Can You Tell The Difference Between Economics And Politics? (@EconTalker, @EconLib, #economics, #politics)

These days, it is trendy to practice political punditry under the guise of a thoughtful economist handing out enlightened “economic policy” suggestions.

A recent case in point is the interview with Harvard’s Ed Glaeser with EconTalk’s Russ Roberts, wherein Glaeser shared the following ideas about reforming city governance with respect to “historic preservation districts”:

In the case of the city historic preservation districts I would probably replace the ever-increasing swatch of territories–15% of the land area in Manhattan south, in the bottom half of Manhattan excluding Central Park as an historic preservation district right now–and areas go into historic preservation districts but they rarely come out of them. So, it seems like it’s going to be an ever-increasing swath of the city. I don’t much like the idea of cities being museum pieces. There are a few which are appropriate, like Bruges, but I think it’s good that cities change and that they develop new space, combination of new activities and people. So, I would in terms of preservation–my father was an architectural historian so I do really believe in the value of preserving some old, beautiful buildings–but I would have a fixed number of the total number of buildings that they are able to set aside as being preserved rather than allow them to just keep on getting new areas for preservation districts.

Here is what an economist would say:

Land and property use should be conditioned on “most highly valued use”, as evidenced by voluntary exchanges agreed to by participants in the property market. For some, purchasing historic properties for the purposes of preserving them, perhaps for commercial exploitation as a tourist attraction or simply to be kept out of the hands of the public or those who might privately redevelop them, might be the “most highly valued use” for which a person would exchange their wealth to control these properties. For others, tearing the historic buildings down or otherwise modifying them from their original, historic state, may be the “most highly valued use”, perhaps for the purpose of providing new housing or areas of commerce and industry.

There is no moral reason why future generations should be beholden to the land-use decisions of ancient generations, and even if there was, it is not an economist’s place to discuss such topics.

Notice– Glaeser said none of this, and in fact violated the statement at the end while complementing it all with a bit of arbitrary personal psychological projection, the idea that because his father was an architectural historian he has some kind of special need or special knowledge into the value of preserving historic properties that necessitate the violence of the State to protect such value impositions.

In fact, the closest Glaeser came to say anything “economic” about the subject was his attempt to calculate a “fixed number of total buildings” which would be available for historic preservation. But even here, his theorizing falls flat on its face, for Glaeser does not explain how his arbitrary calculus would be superior to the outcomes of voluntary exchanges between market participants.

How many is a “fixed number”? What constitutes a “building” for purposes of this policy? Which “buildings” shall be a part of this “fixed number” and which shall remain outside it, and how are such decisions evaluated in an objective way?

Such policies are an invitation for gross, arbitrary and wild government intervention and special interest group politicking that Glaeser claims earlier in the interview he is strongly against. Yet, he opens the intellectual door to them in moments like these when he places his economist costume over his political self and attempts to perpetrate a theoretical deception.

More Banking Confusion: Liquidity Versus Solvency (@EconTalker, #banking, #liquidity)

Here is a choice quote from the recent EconTalk podcast with Anat Admati of Stanford University:

Well, they have fancy ways to talk about banks, and we try to unpack those. They talk about maturity transformation, liquidity transformation. What that means is really that the depositor, the people who lend to the banks, often time want their money quickly, especially demand deposits. But when they invest it, they kind of invest it longer term and in less liquid things. So there is a sort of imbalance between the money that they use to fund and their investment in the sense of the length of time until something has to happen and also the speed with which they have to pay versus get paid. And so that mismatch creates fragility by itself, which also means for example if all of us run to the bank at the same time then the bank may not be able to cover all of that. Even if it technically would be solvent, it has everything, that’s kind of an inefficient run that you could have, in principle. So basically the banks tend to run a little bit more than other people into liquidity problems. You could say that, just, I have the money but I didn’t go to the ATM kind of thing–I can pay you back but we’re going to have to find a liquidity solution, sort of a rolling back my debt. Their funding is kind of fragile almost by definition because of the way it comes and the way people can come back for their money on short notice or any time they want. So that’s part of the funding. And the investments are not as liquid or longer term than that. (emphasis added)

This is an utter confusion. This is not a “liquidity problem”, it’s a solvency problem.

Money-in-an-ATM is not the same economic good as money-in-my-hand. That is, money-five-minutes-from-now is not the same as money-right-now.

They are separate economic goods due to the time value of money. What Admati has done is create an arbitrary distinction between a future money good and a present money good, by projecting her preference/judgment onto an exchange involving two other parties of which she is not one.

If party A demanding “liquidity” from the bank B truly saw no difference between money-right-now and money-a-few-days-from-now, for example, then a bank run would never happen and these items would trade at the same price, which they do not.

This is a fundamental error of economic reasoning. I expect a professor of finance and economics to understand something like this and as a result I find myself disappointed to see that she does not.

Economists and politicians only let banks get away with this. If anyone else were to be so arbitrary and haughty toward contracts they’d be thrown in prison, but for banks insolvency never comes so long as you can contort logic to the point that you convince yourself that all that’s missing is a bit of liquidity.

