Sorry, The Economy Is Officially Closed

One way to describe what I do for a living is “capital allocation.” Really, I am like an internal strategic consultant to a family business (a family of which I am a part) so there is more to it than that, but thinking about where to put our capital is one of the primary functions I serve.

One interesting problem to have when one owns things of value is receiving bids on those things from people interested in buying them when you’re not sure you want to sell. The further above your own estimate of “fair value” their bid goes, the stronger the temptation to take advantage and sell your asset. It seems like a pretty straight forward problem to solve.

The only problem is the market context of the potential sale. Generally, if you’re in a position to get more than fair value for what you’re selling, you’re going to have a hard time finding another asset to buy where the seller isn’t facing the same dynamic. In other words, you can potentially sell one asset at an inflated price and buy another at an inflated price– you’re probably better off just holding on to what you have because there’s no arbitrage in that and it could very well cost you money in terms of frictional costs like brokerage commissions and taxes on imaginary capital gains.

One thing you could do is sell your asset at an inflated value and sit and wait in cash for a better buying opportunity. The problem with that is that cash is, currently, a seemingly barren asset. If you stuff your haul into T-Bills, you’re lucky to earn a few basis points every 90 days– it might as well be zero, and when you factor in the effect of inflation and those damned capital gains taxes once again, it probably is. You could go further out on the yield curve and buy some 10YR Treasury notes, but then you’re exposing yourself to substantial interest rate risk with yields flirting with historic lows.

Meanwhile, most asset owners are earning strong internal returns on their invested capital right now. Say you’re earning 20% a year on your investments, why would you sell them to collect 1.5% over the next 10 years while taking enormous interest rate risk? Or to collect zero for some unknown amount of time sitting in T-bills or cash in a savings account? Every year you stay invested, you get ahead by almost 20% more. Could the value of your investment really drop by that much?

The business cycle is an inevitable fact of owning and operating a business in a modern economy. The question is not could it, but when will it drop by that much, or more? For many business owners and investors, the waiting is the hardest part. Giving up 20% a year for some period of time and avoiding the risk of a 50-60% or greater decline in asset values just isn’t attractive. It isn’t even attractive when thinking about the fact that buying back those same assets at half price could potentially double your return on invested capital during the next boom, an interesting strategy for shortening the compounding time necessary to achieve legendary riches.

For many, this inevitable decline in asset prices is inconceivable. It’s embedded deeply in the fear of selling and going to cash. The implication of this premise is that the economy is officially closed to additional investment. Those who invested earlier in the cycle can stay inside and watch a magnificent show as they earn outstanding returns on their capital while the boom goes on. But for everyone who sold too early, or never bought in, they have to wait outside, indefinitely, and wonder what it’s like– the cost of admission is just too high.

What makes this a stable equilibrium? By what logic has a competitive market economy become permanently closed to new investment, or a change in asset values, or a change in ownership of assets? Under what set of premises could this condition last for a meaningful amount of time and leave people who sell now out in the cold, starving and bitter for returns on capital, forever, or for so long that they would be losing in real terms over time in making such a decision?

To me, this “new normal” is absurd. It is juvenile to believe that the economy is closed and no one else is getting in. It’s silly to think that the people willing to pay those astronomical prices for admission are making a good decision, that they’re going to have a comfy seat and years of entertainment, rather than paying more than full price for a show that’s about to come to an abrupt end. It’s a topsy-turvy world in which the reckless and courageous high-bidders are the ones who get rich. If paying too much for things was the path to riches, we’d all be there by now. I think when everyone’s perception of reality and value skews toward a logical extreme like this, we’re closer to the show being over than the show must go on.

In the meantime, sorry, the economy is officially closed.

Notes – Best Practices in Deal Flow Origination

These notes are from an article entitled “Where Are The Deals?” by David Teten. He also has resources on adding value to portfolio companies which are worth browsing. For notes on a related topic, check out the “Notes – Stanford Graduate School of Business Search Fund Primer” post.

  • the median investor in private companies had to review 80 companies in order to close one transaction
  • investments sourced through personal and professional networks have been shown to yield better results
  • in order to train your relationships, it is important that you provide them with simple, clear investing criteria, not lengthy checklists; provide them a narrowly defined niche of interest (“Retail brands with $50M in annual revenues”)
  • on average it can take 1-2 years between the first meeting with a target CEO sourced through a network and the close of the deal
  • market mapping, identifying key macro and micro drivers of an industry and creating a database of all key companies; identify those with greatest growth potential or competitive white space
  • specialization enhances deal origination through deeper knowledge base, ability to add value through enhanced network and likelihood of being top of mind to key deal sources
  •  monitor target sector for cyclical opportunities and structure shifts; M&A creates orphan divisions and downturns cause strategy refocuses; 30-46% of PE returns over last 30 years driven by EBIT arbitrage (market timing)
  • other valuable sources of deal flow:
    • regional surveys
    • “fastest growing company” lists
    • trade association membership lists
    • commercial vendors
      • Amadeus
      • Capital IQ
      • Dun & Bradstreet
      • Hoover’s
      • InfoUSA
      • Lexis-Nexis
      • Thomson-Reuters
      • OneSource
  • set up alerts in a blog reader based on key words important to your target or industry focus
  • “A large portion of my deal flow comes from people I have rejected in the past.” be kind to everyone, even those you don’t do a deal with
  • consider having a dedicated, SEO-optimized website and blog for your acquisition fund/team that explains what you’re looking for, why, what you bring to the table, etc.; many VCs and most PE investors are not using basic internet marketing techniques (competitive advantage opportunity)
  • Accel Partners and Khosla Ventures post detailed analyses of their target investment sectors; blogging and posting of internal analyses is the “VC freemium model”
  • PE investing is a relationship business and the most important relationships are with LPs, entrepreneurs, executives and intermediaries which are relatively few in number
  • blogging is the best tool for VC investing according to one experienced observer; helps investor gain information, credibility and relationships through improved visibility
  • look for access to secondary interests through directly approaching funds (particularly distressed), markets for secondary interests (SecondMarket, NYPPEX, PORTAL Alliance) and approaching ibanks specializing in secondary interests (Cogent Partners, Probitas, Triago, UBS)
  • service providers such as accountants, lawyers, etc., are typically not good sources of deal flow because they require too much education and often have a fiduciary responsibility to their client; on the other hand, connecting with service providers in a specialized domain that is being targeted can be a good source of insight
  • trawl the Q&A portion of sites such as LinkedIn to identify domain experts for further outreach
  • measure your deal origination efforts with activity measures, deal flow by source, pipeline analytics and industry benchmarking measures
  • many professional services firms do not use a global CRM system such as Salesforce.com, Act, Saleslogix, Microsoft Access or Angelsoft (angel/VC network)
  • Key data sources for CRM systems include employee networks (ContactNet Enterprise Relationship Management), business cards (Cardscan, IRIS, Neat, Presto), data from email and files (eGrabber, Gwabbit, Grab-Text, Broadlook), the “cloud” (LinkedIn, Spoke, Plaxo) and direct from target companies’ websites, media, etc.

