Interviews for Intelligent Investors Toby Carlisle Wes Gray

7/27/2019 Interviews for Intelligent Investors Toby Carlisle Wes Gray

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Interviews for Intelligent InvestorsAConversationwithQuantitativeValueInvestors:TobyCarlisle&WesGray.MiguelABarbosa2013


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TableofContents

Part1:Introduction................................................................................................................3

The Historical Case For Quantitative Value Investing ................................................... 4Models vs Experts........................................................................................................... 4

Part2:TheQuantitativeValueModel...............................................................................7

The Checklist Approach To Quantitative Value Investing.............................................. 8Cheapness is Everything ............................................................................................... 11Obstacles ....................................................................................................................... 12Protecting Capital.......................................................................................................... 13Criticisms & Misconceptions........................................................................................ 14

Part3:ApplyingTheQuantitativeValueModel.........................................................16

Rebalancing, Holding Cash, & Managing Taxes .......................................................... 16International Investing .................................................................................................. 17

Part4:ClosingThoughts....................................................................................................18

Appendix:Toby&WesonJoelGreenblattsMagicFormula..................................19


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Part1:IntroductionMiguel: My name is Miguel Barbosa. With me are my friends Toby and Wes.

They've written a book called Quantitative Value: A Practitioner's Guide

to Automating Intelligent Investment and Eliminating Behavioral Errors.

Miguel: Thanks for joining me.

Toby: Pleasure. Thank you for having us.

Wes: Thanks.

Miguel: What question does your book answer?

Wes: The book is about answering the question, What is the most efficient and

effective way to screen for stocks? The initial answer is that it probablyinvolves a value approach. The philosophy of trying to buy stocks that are

cheap works- and theres a lineage of research and evidence supportingthis approach.

Miguel: So tell me more about the research on why value works.

Toby: Value strategies outperform but the reasons are not because cheap stocks

are more risky. It's more likely that value strategies are exploiting thenaive strategies that other investors employ. A naive investormight not

fully appreciate the phenomenon of mean reversion, which is that an

undervalued stock tends to mean revert to about the average valuation.Value investing seeks to exploit the behavioral and suboptimal errors thatother investors make.

Miguel: So why did you decide to use a quantitative approach (along with a value

approach)?

Wes: Although, we wanted to screen for cheap stocks, screening for cheapnesswithout accounting for risk concerned us. We were also worried about our

own behavioral biases. So, we decided to use a quantitative model toremain objective and disciplined.

We married the two approaches -value investing & quantitative model

trading- and call it quantitative value. We argue that quantitative valueinvesting is the most efficient and effective way to screen for stocks.


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TheHistoricalCaseForQuantitativeValueInvesting

Miguel: So, you are combining value investing (a la Graham/Buffett) with thequantitative (from Ed Thorp).Tell me more about the historical basis forusing the quantitative value approach.

Wes: Theres certainly a precedent starting with Ben Graham. In much of hiswriting he outlines quantitative value strategies. Grahams strategies are

not as sophisticated as ours, not because he didn't know about this stuff,but rather because he didn't have computers or the ability to mine

databases. Grahams principles -of focusing on simple value indicators andmaintaining discipline-still apply.Of course, another value approach that developed from Grahams was

Warren Buffetts, which, evolved to analyzing the whole spectrum ofvalue. Buffett realized that although value investing meant purchasing a

manufacturing firm at five times earning, it could also be Coca Cola at 15-20 times earning because they may have some sort of economic [moat]

that could generate enormous returns on capital. What we are doing istaking Ben Graham's and Buffetts principles to the next level.

ModelsvsExperts

Miguel: Tell me about your argument for quantitative models beating experts.

Toby: We already mentioned that value investing tends to outperform because of

behavioral mistakes made by investors. Unfortunately, these errors applyto both inexperienced and experienced investors (ie. experts). It turns out

that there's a lot of research on experts making errors. Most of thisresearch exists outside of finance but it applies to financial decision-

making.

One research study along these lines involves a simple test given topatients as they were admitted to hospitals to determine whether they were

psychotic or depressed.Apparently the symptoms are quite similar.So in this study, the results of a simple test were compared against thediagnoses of inexperienced and experienced doctors. They found that

experienced doctors make the correct diagnosis more often thaninexperienced doctors. But the simple model -with a few questions

administered by an inexperienced person-performed even better than themost experienced doctor.

