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Alpha Theory Blog - News and Insights

23 posts categorized "Behavioral Finance"

December 29, 2021

A Lack of Freshness Spoils Returns

 

Alpha Theory can’t tell you how to do your research, but it can tell you when. Using insights from the Alpha Theory All-Manager dataset, we can provide guidance on some of the basics in managing your research process. 

 

Managers understand intuitively that producing investment research and updating that research regularly (i.e. freshness) is important. But how frequently? Should I update my research every 60 days? Every two weeks? Do I need to produce scenarios for all my positions?

 

Key conclusions: 

1. Assign price targets and probabilities to every investment

2. Update them once a month

 

To determine the impact of freshness and coverage on returns, we measured the one-year forward return for the optimal long portfolio for each fund in the Alpha Theory All-Manager dataset on a quarterly basis1. We then put each fund into four buckets based on their average freshness (days since the last update or DSLU) and coverage (percentage of positions with price targets). Next, we calculated the return of each quartiled bucket to see if returns correlated to freshness and coverage.

 

We found that funds that were diligent enough to place in the top quartile produced more than four times as much alpha as the bottom quartile, increasing monotonically from bottom to top. The median update frequency for the top quartile was 25 days (once a month updates), meaning the top funds updated more than 10x as often as managers in the bottom quartile. Additionally, managers in the top quartile had research on all active positions.  

 

A Lack of Freshness Spoils Returns

 

As a fundamental manager, you may argue that very rarely does something meaningful happen every 30-days that warrants a forecast update. We would counter that price is an important signal. For example, let’s say you initiated coverage on a position at $100 with a 70% chance of going to $150 and a 30% chance of going to $50. If the price moves from $100 to $125, wouldn’t you say the probability of reaching your bull target has changed? While $150 may still be the price suggested by your model, updating the probabilities of your scenarios to more accurately reflect likely outcomes allows the OPS model to make better sizing recommendations.

 

In addition, Daniel Kahneman’s new book “Noise” describes how the same expert can take the same information and come to different conclusions at different times. And, that the best answer is the average of those forecasts. This means that an analyst may come to a different conclusion for price target and probability on a different day and that the constant refinement (updating once a month) is healthy and leads to more accurate forecasts.

 

Finally, research from our friends at Good Judgement Inc. shows that over the past six years, their top forecasters (orange) update roughly 4x as often (11 updates vs 3 updates per question) as non-Superforecasters. Update frequency has a high correlation with outperformance and incorporating even small additional bits of information (Superforecaster updates were roughly half the size of non-Superforecasters) that either support or detract from the probability of a given outcome lead to better results over time.

 

A Lack of Freshness Spoils Returns Chart 2

 

We are always interested in learning more about your research process and where Alpha Theory can help. Alpha Theory is a process enhancement tool, creating a space that systematizes how you conduct and use research for allocation decisions. Please reach out to us with any questions so we can better optimize your workflow to generate more alpha.

 

1To normalize for different benchmarks, we calculated alpha on an idio+sector basis using the Axioma World-Wide Equity Factor Risk model, which removes performance derived from all their tracked factors, excluding sector. 

 

November 29, 2021

Getting Comfortable with Many, Micro Updates

 

For years we’ve worked closely with the folks at Good Judgement Inc. from “Superforecasting” fame. One of our friends there, Chris Karvetski, recently published a white paper called “Superforecasters: A Decade of Stochastic Dominance” on Superforecasters’ attributes and skills. For analysis, Chris studied 108 forecast questions with 167,000 forecasts to compare the differences between accuracy and approach between Superforecasters and everyone else.

 

From an accuracy perspective, Superforecasters dominate with accuracy that is 36% better (0.166 error for Superforecasters versus 0.259 for general forecasters).

 

Picture1

 

Alpha Theory clients forecast stock price movement. As such, the question we should ask is “how can we be more like Superforecasters?” Well, Chris broke down the frequency and magnitude of updates and I believe the answer is clear.

 

Picture2 Picture2

 

Superforecasters update their forecasts ~4x more often which leads them to adjustments that are about half the size. Imagine steering a boat towards a lighthouse. You can choose to make 3 major adjustments or 11 minor adjustments. Which method is going to get you closer to the lighthouse?

 

As analysts, to gain better forecast accuracy, we should frequently update our price targets and probability forecasts. Obviously, new information warrants updates but we should still make updates even when there is no new information. As The Verve says, “we’re a million different people from one day to the next.” We all have what Daniel Kahneman calls, Occasion Noise, which basically means that we change our opinions without the facts changing. Our mood impacts our forecasts. To get a truer sense of our own opinions, we should ask ourselves the same question at different times.

