Tag Archives: investment process

Review – Quantitative Value (#valueinvesting, #quant, @greenbackd, @turnkeyanalyst)

Quantitative Value: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors + website (buy on Amazon.com)

by Wesley R. Gray, PhD & Tobias E. Carlisle, LLB, published 2012

A “valueprax” review always serves two purposes: to inform the reader, and to remind the writer. Find more reviews by visiting the Virtual Library. Please note, I received a copy of this book for review from the publisher, Wiley Finance, on a complimentary basis.

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 rule), 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.

This One Is Personal

The year 2012 has come and gone, but what do I have to show for it?

From a blogging standpoint, 173 new posts, quite a few of which were one-line quotes of interest but many more still were comprehensive book reviews or annotated videos and other reference materials related to business, investing and other subjects.

But this isn’t about what I accomplished on my blog, because if you want to know what I accomplished on my blog all you need to do is read it. No, this is a retrospective on one area of the life I lived this year past.

A little bit of background is in order: in the last quarter of 2011, I voluntarily left a position in the investment industry and changed my geographic location by several thousand miles, as well. At the time I made the decision, I was not sure what I would do next with myself nor where, exactly, I thought I was going in a general sense. I took a temporary position in sales because it was a professional environment that had always interested me and involved a skill set I did not possess but which I had always hoped to acquire. That got me through to the end of 2011, at which point I decided a break was in order so that I could rest, reset and ponder redirection for my life.

What transpired approximately a week into my mini-sabbatical was fortuitous– I received an e-mail from a good friend encouraging me to visit CSInvesting.org (it was but a mere shadow of itself then, hosted on a WordPress.com subdomain just like this esteemed journal) as a good resource for learning more about investing. What’s fortuitous is not just the fact that he sent it, and the timing, but also the fact that I followed through and visited it immediately, rather than letting it languish in my inbox for weeks or, worse, giving it a cursory glance and then ignoring it just so I had the mental satisfaction that I wasn’t ignoring the suggestions of my friends.

I feel comfortable in admitting that reading that blog changed my life, for the better (or at least “for the different”, but the different was undeniably good). My mind started racing in a million directions at once and a path revealed itself to me at a time I was ready and willing to take the first step. I ended up exchanging correspondence with the proprietor, John Chew, and also made notes of some of his most profound comments later on the blog. If you joined the audience sometime after January of 2012 and haven’t yet read it, I encourage you to do so now.

Taking the motivation and principles I derived from reading CSInvesting.org and combining them with a specific strategy shared with me by a close friend from back home called the “personal MBA” for short, I set out to make the next 12 months a self-guided deep-dive into all things investing and business. I began developing a reading list, which was added to repeatedly as the year wore on, and shipped small libraries-worth of books on the subject to myself to read, annotate and review. The results of those efforts so far can be found in the Virtual Library.

However, this was really just the kickstart. The principle I had come to adopt in this time was that life is a journey, not a place, and personal growth and development and sound investment strategy are no different. The idea is to create a process and fine-tune it with each pass through. You have no final destination and your ultimate control over the result at any given moment in time is not total and often limited. All you can do is focus on that which you do have control over, the process you employ, and improve it as much and as many times as you can.

The Personal MBA, my twelve month commitment to an intensive course of self-study in business and value investing, was just a process for learning and growing (which itself is a process for living). I modified it numerous times along the way, adding some elements and dropping others. At all times, life-at-large intervened in numerous and chaotically unpredictable ways and I learned to course correct along the way. I knew that, come December, I wouldn’t have  come to the end but just another beginning.

As I set out down the path of 2013, I’ve got an idea of some of the sights I’d like to see and I have a general sense of the direction I am heading. Phrased differently, I know what processes I’d like to make a part of my life in the future, and I have an idea of what processes I can use to increase the likelihood they eventually become integrated into my life.

I plan to continue learning about business and investing. I’ve also taken on new professional responsibilities which will afford me additional opportunities to learn, observe and practice, as I am now involved in operational management at a large-scale retail concern. However, a lot of my time will be consumed by these efforts so I have had to adapt my process to the concomitantly reduced time and attention inventory I now possess. This is not a year where I’ll be able to pull off another Personal MBA-type effort, in other words, as far as my investment study process is concerned.