Key attributes of top originators in order of importance

  1. persistence (every no gets you closer to a yes)
  2. personality (people do business with those they like)
  3. business and financial judgment
  4. adequate financial sophistication
  5. seniority and appropriate title (decision-maker)
  6. internal authority to get transaction executed
  7. creativity

Important deal signals when identifying targets (utilize commercial databases, social media, data mining and targeted phone research to uncover)

  • Status of the major equity owner
    • PE funds motivated to sell due to fully invested, raising next fund or current fund has aged beyond 5-7 years
    • Large corp raising cash by selling subsidiaries
    • Time limited tax incentives
    • Family in midst of succession battle
    • Death, disease and divorce (“three Ds”)
  • Status of CEO
    • retirement
    • age
    • acknowledgement of limited competence
  • Corporate performance
    • growth too rapid for self-funding
    • underperforming/distressed
  • Industry/economic trends
    • industry consolidation
      • competitive pressure
      • seeing competitors liquidating equity for large gains
    • competitors raising capital; pressure to maintain parity
    • growth sector

Top considerations for deal intermediaries in directing deal flow

  1. Possibility of future revenue
  2. Integrity
  3. Timely responses
  4. “Fair” treatment of sellers
  5. Experience with the industry or owner type
  6. High certainty to close
  7. Friendship
  8. Feedback and referrals
  9. Maintaining a single point of contact

Most valued aspects of acquiring companies by the acquired

  1. Added operational value
  2. No extra costs
  3. Fair treatment of employees post-transaction
  4. Brand
  5. Long holding periods (no buy-to-flip)

Leading databases of institutional investors (use principles of SEO to optimize your profile here)

  • Galante’s
  • Grey House
  • VentureXpert
  • PE funds
    • Eurekahedge
    • Pitchbook
  • VC funds
    • Angelsoft
    • CrunchBase
    • PWC MoneyTree
    • TheFunded
    • VentureDeal

Market Mapping steps

  1. choose industries and geographies of initial interest
  2. define your proprietary point of view
  3. translate into investment theme (industries/geographies of interest)
  4. list major players in target industry/geography
  5. improve market map with feedback from industry contacts and investment targets
  6. determine which activities offer the highest return and outsource the rest
  7. identify areas of future growth
  8. asses fit with your overall strategy
  9. regularly update the market map with additional feedback and lessons

10 Simple Steps to Improve Your Origination

  1. Analyze your network
  2. Use market mapping to develop deep, proprietary insights about your target
  3. Monitor target ecosystem for cyclical/structural opportunities
  4. Align internal interests
  5. Divide and conquer
  6. Centralize data and become an information sponge
  7. Develop a network with limited overlap
  8. Take control of your virtual presence (marketing)
  9. Join the in-person and virtual communities of your target market
  10. Take a leadership role; find a way to stand out and attract others to you

Notes – The Snowball, By Alice Schroeder: Part V, Chap. 43-52

The following are reading notes for The Snowball: Warren Buffett and the Business of Life, by Alice Schroeder (buy on Amazon.com). This post covers Part V: The King of Wall Street, Chap. 43-52

The modern Buffett

In Part V of the Snowball, we see Buffett’s transformation from the early, cigar butt-picking, Grahamian value-minded Buffett, through the filter of his Fisherite partner, Charlie Munger, into the mega cap conglomerator and franchise-buyer Buffett who is popularly known to investors and the public the world round.

It is in this part that we also see Buffett make one of his biggest missteps, a stumble which almost turns into a fall and which either way appears to shock and humble the maturing Buffett. It is in this era of his investing life that we see Buffett make some of his biggest rationalizations, become entangled in numerous scandals he never would’ve tolerated in his past and dive ever deeper into the world of “elephant bumping” and gross philanthropy, partly under the tutelage of his new best friend and Microsoft-founder, Bill Gates.

The lesson

Buffett made a series of poor investments but ultimately survived them all because of MoS. There will be challenges, struggles, and stress. But after the storm, comes the calm.

The keys to the fortress

From the late seventies until the late nineties, despite numerous economic and financial cycles Buffett’s fortune grew relentlessly under a seemingly unstoppable torrent of new capital:

Much of the money used for Buffett’s late seventies spending spree came from a bonanza of float from insurance and trading stamps

This “float” (negative working capital which was paid to Buffett’s companies in advance of services rendered, which he was able to invest at a profit in the meantime) was market agnostic, meaning that its volume was not much affected by the financial market booming or crashing. For example, if you owe premiums on your homeowner’s insurance, you don’t get to suspend payment on your coverage just because the Dow Jones has sold off or the economy is officially in a recession.

