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Alpha Theory Blog - News and Insights

February 27, 2021

Alpha Theory 2020 Year in Review

Alpha Theory clients continue to outperform! Over the past nine years, Alpha Theory clients have outperformed their peers seven times, leading to over 2% per year performance improvement over the average hedge fund. Over that same period, Alpha Theory’s systematic position sizing outperformed clients’ actual return every year by an average of 6%!

Table1

What does this mean? Our clients are self-selecting, better-than-average managers that would be world-class if they more closely followed the models they built in Alpha Theory.

In fact, over the period, the compound return is twice that of their actual performance (239% vs. 112%) and three times that of the average hedge fund (112% vs. 74%). *Sidenote: 6% per year equals double the returns. Isn’t compounding amazing?

 

2020 was a really good year for clients as they beat the primary Equity Hedge index by 5.0% despite missing out on 2.5% of return if they would have systematically sized positions using Alpha Theory. 

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Note that the difference in returns between the charts is due to leverage. The chart above is total return (varying leverage per manager). The chart below is based on 100% gross exposure per manager (ROIC) and is thus a better apples-to-apples comparison.

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HOW OFTEN DOES IT WORK?

At the end of each year, we sit down with clients and go through an analysis of actual versus optimal performance. The question is how often does optimal outperform. As you can see above, we’ve been reporting on the annual results for nine years and, on average, systematic sizing has won every year. But it doesn’t win for every client and every position. Across all-time, if we randomly select a client in a given year, systematic sizing is better 69% of the time. If we do the same thing but randomly select a position, systematic sizing wins 59% of the time.

This means that our clients have, on average, predictive research because the systematic sizing is based on their forecasts. What we see in the results is the benefit of consistently applying process. The more time spent applying process, the more likely the process is to win[i].

 

PROCESS ENHANCES PERFORMANCE

Alpha Theory clients use process to reduce the impacts from emotion and eliminate guesswork as they make position-sizing decisions. Alpha Theory gives a true ranking of ideas in the portfolio, so managers can size them accordingly. It does this with a rules engine 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

Enables real-time incorporation of market and individual asset moves into sizing decisions

Our clients are a self-selecting cohort who believe in process and discipline; process orientation goes together with the Alpha Theory software that serves as a disciplining mechanism to align the best risk/reward ideas with rankings in the portfolio. Below are some of the best lessons for how to turn process into performance.

START WITH PRICE TARGETS

Alpha Theory research shows that ROIC for assets with price targets is 7.8% higher than for those without price targets. Some investors chafe at price targets because they smack of “false precision.” These investors miss 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 price targets foster are central to any good process.

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NEXT, KEEP THE PRICE TARGETS FRESH

Once you establish targets, keeping them fresh matters. See below for a chart comparing Fresh vs. Stale Price Targets (stale is defined as older than 90 days).

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FINALLY, CREATE A SYSTEMATIC APPROACH TO SIZING POSITIONS

Once you create a research process based on fresh price targets, the next step is to create a systematic process to highlight when positions are out of line with research. That’s what Alpha Theory does in the form of Optimal Position Sizing. As you can see below, there is a marked improvement in almost every metric with systematic position sizing. Again, this is based on 9 years of data across 100+ managers. We can say with high confidence that the managers using Alpha Theory are great price target forecasters. Still, they could do even better if they followed the system they built in Alpha Theory more closely.

Picture6

 

In the future, finding alpha will not become easier. It is imperative that the funds of the 21st century develop plans to evolve to new realities. Data and process are critical to that evolution. Let Alpha Theory help you and your team grow to meet the challenges of tomorrow.

 

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[i] http://success-equation.com/tennis.html - Tennis match simulator from Michael Mouboussin showing the benefit of compounding small edges.

 

January 18, 2021

Alpha Theory Is Giving Back

This past holiday season, the Alpha Theory Team wanted to give back to help those most hurt by what has been a tough year for a lot of people. To that end, we worked with our clients to donate on their behalf if they cleared certain usage hurdles. Unsurprisingly, our clients answered the call, and we were able to make $7,000 in donations to these wonderful charities:

 

1. American Foundation for Suicide Prevention

2. Feeding America

3. World Resources Institute

 

2020 has been a tough year for so many and seeing how hard our clients worked to up their freshness & coverage to give back was truly remarkable. We look forward to continuing to partner with our clients to help build smarter portfolios AND try to do some good throughout our communities.

 

If you would like to donate directly to any of the above organizations, simply follow the links to their sites.