 

Let’s be like Superforecasters and get comfortable with many, micro updates. In our next blog post, we’ll explore the impact that update frequency has on returns.

 

July 31, 2021

Gaining Confidence in Your Confidence

 

Alpha Theory helps managers streamline the capital allocation process by combining all the investment-process inputs into a model that calculates an optimal size (OPS) for each position. While the primary inputs are quantitative including price targets and probabilities, there is also a qualitative perspective that is just as important to capture.

 

Alpha Theory helps managers create a Confidence Checklist which contains the more subjective aspects of each manager’s investment process. The individual Checklist items are combined into a Checklist Confidence Score for each security. Formalizing these mental rules and tracking their performance over time creates a feedback loop through which our clients can learn which questions are most important for generating an excess return.

 

We wanted to investigate if the Checklist Confidence Score was a predictive signal of forward returns. After rigorous analysis of 500,000+ checklist scores, we found a statistically significant signal at the 99% confidence level that showed having a confidence checklist results in positive forward returns. This demonstrates why it is important to explicitly capture and formalize checklists into an investment process.

 

The Confidence Checklist is a combination of the qualitative, statistical, and fundamental metrics that normally are kept in a manager’s mental model. We think of this mental model as everything that is not clearly captured by the price targets and probabilities. There are infinite possibilities for checklist items, and after more than a decade of helping managers make the most optimal decisions, we are able to help build a meaningful and impactful checklist with our managers to help them find more alpha in their qualitative ideas.

 

80% of Alpha Theory clients have checklists that are built with customized inputs to fit their process, each of these inputs can take on several values. For example, Management Team could have a drop-down that consists of selections such as Strong, Neutral, and Weak which contribute to the overall confidence score according to the weight applied by the selection.

 

Each checklist item has a selection, and the total weights combine to create confidence, for example, a final score could be 85%. The confidence checklist score then adjusts the optimal position size and provides the base optimal position before any other factors are applied.

 

We can see that having a confidence checklist for each position is an important factor in investing. When thinking about how to improve your fund’s performance, think about how your own qualitative checklist contributes to the decision-making process. Is scoring consistent across names? Do you have a way to measure the importance of a checklist item? While you can’t quantify everything, these results prove that adding a little science to the art of investing can improve future returns.

 

March 30, 2021

Capital Allocators Book release by Ted Seides

Our friend Ted Seides has recently released a great book titled “Capital Allocators” and WE’RE IN IT! The book distills the learnings and best practices of his 180+ podcasts and is a treasure trove of great insights. There are four things that make the book special:

1. Ted gets amazing people.

2. Many of these people don’t publish their thoughts and this is our only access to them

3. Ted has distilled the best of these learnings into a “toolkit” you can apply to your own investing.

4. ALPHA THEORY IS INCLUDED!

 

See below for the section on Alpha Theory (italicized paragraph is edited to focus on Alpha Theory):

 

Cameron Hight was a frustrated former hedge fund manager at a smaller shop who felt he did not have the requisite tools to improve their own skills. He set aside managing money to create a software company that would help portfolio managers.

 

Cameron Hight had an insight that has helped hedge fund managers big and small optimize portfolio construction. He believed markets move so quickly that a portfolio manager cannot consider all the variables to optimize position sizing in real time. His business, Alpha Theory, strives to make the implicit explicit by putting numbers and probabilities on position sizing decisions.

 

Alpha Theory uses the investment team's research to calculate risk and reward in real time. A thorough analyst already has models and probability scenarios for the potential path a stock might take. Absent new Information, each movement in the stock price changes the attractiveness of risk and reward. Alpha Theory models conviction-weighted sizing based on the investment team's research and compares the result to the actual portfolio position size. Over 15 years of operation, Cameron has teams of data showing that his seemingly simple tool has added substantial returns for clients who employ it in their practice.

 

His data also revealed an important conclusion about many fundamental managers. Good active managers perform far better in their larger positions than they do in smaller names. Alpha Theory wrote “The Concentration Manifesto," preaching that managers and allocators would both be better served if managers focus on more concentrated portfolios of their best ideas.

 

Summary

 

Data analysis almost never gives an allocator the answer, but the tools employed are useful in measuring risk and return at the portfolio and manager level, and in making informed judgements about manager selection. The availability of data and the entrepreneurs at the forefront of assessing it enable CIOs to be more informed. Asking the right questions may reveal managers who eschew modern technology and are a step behind the pack.

 

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. 

Picture2

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.

Picture3

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.

Picture4

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).

Picture5

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.

 

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

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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.

 

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