The really interesting thing is that in a repeat study, experienced and

inexperienced doctors were provided with a copy of the test and asked to


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administer it. What they found is that; although their ability to diagnoseimproved, the simple test still outperformed both the experienced and the

inexperienced doctors. The conclusion was that simple modelsoutperform(ed) even the best experts.

Wes:In general experts or people doing forecasting get evidence

andset up

their prediction. Forecasters then collect additional information, but, onaverage, this extra information doesnt improve the accuracy of theirforecast. However, the additional information does increase theconfidence of their forecast. In the end, the forecasters new prediction is

no more accurate than the original, but they are much more confident(often overconfident) in their predictions. This can lead to erroneousdecision making.

Toby: An example would be astudy that we discuss in thebook, which looks athorserace handicapping. This study is relevant because like investors

handicappers have to makeprediction

sunderuncertainty

.

The experiment goes as follows: Researchers asked them for 20 factorsthat would determine the outcome of a race. Researchers then broke these

20 factors into five groups of four and randomized them so eachhandicapper would get a different group of variables. Each handicapper

was then given pieces of information to make a prediction about theoutcome of a horse race and was required to make a prediction. They were

subsequently provided with another group of factors and asked whetherthey wanted to change their prediction and then how confident they felt.

What the researches found is that the accuracy (based on the predictions vs

actual outcomes) didn't improve when handicappers were provided withadditional blocks of information but their confidence in the accuracy of

their predictions did increase. The reason that this occurred is that wecollect additional information we filter it on the basis of the information

that we already understand. So, we create a story, and as we encounternew information weaccept it or reject it based on whether it fits our initialstory.

So two experts could be sitting next to each other with differentperspectives generated from getting different pieces of (initial)

information and they wouldn't change their mind even if the next piece ofinformation they got was the one that the guy sitting beside him had

received.

Wes: What Toby is describing happened to mewhen I was a stock picker. Iremember a stock I invested incalled Combimatrix.I was like "Oh man,this is great," I did my research, visitedthe company, met the CEO, etc. Iuncoveredeverything you could ever want to know about the company.


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Well, I lost 95% of my investment and it was because of what Toby isdescribing. Every single time I collected more information I was moreand more convinced that itwas a greatinvestment.Unfortunately,all myadditional research was reinforcing my original hypothesisand making memore confident.

As the stock dropped, I bought more--how could I lose?

I think this phenomenon of more information translating into higher

confidence but no corresponding increase in accuracy, represents a hugedanger for bottom up investors. These investors often think they areconducting value-add research when in fact they are just convincingthemselves of the soundness of an ill formed idea.

Miguel: Yeah, my ex-boss used to say, "Within the first 30 minutes to an hour Iknow if it's the type of thing I want to buy or not and then Imjust lookingfor details to disprove mythesis."And he used to tell me all the time, "Be careful, because you can know too

much." This happened to me often. I would go into such detail, that I lostall sense of perspective. I couldnt zoom out and weigh the factors that

really mattered.

Wes: Yeah.


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Part2:TheQuantitativeValueModel

Miguel: Let's talk about your quantitative model. What data sets did you use andwhat universe did you cover?

Wes: The data sets we use are pretty much the standards in academic andpractitioner research. For fundamentals data, we use Compustat and forreturn data as well as delisting data we use CRSP, which stands for the

Center for Research in Security Prices.

Nowas far as universe of stocks, we know that you can make the returnslook very favorable (i.e. 20%-25% CAGRS) when you include microcaps,

small caps, etc. Our research process didnt involve data mining for rareartifacts. We looked for robust strategies that could work in liquid markets.

This way we could develop strategies that werebelievable and useable for99% of people (and institutions).

To do this, we focused our analysis and strategies on a market cap break

point of the 40thpercentile. This means that every single year on June 30 th

(before rebalancing) we look at the entire universe of NYSE stocks (at that

point and time.) We then percentile rank stocks based on their market capsand use the 40 percentile rank as the minimum cutoff for our universe. As

of December 31 2012, the cutoff mark was around 1.5 billion. So the

model would include stocks that are pretty large. It's really important that

over time you dynamically adjust the market cap cutoff becauseeverything is relative to a point and time. 1.5 billion is not huge today, but

50 years ago it might represent the largest firm in the market.