The growth in Buffett’s fortune, the wilting of his family

Between 1978 and the end of 1983, the Buffetts’ net worth had increased by a stunning amount, from $89 million to $680 million

Meanwhile Buffett proves he’s ever the worthless parent:

he handed the kids their Berkshire stock without stressing how important it might be to them someday, explaining compounding, or mentioning that they could borrow against the stock without selling it

Buffett had once written to a friend when his children were toddlers that he wanted to see “what the tree has produced” before deciding what to do about giving them money

(he didn’t actively parent though)

Buffett’s private equity shop

Another tool in Buffett’s investment arsenal was to purchase small private companies with dominant franchises and little need for capital reinvestment whose excess earnings could be siphoned off and used to make other investments in the public financial markets.

Continuing on with his success in acquiring the See’s Candy company, Buffett’s next private equity-style buyout involved the Nebraska Furniture Mart, run by a devoted Russian immigrant named Rose Blumkin and her family. And, much like the department store chain he once bought for a song from an emotionally-motivated seller, Buffett beat out a German group offering Rose Blumkin over $90M for her company, instead settling with Buffett on $55M for 90% of the company, quite a discount for a “fair valuation” of practically an entire business in the private market, especially considering the competing bid.

An audit of the company after purchase showed that the store was worth $85M. According to Rose Blumkin, the store earned $15M a year, meaning Buffett got it for 4x earnings. But Rose had buyers remorse and she eventually opened up a competing shop across the street from the one she had sold, waging war on the NFM until Buffett offered to buy her out for $5M, including the use of her name and her lease.

One secret to Buffett’s success in the private equity field? Personality:

“She really liked and trusted me. She would make up her mind about people and that was that.”

Buffett’s special privileges

On hiding Rose Blumkin’s financial privacy: Buffet had no worries about getting a waiver from the SEC

Buffett got special dispensation from the SEC to not disclose his trades until the end of the year “to avoid moving markets”

The gorilla escapes its cage

Another theme of Buffett’s investing in the late 1980s and 1990s was his continual role as a “gorilla” investor who could protect potential LBO-targets from hostile takeover bids. The first of these was his $517M investment for 15% of Tom Murphy-controlled Cap Cities/ABC, a media conglomerate. Buffett left the board of the Washington Post to join the board of his latest investment.

Another white knight scenario involved Buffett’s investment in Ohio conglomerate Scott Fetzer, which Berkshire purchased for $410M.

Then Buffett got into Salomon Brothers, a Wall Street arbitrage shop that was being hunted by private equity boss Ron Perelman. Buffett bought $700M of preferred stock w/ a 9% coupon that was convertible into common stock at $38/share, for a total return potential of about 15%. It even came with a put option to return it to Salomon and get his money back.

But Buffett had stepped outside of his circle of competence:

He seemed to understand little of the details of how the business was run, and adjusting to a business that wasn’t literally made of bricks-and-mortar or run like an assembly line was not easy for him… he had made the investment in Salomon purely because of Gutfreund

Buffett’s disgusting ignorance and hypocrisy

Buffett:

I would force you to give back a huge chunk to society, so that hospitals get built and kids get educated too

Buffett decides to sell the assets of Berkshire’s textile mills– on the books for $50M, he gets $163,122 at the auction. He refused to face his workers and then had the gall to say

“The market isn’t perfect. You can’t rely on the market to give every single person a decent living.”

Buffett on John Gutfreund:

an outstanding, honorable man of integrity

Assorted quotes

Peter Kiewit, a wealthy businessman from Omaha, on reputation:

A reputation is like fine china: expensive to acquire, and easily broken… If you’re not sure if something is right or wrong, consider whether you’d want it reported in the morning paper

Buffett on Wall St:

Wall Street is the only place people ride to in a Rolls-Royce to get advice from people who take the subway

Notes – Stanford Graduate School of Business Search Fund Primer

Notes on “A Primer On Search Funds” produced by the Stanford Graduate School of Business

“The Search Fund”

  • Greater than 20% of search funds have not acquired a company
  • Stages of the Search Fund model:
  • Raise initial capital (2-6mos)
  • Search for acquisition (1-30mos)
  • Raise acquisition capital and close transaction (6mos)
  • Operation and value creation (4-7+ years)
  • Exit (6mos)
  • SFs target industries not subject to rapid tech change, easy to understand, fragmented geographic or product markets, growing
  • Highest quality deals are found outside broker network/open market due to lack of auction dynamics
  • Research shows that partnerships are more likely to complete an acquisition and have a successful outcome than solo searchers (71% yielded positive return, 15 of top 20 performing funds were partnerships)
  • Principals budget a salary of $80,000-120,000 per year w/ median amount raised per principal $300,000~
  • Majority of the economic benefit of SF comes through principal’s earned equity; entrepreneur/partners receive 15-30% equity stake in acquired company in three tranches
  • Investors typically receive preference over the SFer, ensuring investment is repaid, with return attached, before SFer receives equity value
  • Individual IRR from 2003-2011 median was not meaningful, heavily skewed toward 75th percentile where median was 26% in 2011; 57% of individual IRRs were not meaningful in 2011; the median fund destroyed capital in 2009 (0.5x) and 2011 (0.8x); 58% in 2011 broke even or lost money
  • Half of the funds that represent a total or partial loss were funds that did not acquire a company; biggest risk is in not acquiring a company at all
  • Median acquisition multiples: 1.1x revenues; 5.1x EBITDA
  • Median deal size, $8.5M

“Raising a Fund”

  • Search fund capital should come from investors with the ability and willingness to participate in the acquisition round of capital raising

“Search Fund Economics”

  • Search fund investors often participate at a stepped up rate of 150% of original investment in acquired company securities

“Setting Criteria and Evaluating Industries”

