERING: Break complex topics into individual components that are better able to be forecast and use the combination of the smaller forecasts to forecast the more complex. (ie. Will AAPL break $200 is a complex forecast that can be broken down into Will iPhone X ship more than 400m units? / Will Samsung’s technology outpace Apple’s? / etc.)


10. INDEXING: Individual clustering questions can be weighted to come up with a forecast for the complex topic instead of using simple equal weighting.


11. DIVERSITY OF FORECASTS MATTER: Forecasts made from similar perspectives are less accurate than those made from multiple perspectives (see Boosting below).


12. BOOSTING: If you have three forecasters with different perspectives that all arrive at a 70% probability of an event occurring then the actual probability is greater than 70%.


13. GISTING: We didn’t get to spend much time here, but the idea is that complex subjects, reports, presentations, etc. can be distilled down into gists that the team votes on and refines into supergist. Full understanding is never just quantitative or qualitative. Superforecasting is quantitative. Supergisting attempts to provide the qualitative piece. 


14. HYBRID FORECASTING COMPETITION: IARPA, the defense agency that sponsored the forecasting tournament that launch the Superforecasters (Good Judgment) is sponsoring a new Man+Machine Forecasting Tournament. For those interested in Forecasting and Machine Learning, this is your spot: https://www.iarpa.gov/index.php/research-programs/hfc


September 14, 2017

Asset Manager Reliance on Human Judgement vs Machine

Asset management is the industry most reliant on human judgement according to a recent Price Waterhouse study on Data Analytics.


Screen Shot 2017-09-14 at 10.14.59 AM


Asset managers rely on human judgement 3x more than the next industry. For an industry with some of the best and brightest, we seem to be far behind. There is no expectation that this will happen overnight, but at a bare minimum we need to be experimenting with ways to enhance our judgement with machines.

Alpha Theory has been doing just that for over 10 years and our clients have outperformed the average hedge fund by over 2x. Getting started is not hard. Adopting “machine” does not require a wholesale change as all of our clients operate with Man + Machine. What it does require is an acceptance that Man alone is generally inferior to Man + Machine and a cultural embrace of the “machine” as an enhancement to the daily judgements we all make.

The reliance on human judgement will fall over time for asset managers. Do not be the last the change.



April 17, 2017

Investor Bias Seen in Data

By Cameron Hight and Justin Harris


Alpha Theory’s Analytics Department studies clients’ historical data to provide useful insights. Over time, we have identified patterns that point to certain investor biases. Typically, biases are highlighted by deviations between actual and optimal position sizes. Said another way, biases occur when managers size positions different than what the risk-reward would suggest.


Here are a few examples:


1. NOT ADJUSTING POSITION SIZE AFTER A BIG PRICE MOVE: One of the most common biases we see in the data, is that after large positive price changes, managers are less likely to cut exposure, even though the probability-weighted return has diminished due to the move. The potential damage from this willful ignorance is compounded by a much larger position with a lower expected return. The typical behavior of investors is to let winners run, however, we’ve found that to be sub-optimal for fundamental funds.

The first step to alleviating this bias is to force re-underwriting names when they reach an unacceptable PWR. If the new assumptions justify the size, then all is good. If not, then the manager knows there is some bias that is causing them to stay in the position. Forcing re-underwriting at critical levels ensures that checks and balances are in place so that profits are kept and not lost on reversals.


2. NOT SIZING UP GOOD PROBABILITY-WEIGHTED RETURN WHEN INITIATING A POSITION: When analysts input price targets into Alpha Theory, and a manager decides to act on that information, what we’ve seen in the data is a tendency to build a position over time. We’ve found, on average, this is detrimental to returns. Slowly scaling into a high conviction and high probability weighted return name causes investors to miss some of the return potential.


3. UNDISCIPLINED APPROACH: Our data has shown that managers who are more disciplined (i.e. have more of their portfolio with price target coverage and size closer to optimal position sizing) tend to outperform those who don’t. Unfortunately, running complex sizing algorithms through our heads is not something we do well. What we see in the data is that positions without explicit price targets underperform. Be it hubris or any other number of reasons, it’s almost always detrimental to returns.


4. DIVERSIFYING: Our research shows that the largest positions in client portfolios outperform smaller names by a big margin, mostly because the batting average on top holdings is high. Most clients nullify this benefit by taking on many more names in the portfolio at much lower probability-weighted returns. We’ve done research which shows that concentrated portfolios outperform diversified portfolios by 2.2% on an alpha basis (run as a Monte Carlo study using batting averages calculated for various portions of client portfolios 2011-2016). The cost of diversification is a loss of alpha without a commensurate improvement in risk protection.


For 2016 returns, if clients sized using the suggested Optimal Position Size, they would have been better off by 5.1%. Clearly we recognize that not every position was able to be sized optimally, but even if half of that difference could have been captured, there was a lot of money left on the table. The biases above highlight why some of the difference occurs. It’s hard to beat an unemotional version of yourself, especially when we’re not psychologically built for the game.

September 13, 2010

Institutional Investor | 8 Mistakes Series – Final Installment Released Today

The last installment of the "8 Mistakes Money Managers Make" series on Institutional Investor (www.InstitutionalInvestor.com) was released today. The series has been highlighted in their weekly electronic newsletter and posted on their homepage. The articles focus on poor position sizing's effect on portfolio risk and return. The root cause being a basic misunderstanding of an asset's impact on the portfolio and how it should be used to determine position size.

A link to the entire series can be found here or by visiting www.InstitutionalInvestor.com and clicking on the "8 Mistakes Money Managers Make" link under the "Asset Management" section.


September 02, 2010

Institutional Investor Article Series: 8 Mistakes Money Managers Make

Institutional Investor (www.institutionalinvestor.com) will feature a daily article series I authored beginning today. The series, “The 8 Mistakes Money Managers Make,” was highlighted in their weekly electronic newsletter today and posted on their homepage. The articles focus on poor position sizing's effect on portfolio risk and return. The root cause being a basic misunderstanding of an asset’s impact on the portfolio and how it should be used to determine position size.

The initial article, “Mistake #1: Discounting the Downside” is located under the “Asset Management” portion of the website and can be found here. Be sure to visit www.InstitutionalInvestor.com tomorrow for the solution to Mistake #2: The Good Stock Paradox.

December 04, 2009


I recently wrote a second article for Institutional Investor magazine (http://www.iimagazine.com/) called “The Problem with Dividends.” You can read the full article here.

Here is an excerpt from that article:

To paraphrase Einstein, “matter cannot be created or destroyed,” yet this fundamental tenet seems to be ignored when discussing dividends. Despite the popularity of dividend-paying stocks, serious analysis dictates that dividends are a net drain of company enterprise value. Why are we so confused about dividends? There seems to be a misunderstanding of enterprise value and tax-effect because dividend payments by their very nature have a negative expected return.

If a company has just paid $10 million in dividends then its enterprise value has decreased by a corresponding $10 million dollars. No value has been created in the dividend payment, but investors are quick to praise the merits of a dividend-paying stock. I believe the flawed logic of this problem is ingrained in the dogma of investing…