Miguel: You mention that people misunderstand the dynamic nature of your model.What do you mean?

Wes: Lets use an example.

Lets say that afterperforming some sort of liquidity cutoff we have 1000

stocks in our universe. First,we kick out firms that have a potential forpermanent loss of capital. This means we remove approximately 10% of

firms in our sample--so now we have 900 firms. Next, we filter forcheapness by decile and go to the cheapest decile--this means we have

only 90 firms left. We then calculate quality measures for every singlefirm amongst those 90 firms, and rank them. Finally,we buy the top halfof the 90cheapest firms. So our portfolio for the year would be 45.You see that if you start with a different universe at the initial stepofsay500, the portfolio may only contain 22-23 firms. So thats what we mean

by the model being dynamic, because the construction of portfoliosdepends on the initial sample as well as the number of firms that get


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eliminated. Historically,our portfolios hold between 30 to 40 names.TheChecklistApproachToQuantitativeValueInvesting

Miguel: You present your quantitative model as a mechanized checklist. Thechecklist contains factors for screening stocks (i.e. measures forcheapness, risk, quality, etc). Tell us about the checklist.

Toby: We built our model around a checklist because thats how most value

investors would operate. So we start with cheap measures. Next we lookfor measures that identify risky stocks. That is stocks where you can never

be confident that they have any intrinsic value. There might be earningsmanipulation or fraud etc. We also wanted to consider quality and by

quality we mean, not return on equity, but other quality measures likemargin strength and margin consistency.

Miguel: Tell me more about the individual measures included in the checklist.

Toby: Many of these measures already exist in academia. In some instances we

improve upon these measures. For example, we rearrange the Piotroski Fscore and change several of the variables.

Wes: One of the most important findings in developing the searching algorithm

is the step in our checklist that relates to avoiding permanent loss ofcapital. Most value investors complete this step at the end. We do it at the

beginning because there are a lot of models in academic research shown topredict with statistical significance future frauds, earnings manipulations,

andpotential bankruptcies.

Miguel: Tell me about some of these financial risk measures:

Wes: A lot of people know about a few of the manipulation measures we usesuch as accruals. Broadly speaking, firms that use a lot of accruals are

prone to earnings manipulation--most folks know that. So lets talk aboutsome that your readers will be less familiar with.

One tool for detecting manipulation is a statistical model called the

PROBM Model. This model is based on research that uses a sample of

firms that engaged in past manipulation to identify a set of predictive

variablesthat can identify manipulators in out of sample. Themodel minesthe relationship between a set of firms with a history of manipulation and

these predictive variables. Finally, the model calculates the probabilities offuture manipulation.

There's a second model that can be used to predict financial distress orbankruptcy. It comes from a paper titled, In Search of Distress Risk.


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This is different from measures that people typically use like the AltmanZ. Before elaborating on this model, I want to take a second to talk about

the Altman Z and why it doesnt work (anymore).

Altman Z became popular because Ed Altman published a paper in 1968and became a con

sultant to everyone. Well, it turns out that customersbuying the consulting work havent been paying attention to the last 40years of research because about ten years after Altman wrote that paper,

another paper came out and said, Altman's paper is not correct because hehad look ahead bias in his data. Fast-forward 34 years, and people say,Altman's stuff doesn't even work out of sample.

We like to use the latest in financial technology, so we ditched the AltmanZ approach and focused on this improved financial distress model.Theframework for the model works is exactly the same as the PROBM model.You start by identifingfirms that actually went bankrupt and/or had somesort of serious financial distress problem. Then you find variables that arehypothesized to matter for predicting distress. Variables like recent highvariability in stock prices, recent under performance in the stock price,high debt relative to your assets, no income, etc. Then theauthorsfit thismodel with the sample to data-mine the relationship between actualbankrupt firms and these hypothesized factors (that should be useful in

predicting bankruptcy).

When you apply this model out of sample, it works. It increases theprediction ability by about 15 to 16 percent. It's not perfect and there's

certainly a lot of instances where you're going to get false positives,but inthe context of value investing -especially for long-only investorsthe key

is to eliminate the left tail.

Miguel: What other measures do you find to be extremely important?

