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Alpha Theory Blog - News and Insights

34 posts categorized "Institutional Investor"

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

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.

 

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

 

April 4, 2020

A Fundamentalist’s Attempt to Take Advantage of Factor Moves

 

This article is written in collaboration with Omega Point

  

Introduction

For many fundamental investors, factors play little to no role in their day-to-day portfolio construction process. In fact, many fundamental investors aren’t quite sure what factors are. Below we’ll give a basic primer on factors and a potential strategy for fundamental investors to take advantage of irrational movements in their investments caused by factors.

 

What are Factors?

Factors are tools designed to help explain why stocks move. The simplest factor is the market. If the market is down 15% in a month, you expect the average stock would be down 15% as well. So if a particular stock is down 10% (ignoring Beta for this simplified example), then it has generated 5% of positive alpha (to make it really confusing, it is also called idiosyncratic return and specific risk). Basically, alpha is the fundamental piece left over.

 

But the market is not the only “factor” that could explain why a stock moved. It was just the first. Academics and quants have been coming up with new factors since Fama-French added Size and Value to Market in the early ‘90s. Below are some common ones but there are many more:

 

Picture1

 

How is a Factor Measured?

Let’s use the Value factor as an example. Imagine I take every stock and measure an average Price-to-Book (P/B) of 3.5x and a standard deviation of 1.5x. Then I take every stock and measure its Z-Score which is simply the P/B of the stock minus the average of 3.5x divided by the standard deviation of 1.5x. If a stock has a P/B of 5.0x then it has a Z-Score of 1.0x ((5.0x-3.5x)/1.5). To drive the point home, a stock with a P/B of 3.5x (equal to the average) would have a Z-Score of 0.0x and one that had a P/B of 2.0 would have a Z-Score of -1.0x. This is simply how many standard deviations away from the average a particular stock sits.

 

To drive the point home, if you hold a 5% position in a stock with Value Z-Score of 2.0x and another 5% position in a stock with a Value Z-Score of -2.0x, your portfolio has no exposure to the Value factor. This works the same if you are long and short equal amounts of securities that have the same Value Z-Score.

 

How Have Factors Been Moving Recently?

Market turbulence spurred by the COVID-19 outbreak has pressed many factors to multiples far outside of their historical averages. To illustrate this dispersion, the table below lists 12 of the most common style factors used by investors and ranks them based on the number of standard deviations that last month’s performance represented compared to their 10-year average (Jan 2010 – Jan 2020).

 

Picture2

Source: Omega Point

 

If we highlight a few of the factors that typically see minimal month-to-month movement, we see that Profitability sits at the top of the list with a 7.2x standard deviation. Profitability is typically referred to as the ‘Quality’ factor and comprises quality-related metrics such as return-on-equity, return-on-assets, cash flow to assets, cash flow to income, gross margin, and sales-to-assets. It’s abundantly clear that in the present environment, investors have been flocking in droves to quality names.

 

Not at all dissimilar to what occurred during the Financial Markets Crisis of 2008, investors have been dumping Leverage. Leverage is composed of metrics related to total debt-to-total assets and total debt-to-equity and is a factor that moves glacially in a normal market environment.  It ended down 3.44% last month, which actually represented a pull-back as its nadir for the month was -4.13%. Investors are seeing highly levered companies as much riskier than normal in the current environment.

 

Investors are also punishing companies that have supply and distribution chains more exposed to global foreign exchange movements (Exchange Rate Sensitivity) but are more prone to flock to larger market cap names (Size). 

 

These large factor moves may be presenting great buying opportunities for fundamental investors who have seen their stocks move for non-fundamental reasons. But what are some practical methods that you can use to measure it?

 

Factors Movements Create Fundamental Investment Opportunities

As the markets become less and less rational, this may represent a golden opportunity for fundamental investors who track recent factor movements. When looking at their particular universe of stocks, fundamental investors should ask themselves, how much of recent moves are fundamentally-driven, and how much are non-fundamental? This environment presents potential arbitrage opportunities that they can dig deeper on.

 

Using the Omega Point platform (a tool to measure portfolio risk - Alpha Theory has a partnership driven by clients that use both products), below we will go through three examples of individual stocks that represent various sides of the factor opportunity spectrum for fundamental investors:

 

Moderna (MRNA)

Picture3

Source: Omega Point

 

Moderna is a biotechnology company focused on drug discovery and drug development. In January, Moderna announced the development of a vaccine to inhibit COVID-19 coronavirus with a subsequent announcement of an ETA in 2021. If you look more closely at its recent performance, it’s clear that the most significant component of this stock’s move is related to alpha, and much less so for irrational, non-fundamental reasons. The fundamental investors using a factor-based lens to uncover opportunities should skip this one and look at other names in their universe.