In 2012, I lacked courage and conviction when it came to practicing the art of business and investing. Now that the year is behind me, I am confident that my theoretical knowledge is built on a sound and deep foundation. This coming year is the year of action, of taking that knowledge and putting it in practice, much more so than I managed to do so in 2012. Following the 80/20 principle, I believe I’ve covered the 80% of the literature and resources that are valuable and the remaining 20% are not as worthy of my time. Now I’d like to spend 80% of my time practicing and only 20% reading and thinking.

Value investors talk a lot about the example of Warren Buffett. Something I rarely hear mentioned is the fact that Buffett has spent an inordinate amount of time simply looking at stuff: rejecting the multitude of bad deals, getting tangible experience with the good ones and learning to identify the difference between the two so thoroughly that he had developed an intuitive pattern memory so that the contrast of a good opportunity began to leap off the page at him when he came across it.

That’s what I am missing right now as an investor and businessman (one thing, anyway), and that’s what 2013 is going to be partly about getting.

Notes – How Did I Come Up With My 16 JNets? (#JNets, #NCAV)

A couple days ago someone who follows my Twitter feed asked me what criteria I had used to pick the 16 JNets I talked about in a recent post. He referenced that there were “300+” Japanese companies trading below their net current asset value. A recent post by Nate Tobik over at Oddball Stocks suggests that there are presently 448 such firms, definitely within the boundaries of the “300+” comment.

To be honest, I have no idea how many there are currently, nor when I made my investments. The reason is that I am not a professional investor with access to institution-grade screening tools like Bloomberg or CapitalIQ. Because of this, my investment process in general, but specifically with regards to foreign equities like JNets, relies especially on two principles:

  • Making do with “making do”; doing the best I can with the limited resources I have within the confines of the time and personal expertise I have available
  • “Cheap enough”; making a commitment to buy something when it is deemed to be cheap enough to be worthy of consideration, not holding out until I’ve examined every potential opportunity in the entire universe or local miniverse of investing

That’s kind of the 32,000-ft view of how I arrived at my 16 JNets. But it’s a good question and it deserves a specific answer, as well, for the questioner’s sake and for my own sake in keeping myself honest, come what may. So, here’s a little bit more about how I made the decision to add these 16 companies to my portfolio.

The first pass

The 16 companies I invested in came from a spreadsheet of 49 companies I gathered data on. Those 49 companies came from two places.

The first place, representing a majority of the companies that ultimately made it to my spreadsheet of 49, was a list of 100 JNets that came from a Bloomberg screen that someone else shared with Nate Tobik. To this list Nate added five columns, to which each company was assigned a “1″ for yes or a “0″ for no, with category headings covering whether the company showed a net profit in each of the last ten years, whether the company showed positive EBIT in each of the last ten years, whether the company had debt, whether the company paid a dividend and whether the company had bought back shares over the last ten years. Those columns were summed and anything which received a “4″ or “5″ cumulative score made it onto my master spreadsheet for further investigation.

The second place I gathered ideas from were the blogs of other value investors such as Geoff Gannon and Gurpreet Narang (Neat Value). I just grabbed everything I found and threw it on my list. I figured, if it was good enough for these investors it was worth closer examination for me, too.

The second pass

Once I had my companies, I started building my spreadsheet. First, I listed each company along with its stock symbol in Japan (where securities are quoted by 4-digit numerical codes). Then, I added basic data about the shares, such as shares outstanding, share price, average volume (important for position-sizing later on), market capitalization, current dividend yield.

After this, I listed important balance sheet data: cash (calculated as cash + ST investments), receivables  inventory, other current assets, total current assets, LT debt and total liabilities and then the NCAV and net cash position for each company. Following this were three balance sheet price ratios, Market Cap/NCAV, Market Cap/Net Cash and Market Cap/Cash… the lower the ratio, the better. While Market Cap/Net Cash is a more conservative valuation than Market Cap/NCAV, Market Cap/Cash is less conservative but was useful for evaluating companies which were debt free and had profitable operations– some companies with uneven operating outlooks are best valued on a liquidation basis (NCAV, Net Cash) but a company that represents an average operating performance is more properly considered cheap against a metric like the percent of the market cap composing it’s balance sheet cash, assuming it is debt free.

I also constructed some income metric columns, but before I could do this, I created two new tabs, “Net Inc” and “EBIT”, and copied the symbols and names from the previous tab over and then recorded the annual net income and EBIT for each company for the previous ten years. This data all came from MSN Money, like the rest of the data I had collected up to that point.