This is more free lunch thinking.

Observations On Expectations (#publicschool, #expectations, #psychology)

A story of expectations met and unmet, in two parts.

Part the first. I spoke in front of a group of students at a local continuation high school this morning. The original topic was my career (what do I do? what do I like/dislike about it? etc.) and my career path (what’s my background? education? how’d I get to where I am?) but I never quite got there. I mostly ended up talking about economics as I was speaking to an economics class, nominally, and the program coordinator kept prompting me on that subject.

I introduced two economic concepts to the assembled: TNSTAAFL/opportunity cost, and subjective value theory. I tried to apply them to “real life” to make them tangible and interesting to the audience. I talked about how everyone got suckered into the Housing Bubble, which cost a lot of people their homes, their personal finances, their jobs and sometimes more. I suggested that a person who understood that TNSTAAFL wouldn’t have gotten suckered in because he would’ve recognized the bubble for what it was and played it safe as he could. Subjective value theory I used to explain why we have an economy and why people work jobs, to serve each other’s subjective needs. I encouraged the class to think about their own values and to pursue them, and recognize that when people tell them what to do they’re simply telling them they should follow subjective values other than their own. I tried to highlight the role opportunity cost plays in pursuing subjective values, for example, people often get into traps such as pursuing money to provide for their families in such a way that they don’t get to spend time with their families. This opportunity cost is forgotten or ignored.

I also covered time value of money and the function of credit during a brief tangent, prompted by the program coordinator emphasizing the importance of personal finance principles.

The instructor goaded the students into applauding me before I had even spoke, as some kind of polite welcome for someone who had taken the time to stand before them and pontificate on a subject they cared little about. I said, “We’ll see if you still feel like applauding me at the end” and then began my talk. At the end of it, as the students rose to leave at the sound of the Pavlovian bell, one of the young men closest to me in the front of the room turned to his classmate and said in a quite intentionally audible way, “Thank GOD that is over!”

The morning’s events completely met my expectations and as a result, I was satisfied with myself when I myself left. I had entered a prison, whose inmates were being held against their will, by force of law, who had been assembled before me because they had no other choice save punishment and who had little to no interest in the subjects I had been invited to speak about before them. You certainly can’t blame a person in such circumstances for being disengaged, melodramatic and at times downright hostile.

If you put me in a cage I’d be uncomfortable and not in a friendly mood, either.

I didn’t expect to touch anyone, change a life or spark a fire or interest in anyone for the subjects I spoke about (economics, careers, my career, me) and if I happened to do that despite my intentions, that’s fine. I expected to go in there, treat the poor beasts with respect and maybe a bit of sympathy, having once been caged in a similar manner myself, and deliver my thoughts as articulately and coherently as I could. I expected to get practice speaking before an audience and trying, not necessarily succeeding, at making a foreign subject engaging or relatable for them.

In this, I met my expectations and so I believe I succeeded and thus I felt satisfied.

Part the second. For some time now I have watched in despair as a previously favorite blog of mine has gone into seemingly terminal decline. What was once a source of original thinking, unique coverage and respectable ideological consistency has in time become a haven for hacks and simpletons, its content hollowed-out and refocused on a few topics I just don’t have much interest in. The purveyor of the site has taken numerous opportunities, on his blog and his new webcast radio show, to demonstrate qualities of his personality I’ve found surprising, disappointing and at times reprehensible.

My distress with this reached a fever pitch early this week when a long-awaited debate on the subject of “intellectual property” was joined by the purveyor and another popular blogger on the subject. While the purveyor’s behavior leading up to the discussion gave me no reason to believe it’d be an intelligent, objective attempt at sussing out the truth by the two parties, but rather much evidence that it would be a battle of wills and ego characterized by willful blindness of reason and savage emotional assaults on each respective victim, the final product was so shockingly extreme in terms of all the undesirable qualities I suspected it would contain that I almost couldn’t believe these two adults had allowed themselves to be recorded, their outrage to be shared in front of a public audience of strangers.

I found myself so disappointed with the whole thing. It was anti-intellectual and truly uncivilized, the kind of stuff blood feuds at made of (gusto about sacred honor and the like that can never be satiated by way of reasonable argument). I knew both men were capable of a bit of underhandedness, but at least in the past the underhandedness seemed to have some kind of productive point. This time, after I finished sitting through two and a half hours of two middle-aged men calling each other names and screaming at one another, waiting for a point, I realized too late that there was none beyond sharing pure hate and distrust.

Who was to blame for my dissatisfaction in this instance? Initially, I found myself disgusted with these two people for subjecting me to this idiocy. “How dare they!” Then I thought about it some more. They are who they are. Their current skills and capabilities with regards to interpersonal communication and intellectual reasoning are aspects of their identity that exist as they do, whether I find them appealing or satisfying or not. I expected them to work hard to please me in their debating efforts (despite, I should add, much evidence that they were capable of no such thing) and when they didn’t live up to my expectations, I was disappointed.