  • Desirable characteristics for a target industry: fragmented, growing, sizable in terms of revenues and number of companies, straightforward operations, early in industry lifecycle, high number of companies in target size range
  • Desirable characteristics for a target company: healthy and sustainable profit margins (>15% EBIT), competitive advantage, recurring revenue model, history of cash flow generation, motivated seller for non-business reasons, fits financial criteria ($10-30M in revs, >$1.5M EBITDA), multiple avenues for growth, solid middle management, available financing, reasonable valuation, realistic liquidity options in 3-6 years
  • Key challenge is “know when to take the train” lest a SF never leaves the station waiting for the perfect opportunity
  • Ideally, seller is ready to transition out of the business for retirement or personal circumstances or has something else they’d like to do professionally
  • Experience shows it is better to pay full price for a good company than a “bargain” for a bad one
  • Idea generation: SIC and NAICS codes, Yahoo! Finance, Thomson Financial industry listings, Inc. 5000 companies, public stock OTC and NASDAQ lists and even the Yellow Pages; generate a list of 75 potential industries to start
  • Target industries buoyed by a mega-trend
  • Can also target an industry in which the SFer has worked and possesses an established knowledge base and network
  • Some focus on 2-3 “super priority” industry criteria (eg, recurring revenues, ability to scale, min # of potential targets, etc.)
  • Objective is to pare down the industry target list to 5-10 most promising
  • Basic industry analysis (Porter’s five forces, etc.) is then used to narrow from 10 to 3; SFers use public equity research and annual reports for market size, growth, margin benchmarks; also Capital IQ, Hoover’s, Dun & Bradstreet and One Source
  • Industry insiders (business owners, trade association members, sales or business development professionals) and industry trade associations or affiliated ibanks and advisory firms are primary methods of research and often have general industry research or white papers available
  • Next step is to create a thesis to codify accumulated knowledge and compare opportunities across common metric set in order to make go/no-go decision
  • In order to become an industry insider, SFers typically attend tradeshows, meet with business owners, interview customers and suppliers and develop “River Guides”

“The Search”

  • Median # of months spent searching, 19
  • 54% spend less than 20 months searching, 25% spend 21-30 months, 21% spend 30+ months
  • Track acquisition targets with CRM software such as Salesforce, Zoho, Sugar CRM
  • Bring up financial criteria and valuation ranges as early as possible when speaking to potential acquisition targets to save everyone time
  • A company that is too large or too small as an acquisition target may still be worth talking to for information
  • You must immediately sound useful, credible or relevant to the owner; deep industry analysis should already have been performed at this stage
  • Trade shows can be a critical source of deal flow
  • If a particular owner is not willing to sell, ask if he knows others who are
  • “River Guides” are typically compensated with a deal success fee, usually .5-1% of total deal size
  • Boutique investment banks, accounting firms and legal practices specializing in the industry in question are also a good source of deals
  • The business broker community itself is extremely large and fragmented; could be a good rollup target?
  • Often, brokered deals are only shown if a private equity investor with committed capital has already passed on the deal, presenting an adverse selection problem
  • Involve your financing sources (such as lenders and investors) early in the deal process to ensure their commitment and familiarity

“Evaluating Target Businesses”

  • Principles of time management: clarify goals of each stage of evaluation and structure work to meet those goals; recognize that perfect information is an unrealistic goal; keep a list of prioritized items impacting the go/no-go decision
  • Stages: first pass, valuation/LOI, comprehensive due diligence
  • It is in the best interest of the SFer to tackle core business issues personally during due diligence as it is the best way to learn the details of the business being taken over
  • Adding back the expenses of a failed product launch rewards the seller for a bad business decision; adding back growth expenses gives the seller the double benefit of capturing the growth without reflecting its true cost
  • Due diligence may also uncover deductions to EBITDA or unrealized expenses that reduce the “normalized” level of earnings (undermarket rents, inadequate insurance coverage, costs to upgrade existing systems, etc.)

“Transitioning Ownership and Management”

  • Create a detailed “Transition Services Agreement” with the seller, a legal contract where specific roles, responsibilities, defined time commitments and compensation are agreed prior to the transaction close
  • The first 100 days should be dedicated to learning the business
  • Businesses consist of people, and people need communication; great leaders are always great communicators
  • “Don’t listen to complaints about your predecessor, this can lead to a swamp and you don’t want to be mired there.”
  • The goal is to learn, not to make immediate changes
  • Outwork everyone; be the first person in and the last to leave
  • Many SFers insert themselves into the cash management process during the transition period by reviewing daily sales, invoices and receipts and signing every check/payment made by the company
  • The company’s board should be a mix of deep operational experience, specific industry or business model experience and financial expertise
  • The seeds of destruction for new senior leaders are often sown in the first 100 days

Review – Good To Great

Good To Great: Why some companies make the leap and others don’t

by Jim Collins, published 2001

The G2G Model

“Good To Great” seeks to answer the question, “Why do some good companies become great companies in terms of their market-beating stock performance, while competitors stagnate or decline?” After a deep dive into varied data sources with a team of tens of university researchers, Collins and his team arrived at an answer:

  1. Level 5 Leadership
  2. First Who… Then What
  3. Confront The Brutal Facts (Yet Never Lose Faith)
  4. The Hedgehog Concept (Simplicity Within The Three Circles)
  5. A Culture Of Discipline
  6. Technology Accelerators

The first two items capture the importance of “disciplined people”, the second two items refer to “disciplined thought” and the final pair embodies “disciplined action”. The concepts are further categorized, with the first three components representing the “build up”, the ducks that must be gotten into a row before the second category holding the last three components, “breakthrough”, can take place. The entire package is wrapped up in the physical metaphor of the “flywheel”, something an organization pushes on and pushes on until suddenly it rolls forward and gains momentum on its own.

This book found its way onto my radar several times so I finally decided to read it. I’d heard it mentioned as a good business book in many places but first took the idea of reading it seriously when I saw Geoff Gannon mention it as part of an essential “Value Investing 101” reading list. I didn’t actually follow through on the initial impulse until I took a “leadership science” course recently in which this book was emphasized as worth covering.

I found G2G to be almost exactly what I expected– a rather breathless, New Age-y, pseudo-philosophical and kinda-scientific handbook to basic principles of organizational management and business success.  The recommendations contained within range from the seemingly reasonable to the somewhat suspect and the author and his research team take great pains to make the case that they have built their findings on an empirical foundation but I found the “We had no theories or preconceived notions, we just looked at what the numbers said” reasoning scary. This is actually the opposite of science, you’re supposed to have some theories and then look at whether the data confirms or denies them. Data by itself can’t tell you anything and deriving theory from data patterns is the essence of fallacious pattern-fitting.