Toby: The valuation measures are the price ratios. We studied a number of priceratios, free cash flow to enterprise value, the enterprise multiple that is

EBITDA to enterprise value and the EBIT version. Then we looked attheir performance over a period and found that an EBIT to enterprise value

ratio performed the best.

This finding is something I thought would be likely before we conductedthe testing. But, what I found really surprising was when we tested

permutations of those price ratios such as multi-year averages (so multi-year averages of P/E, EV/Ebitda etc). Those multi-year averages didn't

perform as well as the single slice of EBIT to total enterprise value, whichis interesting. The simple version gives better performance.

The other thing that we did was construct averages of all of those price


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ratios to produce composites. I've seen in various research reports wherecomposites have performed very well and those were my expectations.

When we did the testing it just didn't work out that way. I think weexamined eith year data and it doesn't improve returns. And the other

thing that we looked at was combinations of price ratios. So, free cash

value to enterprise value, EBIT to total enterprise value, price to book,price to earnings, and I've seen in other places where those combinationshave worked and have improved returns but we didn't find that. We found

that they still under-performed the best performing price ratios. It seemsthat as unlikely as it sounds, the EBIT to total enterprise price ratio is the

best measure for value beating out multi-year averages and composites.

Miguel: Wow.Toby: It's completely unexpected.

Wes: Its all aboutrobustness;

it's not that you can't find certain time periodswhere certain ratio worksbetter than another. But the real trick in

performing simulations is looking at performance over awhole cycle.Onaverage, the more complicated the formula, the more the performancelagged.

Miguel: Lets keep talking about checklist measures. Just to reiterate your modelstarts by eliminatinghigh-risk companies (ie protecting downside)out of auniverse of cheap stocks. So what are the next factors that the modelanalyzes?

Toby: Sooncewe've ranked the cheapest companies by decile we look for highquality stocks. We do it, by examining two categories of factors;operationalfactorsand financial strength factors.Ill take a second to talk about the financial strength factors and Wes will

talk about the operational factors.

The financial strength score is similarto the Piotroski F Score. In fact, it'salmost exactly the samebut we change Piotroskisequity issuance gate toa net equity issuance test and we rearrange the sum of the other factorsinto something a value investor would recognize and we call this thefinancial strength score. So the higher the financial strength score, thehigher the quality of the company.

Wes: Lets talk about the operational factors that we use to find quality

companies.

Warren Buffett talks about a durable competitive advantage as a proxy

forqualityand calls it an economic moat or franchise.It's an indicator that


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a firm can earn returns on capital that arehigher than what they shouldearn. Our goal is to try to find factors that would signal the presence of a

durable competitive advantage. To do this we look at eight years of returnson capital,returns onassets, & margins.Let me give you an example ofwhy its important to take a long term view:

Lets say you have a firm that last year that had 100% return oncapital, that's not really necessarily a quality firm because maybe

the prior yearhad -50% return on capital. Maybe they're an oilproducer or maybe the business is just responding to acommodities cycle.

However, if you could identify firms that, say, had 15-20% returns oncapital everyear forthe past eight years that would be an indication that

this firm might be special. So we have fined tuned measures to detect thehistoricalpresence of an economic franchise.

Miguel: So in conclusion what have you found about most factors/measures thatyou have tested(andthat you includein your checklist-model).

Toby: We found that using simple measures results in good returns. But addingwell-known quality type dimensions doesn't provide much of a boost. Atbest you receive a tiny marginal return on top of the return provided by thesimple measures.

Wes: Thats right. One of our board members is an accounting professor at

Columbia. They have a new method for capitalizing R&D, which resultsin more accurate calculations of book to market. We researched the more

complicated and nuanced method and it simply doesnt add muchmarginal value.

Most variable fine-tuning(beyond the basics)doesnt work.Complicationand sophistication dont equate to better results, merely overconfidence inthe model. The key insight is to capture movements of cheap stocks byusing basic metrics. If you can keep things simple, youll capture 95% ofthe result andfilter out the noise. In the end, asimple checklist of simplevariables ends up adding the most value.

CheapnessisEverything

Toby: It also goes back to the fact that the main driver of returns is value .

Wes: Cheapness is everything. That's the biggest takeaway anybody can take

from any of the research we have performed. Quality matters at themargin, but the minute you move out of cheap is the minute you shoot

yourself in the foot.