Then I carried this info back to my original “Summary” tab via formulas to calculate the columns for 10yr average annual EBIT, previous year EBIT, Enterprise Value (EV), EV/EBIT (10yr annual average) and EV/EBIT (previous year), as well as the earnings yield (10yr annual average net income divided by market cap) and the previous 5 years annual average as well to try to capture whether the business had dramatically changed since the global recession.

The final step was to go through my list thusly assembled and color code each company according to the legend of green for a cash bargain, blue for a net cash bargain and orange for an NCAV bargain (strictly defined as a company trading for 66% of NCAV or less; anything 67% or higher would not get color-coded).

I was trying to create a quick, visually obvious pattern for recognizing the cheapest of the cheap, understanding that my time is valuable and I could always go dig into each non-color coded name individually looking for other bargains as necessary.

The result, and psychological bias rears it’s ugly head

Looking over my spreadsheet, about 2/3rds of the list were color-coded in this way with the remaining third left white. The white entries are not necessarily not cheap or not companies trading below their NCAV– they were just not the cheapest of the cheap according to three strict criteria I used.

After reviewing the results, my desire was to purchase all of the net cash stocks (there were only a handful), all of the NCAVs and then as many of the cash bargains as possible. You see, this was where one of the first hurdles came in– how much of my portfolio I wanted to devote to this strategy of buying JNets. I ultimately settled upon 20-25% of my portfolio, however, that wasn’t the end of it.

Currently, I have accounts at several brokerages but I use Fidelity for a majority of my trading. Fidelity has good access to Japanese equity markets and will even let you trade electronically. For electronic trades, the commission is Y3,000, whereas a broker-assisted trade is Y8,000. I wanted to try to control the size of my trading costs relative to my positions by placing a strict limit of no more than 2% of the total position value as the ceiling for commissions. Ideally, I wanted to pay closer to 1%, if possible. The other consideration was lot-sizes. The Japanese equity markets have different rules than the US in terms of lot-sizes– at each price range category there is a minimum lot size and these lots are usually in increments of 100, 1000, etc.

After doing the math I decided I’d want to have 15-20 different positions in my portfolio. Ideally, I would’ve liked to own a lot more, maybe even all of them similar to the thinking behind Nate Tobik’s recent post on Japanese equities over at Oddball Stocks. But I didn’t have the capital for that so I had to come up with some criteria, once I had decided on position-sizing and total number of positions, for choosing the lucky few.

This is where my own psychological bias started playing a role. You see, I wanted to just “buy cheap”– get all the net cash bargains, then all the NCAVs, then some of the cash bargains. But I let my earnings yield numbers (calculated for the benefit of making decisions about some of the cash bargain stocks) influence my thinking on the net cash and NCAV stocks. And then I peeked at the EBIT and net income tables and got frightened by the fact that some of these companies had a loss year or two, or had declining earnings pictures.

I started second-guessing some of the choices of the color-coded bargain system. I began doing a mish-mash of seeking “cheap” plus “perceived quality.” In other words, I may have made a mistake by letting heuristics get in the way of passion-less rules. According to some research spelled out in an outstanding whitepaper by Toby Carlisle, the author of Greenbackd.com, trying to “second guess the model” like this could be a mistake.

Cheap enough?

Ultimately, this “Jekyll and Hyde” selection process led to my current portfolio of 16 JNets. Earlier in this post I suggested that one of my principles for inclusion was that the thing be “cheap enough”. Whether I strictly followed the output of my bargain model, or tried to eyeball quality for any individual pick, every one of these companies I think meets the general test of “cheap enough” to buy for a diversified basket of similar-class companies because all are trading at substantial discounts to their “fair” value or value to a private buyer of the entire company. What’s more, while some of these companies may be facing declining earnings prospects, at least as of right now every one of these companies are currently profitable on an operational and net basis, and almost all are debt free (with the few that have debt finding themselves in a position where the debt is a de minimis value and/or covered by cash on the balance sheet). I believe that significantly limits my risk of suffering a catastrophic loss in any one of these names, but especially in the portfolio as a whole, at least on a Yen-denominated basis.

Of course, my currency risk remains and currently I have not landed on a strategy for hedging it in a cost-effective and easy-to-use way.