Those caveats out of the way, the book is still hard to argue with. Why would an egotistical maniac for a leader be a good thing in anything but a tyrannical political regime, for example? How would having “the wrong people on the bus” be a benefit to an organization? What would be the value in having an undisciplined culture of people who refuse to see reality for what it is?

What I found most interesting about the book is the way in which all the principles laid out essentially tend to work toward the common goal of creating a controlled decision-making structure for a business organization to protect it from the undue influence of big egos and wandering identities alike. In other words, the principles primarily address the psychological risks of business organizations connected to cult-like dependency on great leaders, tendency toward self-delusional thinking and the urge to try everything or take the easy way out rather than focus on obvious strengths. This approach has many corollaries to the value investing framework of Benjamin Graham who ultimately saw investor psychology as the biggest obstacle to investor performance.

I don’t have the time or interest to confirm this hypothesis but I did wonder how many of the market-beating performances cataloged were due primarily to financial leverage used by the organization in question, above and beyond the positive effects of their organizational structure.

A science is possible in all realms of human inquiry into the state of nature. Man and his business organizations are a part of nature and thus they fall under the rubric of potential scientific inquiry. I don’t think we’re there yet with most of what passes for business “research” and management or organizational science, but here and there the truth peeks out. “Good To Great” probably offers some clues but it’s hard to know precisely what is the wheat and what is the chaff here. Clearly if you inverted all of the recommendations of the book and tried to operate a business that way you’d meet your demise rather quickly, but that is not the same thing as saying that the recommendations as stated will lead in the other direction to greatness, or that they necessarily explain the above-average market return of these public companies.

I took a lot of notes in the margin and highlighted things that “sounded good” to me but on revisiting them I am not sure how many are as truly useful as they first seemed when I read them. I think the biggest takeaway I had from the book was the importance of questioning everything, not only as a philosophical notion but also as a practical business tool for identifying problems AND solutions.

Review – Quantitative Value

Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors + website

by Wesley R. Gray and Tobias E. Carlisle, published 2012

The root of all investors’ problems

In 2005, renowned value investing guru Joel Greenblatt published a book that explained his Magic Formula stock investing program– rank the universe of stocks by price and quality, then buy a basket of companies that performed best according to the equally-weighted measures. The Magic Formula promised big profits with minimal effort and even less brain damage.

But few individual investors were able to replicate Greenblatt’s success when applying the formula themselves. Why?

By now it’s an old story to anyone in the value community, but the lesson learned is that the formula provided a ceiling to potential performance and attempts by individual investors to improve upon the model’s picks actually ended up detracting from that performance, not adding to it. There was nothing wrong with the model, but there was a lot wrong with the people using it because they were humans prone to behavioral errors caused by their individual psychological profiles.

Or so Greenblatt said.

Building from a strong foundation, but writing another chapter

On its face, “Quantitative Value” by Gray and Carlisle is simply building off the work of Greenblatt. But Greenblatt was building off of Buffett, and Buffett and Greenblatt were building off of Graham. Along with integral concepts like margin of safety, intrinsic value and the Mr. Market-metaphor, the reigning thesis of Graham’s classic handbook, The Intelligent Investor, was that at the end of the day, every investor is their own worst enemy and it is only by focusing on our habit to err on a psychological level that we have any hope of beating the market (and not losing our capital along the way), for the market is nothing more than the aggregate total of all psychological failings of the public.

It is in this sense that the authors describe their use of “quantitative” as,

the antidote to behavioral error

That is, rather than being a term that symbolizes mathematical discipline and technical rigor and computer circuits churning through financial probabilities,

It’s active value investing performed systematically.

The reason the authors are beholden to a quantitative, model-based approach is because they see it as a reliable way to overcome the foibles of individual psychology and fully capture the value premium available in the market. Success in value investing is process-driven, so the two necessary components of a successful investment program based on value investing principles are 1) choosing a sound process for identifying investment opportunities and 2) consistently investing in those opportunities when they present themselves. Investors cost themselves precious basis points every year when they systematically avoid profitable opportunities due to behavioral errors.

But the authors are being modest because that’s only 50% of the story. The other half of the story is their search for a rigorous, empirically back-tested improvement to the Greenblattian Magic Formula approach. The book shines in a lot of ways but this search for the Holy Grail of Value particularly stands out, not just because they seem to have found it, but because all of the things they (and the reader) learn along the way are so damn interesting.

A sampling of biases

Leaning heavily on the research of Kahneman and Tversky, Quantitative Value offers a smorgasbord of delectable cognitive biases to choose from:

  • overconfidence, placing more trust in our judgment than is due given the facts
  • self-attribution bias, tendency to credit success to skill, and failure to luck
  • hindsight bias, belief in ability to predict an event that has already occurred (leads to assumption that if we accurately predicted the past, we can accurately predict the future)
  • neglect of the base case and the representativeness heuristic, ignoring the dependent probability of an event by focusing on the extent to which one possible event represents another
  • availability bias, heavier weighting on information that is easier to recall
  • anchoring and adjustment biases, relying too heavily on one piece of information against all others; allowing the starting point to strongly influence a decision at the expense of information gained later on

The authors stress, with numerous examples, the idea that value investors suffer from these biases much like anyone else. Following a quantitative value model is akin to playing a game like poker systematically and probabilistically,

The power of quantitative investing is in its relentless exploitation of edges

Good poker players make their money by refusing to make expensive mistakes by playing pots where the odds are against them, and shoving their chips in gleefully when they have the best of it. QV offers the same opportunity to value investors, a way to resist the temptation to make costly mistakes and ensure your chips are in the pot when you have winning percentages on your side.

A model development

Gray and Carlisle declare that Greenblatt’s Magic Formula was a starting point for their journey to find the best quantitative value approach. However,

Even with a great deal of data torture, we have not been able to replicate Greenblatt’s extraordinary results

Given the thoroughness of their data collection and back-testing elaborated upon in future chapters, this finding is surprising and perhaps distressing for advocates of the MF approach. Nonetheless, the authors don’t let that frustrate them too much and push on ahead to find a superior alternative.