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Toby: We've done a whole heap of back testing to essentiallyprove whatBenjamin Graham said50-60 years ago.

Toby: This reminds me ofa 1976 article where Graham concludes; that all thateffort in security analysis, might not add a

great deal, because you can

outperform by using cheapness and quality measures. His measure ofcheapness is a PE of less than 10. And his measure for quality is a debt toasset ratio of less than 50%. He said this based on 25 years of data that hehad collected. We researched the same factorsandfound the same resultsexcept that we had much more data.

Wes: Yeah. It's incredibly volatile, but if you want a15% return, this is the wayyou do it. But you betterready for a wild ride.

Obstacles

Miguel: What are the biggest obstacles in applying your model?Wes: I think the biggest obstacle is tracking error. With any value strategy

you're going to get a lot of tracking error, and that's just the nature of the

beast. If you want to be tightly bound to an index, these strategies simplyaren't going to do it for you. They've got huge swings and you're probably

going to lose your job if you're marked to market on a monthly basis. So Ithink that's really the biggest challenge for an institutional money

manager.

Miguel: Toby, whats your take on obstacles to quantitative value investing?

Toby: I thinkthe biggest obstacles arebehavioral. It's extremely difficult to buysome of the stocks in the list. Joel Greenblatt describes a situation where

he compared accounts using the magic formula. There were two types ofaccounts: self-directed and automatic. The automatic accounts boughteverything in the screen and the self-directed ones systematically avoidedthe biggest winners. This happened because they don't look like they're

going to generate a good return, which is exactly what makes them suchdeep value in the first place.

An example would be buying Blackberry.

I had conversations with peoplein June of 2012 and at the time that they said, " there are a variety ofreasons why you shouldn't be buying the stock. One its atechnologyfirm,and it's losing market share to Apple and Samsung," but Wes bought itanyways.

Wes: Yeah, that's rightwe bought RIMMin June of 2012. I really didnt want todo it. But I follow models. Jim Simmons says, If you're going to use


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models, slavishly follow your model.

Another classic example is GameStop. A very well known short sellertouted GameStopas a short--Jim Chanos. He is so well known that if hesays something bad about your stock, you know everybody hates it, even

value investors. But GameStop showed

up in the screenon June 2012and

has doubled since then. So I'm of the opinion that this quant value systemis not only contrarian to what the market says, but also to most value

investors. Its picks the safest trash that nobody would ever want to touch--even value investors!

ProtectingCapitalMiguel: If I wanted to apply this strategy, one of the things I'd be worried about is

protecting my capital duringmarket-widedownturns. Tell us about howyour approach protects capital in a variety of market climates.

Wes: The question of how well our model protects capital is an empirical

question. If you compare risk measures such as max drawdowns, ourmodel outperforms standard mechanical value strategies and value

managers. Even in the most turbulent times a long-only fund using ourstrategy has a 37% drawdown during the financial crisis versus the S&P

500 which endured a 50.21% max drawdown.

The real question is why do we outperform? The answer is that downsideprotection isbuilt into the model. Starting with the first step of avoidingpermanent loss of capital (i.e. frauds etc). The model cuts off the left tailof potential -100% percent investments. Even if you have a diversified

portfolio of say 30-40 names, a negative 100 percent position hurts yourreturns. So this is one of the reasons we outperform from a risk

management perspective.

Next the model protects downside by focusing on cheapness. We alreadyknow from Ben Graham that cheapness provides a margin of safety, which

prevents permanent losses of capital. But we go even further.

After looking for a margin of safety we look at quality measures, whichcorrelate with economic moats. Now I have to clarify that having a moat

isnt a surefire way to protect capital because a business can have an

economic moat but also be unable to pay a debt payment in which case its

going to go bankrupt. But we do look at economic moats because cheapfirms with moats can compete effectively (and outperform) during down

terms.

Finally, after taking all these steps we incorporate what I call a preflightchecklist which is literally a ten point checklist, akin to a PiotroskiF scorebut with slight improvements. This checklist looks at factors like:


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1. Debt levels2. Working capital3. Operations improving4. Capital allocation/Net Stock Issuance5.

Profitability

So when markets tank our process minimizes capital loss. This is what we

think drives the empirical evidence for our outperformance.