I suppose the only concern I have at this point is whether my portfolio is “cheap enough” to earn me outsized returns over time. I wonder about my queasiness when looking at the uneven or declining earnings prospects of some of these companies and the way I let it influence my decision-making process and second-guess what should otherwise be a reliable model for picking a basket of companies that are likely to produce above-average returns over time. I question whether I might have eliminated one useful advantage (buying stuff that is just out and out cheap) by trying to add personal genius to it in thinking I could take in the “whole picture” better than my simple screen and thereby come up with an improved handicapping for some of my companies.

Considering that I don’t know Japanese and don’t know much about these companies outside of the statistical data I collected and an inquiry into the industry they operate in (which may be somewhat meaningless anyway in the mega-conglomerated, mega-diversified world of the Japanese corporate economy), it required great hubris, at a minimum, to think I even had cognizance of a “whole picture” on which to base an attempt at informed judgment.

But then, that’s the art of the leap of faith!

A Record Of Some Misgivings ($DWA, $DIS, $FRMO, @WhopperInvest, #valueinvesting)

I’ve had a little back and forth with some other value investors recently on my concerns about some of DreamWorks Animation’s outstanding corporate governance and capital allocation issues. I figured it was probably time to put pen to paper and formally record some of these thoughts.

Capital mis-allocation

To start, I want to mention the capital allocation issues. Over the last four years (2008-2011), DWA generated approximately $508M in operating cash flow, or about $127M/yr. In that same period, DWA invested $217M in their business, or about $54M/yr, while it bought back $389M, or about $97M/yr, worth of stock and finally they retired $73M worth of debt, which occurred in one year (2009) and represented the last of their LT debt on the books at that time.

As you can quickly surmise, there was only $291M of FCF or about $73M/yr over that period to support $462M in buybacks and debt paydown, a deficit of $171M which appears to have been financed by drawing down cash on the balance sheet and potentially leaning on the revolving credit facility as well.

I see a couple problems here:

  1. This is a growth company but the company will not be able to finance its growth ambitions on its own now because it has used a ton of its own financial resources buying back stock, which means it’ll have to either issue substantial new equity at low prices or take on more debt to finance its future growth
  2. The buybacks occurred at a range of prices and therefore market valuations of the company, with many of them clustered at the high end of that range, implying the company is not good at determining its own value and buying back only when the company is on sale

The first issue concerns me especially so given the nature of DreamWorks Animation’s business– in the end, it is highly speculative and could easily fail, meaning the most appropriate financing type is equity, not debt. Debt is more appropriate for a low-risk, predictable, consistent enterprise (such as financing a real estate venture). Equity provides the kind of flexibility and endurance one needs to weather the potential storms in a business like DWA’s.

But by using up much of its cash, DWA has put itself in the position where it will have to either dilute existing shareholders at potentially disadvantageous prices, or else it’ll have to raise debt which I believe adds substantial extra risk because of the way it mismatches with their business fundamentals.

The second issue concerns me because I think it directly explains a lot of the apparent value destruction that has occurred at DWA over the last 4 years as communicated by the fluctuating market capitalization and I think it sets a precedent that is in the long-run bad for minority shareholders, not good, as people of the “buybacks are good no matter what” school of thought seem to believe.

In 2008, the peak price of DWA was $32/share and with 91M FDSO at the time, that amounted to a market cap of $2.9B. In early 2010, the company climbed to an all-time peak price of nearly $43.50/share and with 87M FDSO that amounted to a market cap of nearly $3.8B. The shares now linger back below their 2009 low of $18.56/share and very close to the all-time low of $16.52/share reached in January of 2012, trading around $17/share for a total market cap of about $1.43B.

Slice it how you like it but according to the market the company has conservatively destroyed almost $1.5B of value in that time and I’d say that’s primarily due to spending $460M on buybacks and debt reduction that could’ve been spent on growing the business or waiting for opportunities to grow the business. If you add that capital back into the business you’d get a market cap closer to $2B right now.

Most of the buybacks occurred near the $30/share range with relatively little of the buybacks occurring near the lows of around $17/share. This kind of capital allocation “discipline” can not be put to bed by arguing that “share buybacks are good if they happen at all”– the latter price represents a 50% discount to the former (or the former a nearly 100% premium to the latter, depending on how you want to look at it)! Are we supposed to be comforted by the fact that DWA’s management and board seem to think the company is cheap anywhere between $3B and $1.5B in market cap?