They begin their search with an “academic” approach to quantitative value, “Quality and Price”, defined as:

Quality, Gross Profitability to Total Assets = (Revenue – Cost of Goods Sold) / Total Assets

Price, Book Value-to-Market Capitalization = Book Value / Market Price

The reasons for choosing GPA as a quality measure are:

  • gross profit measures economic profitability independently of direct management decisions
  • gross profit is capital structure neutral
  • total assets are capital structure neutral (consistent w/ the numerator)
  • gross profit better predicts future stock returns and long-run growth in earnings and FCF

Book value-to-market is chosen because:

  • it more closely resembles the MF convention of EBIT/TEV
  • book value is more stable over time than earnings or cash flow

The results of the backtested horserace between the Magic Formula and the academic Quality and Price from 1964 to 2011 was that Quality and Price beat the Magic Formula with CAGR of 15.31% versus 12.79%, respectively.

But Quality and Price is crude. Could there be a better way, still?

Marginal improvements: avoiding permanent loss of capital

To construct a reliable quantitative model, one of the first steps is “cleaning” the data of the universe being examined by removing companies which pose a significant risk of permanent loss of capital because of signs of financial statement manipulation, fraud or a high probability of financial distress or bankruptcy.

The authors suggest that one tool for signaling earnings manipulation is scaled total accruals (STA):

STA = (Net Income – Cash Flow from Operations) / Total Assets

Another measure the authors recommend using is scaled net operating assets (SNOA):

SNOA = (Operating Assets – Operating Liabilities) / Total Assets

Where,

OA = total assets – cash and equivalents

OL = total assets – ST debt – LT debt – minority interest – preferred stock – book common equity

They stress,

STA and SNOA are not measures of quality… [they] act as gatekeepers. They keep us from investing in stocks that appear to be high quality

They also delve into a number of other metrics for measuring or anticipating risk of financial distress or bankruptcy, including a metric called “PROBMs” and the Altman Z-Score, which the authors have modified to create an improved version of in their minds.

Quest for quality

With the risk of permanent loss of capital due to business failure or fraud out of the way, the next step in the Quantitative Value model is finding ways to measure business quality.

The authors spend a good amount of time exploring various measures of business quality, including Warren Buffett’s favorites, Greenblatt’s favorites and those used in the Magic Formula and a number of other alternatives including proprietary measurements such as the FS_SCORE. But I won’t bother going on about that because buried within this section is a caveat that foreshadows a startling conclusion to be reached later on in the book:

Any sample of high-return stocks will contain a few stocks with genuine franchises but consist mostly of stocks at the peak of their business cycle… mean reversion is faster when it is further from its mean

More on that in a moment, but first, every value investor’s favorite subject– low, low prices!

Multiple bargains

Gray and Carlisle pit several popular price measurements against each other and then run backtests to determine the winner:

  • Earnings Yield = Earnings / Market Cap
  • Enterprise Yield(1) = EBITDA / TEV
  • Enterprise Yield(2) = EBIT / TEV
  • Free Cash Flow Yield = FCF / TEV
  • Gross Profits Yield = GP / TEV
  • Book-to-Market = Common + Preferred BV / Market Cap
  • Forward Earnings Estimate = FE / Market Cap

The result:

the simplest form of the enterprise multiple (the EBIT variation) is superior to alternative price ratios

with a CAGR of 14.55%/yr from 1964-2011, with the Forward Earnings Estimate performing worst at an 8.63%/yr CAGR.

Significant additional backtesting and measurement using Sharpe and Sortino ratios lead to another conclusion, that being,

the enterprise multiple (EBIT variation) metric offers the best risk/reward ratio

It also captures the largest value premium spread between glamour and value stocks. And even in a series of tests using normalized earnings figures and composite ratios,

we found the EBIT enterprise multiple comes out on top, particularly after we adjust for complexity and implementation difficulties… a better compound annual growth rate, higher risk-adjusted values for Sharpe and Sortino, and the lowest drawdown of all measures analyzed

meaning that a simple enterprise multiple based on nothing more than the last twelve months of data shines compared to numerous and complex price multiple alternatives.

But wait, there’s more!

The QV authors also test insider and short seller signals and find that,

trading on opportunistic insider buys and sells generates around 8 percent market-beating return per year. Trading on routine insider buys and sells generates no additional return

and,

short money is smart money… short sellers are able to identify overvalued stocks to sell and also seem adept at avoiding undervalued stocks, which is useful information for the investor seeking to take a long position… value investors will find it worthwhile to examine short interest when analyzing potential long investments

This book is filled with interesting micro-study nuggets like this. This is just one of many I chose to mention because I found it particularly relevant and interesting to me. More await for the patient reader of the whole book.

Big and simple

In the spirit of Pareto’s principle (or the 80/20 principle), the author’s of QV exhort their readers to avoid the temptation to collect excess information when focusing on only the most important data can capture a substantial part of the total available return:

Collecting more and more information about a stock will not improve the accuracy of our decision to buy or not as much as it will increase our confidence about the decision… keep the strategy austere

In illustrating their point, they recount a funny experiment conducted by Paul Watzlawick in which two subjects oblivious of one another are asked to make rules for distinguishing between certain conditions of an object under study. What the participants don’t realize is that one individual (A) is given accurate feedback on the accuracy of his rule-making while the other (B) is fed feedback based on the decisions of the hidden other, invariably leading to confusion and distress. B comes up with a complex, twisted rationalization for his  decision-making rules (which are highly inaccurate) whereas A, who was in touch with reality, provides a simple, concrete explanation of his process. However, it is A who is ultimately impressed and influenced by the apparent sophistication of B’s thought process and he ultimately adopts it only to see his own accuracy plummet.

The lesson is that we do better with simple rules which are better suited to navigating reality, but we prefer complexity. As an advocate of Austrian economics (author Carlisle is also a fan), I saw it as a wink and a nod toward why it is that Keynesianism has come to dominate the intellectual climate of the academic and political worlds despite it’s poor predictive ability and ferociously arbitrary complexity compared to the “simplistic” Austrian alternative theory.