 

Have a prosperous 2021! 

Alpha Theory Team

December 29, 2020

Optimizing Usage for Optimal Returns (Part 2) - Position Level Analysis

 

This article was co-written by Billy Armfield, Data Scientist of Alpha Theory, and Cameron Hight, CEO of Alpha Theory.

 

In our last edition, “Optimizing Usage for Optimal Returns”, we explored the relationship between forecast freshness and portfolio coverage on one-day forward returns at the fund level. Freshness and performance showed a nearly monotonic relationship. Coverage, on the other hand, had a more parabolic shape with the lowest performance coming from the middle of the range. To explore these relationships further in this edition, we are investigating data at the ticker level instead of the fund level. We ask:

1. Does coverage show more predictive power at the ticker level than it did at the fund level?

2. Is freshness correlated with a performance at the ticker level?

 

Coverage

Ten years of data on Alpha theory clients have shown that process-oriented investing yields higher returns. A large part of the process centers around entering and updating scenarios forecasting future stock prices, and the probability of them reaching that price. Implicit in this philosophy is the idea that if you are going to make an investment, it should be supported by research. As part of our exploration of coverage and freshness at the ticker level, we regressed coverage against a one-day forward price change. In this case, coverage is treated as a binary variable based on whether a forecast has been made on the position or not.

 

B1

 

Unlike the results of measuring coverage by the fund, when measured at the ticker level, there is a distinct positive relationship with one-day forward price change. Simply stated, positions with price targets are more likely to outperform positions without price targets. 

 

Freshness

There is more than one way to bake a Christmas cookie, so we first examined which measure of freshness has a stronger relationship with one-day forward price change. We examined two variations. The first measures freshness in terms of the number of days since a forecast was last updated (DSLU). The second treats it as a binary feature, where its value is 1 if price targets were updated in the last ninety days, and zero otherwise. We regressed both features against a one-day forward price change for all positions in Alpha Theory’s historical database. The results for the DSLU method can be found in figure 1, and the results for the binary method in figure 2.

 

Figure 1 (Days Since Last Update)

B2

 

Figure 2 (Binary: Updated in last 90 days)

B3

 

The number of days since the last update has a negative coefficient and t-statistic, which makes sense, given that one might reasonably expect a lower degree of certainty of positive future returns when forecasts are out of date. While intuitive, its lower t-statistic and higher p-value means this relationship deserves further investigation before drawing any conclusions. The binary feature, however, has more conclusive results. Having coverage no older than ninety days does have a positive relationship with the one-day forward price change, with a higher t-statistic and lower p-value.

 

The importance of creating price targets and keeping coverage fresh can be summarized by the annualized price change of fresh positions vs. stale and uncovered positions.

 

B4

 

Scattered, Fresh, and Covered

Based on the analysis above, it is well worth the time to create price targets and keep them fresh as they are both important predictors of future returns. When thinking through how your firm is going to improve in 2021, use these empirical proofs as evidence that the team should seek process as a proven way to improve returns.

 

November 30, 2020

Don't Be A Turkey: Optimizing Usage for Optimal Returns

 

This article was co-written by Billy Armfield, Data Scientist of Alpha Theory, and Cameron Hight, CEO of Alpha Theory.

 

Alpha Theory makes it easier for process-oriented investment managers to do their work, from uploading price targets through Excel Connect, to monitoring Optimal Position Size in the application. Previous analyses have shown a strong positive relationship between increased usage of the app and increased ROIC. Those results showed that for usage, like turkey, if some is good, then more is better. In that spirit, we decided to investigate different forms of usage to see what gives the greatest return on investment.

 

Two common ways in which we measure client usage are coverage (how much of the portfolio has research?) and freshness (how recently were your price targets updated?). In the same way we help our clients optimize exposure to their positions, we also want to make sure they are optimizing how they spend time engaging with Alpha Theory.

 

We ask:

1. Which element of usage has the strongest relationship with returns?

2. How much is enough?

 

Coverage

There are several ways to think about coverage. The first way we will examine is to think of it as the percentage of names in the book which has research. Simple enough. But this method can provide a false sense of security. Say, for example, you have a five-position portfolio weighted at 80%, 5%, 5%, 5%, 5%. You have price targets for your smaller positions, but not the big ones, giving you coverage of 80% under this definition. But are you really covered?

 

The second method of measuring coverage is by calculating the ratio of the gross exposure of the portfolio which has research, to the gross exposure of the portfolio as a whole. Both methods have their place, but we were interested to know which was more helpful as a metric to track and optimize.