That isn’t a reasonable way to manage capital. You’ll never catch Warren Buffett making that kind of argument and I highly doubt you’d have much money to manage on your own if you adhered to that philosophy for long.

One of the replies I got back from another investor (see below) on this was that “what’s done is done.” That is an unacceptable response. What’s done is not done because it could very easily happen again and it is more than likely to do so given that the pattern set, the discipline demonstrated so far, is that the management and board of DWA is incompetent when it comes to allocating capital to share buybacks. This is a red flag and a way they could continue to destroy whatever value they create through their growth strategy in the future.

Golden parachutes for the pilot and the flight crew, but not the passengers

At the behest of another money manager with a value-based approach I had been communicating with, I decided to review the Form DEF-14A filed 4/11/12 for DWA. I had (admittedly) skim-read the thing when first performing due diligence several months ago, but I had not read it line-by-line as he had urged me to do, more on that fact in a bit.

As I read through it, I noticed a few things.

For one, I noticed that FRMO-owned companies own 9,614,089 shares or 13.1% outstanding, ostensibly for their ETF products. From a recent post here at valueprax you’ll remember that I am impressed with the strategic thinking of this organization and for the purposes of their own business they seem to be great capital allocators (of course, I have no idea at what prices they accumulated their position). But then it dawned on me that most of their products are passively-managed index ETFs and that took the wind out of my sails. I’m not necessarily under the impression at this point that they hold a stake because they think it’s a great buy, but just because it fits some strategy or theme for one of their proprietary indexes. So, that’s about 13% of the company potentially owned by “dumb money” in this case.

Then I noticed that the company utilizes Exequity and Frederic W. Cook & Co., compensation consultants, to determine executive pay. I’m working on a “digest” post of articles I’ve been reading about corporate governance and activism over at a now-defunct website nominally belonging to Carl Icahn (man, that guy seems a bit ADD at times the way he starts and stops investments, grass roots activism platforms, etc.) and I came across this post on compensation consultants which really set off alarm bells for me.

Think about it for a second– the managers are using company money, which belongs to shareholders, to hire consultants (multiples in this case) who charge millions of dollars and spend hundreds of hours trying to outdo each other in justifying outlandish executive compensation packages. In other words, they use your money to figure out how much they should pay themselves at your expense. It’s kind of like gilt-edged unionism for corporate executives. Why the hell is this such a mystery? Why do you need consultants to figure stuff like this out for you?

This is a corporate governance red flag– this is not treating minority shareholders like equal partners but rather treating them like the sucker at the table. After all, Katzenberg owns about 15% of the company and because of the dual class share structure (another red flag, by the way), effectively controls the company himself which makes him an owner-operator (to be fair, a good thing)… you think he can’t figure out how much to pay his other executives in terms of what’s good for K-man and what’s not?

Preposterous!

Then I get to the actual executive compensation itself. Katzenberg is now paid a $1 annual salary, choosing to receive most of his compensation via stock options and other perks. Other executives are compensated quite generously and compensation has been growing. The value of options grants is $17M annually, or over 1% of market cap each year. Long-term incentive compensation is worth another $9.2M. Combined, that is $26M or almost 2% of the company’s market cap for a handful of top execs and board members.

Other things of note:

  • Lew Coleman, president and CFO, recently exchanged higher annual cash salary structure in return for decreased long-term incentive awards, does this show lack of faith in the long-term value of the company?
  • Ann Daly, the COO, has part of her compensation tied to performance of the company’s stock price, which is an idiotic practice given that it incentivizes her to manipulate the company’s operations to game short-term numbers meanwhile the company’s management has no direct control, in the long-run, over what the investing public thinks of the value of the company (yes, their actions will translate into better or worse valuations but in the end it’s like tying someone’s compensation to the weather)
  • Overall, tons of golden parachutes for just about everyone in the case of a change of control or a termination with or without cause, which are more blatant red flags and give minority shareholders an unfair shake

Then there’s the income tax savings-sharing agreement with Paul Allen, a former shareholder and financial enabler of the company which the proxy explains constitutes “substantial” payments to Mr. Allen over time (this fact being confirmed by the multi-hundred million dollar payable on the balance sheet). To put it simply, I don’t get this or how it works and so far no one has been able to explain it to me. It could be harmless, it could be disastrously unfair to minority shareholder. I really have no clue, it’s beyond my accounting and income tax liability knowledge.

