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Alpha Theory Blog - News and Insights

17 posts categorized "Probability-Weighted Return"

July 31, 2020

Performance During The Pandemic (Part 1)

 

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

 

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

 

Returns

 

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

 

Net exposure

 

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

 

Long exposure

 

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

 

Short Exposure

 

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

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

 

Long ROIC

 

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

 

Short ROIC

 

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

 

ROIC

 

June 19, 2020

The Short End of the Stick

 

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

 

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

 

Short_end

Source: Omega Point

 

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

 

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

 

Screen Shot 2020-06-19 at 11.21.34 AM

 

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

 

May 15, 2020

Alpha Theory Announces Partnership with New Constructs

 

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

 

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

 

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

 

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

 

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

 

Earningsdist

 

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

 

Full press release can be found here

 

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)

Picture5

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.

 

Picture6

 

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).

Picture7

 

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%!

 

Screen Shot 2020-03-06 at 4.24.13 PM

 

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. 

 

Chart1

 

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.

 

Chart2

 

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

 

Screen Shot 2020-03-06 at 5.10.04 PM

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.

 

Screen Shot 2020-03-06 at 4.25.21 PM

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.

Screen Shot 2020-03-06 at 4.25.28 PM

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.

 

February 8, 2020

Doing More with Less – Cliff Asness Illiquidity Discount Article

 

We’re all familiar with controls that point you towards the right decisions because knowing what to do and doing it are not the same thing. This is why our car dings until we put our seatbelt on, why there are signs reminding your server to wash their hands, and why we hire personal trainers. But what about blinders that help you avoid making bad decisions?

 

There are studies that show how a store that offers more options can cause customers to buy less because the extra information confuses the buyer's decision and causes them to make no-decision. More germane to our field, I know of funds where the PM restricts themselves and their analyst teams from checking P&L because they’ve attributed it to poor decision making. In this case, less information is more. Would you pay for less information?

 

In Cliff Asness’s latest piece, The Illiquidity Discount, he discusses that concept in the context of asset pricing. What is it worth to not know the price of an asset if knowing the price caused you to sell and buy at the exact wrong times? Where the artificially smoothed volatility of infrequent pricing was a feature.

 

The preference for illiquid, infrequently-priced assets that don’t smash you in the face with their volatility (even though it’s really there) could be rational in the same sense. Perhaps a levered small-cap portfolio is a rational investment for long-term investors, but there’s little chance they’d stick with it full-cycle. However, they find PE easy to stick with? It’s not hard for me to imagine these are both true for some (or many).

 

Finally, to address our main topic, what’s the next implication of extreme illiquidity and pricing opacity being a feature, not a bug? Well, you pay up in price (and give up in expected return) for features you value (not bugs you can’t stand). Attractive smoothness of returns may not come for free. If illiquidity is more positive than negative to many investors, it could easily mean paying a higher price and accepting a somewhat lower return to obtain it. Sounds really counter-intuitive, I know. But it also sounds, to me, pretty plausible.

 

I appreciate those that question conventional wisdom. I especially appreciate it when it is done in the pursuit of better decision making. There is something beautiful about simple hacks that help us make better decisions (i.e. that’s what we do at Alpha Theory). I think we’ll be spending more time at Alpha Theory in the coming months (years) thinking about if there is the information we present (or maybe the timing of that information) that may lead to sub-optimal decision making and what changes we can make to improve how/when information is delivered.  

 

December 1, 2019

Capturing Alpha in Risk Rewards - Morgan Stanley

 

Morgan Stanley has one of the most robust sets of scenario-based price target forecasts in the world with around 70,000 forecasts over 10 years. Naturally, they decided to evaluate the quality of their analysts’ forecasts and the results were positive. In the chart below, their price target, scenario-based strategies consistently created positive alpha.  

 

Screen Shot 2019-11-25 at 2.18.00 PM

 

The model was built by evaluating analysts’ scenario analysis to determine buy and sell signals by using measurements and trends on the variables of Downside, Tilt, and Uncertainty. The magnitude and number of those signals determined the weighting in the hypothetical portfolio.

 

Screen Shot 2019-11-25 at 2.22.41 PM

 

They determined that there was a demonstrable benefit in using a scenario-analysis instead of a single price target.

 

Screen Shot 2019-11-25 at 2.22.56 PM

Screen Shot 2019-11-25 at 2.23.12 PM

 

Breaking the analysis down to its components (individual scenario analyses) showed consistent predictive quality from the scenarios analysis as measured by the pre-cost hit ratio (the percentage of long/short signals that generate higher/lower returns than the total return of the equity index). While a mid-50s hit rate may seem marginal, it is substantial. It is enough to create consistent outperformance, as we measured by observing our managers with consistent hit rates above 50%.