Not by them, but by myself. For expecting people to live to serve my intellectual and emotional needs.

In the first part, I participated in something that could easily be seen as a disastrous waste of everybody’s time. Yet, I walked away from it in a positive state of mind. In the second part, I witnessed a true social tragedy and felt depressed and upset. Both circumstances were undesirable, but my reaction was different each time because my expectations were different.

Expectations can glorify our existence or cast the light of our lives down a dark abyss. I hope to remind myself of this fact more often.

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.

The Best Interview On Gold, The Gold Market And Investment Implications I’ve Ever Read (#gold, #economics)

In “What is the key for the price formation of gold?” at GoldSwitzerland.com, SF-based software developer Robert Blumen covers a lot of fascinating and, to my eyes, original ground in an interview with the site’s host.

This has got to be the best interview on the subject of gold in general, the functioning of the gold market and the implications for investors that I’ve ever come across. Blumen not only covers these specific subjects related to gold, but also discusses the Chinese economy, the US economy and the state of monetary and fiscal affairs and even the attitudes of value investors, demonstrating thoughtful familiarity with all he touches. Blumen is well-versed in Austrian economic philosophy and applies this theory to the various practical considerations resulting in surprising new perspectives on common themes.

It’s a long interview and it will only fully reward those determined to dive all the way in. Here’s an excerpt:

There are two different kinds of commodities and we need to understand the price formation process differently for each one. The first one I’m going to call, a consumption commodity and the other type I’m going to call an asset.

A consumption commodity is something that in order to derive the economic value from it, it must be destroyed. This is a case not only for industrial commodities, but also for consumer products. Wheat and cattle, you eat; coal, you burn; and so on. Metals are not destroyed but they’re buried or chemically bonded with other elements making it more difficult to bring them back to the market. Once you turn copper into a pipe and you incorporate it hull of a ship, it’s very costly to bring it back to the market.

People produce these things in order to consume them. For consumption goods, stockpiles are not large. There are, I know, some stockpiles copper and oil, but measured in terms of consumption rates, they consist of days, weeks or a few months.

Now for one moment I ask you to forget about the stockpiles. Then, the only supply that could come to the market would be recent production. And that would be sold to buyers who want to destroy it. Without stockpiles, supply is exactly production and demand is exactly consumption. Under those conditions, the market price regulates the flow of production into consumption.

Now, let’s add the stockpiles back to the picture. With stockpiles, it is possible for consumption to exceed production, for a short time, by drawing down stock piles. Due to the small size of the stocks, this situation is necessarily temporary because stocks will be depleted, or, before that happens, people will see that the stocks are being drawn down and would start to bid the price back up to bring consumption back in line with production.

Now let’s look at assets. An asset is a good that people buy it in order to hold on to it. The value from an asset comes from holding it, not from destroying it. The simplest asset market is one in which there is a fixed quantity that never changes. But it can still be an asset even when there is some production and some consumption. They key to differentiating between consumption and asset is to look at the stock to production ratio. If stocks are quite large in relation to production, then that shows that most of the supply is held. If stocks are small, then supply is consumed.

Let me give you some examples: corporate shares, land, real property. Gold is primarily an asset. It is true that a small amount of gold is produced and a very small amount of gold is destroyed in industrial uses. But the stock to annual production ratio is in the 50 to 100:1 range. Nearly all the gold in the world that has ever been produced since the beginning of time is held in some form.

Even in the case of jewelry, which people purchase for ornamental reasons, gold is still held. It could come back to the market. Every year people sell jewelry off and it gets melted and turned into a different piece of jewelry or coins or bars, depending on where the demand is. James Turk has also pointed out that a lot of what is called jewelry is an investment because in some parts of the world there’s a cultural preference for people to hold savings in coins or bars but in other areas by custom people prefer to hold their portable wealth as bracelets or necklaces. Investment grade jewelry differs from ornamental jewelry in that it has a very small artistic value-added on top of the bullion value of the item.

So, now that I’ve laid out this background, the price of a good in a consumption market goes where it needs to go in order to bring consumption in line with production. In an asset market, consumption and production do not constrain the price. The bidding process is about who has the greatest economic motivation to hold each unit of the good. The pricing process is primarily an auction over the existing stocks of the asset. Whoever values the asset the most will end up owning it, and those who value it less will own something else instead. And that, in in my view, is the way to understand gold price formation.

Many of the people who follow and write about this market look at it as if it were a consumption market and they look at mine supply and industrial fabrication as the drivers of the price as if it were tin, or coal, or wheat. People who look at gold as if it were a consumption market are looking at it the wrong way. But now you can see where the error comes from. In many financial firms gold is in the commodities department, so a commodities analyst gets assigned to write the gold report. If the same guy wrote the report about tin and copper, he might think that gold is just the same as tin and copper. And he starts by looking at mine supply and industrial off-take.

I wonder if more equity analysts or bond analysts were active in the gold area, if they would be more likely to look at it the same way they look at those assets.