Criticisms&MisconceptionsMiguel: What are the biggest criticisms and misconceptions of your quantitative

value model?

Wes: The biggest criticism is that people dont believesimplemodels can beatexperts. It's very difficult for an expert, who's invested 30 years of their

life, to hear people say that a model can beat their instincts. So the biggestcriticism we get is that itjust can't be trueand wontwork. There's noway a model can beat me because I've put all this time and effort intolearning about finance.

My only defense against these critiques is the data. It's an empiricalargument and so I point naysayers to the data. What makes our systemwork is the presence of people who think they can rely on gut instincts to

outperform. Some will outperform but of course most wont. So, I wouldsay, we have a different philosophical view and our view is based on data.

Toby: Many people argue that the people using models arent experts. But, wefind that even when experts apply models we get similar results themodels win.

Toby: Another criticism I get is from value investors. They tell me that it's the

undiscovered footnote in the financial statements that makes the difference

between something being a buy, sell, or pass. They claim that we're not

going to be able to find these issues because we're not performing agranular analysis of the footnotes.

We addressed this earlier with the horserace handicapper experiments.

When we mentioned that there is research showing that collecting moreinformation when making afuture prediction doesnt increase the accuracyof the prediction. Rather it only increases the confidence in the accuracyof the prediction.

As an investor gets more and more granular information (i.e. footnotes)they run the risk of becoming wedded to a position and filtering


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everything through the same lens. And in this case even though they thinkmore information will increase their returns it only increase their

confidence in their returns.

Wes: I also want to stress that Toby and I have not been quants our whole life. Iwas raised on the religion of value investing and picking individual

stocks.It took me 15 years to get over the hurdle of using models instead ofpicking stocks.

It's also not the case that either of us are quant geeks with zero market

experience. People often say, All these PhDs, they just don't understand.Well, in our case thats wrong because we do understand. We've been

there. I've managed a fund as a stock picker. It was terribly stressful andmade my hair turn gray.

I think we both generally came to the philosophy of quantitative value

after being stock pickers.We both tried many different value approaches.But eventually both asked, Am I really that smart? Am I smart enough to

outsmart myself? I came to the realization that I'm just not that smart.

Miguel: So what othercriticisms are you receiving?

Toby: One criticism is that there's some disappointment that the price ratio weended upusingwas EBIT to total enterprise value, but we've tested longterm averages and we've tested composites in the book and we didn't findanything that beat EBIT to total enterprise value, which I think issurprising, because Greenblatt uses the same ratio and found that itmassively outperforms the market.


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Part3:ApplyingTheQuantitativeValueModel

Miguel: How can the average intelligent investor apply your quantitative model?

Wes: There's multiple ways you can go about it. Weve built some free tools and

you can see them at TurnkeyAnalyst.com. On this site we run the modelseach month and provide names for investors to investigate.

Then if you're a do-it-yourself type, go to Yahoo! Finance. Run screensusing EBIT to enterprise value and then screen for some of our measures

(in the checklist). So for example after screening with Ev/Ebit use grossprofits to total assets. If you do this you're going to capture 90 -95 percent

of the benefits of being systematic. Maybe you're not going to get theextra 100-200bps but you're going to get the majority of our results.

That said, you have to remember that with systematic investing you have

to follow the models otherwise the strategy wont work. So, if the modeltold you to buy RIMM, would you actually do it? If you are the type of

person that's going to tinker with the model -picking the stocks you likeand throwing out ones you don't like-you're running a serious risk.

Rebalancing,HoldingCash,&ManagingTaxes

Miguel: Lets say Im a do-it yourself investor interested in applying this strategy.How often do you rebalance your portfolio and what do you think about

holding cash?

Wes: Effectively we have tax-managed versions of the model and non-taxversions. There is better performance with more frequent rebalancing but

it comes with a huge tax burden. For the tax-managed version werebalance annually and perform tax harvesting at the end of the year. As

for which month to rebalance; if you don't care about taxes, then eachmonth you add in the latest names that are highest ranked and sell your

lowest ranked holdings. Also, during this time you would rebalance toequal weight (per position) for risk management.

Cash management can be tricky. A typical example where this would

occur would be mergers and acquisitions. A lot of times you have portfoliocompanies being bought-out. This happens because you're buying a bunchof cheap companies that private equity funds eventually takeout. You need

a system to deal with these situations.