My overall impressions were thus: it takes 66 pages to explain/justify DWA’s compensation practices and related-party special transactions. The company hires compensation and other consultants with shareholder money to determine what management should be paid. And shares are locked up and all change of control decisions will be made by Katzenberg. This company gets maybe a C in terms of corporate governance, which is average in relative terms but sucks in my absolute opinion.

In general, I am concerned about my own ability to understand the accounting behind the company’s compensation practices. And this dovetails with my lingering concern that neither I nor anyone else seems to be able to confidently and accurately model just how much cash specific or even any single movie title in DWA’s library generates for the company at different points over its life.

Bringing it full circle

A few days ago I posted a video interview of Rahul Saraogi, a value investor operating in India, along with my notes of the interview. I found the interview surprisingly impactful (I’ve been watching other interviews from the Manual of Ideas folks and unfortunately none of them have come anywhere close in terms of profundity) and the item that stuck out the most from the whole thing was Saraogi’s comments on the importance of corporate governance and capital allocation for the long-term investment results of minority shareholders.

To reiterate, according to Saraogi good corporate governance means dominant shareholders who treat the minority shareholders like equal partners, who do not treat the company like a personal piggy bank or a tool for furthering their own personal agendas at others’ expense. He says good corporate governance is binary– it either exists or it doesn’t, there are no shades of grey here. The issues I’ve cited above make it clear that DWA does not have good corporate governance practices. The fact that the Form 14A discloses the fact that both David Geffen and Jeffery Katzenberg are essentially using the company resources to the tune of over $2M per year to subsidize their ownership and maintenance of private aircraft is another good example– it is one thing to have the company reimburse them for expenses occurred in doing business but it is quite obvious from the way this agreement is structured that the company is basically paying for the major costs of ownership while they are deriving the personal benefits and exercising discretion as owners in name and title.

Similarly, capital allocation is critical in Saraogi’s mind and many companies and their management don’t get it– they either don’t understand it’s importance or how to do it, or they don’t care because they’re rich enough. I think a little bit of both is operating here. Certainly Jeffery Katzenberg is “rich enough” at this point. He’s worth several hundred million dollars at least, he has the company paying for his private aircraft and other perks and he has even said in interviews I’ve read that he’s got all the money he could need or want at this point and continues to work out of passion and interest. Normally that’s a good thing but in this respect it’s a bad thing because a person who operates as an artist rather than a businessman probably doesn’t care what their ROC looks like as long as they get to put their name on the castles they build.

And people who get capital allocation don’t pay prices that range nearly 100% in value for shares they purchase, unless of course they’re absolutely convinced the intrinsic value still far exceeds such prices. I note here that while there is no evidence from the company that this isn’t the case, there’s similarly no evidence that there is, and I don’t think faith is a good basis on which to form a valuation. As an aside, none of the grade-A elite Wall St analysts on the earnings calls ever ask about this, and my e-mail to DWA’s IR on this topic and numerous others went completely unanswered, which is another embarrassing black mark for the company in terms of corporate governance.

Other voices in the wild

For those who are interested, there are now two recent write-ups on DWA over at Whopper Investments, the first on the value case for DWA and the second analyzing the company’s potential takeover value when compared to Disney’s acquisition of Pixar in 2004.

I really enjoy Whopper’s blog for the most part but I consider these two posts to be some of his weaker analytical contributions to date (which should be obvious from my remarks in the comments section of each, 1 and 2) and if anything that makes me even more queasy with this one– he mimicked a lot of my own unimpressive reasons for investing and I don’t generally find the sound of my own voice that soothing in cases like these, and he seemed unable to answer some of my deeper concerns, which could be evidence of his own shortcomings as an analyst or it could be evidence that these are questions with unsatisfactory answers by and large (I prefer to believe the latter at this point).

And in case anyone missed it, I wrote up my first bullish thesis on the company several months ago and I have since covered some of the more bullish developments with posts tagged $DWA.

In a nutshell, at this point my major concern is that, even if the company successfully executes on its grand growth strategy it might not mean as much for minority shareholders as we might like due to outstanding corporate governance and capital allocation concerns. I seriously wonder if I and many other value investors like me are not blinding themselves to these “binary” concerns because the potential home-run hit possibility of getting in near all-time lows on “the next Disney” is just too exciting to resist.

Whatever I do, I’ve now written this post and put it in the public domain so I won’t be able to excuse myself later on by claiming I hadn’t thought about these issues.