But I digress.

Focusing on the simple and most effective rules is not just a big idea, it’s a big bombshell. The reason this is so is because the author’s found that,

the Magic Formula underperformed its price metric, the EBIT enterprise multiple… ROC actually detracts from the Magic Formula’s performance [emphasis added]

Have I got your attention now?

The trouble is that the Magic Formula equally weights price and quality, when the reality is that a simple price metric like buying at high enterprise value yields (that is, at low enterprise value multiples) is much more responsible for subsequent outperformance than the quality of the enterprise being purchased. Or, as the authors put it,

the quality measures don’t warrant as much weight as the price ratio because they are ephemeral. Why pay up for something that’s just about to evaporate back to the mean? […] the Magic Formula systematically overpays for high-quality firms… an EBIT/TEV yield of 10 percent or lower [is considered to be the event horizon for “glamour”]… glamour inexorably leads to poor performance

All else being equal, quality is a desirable thing to have… but not at the expense of a low price.

The Joe the Plumbers of the value world

The Quantitative Value strategy is impressive. According to the authors, it is good for between 6-8% a year in alpha, or market outperformance, over a long period of time. Unfortunately, it is also, despite the emphasis on simplistic models versus unwarranted complexity, a highly technical approach which is best suited for the big guys in fancy suits with pricey data sources as far as wholesale implementation is concerned.

So yes, they’ve built a better mousetrap (compared to the Magic Formula, at least), but what are the masses of more modest mice to do?

I think a cheap, simplified Everyday Quantitative Value approach process might look something like this:

  1. Screen for ease of liquidity (say, $1B market cap minimum)
  2. Rank the universe of stocks by price according to the powerful EBIT/TEV yield (could screen for a minimum hurdle rate, 15%+)
  3. Run quantitative measurements and qualitative evaluations on the resulting list to root out obvious signals to protect against risk of permanent loss by eliminating earnings manipulators, fraud and financial distress
  4. Buy a basket of the top 25-30 results for diversification purposes
  5. Sell and reload annually

I wouldn’t even bother trying to qualitatively assess the results of such a model because I think that runs the immediate and dangerous risk which the authors strongly warn against of our propensity to systematically detract from the performance ceiling of the model by injecting our own bias and behavioral errors into the decision-making process.

Other notes and unanswered questions

“Quantitative Value” is filled with shocking stuff. In clarifying that the performance of their backtests is dependent upon particular market conditions and political history unique to the United States from 1964-2011, the authors make reference to

how lucky the amazing performance of the U.S. equity markets has truly been… the performance of the U.S. stock market has been the exception, not the rule

They attach a chart which shows the U.S. equity markets leading a cohort of long-lived, high-return equity markets including Sweden, Switzerland, Canada, Norway and Chile. Japan, a long-lived equity market in its own right, has offered a negative annual return over its lifetime. And the PIIGS and BRICs are consistent as a group in being some of the shortest-lifespan, lowest-performing (many net negative real returns since inception) equity markets measured in the study. It’s also fascinating to see that the US, Canada, the UK, Germany, the Netherlands, France, Belgium, Japan and Spain all had exchanges established approximately at the same time– how and why did this uniform development occur in these particular countries?

Another fascinating item was Table 12.6, displaying “Selected Quantitative Value Portfolio Holdings” of the top 5 ranked QV holdings for each year from 1974 through 2011. The trend in EBIT/TEV yields over time was noticeably downward, market capitalization rates trended upward and numerous names were also Warren Buffett/Berkshire Hathaway picks or were connected to other well-known value investors of the era.

The authors themselves emphasized that,

the strategy favors large, well-known stocks primed for market-beating performance… [including] well-known, household names, selected at bargain basement prices

Additionally, in a comparison dated 1991-2011, the QV strategy compared favorably in a number of important metrics and was superior in terms of CAGR with vaunted value funds such as Sequoia, Legg Mason and Third Avenue.

After finishing the book, I also had a number of questions that I didn’t see addressed specifically in the text, but which hopefully the authors will elaborate upon on their blogs or in future editions, such as:

  1. Are there any reasons why QV would not work in other countries besides the US?
  2. What could make QV stop working in the US?
  3. How would QV be impacted if using lower market cap/TEV hurdles?
  4. Is there a market cap/TEV “sweet spot” for the QV strategy according to backtests? (the authors probably avoided addressing this because they emphasize their desire to not massage the data or engage in selection bias, but it’s still an interesting question for me)
  5. What is the maximum AUM you could put into this strategy?
  6. Would more/less rebalancing hurt/improve the model’s results?
  7. What is the minimum diversification (number of portfolio positions) needed to implement QV effectively?
  8. Is QV “businesslike” in the Benjamin Graham-sense?
  9. How is margin of safety defined and calculated according to the QV approach?
  10. What is the best way for an individual retail investor to approximate the QV strategy?

There’s also a companion website for the book available at: www.wiley.com/go/quantvalue

Conclusion

I like this book. A lot. As a “value guy”, you always like being able to put something like this down and make a witty quip about how it qualifies as a value investment, or it’s intrinsic value is being significantly discounted by the market, or what have you. I’ve only scratched the surface here in my review, there’s a ton to chew on for anyone who delves in and I didn’t bother covering the numerous charts, tables, graphs, etc., strewn throughout the book which serve to illustrate various concepts and claims explored.

I do think this is heady reading for a value neophyte. And I am not sure, as a small individual investor, how suitable all of the information, suggestions and processes contained herein are for putting into practice for myself. Part of that is because it’s obvious that to really do the QV strategy “right”, you need a powerful and pricey datamine and probably a few codemonkeys and PhDs to help you go through it efficiently. The other part of it is because it’s clear that the authors were really aiming this book at academic and professional/institutional audiences (people managing fairly sizable portfolios).