This is how we handle cash; if at the end of the month there's over 1%

cash we rerun the model and add a new name. Otherwise, if cash is below

one percent we wont add anything. This will result in a slight drag but the


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tradeoff between the performance and costs of trading and taxes offset.That's just our internal rule. People could do it differently but the

execution is tricky.

InternationalInvesting

Miguel: One quick follow-up. How does the strategy work with international

investments?

Wes: We don't do it. We'recurrently developing the systematic models to investinternationally. The problem with international companies is data. Our

model requires a massive amount of data with a long tail of at least eightyears. In many international markets the data's just notavailable (exceptfor very large companies). In those cases you have to simplify the modeland screen for a few factors. Toby can tell you more about his approach

because he focuses globally.

Toby: That's exactly what I do. I use a simplified version of the model thatdoesn't require data that doesn't exist. It allows me to buy stocks in

developed markets around the world.

Recently, the best place to investover the last year has been in the U.S. Tothe extent that you weren't exposed to the U.S., you slightly

underperformed. I've been balancing towards other countries as they'vebecome cheaper and away from the U.S. I just have too short of a period

of data to give you an answer. I think it needs a full cycle to becomparable to the research we have done in the U.S.


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Part4:ClosingThoughts

Miguel: What would be the best way for us to follow your work?

Toby: Well you can see what I write on Greenbackd.com. I post research both

my own and as well as academic research from others. I also run aRegistered Investment Advisor called Eyquem.

Miguel: Wes, what about you?

Wes: We have two venues. First would be the TurnkeyAnalyst which is awebsite designed to democratize quant strategies and release research for

retail investors. I also run an SECRegistered Investment Advisor calledEmpiritrage LLC. At Empiritrage we provide research that's tailored to

institutions and full timeprofessionals.Miguel: Thank you guys for taking the time to answer our questions.

Toby: Thank you very much, Miguel.

Wes: Thank you, Miguel.


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Appendix:Toby&WesonJoelGreenblattsMagicFormulaMiguel: The value community is obviously very keen of the magic formula. What

have you guys learned aboutmagic formula?Wes: Lets start with some background. Ben Grahams quantitative model was

designed around only buying cheap stuff. Buffetts approach is to look for

value across the spectrum; where value is not just what you pay but alsowhat you get. Buffetts strategy was very successful and also seems to be a

very sexy idea to quantify and automate. The magic formula is aquantitative Warren Buffett strategy.

The magic formula quantifies the Buffett criteria. Say you have a universe

of 1000 firms. Magic Formula ranks them on cheapness (using EBIT totalenterprise value) and on quality (using EBIT over capital) from one to

1000. So you have two columns a cheapness column (in order fromcheapest to most expensive) and a quality column (in order from highest

quality to lowest quality).

Next, the Magic Formula takes the average of those two ranks and re-ranks all the firms. The idea is that now you have a quantitative Buffett

system where it's not just focused on cheap stuff, it's focused on all valuestuff. This means, that in Magic Formula, a firm that's of high quality,

even if it's expensive on a price ratio still might be better than buying areally cheap low quality firm.

So the magic formula is attractive because it quantifies Buffett'sphilosophy and anyone who looks at Buffett's track record says, Wow,that's something we should probably think about. Fundamentally, the

question is an empiricalone because Buffett is an anecdote. He is not arobust study of the data. We like to do robust studies because there's

always going to be some sort of outlier.

When we perform the tests what we've found is that it's essential for aframework to start with cheapness and then focus quality. So the order in

which you look for things matters. The minute you step away from thebargain bin is the minute, -that from an on-average perspective-you are

shooting your self in the foot.

We talk a lot about this in the book and demonstrate empirically that themagic formula underperforms our quantitative value system because it

sometimes buys firms that on a straight price basis are expensive. Eventhough it may feel like a good deal to pay 20 times for Coke, on average

that's actually a bad bet. It's critical that your framework gets to cheap firstthen looks at quality within the cheapest stuff. That's going to maximize


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the probability of you capturing high risk-adjusted returns. So thesummary is that from a quantitative perspective Warren Buffett was wrong

and that his teacher, Ben Graham, was right.

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Interviews for Intelligent Investors Toby Carlisle Wes Gray