 

Screen Shot 2019-11-25 at 2.46.37 PM

 

The Morgan Stanley analysis is substantive in two ways. First, it supports our research that scenario analyses have predictive power that can be utilized to create positive alpha strategies. The second is our suspicion that buy-side manager scenario analyses are superior to sell-side forecasts because of their real-world application, their lack of administrative constraints, and lack of investment-banking conflict. If that is the case, Alpha Theory forecast dataset should have predictive power superior to that in the Morgan Stanley analysis.

 

This article is one of a long series of “Empirical Proofs” of active manager skill that we’ve been collecting. To see the full list, download a full version of the Concentration Manifesto.

 

November 1, 2019

Concentrating on Concentration: New Data on Portfolio Concentration

 

As most of our readers know, we are proponents of more concentrated portfolios. In May of 2017, we released our Concentration Manifesto which was an attempt to get a critical dialogue started between managers and allocators to ultimately improve the active management process. A conversation that requires both sides cast aside outdated thinking and embraces the notion that concentration is in their best interest.

 

And we’re seeing it in external data:

 

Exhibit 19

 

And in our own managers:

 

AveragePositionSize

 

This conversation began well before our Concentration Manifesto. We recently found an April 2014 study by Cambridge Associates outlining the “Hallmarks of Successful Active Equity Managers.

 

Cambridge Associates analyzed a selection of managers to isolate attributes that lead to success. In their findings, active share and concentration were major contributors. Their analysis1 found that concentrated portfolios (US equity less than 30 positions and US Small-Cap & EAFE Equity less than 40 positions) generated between 100bps and 170bps of additional performance over non-Concentrated portfolios.

 

Table-3.-Results-of-Active-Share-Analysis

 

The performance difference for concentrated managers held after fees and worked across various strategies. The fractal nature (it still works when you break it into different strategies) lends additional validation for concentration’s benefits.

 

In the Cambridge article, we found a reference to another concentration study.

 

Baks, Busse, and Green published “Fund Managers Who Take Big Bets: Skilled or Overconfident” in 2006. The abstract says it all:

 

We document a positive relation between mutual fund performance and managers' willingness to take big bets in a relatively small number of stocks. Focused managers outperform their more broadly diversified counterparts by approximately 30 basis points per month or roughly 4% annualized. The results hold for mimicking portfolios based on fund holdings as well as when returns are measured net of expenses. Concentrated managers outperform precisely because their big bets outperform the top holdings of more diversified funds. The evidence suggests that investors may enhance performance by diversifying across focused managers rather than by investing in highly diversified funds.

 

Their sample covers funds from 1979-2003 and the return advantage per month ranges between +1 and +67 basis points depending on the methodology for measuring fund concentration and how many deciles to included. That equates to a range between +0.12% and +8.34% on an annualized basis for concentrated managers.

 

Fund perf vs. portf weight

 

We continue to believe that there is a demonstrable skill in equity managers and that the skill could be harnessed in better ways than is typically demonstrated by the average manager and that concentration is the simplest way to improve a manager who possesses positive stock-picking skill.

 

1 eVestment Alliance Database: September 2007 to June 2013 US large-cap core equity, US large-cap growth equity, US large-cap value equity, US small-cap core equity, US small-cap growth equity, US small-cap value equity, and all EAFE equity

 

Download full version of the Concentration Manifesto

 

October 4, 2019

The Difference between Intrinsic and Extrinsic Value – A Case Against WACC

 

In one of our blogs, we highlighted how our clients’ returns would have enhanced returns by more closely following a position-sizing optimization based on probability-weighted return. We also noted how the quality of the probability-weighted returns impacted the improvement generated by the optimization (garbage-in garbage-out). The return our clients calculate is the difference between the market’s valuation and the manager’s calculation of intrinsic value (probability-weighted return). Said another way, gaining a sense of the intrinsic value is the core task of a portfolio manager.

 

For managers that use discounted cash flow analysis to determine intrinsic value, the discount rate is one of the most subjective, yet important, inputs. For managers that do a scenarios analysis, we pointed out a straightforward approach that dramatically reduces subjectivity (June ‘18 blog). However, for those that don’t do scenario analysis, determining the discount rate can be substantially more complicated.