 

Avis Budget Group (AVIS)

Picture4

Source: Omega Point

 

Avis represents a more middle-of-the-road example based on a mix of factors and alpha driving its recent movement. Approximately 50% of its move has been related to fundamental factors, while the other half is alpha related. This makes sense, as Avis is in an especially difficult situation right now based on the global travel environment that impacts its core business. Some further analysis may be in order for Avis, but better opportunities may lie with names barely being driven by alpha.

 

Dupont (D)

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Source: Omega Point

 

As shown in the Omega Point screenshot above, Dupont is down over 20% in March but what interests us most here is that 95% of that movement is purely factor related. If we hone in on the sidebar, we can see the breakout of the different factor components. While the market and sector moves seem plausible, there is a 10% downward movement in style factors that may represent instant upside once the factor effect is neutralized. Dupont’s price has been driven almost entirely by factors (i.e. non-fundamentals) and may be a strong candidate for a buy if you agree the move is largely for non-fundamental reasons.

 

There may be several names in your coverage universe that share the factor characteristics of a Dupont, but we need to remind readers that fundamental investors shouldn’t take this type of analysis at face value. Although the increasing precision of factors to describe stock movements have been a huge boon to many investors, it’s still an imperfect science but in our view can give fundamental investors a powerful punch list of ideas which they can pursue.

 

Identifying Good Buy Candidates in Your Portfolio

The table below sorts a group of stocks by the impact of factors vs. their total overall return. Names higher on the list such as Dupont exhibit returns that are much less fundamental, and may warrant additional research by you and your team.

 

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Coupled with a strong fundamental story, a likely good bet in the long term:

- A buy: large negative factor move as a percentage of the total move

- A sell: large positive factor move as a percentage of the total move

 

We encourage you to perform this type of analysis to highlight names that have moved for non-fundamental reasons and compare them to where your position-sizing system is suggesting you make the biggest adds (see below).

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While the current depressed market has been more favorable to uncovering potential buying opportunities, this analysis can be effective for finding both buys and sells in a more normal market environment.

 

Special Offer

While uncovering, researching, and selecting superior stocks will always remain the core focus of fundamental investors, a better grasp of how factors are impacting our portfolios can help us take advantage of irrational behavior. 

 

And to that end, our friends at Omega Point are offering to provide Alpha Theory clients a customized factor-based analysis including the Factor Move for each security in your portfolio. Reach out to support@alphatheory.com or your Customer Success representative to get the details.

 

 

March 6, 2020

Alpha Theory 2019 Year in Review

 

Alpha Theory clients continue to outperform! Over the past eight years, Alpha Theory clients have outperformed their peers seven times, leading to an almost 3% per year performance improvement over the average hedge fund. Over that same period, Alpha Theory’s suggested optimal return outperformed our clients’ actual return every year by an average of 5.5%!

 

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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 (174.8% vs 85.6%) and three times that of the average hedge fund (174.8% vs 51.3%). *Side note: Isn’t compounding amazing?

 

2019 was a really good year for clients as they beat the primary Equity Hedge index by 5.9% despite missing out on 3.4% of return if they would have more closely followed 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) and 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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PROCESS ENHANCES PERFORMANCE

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

 

1.    Centralizes price targets and archives them in a database

2.    Provides notifications of price target updates and anomalies

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

4.    Enhances returns

5.    Mitigates portfolio risk 

6.    Saves time

7.    Adds precision and rigor to the sizing process

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

 

DISCIPLINED USAGE REDUCES RESEARCH SLIPPAGE

Our clients are a self-selecting cohort who believe in process and discipline; process orientation goes together with Alpha Theory software that serves as a disciplining mechanism to align the best risk/reward ideas with rankings in the portfolio. Shown below, the most active users as measured by frequency of updates, research coverage, and model correlation have the highest ROIC.

 

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

 

Usage intensity is determined by:

1. Percent of Positions with Research

2. Correlation with Optimal Position Size

3. Login Frequency

 

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

 

OPTIMAL POSITION SIZING REDUCES RESEARCH SLIPPAGE

Comparing clients’ actual versus optimal returns shows:

 

HIGHER TOTAL RETURNS

ROIC is 4% higher.

 

IMPROVED BATTING AVERAGE

Batting Average is 9% higher. Explanation: many of the assets that don’t have price targets or have negative probability-weighted returns (PWR) are held by the fund but recommended as 0% positions by Alpha Theory. Those positions underperform and allow Alpha Theory’s batting average to prevail.

 

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

2.    Before trading costs

 

PRICE TARGETS REDUCES RESEARCH SLIPPAGE

Alpha Theory has further found that ROIC for assets with price targets is 4.8% higher than for those without price targets. Some investors chafe at price targets because they smack of “false precision.” These investors are missing the point because the key to price targets is not their absolute validity but their explicit nature which allows for an 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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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.