 

An Experiment

In order to determine which measure of coverage is more helpful, we regressed both forms of coverage against one day forward returns for a sample of Alpha Theory client funds. The results for the first method are in figure 1, and the results for the second method are in figure 2.

 

Figure 1 (% of Positions with Price Targets): 

Picture1

 

Figure 2 (% of Exposure with Price Targets):

Picture2

 

The results for the exposure-based method (figure 2) show a higher coefficient, larger t-statistic, and lower p-value than method 1, giving us a high degree of confidence there is a stronger relationship between one day forward returns and the exposure-based method (p-value < 0.05 is statistically significant for our purposes). This measure of exposure is what we will use going forward.

 

Freshness

We should ask the same questions about freshness that we have for coverage. The first way we can think about freshness is to take the ratio of positions which have had their price targets updated in the last 90 days to all positions with price targets. The second is to take the ratio of gross exposure of positions with research updated less than 90 days to gross exposure of the portfolio.

 

Figure 3 (% of Positions with Price Targets that are Fresh):

Picture3

 

Figure 4 (% of Exposure with Price Targets that are Fresh):

Picture4

 

The exposure-based method is more predictive here as well, with a higher t-stat and lower p-value. These results make sense – small positions can be placeholders and get less attention. The positions with large exposure have targets subjected to greater diligence.

 

Comparing the Variables

We can now compare the results of regression against one day forward returns for the two variables under consideration.

 

Figure 5: Coverage

Picture5

Figure 6: Freshness

Picture6

 

In figures 5 and 6, we see that both coverage and freshness have t-statistics and p-values which are statistically significant, and both variables have positive coefficients. Of the elements of usage examined, higher coverage is most strongly related to higher one day forward returns, followed by freshness.

 

How much is enough?

Unlike at Thanksgiving dinner, we can estimate how much is enough of each. Focusing on Coverage and Freshness, we examined various thresholds to see how the average client performed above and below it. The 85% and 95% thresholds resulted in the greatest difference in annualized ROIC for clients above and below. More clients have been able to reach the upper echelons of coverage than freshness, which makes sense – over time, and given the reasonably low turnover, it isn’t unfathomable that you can make forecasts on > 95% of the exposure of your book.

 

Picture7

 

Keeping everything fresh, however, requires strict discipline, and an emphasis on process. But it appears as if the hard work pays off: the four funds with freshness over 95% have average annualized ROIC 6.6% higher than the average for all the other funds under consideration.

 

What is interesting is the persistent relationship for Coverage where higher Coverage always results in better returns but Freshness reverses course at the 70% level. This phenomenon is counterintuitive given the statistical relationship discovered in the regression. It is clear that more research is required and in next month’s blog post we’ll investigate this discovery and explore what can be learned from examining on a by-position basis instead of by-firm.

 

October 16, 2020

Best Ideas Update

 

The Cohen, Polk, Silli “Best Ideas” paper was first released in 2005 and Alpha Theory incorporated the 2010 draft in the Concentration Manifesto as an empirical proof (#3 to be exact) of why managers should concentrate. An updated version of the “Best Ideas” paper was released in June, it expands the data set from 24 to 37 years and reconfirms the earlier findings that active managers are 1) good at selecting and sizing a few “Best Ideas” and 2) then dilute the “Best Ideas” with a bunch of positions that are basically random noise.

 

The “Best Ideas” portfolio outperforms the rest of the portfolio and benchmarks by 2.8% to 4.5% per year with high statistical significance, across a thousand-plus mutual and hedge fund managers, and with consistency amongst managers and from year-to-year.

 

This abnormal performance appears permanent, showing no evidence of subsequent reversal, even several years later. Interestingly, cross-sectional tests indicate that active managers’ best ideas are most effective in illiquid, growth, momentum stocks, or for funds that have outperformed in the past.

 

Given the strong empirical evidence for concentration, why don’t managers concentrate more on their best ideas? The “Concentration Manifesto” highlights myriad reasons managers should concentrate but does not investigate why they do not. The “Best Ideas” paper does:

 

We identify four reasons managers may overdiversify.