Videos – Rahul Saraogi On Value Investing In India (#valueinvesting, #India, @manualofideas)

The Manual of Ideas presents Rahul Saraogi, managing director of Atyant Capital Advisors

Major take-aways from the interview:

  • Referring to Klarman, finding ideas and doing the analysis is a small part of investing; the two most critical factors to succes in any investment as a minority shareholder are corporate governance and capital allocation
  • Good corporate governance means a dominant shareholder who treats minority shareholders like an equal business partner: even aside from egregious fraud and legal violations, you can face situations where dominant shareholders use the company like a piggy bank or to promote personal agendas
  • Once you’ve cleared the corporate governance hurdle you must consider capital allocation: many times companies follow the same strategy that got them from 0 to a few hundred million in market cap, which will not work to get them to the next level; often by this time the dominant shareholder is sufficiently wealthy and loses interest in capital allocation to the detriment of minority shareholders
  • India’s investment universe:
    • Indian GDP close to $2T
    • Indian market cap $1.5-2T
    • 80-85% of India’s market cap is represented by the top 150 firms: mega-cap banks, steel producers, etc., that trade on ADRs and everyone knows of outside of India
    • Thousands of listed companies below this with market caps ranging from $2-3B to a couple million dollars
    • Rahul finds the next 1200-1300 companies below the top 150, with market caps ranging from $50M-$2B, to be the most interesting opportunity
  • Corporate governance is binary: either a company gets it, or it doesn’t
  • Case study: 1998, invested in a sugar manufacturer trading for $20M generating $20M in annual earnings with a 14% tax free dividend yield, virtually debt free, strong moats, dominant player in its field, grew from $20M to $900M market cap, the owners were very focused on growing capital, no grandiose desire to build empires, not trying to grow the top line at all costs or gain rankings, just allocating capital wisely
  • Every investor is looking for shortcuts and binary decisions, ie, “Should I invest in India or not invest in India?”; the reality is it’s a lot of work, it’s about turning over as many stones as you can– what Buffett has done well is finding people who can compound capital and then staying with them through market cycles
  • You can do what Buffett did in any market but you must dive into it, get your hands dirty, do the work it takes and then maintain the discipline to stick with what you’ve found
  • Home-market bias: most people are going to allocate most of their capital in their home-market, because by definition anything that is not familiar or proximate is considered risky; consequentially, “locals” will disproportionately benefit from economic and financial gains in their local markets
  • India can not and likely will not become a dominant allocation in a foreign investors portfolio; without devoting 100% of your time and energy to understanding that market, or having someone invest on your behalf who does, you will likely not understand the culture, motivation and habits of the people in that market
  • “It is imperative that in any market you go with people who understand it and are focused on it full time because investing is ultimately bottom-up”
  • Accounting, financial reporting and investor relations practices are modeled off the US and UK so they’re similar; however, many businesses are run by one or two entrepreneurs and they’re often too busy to be available to speak with outside investors, but persistence pays off when they realize you’re interested in learning about their business
  • Access to capital in Indian markets has improved, meaning it has become easier for Indian companies to scale
  • Why does India have high rates of capital compounding? India is a 5,000 year old civilization and has had borrowing, lending and private markets for capital that entire time meaning people are aware of capital compounding; that being said, India has companies and management that understand ROC, those that don’t, and those that are essentially professional Ponzi-schemes, issuing capital at every market peak and then trading for less than the issued capital at the trough because they’re constantly destroying wealth
  • Rahul sees the government as incapable of providing the public infrastructure needed by the growing economy; he sees the economy turning toward a “private-public partnership” model that is more private than public– enlightened fascism?
  • As companies rushed into this private-public space, a lot of conglomeration and corporate mission-creep occurred, resulting in systemically low ROC for companies in the infrastructure space as most as poorly run; failure of top-down investing thesis
  • “I’m looking for confirmation in facts, not in other investors’ opinions”
  • I can comment on whether valuations for individual companies make sense, but I can’t make a judgment on the value of a broad market index, I just don’t think that number means anything
  • Risk management: develop assumptions about the company’s business and then periodically analyze what the company is doing relative to original investment hypothesis; if your assumptions prove to be wrong or something changes drastically with the company, that is when you hit a “fundamental stop-loss” and corrective action needs to be taken immediately, even if the stock has done well and the price has risen