 

We’ve recently spent some time with Ryan Guttridge and his colleague Corry Bedwell of the University of Maryland and they have some interesting ideas on setting discount rates that I thought were worth sharing:

 

Let’s Think about What Intrinsic Actually Means

 

An intrinsic property is well defined in sciences such as chemistry. An intrinsic property is an essential or inherent property of a system. Said another way, an intrinsic property is internal to the system being evaluated like specific density is an intrinsic property of water.  In contrast, an extrinsic property is not internally defined by the entity being evaluated. So, think about them this way:

 

Intrinsic – Totally independent of outside influence

Extrinsic – An influence outside the system

 

Why We Discount – The Marshmallow Test

 

Put a group of kids with a plate of marshmallow’s (or any other tasty treat) in a room and leave. What will happen? Chances are by the time you come back the treats will be gone. Why? Well, something now is better than something later. Ok so change the rules, offer the kids a deal. If they don’t eat the treat right away they will get another one at some later time, growing their supply of marshmallows. So, the deal from the kids perspective, I give up eating the marshmallow now and get two later. What makes it worth it? First, he has trust that the marshmallows will be delivered as promised. Second, the time he must wait for a reward can’t be too long, given the fact he can always eat the marshmallow now. In other words, the reward has to be large enough to overcome the opportunity cost of eating the marshmallow now. This is the classic economic definition of the logic of discounting. Forgo today’s marshmallow for two tomorrow.

 

Investing – Bringing two separate but intrinsic concepts together

 

Think about what goes through your head when you make an investment. You are going through the same steps described above. First, you will estimate the series of cash flows you expect the asset to provide (when are and how often are the marshmallows arriving). Second, you are going to decide how much you will pay for those cash flows (how badly do you want that marshmallow now). Each of these steps are independent but intrinsic.

However, according to the financial literature, determining an appropriate discount rate (i.e. the opportunity cost) isn’t straight forward. The efficient market hypothesis logically implies the correct discount rate for our intrinsic valuation models is the company’s weighted average cost of capital, WACC. Which is defined in the following way:

 

Screen Shot 2019-09-16 at 3.19.54 PM

 

That is WACC is the portion of capital from equity plus the portion of capital from debt.

 

WACC – A Tale of an Extrinsic Rate

 

So, let’s take this apart -- our recommended discount rate is a function of capital ‘’we” have from equity and the amount of capital “we” have from debt. Right? This has at least two fundamental problems.

 

First, if the market is efficient, there is no reason for active management. However, there is a growing body of literature showing that while large swarths of the market can be considered efficient, there are pockets of inefficiency. So, if these inefficiencies are going to be taken advantage of, logically a discount rate that requires efficiency cannot be used. 

 

Second, an active manager is required to have an independent sense of value. He is hired to find “mis-priced” assets. The only way to do that is to develop an independent view of the correct valuation (i.e. outside the market). So, what do we have that is independent of the Market? Think back to the marshmallow test.  The value of the deal is a function of the opportunity cost (compensation for not buying something else) of your capital, and the intrinsic productivity (read cash flows) of the asset.  

 

What to do about Risk?

 

One way to deal with this problem is to use scenario analysis as mentioned above. For those who don’t use scenario analysis, you need to focus on these three issues when calculating a discount rate: 1) Opportunity cost of capital (possibly provided by your benchmark), 2) the average cash flow level, and 3) its variation. Not only are these intrinsic to the system, but your analysis offers an independent sense of value. This, in turn, allows for the calculation of “edge” (the difference between Intrinsic Value and Market Value) and proper optimization. In our next post, we will expand on the calculation of an intrinsic discount rate using this method. In the meantime, feel free to check out our paper on the topic: https://arxiv.org/abs/1903.09683

 

September 19, 2019

Superforecasting and Noise Reduction in Financial Analysis

 

Alpha Theory and Good Judgement Inc. hosted a Superforecasting workshop this week with several Alpha Theory clients attending and learning about noise reduction techniques. Warren Hatch, President of GJI, led the discussion on how to reduce noise in forecasting. Warren began the discussion with an overview of the characteristics of Superforecasters and what leads to good individual forecasts. We then shifted to how we can use team dynamics to improve forecast accuracy.

 

Warren started with examples pulled from other noise reduction workshops and showed how the team methods reduced noise (measured by the standard deviation of estimate) and increased accuracy (measured by Brier Score). We did our own example using Good Judgement Inc. software to ask questions of our group that led to a valuation of NFLX:

How many subscribers will Netflix have at the end of 2020?

What will be Netflix's revenue per subscriber in 2020?

What will be Netflix's net margin in 2020?

What will be Netflix's PE multiple in 2020?   

 

We compiled the initial results and compared them to current. We then had a chance to review other contributors forecasts and rationales and vote on the ones we thought were best. Next, the “team” discussed the highest vote-getting rationales and quickly identified an expert in the room. Through the noise reduction exercises and discussion, we narrowed our forecast range (reduced noise) and hopefully improved accuracy. We’ll know in a year when we see if NFLX is at $296.00.

 

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Thanks to Warren and team for putting on a great workshop for Alpha Theory clients. Please contact us with any questions.

info@alphatheory.com