 

1. Regulatory/legal. A number of regulations make it impossible or at least risky for many investment funds to be highly concentrated. Specific regulations bar overconcentration; additionally, vague standards such as the “Prudent man” rule make it more attractive for funds to be better diversified from a regulatory perspective. Managers may well feel that a concentrated portfolio that performs poorly is likely to lead to investor litigation against the manager. Anecdotally, discussions with institutional fund-pickers make their preference for individual funds with low idiosyncratic risk clear. Some attribute the effect to a lack of understanding of portfolio theory by the selectors. Others argue that the selector’s superior (whether inside or outside the organization) will tend to zero in on the worst-performing funds, regardless of portfolio performance. Whatever the cause, we have little doubt that most managers feel pressure to be diversified.

2. Price impact, liquidity, and asset-gathering. Berk and Green (2004) outline a model in which managers attempt to maximize profits by maximizing assets under management. In their model, as in ours, managers mix their positive-alpha ideas with a weighting in the market portfolio. The motivation in their model for the market weight is that investing in an individual stock will affect the stock’s price, each purchase pushing it toward fair value. Thus, there is a maximum number of dollars of alpha that the manager can extract from a given idea. In the Berk and Green model managers collect fees as a fixed percentage of assets under management, and investors react to performance so that in equilibrium each manager will raise assets until the fees are equal to the alpha that can be extracted from their 26 good ideas. This choice leaves the investors with zero after-fee alpha. Clearly in the world of Berk and Green, (and in the real world of mutual funds), managers with one great idea would be foolish to invest their entire fund in that idea, for this would make it impossible for them to capture a very high fraction of the idea’s alpha in their fees. In other words, while investors benefit from concentration as noted above, managers under the most commonly used fee structures are better off with a more diversified portfolio. The distribution of bargaining power between managers and investors may therefore be a key determinant of diversification levels in funds.

3. Manager risk aversion. While the investor is diversified beyond the manager’s portfolio, the manager himself is not. The portfolio’s performance is likely the central determinant of the manager’s wealth, and as such we should expect them to be risk-averse over fund performance. A heavy bet on one or a small number of positions can, in the presence of bad luck, cause the manager to lose their business or their job (and perhaps much of their savings as well, if they are heavily invested in their own fund, as is common practice). If manager talent were fully observable this would not be the case – for a skilled manager, the poor performance would be correctly attributed to luck, and no penalty would be exacted. But when ability is being estimated by investors based on performance, risk-averse managers will have an incentive to overdiversify.

4. Investor irrationality. There is ample reason to believe that many investors – even sophisticated institutional investors – do not fully appreciate portfolio theory and therefore tend to judge individual investments on their expected Sharpe ratio rather than on what those investments are expected to contribute to the Sharpe ratio of their portfolio. For example, Morningstar’s well-known star rating system is based on a risk-return trade-off that is highly correlated with Sharpe ratio. It is very difficult for a highly concentrated fund to get. This behavior is consistent with the general notion of “narrow framing” proposed by Kahneman and Lovallo (1993), Rabin and Thaler (2001), and Barberis, Huang, and Thaler (2006). A top rating even if average returns are very high, as the star methodology heavily penalizes idiosyncratic risk. Since a large majority of all flows to mutual funds are to four- and five-star funds, concentrated funds would appear to be at a significant disadvantage in fundraising. Other evidence of this bias includes the prominence of fund-level Sharpe ratios in the marketing materials of funds, as well as maximum drawdown and other idiosyncratic measures. Both theory and evidence suggest that investors would benefit from managers holding more concentrated portfolios.

Our view is that we fail to see managers focusing on their best ideas for a number of reasons. Most of these relate to benefits to the manager of holding a diversified portfolio. But if those were the only causes, we would be hearing an outcry from investors about overdiversification by managers, while in fact, such complaints are rare. Thus, we speculate that investor irrationality (or at least bounded rationality) in the form of manager-level analytics and heuristics that are not truly appropriate in a portfolio context, play a major role in causing overdiversification.

 

The reasons for diversification (not concentration) are real and will require systematic change and mutual agreement from both funds and LPs. Given the state of flows from active to passive, there may be a strong enough catalyst for that change.

 

September 11, 2020

The Benefits of Manager Aggregation

 

Long/Short equity investing has underperformed for over a decade (see below). Worst of all, it hasn’t protected investors in down markets, when they’ve needed it most. Investors increasingly struggle to justify their investments in Long/Short managers. This is fixable.

 

Picture1

 

Long/Short equity managers are stock pickers at heart. Many LPs ask them to do too much. They want high return and low volatility. To achieve low volatility, Portfolio Managers must become risk managers and diversify. Risk management is not a core competency of most PMs and diversification causes them to hold more names than they have the mental capital to manage.