As much as I like it, though, I don’t think I can give it a perfect score. It’s not that it needs to be perfect, or that I found something wrong with it. I just reserve that kind of score for those once-in-a-lifetime classics that come along, that are infinitely deep and give you something new each time you re-read them and which you want to re-read, over and over again.

Quantitative Value is good, it’s worth reading, and I may even pick it up, dust it off and page through it now and then for reference. But I don’t think it has the same replay value as Security Analysis or The Intelligent Investor, for example.

Review – Losing My Virginity

Losing My Virginity: How I Survived, Had Fun and Made a Fortune Doing Business My Way

by Richard Branson, published 2011

Spoiler alert– this book is choppy and inconsistent in the pacing and entertainment factor of its narrative. You really need to read between the lines a bit to get the most value out of it. That being said, it’s surprisingly literary for a dyslexic former publisher of a student magazine and I found Branson’s repeated reference to his high-altitude balloon voyage trials to be an outstanding metaphor for his life as a businessman and entrepreneur.

You see, in Branson’s ballon journeys, the key factors of any consistency were that: a.) Branson was knowingly and openly taking what he perceived to be a potentially life-threatening risk b.) Branson was almost always underprepared for it, or decided to go ahead with his attempt despite early warnings that something was amiss and c.) nonetheless, he somehow managed to survive one disaster after another, only to try something bigger and bolder the next time around.

And this is quite similar to the way he comported himself as an entrepreneur on so many occasions. Again and again, he’d make a daring foray into a business, market or industry he didn’t quite understand, the company would stumble after an early success leaving them all on the brink of failure and yet, each time they’d double down and somehow win.

In that sense, Branson is a perfect example of survivorship bias. On the other hand, having so many narrow misses that turn into massive accelerators of a person’s fortune start to make you wonder if isn’t mostly luck but rather mostly skill.

As an entrepreneurial profile, “Losing My Virginity” is full of all kinds of great successes and astounding failures. With regards to the failures, something I found of particular interest was the fact that Branson’s company were victims of some of the most common pitfalls of other businesses throughout its early history: taken for a ride by indomitable Japanese owners/partnerships in the 80s, repeated victim of the LBO-boom and the private/public buyout-cycle in the 80s and 90s. When you read these stories in the financial press it always seems to happen to the rubes of the business world, but Branson’s foibles help one to realize even rather sophisticated types can get taken in now and then.

The volatility in Branson’s fortunes do leave one with a major question though, namely, why did Branson’s company ultimately survive?

This isn’t a Harvard Business School case study so I don’t mean to pass this off as a qualified, intelligent answer to that question, but I will attempt a few observations and, in typical HBS fashion, some or all of them may be contradictory of one another and none will be provided with the precise proportional contribution they made to the end result:

  • the group had a cultural commitment to change and dynamism; they were not so much their businesses, but a culture and group of people who did business a particular way, a true brand-over-merchandise, which allowed them to reinvent themselves numerous times
  • the group strategically focused on being the low-cost provider in their industry, usually while simultaneously attempting to pursue the seemingly mutually exclusive goal as being seen as the highest quality offering as well
  • the group focused on serving customers but equally saw treating its employees with concern as an important value
  • the group consciously created a brand that could be applied to diverse businesses (see point #1)
  • the group pursued businesses that seemed “interesting” or sensually appealing to it, which ensured that everyone involved was motivated to do well because they liked the work they had chosen

Another thing I noticed about Branson and the development of his company was the attention he paid to the composition of management and owners and his dedication to weeding out those who were not good fits in a charitable way. Channeling the “best owner” principle, Branson made a conscious effort to buy out early partners whose vision and tastes did not match the current or future vision of the group. In this way, the company maintained top-level focus and concentration on a shared strategic vision at all times, sparing itself the expense and distraction of infighting and wrangling over where to go next and why.

Another aspect of the company’s resilience had to do with its operational structure. Branson built a decentralized company whose debts and obligations were kept separate. In an environment where new ventures were constantly subject to total failure, this arrangement ensured that no one business failure would bring the entire group down.

The final lessons of the Branson bio were most instructive and had to do with the nature and value of forecasting.

The first lesson in forecasting has to do with the forecasts others make of us, or the world around us. For example, Richard Branson had no formal business training, he grew up with learning disabilities (dyslexia) and he was told very early on in his life by teachers and other adult and authority figures in his life that he’d amount to nothing and his juvenile delinquency would land him in prison. Somehow this worthless person contributed a great deal to society, through business and charity, and by most reasonable measures could be considered a success, making this forecast a failure. If one had taken a snapshot of the great Warren Buffett at a particular time in his adolescence, when the young boy was known to often take a “five-finger discount” from local department stores, it might have been easy to come up with a similar forecast about him.

I’m not sure how to succinctly sum up the concept there other than to say, “Things change.” Most forecasts that involve extrapolating the current trend unendingly out into the future will probably fail for this reason.

The second lesson in forecasting has to do with how we might attempt to forecast and plan our own lives. When we have 50, 60, 70 or more years of a person’s life to reflect on, it is easy to employ the hindsight bias and see how all the facts of a person’s life were connected and led them inexorably to the success (or infamy) they ultimately achieved. And certainly there are some people, again using Buffett as an example, who from an early age were driven to become a certain something or someone and so their ability to “predict their future selves” seemed quite strong.

But the reality is that for the great many of us, the well-known and the common alike, we really don’t have much of a clue of who we are and what we’ll ultimately become. The future is uncertain and, after all, that’s the great puzzle of life that we all spend our lives trying to unravel. Richard Branson was no different. He was not born a billionaire, in a financial, intellectual, personal or other sense. He had to learn how to be a businessman and how to create a billion dollar organization from scratch. Most of the time, he didn’t even know he was doing it. In other words, HE DID NOT KNOW AHEAD OF TIME that he would become fabulously wealthy, and while he was hard-working and driven, it doesn’t even appear he purposefully intended to become so.

Maybe we should all take a page from Branson’s book and spend less time trying to figure out what’s going to happen and more time just… happening. We could sit around all day trying to figure life out, or we could follow the Branson philosophy where he says, “As for me, I just pick up the phone and get on with it.”