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Alpha Theory Blog - News and Insights

36 posts categorized "Superforecasting"

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.

 

October 27, 2021

KISS, or How to Make Money by Following Your Research

 

“It is remarkable how much long-term advantage people like us have gotten by trying to be consistently not stupid, instead of trying to be very intelligent.” - Charlie Munger

 

Successful traders and investors encourage entrants to the field to find an “edge”, ideally a strategy that has not already been widely adopted by other market participants. This has led to the proliferation of esoteric strategies, especially in the quantitative arena. In order to generate alpha in the increasingly competitive asset management industry, you need an army of PhD’s, complex strategies, and troves of data, right? Well, not necessarily.

 

KISS_Sep2020

 

Analysis of the Alpha Theory dataset shows that if managers simply exit all positions where probability-weighted return is zero or negative, the average manager’s CAGR would improve by 3%!

 

Alpha Theory managers create a probability-weighted value for each position based on price targets and probabilities for the various scenarios which may play out in the market. In an ideal long scenario, the current market price of a security will increase towards the probability-weighted value. As price and expected value converge, probability-weighted return drops to zero, and the analyst should either revise price targets upward, trim, or exit the position all together. If expected return is zero, Optimal Position Size will recommend exiting the position, as there are other investments with greater expected return.

 

Sometimes, however, managers are slow to update price targets, or to reallocate the portfolio to higher expected return investments. We compared the return on invested capital (ROIC or total return/gross exposure) of the manager’s actual portfolios to what ROIC would have been if managers were only invested in positive probability-weighted return positions. This means a long position would only be in the portfolio if the probability-weighted return was positive, and a short position only if the probability-weighted return was negative.

 

The data below shows the improvement in ROIC over actual for simply removing positions with negative probability-weighted returns (blue column) and then for Alpha Theory’s Optimal Position Size (gray column), which layers on additional sizing logic in addition to zeroing out positions with zero probability-weighted return. The sample includes all Alpha Theory clients from January 1st, 2014 to June 30th, 2021.

 

Improvement Over Actual ROIC

Returns on manager portfolios of only the positions which had a directionally accurate positive probability-weighted return had a 3% higher CAGR, and returns on Optimal Position Size, which uses manager research as well as other portfolio constraints, improved CAGR by 6.7% over actual ROIC.

 

Highly intelligent, sophisticated investors look for ways to improve by default, and the temptation to distinguish oneself with new strategies is intense. But our research suggests that it is more important to focus on the fundamentals. John Wooden’s insight that free throws contribute to national championships also applies to portfolio management. Having high research coverage, updating price targets, and being allocated to positive returns are simple rules which contribute to outperformance, but which are often ignored at the expense of alpha.

 

August 31, 2021

Caveats in Compounding

 

“Compound interest is the eighth wonder of the world. He who understands it, earns it; he who doesn't, pays it.” – Albert Einstein

 

“Compounding is the most powerful force in the universe” – Albert Einstein

 

“My wealth has come from a combination of living in America, some lucky genes, and compound interest.” – Warren Buffett

 

Compounding really is the 8th Wonder of the World. In a recent analysis, we were comparing the CAGR (Compound Annual Growth Rate) of two portfolios and noticed two unique qualities of compounding that are important to remember when using CAGR:

 

1. Small differences in CAGR can compound to large differences over time

2. A 1% difference in two small CAGRs is not the same as a 1% difference in two large CAGRs

 

Small differences in CAGR can compound to large differences over time

 

Imagine you have two portfolios. One generating 8% per year and another 9%. The 1% difference seems trivial. Because of compounding, it is not.

 

Screen Shot 2021-08-31 at 3.19.42 PM

 

The 10-year performance difference is a non-trivial 20.8%. This means that finding investments that may seem marginally different when compared at the small scale of a year, can have profound differences over time.

 

One interesting fact was that the total difference over ten years was 20.8%, which is not the same as the 1% difference compounded over 10 years, which is 10.5%. This leads to the second unique quality…

 

A 1% difference in two small CAGRs is not the same as a 1% difference in two large CAGRs

 

If we bump the performance slightly up but keep the difference 1%, the total difference grows from 20.8% to 22.6%.

 

Screen Shot 2021-08-31 at 3.19.51 PM

 

That 1% difference gets to compound a bigger base and thus results in a larger total return difference. This is counterintuitive. An investor may be indifferent between a 23% and a 24% return while being sensitive to a 2% versus 3% return. The later seems much more meaningful because the relative difference is 50%.

 

In the graph below, the difference between a 2% and 3% return is $12.5M (12.5% on $100M fund) over 10 years. The difference between a 23% and 24% return is $62.1M! They are both 1% differences, but they are not created equal.

 

Screen Shot 2021-08-31 at 3.21.54 PM

 

Compounding is amazing but can be amazingly difficult to conceptualize. As an investor, your job is to be a professional compounder. Keep your tools sharp by remembering that CAGRs don’t tell the whole story. To get a better sense of the return stream, compare the CAGR to the total return for a period of time and then perform some basic sensitivity analysis. This allows the compounding impact on returns to present itself in a way that is easier to put into perspective and help you make better decisions.

 

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.

 

May 28, 2021

We’re Getting Better All The Time

 

When we do our year-end review with clients, a bulk of the conversation is about the performance of the systematic portfolio built by Alpha Theory versus the client’s actual returns. The conversations are always informative but, as you might imagine, the systematic portfolio doesn’t always outperform. We did some recent analysis to understand the frequency of systematic portfolio outperforming.

 

We started by looking at individual positions and took all positions, all time, all managers and simply looked at the lifetime returns of the security in the actual and systematic portfolio. In this case, the systematic position outperformed 55% of the time. That’s a pretty good batting average.

 

We then rolled that up to the analyst level. For analysts with at least 10 positions over time, the hit rate rose to 66%. Said another way, the analyst would have been better off 60% of the time if they would have sized all of their positions using the systematic method versus what actually occurred.

 

The next step was to roll up to the client level and simply comparing the return of the actual and systematic portfolio. The first cut was by year. In any given year, a fund has a 67% chance that the systematic portfolio will outperform its actual portfolio.

 

Then, we rolled it up for each client on an all-time basis. For example, for a six-year-old client, we would compare their six-year actual and systematic portfolio returns. In this case, the systematic portfolio outperformed 76% of the time. 

 

Finally, we rolled all clients together into an actual and systematic portfolio over our clients’ nine years of historical data. 100% of years, the systematic portfolio outperformed.

 

All Funds All Time – 100%

All Funds By Year – 100%

By Fund All Time – 76%

By Fund By Year – 67%

By Analyst All Time – 66%

By Ticker All Time – 55%

 

This data is starting to resonate for clients as the correlation between actual and systematic has increased over time. In 2015, the average correlation was 35%, today it is 57%. The difference between 57% correlation and 100% is why there is a difference between the actual and systematic. The challenge is that the systematic portfolio requires diligence and higher trading activity than typical for our uber-fundamental managers. The best solution may be a hybrid where the fundamental manager does what they do best, fundamental research. Then a systematic overlay is applied to construct the portfolio, manage risk, and ensure the best execution. And, so, we’ve decided to partner with our clients and build that strategy through CenterBook Partners. We look forward to sharing more about it as it develops.

 

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.

 

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.

 

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.