 

A growing group of LPs are recognizing that finding multiple great stock pickers with concentrated portfolios and creating their own risk management and diversification is the ideal strategy. Alpha Theory helped kick off this trend with their paper “The Concentration Manifesto” in May 2017.

 

As mentioned in the paper, concentrated portfolios have better batting averages than diversified portfolios. The benefit is that the average return of the concentrated strategy is higher (2.1% vs. -0.1% after fees). The problem is that concentrated funds have the “red tail” on the left where there is a higher probability of large loss.

 

Picture2

 

If you could invest in several of these managers (10 in this example) you can get the same return and cut off the left tail.

 

Picture3

 

And if you could find 100 of these managers then…

 

Picture4

 

There is tremendous potential in this structure for savvy LPs. In an ideal world, we would overlay this multi-concentrated strategy (+2.2% of return) with Alpha Theory position sizing (average improved returns of +4% per year) to create a vastly superior strategy that would pull investors back into Long/Short equity investing.

 

August 21, 2020

Performance During The Pandemic (Part 2)

 

This article is the continuation of Performance During The Pandemic (Part 1) and was co-written by Billy Armfield, Data Scientist of Alpha Theory, and Cameron Hight, CEO of Alpha Theory.

 

Optimal Position Sizing During the Pandemic

 

Every investor uses mental models that dictate their investment process and portfolio composition. When investment managers join Alpha Theory, they work with our Customer Success team to make those mental rules explicit in the form of a model. This forces managers to think about their process, and to be honest with themselves when they are not following their own rules.

 

These models are defined by variables like position size, liquidity, investment checklist, and analyst price targets, to name a few. The output of the model is a suggested optimal position size (OPS), which allows managers to make sure their actual position size (APS) reflects their research and investment process. In the Concentration Manifesto, we outline our research on how managers would benefit by concentrating their holdings in their best ideas. The OPS models created for every fund tend to suggest larger position sizes for names with a higher probability-weighted return and can suffer from higher volatility when markets undergo a sharp correction.

 

When markets are as volatile as they have been this year, having a way to highlight dislocations between research and position size is more important than ever. The market has declined roughly 4.5% (as of 7/24) since the high in February, whereas our average long/short manager is up by 1.5% over the same period. Had our clients followed the model verbatim, they would be up, on average, by 5.9%.

 

Picture1

 

Paying closer attention to the feedback loop provided by the optimal position sizing model would have led to significant improvement for the bottom quintile. The OPS models for these clients, based on their own research and investment process led optimal to outperform by roughly 20% 

 

Picture2

 

Digging in a little further we see that the bottom performer’s actual and optimal long exposure moved in opposite directions. As markets reached their nadir in mid to late March, OPS recommended increasing exposure to the market while the managers were decreasing exposureAlpha Theory then recommended decreasing exposure as prices recovered. This resulted in their long book losing 14% while the Alpha Theory long book made +6%!

 

Picture3

 

This is because optimal position sizes are based in part on the expected value of a security. If you liked a stock at $100, you love it at $70. As the market went down, expected returns went up, so the models increased exposure. This is one of the key takeaways for how Alpha Theory can help clients avoid emotional decisions, and to invest based on their own research. 

 

This remains a difficult environment and we believe that during times of stress, implementing protections against emotional investment decisions is critical to success. Investors in the top quintile used their research as their anchor, did not overreact, and added to their high conviction names when things seemed bleak.  Process-driven investing, as we practice at Alpha Theory, helps to mitigate the impact of emotions and helps managers harvest more of the alpha they deserve from their research. 

 

July 31, 2020

Performance During The Pandemic (Part 1)

 

This article was co-written by Billy Armfield, Data Scientist of Alpha Theory, and Cameron Hight, CEO of Alpha Theory.

 

As major indices approach, and even surpass, their pre-COVID levels, we wanted to take a step back and look at how our clients weathered the storm. What can we learn from the managers who outperformed, and how we can avoid the mistakes of those who underperformed? Using February 19th, 2020, the all-time high of the S&P 500, as a starting point, we broke our clients into quintiles based on the alpha returns versus the SPY ETF over the period ending July 24th, 2020 (see below for total returns by quintile). We also narrowed our analysis to look exclusively at long/short clients to ensure an apples to apples comparison.

 

Returns

 

All quintiles stayed net long over the duration of the period (chart below). Looking at just the top (green) and bottom (red) quintiles, we get a sense of how important net exposure was over this period. Not only was the top quintile carrying the highest net exposure, but they were also the first to get their net back to pre-COVID levels (April 7th). In contrast, the bottom quintile has yet to exceed pre-COVID levels of net exposure.

 

Net exposure

 

Looking at the long exposure of the return quintiles (chart below), we see almost no difference between the long exposure of the best and worst-performing managers.

 

Long exposure

 

This means that the big difference in net exposure between the best and worst performers was due to higher short exposure for the worst performers. The worst performing funds in the fifth quintile had much higher exposure on the short side, which ate into their returns as markets returned to life. You can also see that their short exposure has only increased since the market bottom, further eroding returns as markets continue to climb.

 

Short Exposure

 

But this is only part of the story, as high long and short exposure with a small net positive exposure should have led to a small gain over the period. In order to understand what’s really going on, we need to look at gross exposure-adjusted returns on the long and short side.

Long Return on Invested Capital (ROIC – what was the return of the average position-weighted stock return over the period – excluding leverage = +15% for the highest quintile, -10% for the lowest quintile) was the major contributor to the difference in quintiles.

 

Long ROIC

 

Short ROIC for the period for all clients was tightly clustered compared to long ROIC, with the best performing cohort losing about 2% on their shorts and the worst performing losing 9%. In fact, the top and bottom performing quintiles from a total return perspective had almost identical short ROIC.

 

Short ROIC

 

The poor long return for the 5th quintile combined with increasing short exposure was a major cause of their underperformance. Investors in the top cohort, despite large losses as markets plunged, were willing to add exposure at the bottom and frankly, just picked better longs.

 

ROIC

 

June 19, 2020

The Short End of the Stick

 

Over the years, we have consistently heard that the short portion of clients’ portfolios have been a major drag on returns. The problem is that when we do portfolio performance reviews with our clients we see that the short book, which is consistently negative, is generally less negative than the S&P 500 and MSCI World.

 

To explore this further, we wanted to test a simple strategy of creating an aggregated portfolio of client short positions to see how they performed against the major indices. The absolute result was an average annualized loss of -4.02% which confirms the industry dogma that the short book is a drag. That being said, the short portfolios provided consistent positive alpha (short book return minus negative index return).

 

Short_end

Source: Omega Point

 

The total return for clients’ short portfolio is -23.74% over the 5+ year period or an annualized return of -4.02%. This compares to a 10.20% annualized return for the S&P 500 or 6.16% annualized alpha and 6.35% for MSCI World or 2.33% annualized alpha. If we take the midpoint, that is roughly 4% of annualized alpha that our clients have generated per year for over 5 years.

 

Breaking it down by year, the alpha contribution was consistent except for 2016 where it was roughly breakeven showing that Alpha Theory managers were dependable alpha generators on the short-side.

 

Screen Shot 2020-06-19 at 11.21.34 AM

 

The sustained positive trend of the overall market over the years makes it almost impossible to create absolute returns from shorting. However, for investors looking to generate a less volatile stream of returns, a short book that has a negative correlation with the long book and provides consistent alpha is extremely valuable. Alpha Theory’s clients are consistent short alpha contributors. This is, of course, because of their stock selection skill but I would posit that their process discipline is just as important and is one of the reasons their alpha returns have been so consistent.

 

May 15, 2020

Alpha Theory Announces Partnership with New Constructs

 

Alpha Theory has partnered with New Constructs, the leading provider of insights into the fundamentals and valuation of private and public businesses, to provide a score based on a firms’ likelihood of beating or missing earnings.

 

This feature works by pulling New Constructs’ Earnings Distortion Scores directly into Alpha Theory. These scores indicate the likelihood of a firm to beat or miss consensus expectations for EPS, revenue, or guidance in the next quarter:

 

    1 – Strong Beat
    2 – Beat
    3 – Inline
    4 – Miss
    5 – Strong Miss

 

“We are excited that Alpha Theory’s clients will now have access to our proprietary consensus earnings prediction tool, which will help them make smarter investment decisions,” said David Trainer, founder and CEO of New Constructs.

 

Earnings Distortion measures the level of non-core income/expense contained within reported earnings. It is a proprietary measure featured by professors from Harvard Business School and MIT Sloan in a recent paper: Core Earnings: New Data & Evidence. The paper empirically demonstrates the superiority of New Constructs’ measure of Core Earnings based on its proprietary adjustments for unusual gains/losses.

 

Earningsdist

 

“Alpha Theory’s goal is to constantly provide new sources of value for our clients and we believe the Earnings Distortion Score from New Constructs is a great addition”, said Cameron Hight, CEO of Alpha Theory.

 

Full press release can be found here