Dear Reader:

If you are interested in the topics discussed on this blog, you will no doubt be interested in reading the articles on the website of my consulting and research company ...

McLelland + Palazzi | Financial economics

... which will have new, but similar and interesting, articles.  I look forward to seeing you there!

Cheers,

MMc
São Paulo

All accountants should be trained in econometrics ...

The proposition about accounting and econometrics, a little more formally, is this:

It is necessary for professional accountants to be trained in econometrics.

Most accountants and, I believe, even most accounting professors would strongly disagree with the proposition. ( It is, I can assure you, nonetheless true. :) )  Before presenting the argument supporting the proposition, it's first necessary to have an understanding of what econometrics is.

    What is econometrics?  To paraphrase the econometrician Jeff Wooldridge, 

Econometrics is the discipline involved in estimation of, and inferences about, causal relationships between economic variables.

For example, suppose economic theory suggests a causal relationship between sunspots and the demand for electronic components; let's say theory suggests sunspots cause an increase in the demand for electronics components.  An inquisitive individual might want to answer at least two questions about the theory:  Is actual data consistent with the prediction that sunspots are, on average, associated with an increase in electronic component demand? And, what is the estimated average effect of (the average) sunspot on electronic component demand?  Both, no doubt, interesting questions ... .  
    Also, attributable to Jeff Wooldridge is the useful idea of a mutually-exclusive-and-exhaustive classification of things we need to know to make decisions: Things we need to know can either be (1) known, (2) estimated, or (3) assumed, where the (rational) decision-making preference ordering is known > estimated > assumed.  Econometric methods are necessary when relationships between economic variables are not known, and when it is a Bad Idea to assume them.  
    In the context of my proposition it is hopefully obvious that simply assuming values in financial statements is a Bad Idea.  It is also hopefully obvious that the values of many things reported in financial statements is not "known".  In any case, with this basic understanding of what econometrics is I'll turn to the main topic.

    Accounting requires econometrics. As background, accounting is basically comprised of the recognition, measurement, and disclosure of economic events and resources in financial statements:

• recognition refers to when events and resources are presented;
• measurement refers to how events and resources are valued; and
• disclosure refers to how such events and resources, recognition, and measurement are described in the statements.

Accounting measurement of many economic events and resources in a way approaching objective measurement requires use of econometric methods (and objectivity is an important characteristic of accounting measurement).  I will explain the idea using FAS 157, Fair Value Measurement and FAS 142, Goodwill and Other Intangible Assets, although I could just as easily use any of a dozen or so other accounting standards, which professional accountants must apply as part of their work.  Roughly speaking,  FAS 142 and 157 in combination require accountants to estimate the fair value of intangible assets, and reduce the financial statement value of the intangibles to the estimated value if less than previously reported.

    So why would estimating the fair value of intangible assets (among other things in financial statements) require use of econometric methods?  To answer, first consider the simplest expression of how fair value of intellectual property ("IP", one broad category of intangibles) is determined, where example numbers for factors influencing value are included for concreteness:

The equation says estimated fair value is a function of (i) expected future marginal profits attributable to the IP, (ii) expected future value of the IP, and (iii) estimated fair market expected risk-adjusted rate of return.  It is relatively easy to show expected future profits and values generally must be estimated using econometric methods, but I will simply focus (very carefully) on the estimated fair market expected risk-adjusted rate of return.  For example, given $1,000 in  expected future profits and value at the end of one year, if the estimated rate of return is .10, then the estimated fair value of the IP is about $909.

    But here's the problem for accountants: They don't know what a fair market's expected risk-adjusted rate of return is for the IP. Why? Because IP is almost by definition unique  and therefore unlike other IP that might have been sold.  Moreover, IP is rarely traded in open, fair markets.  This means that accountants (or someone else) must estimate what a fair market's expected risk-adjusted rate would be; that is, they must predict what the rate would be if the IP was traded in a fair market. 
    So, unless the accountants are willing to abrogate their responsibilities for accounting measurement (which they often do, by the way) or simply make an assumption about the rate (which they also do sometimes), then they must use econometric methods to estimate the rate.

    The argument is essentially complete at this point (QED, quod erat demonstrandum!), but to see this all a little more clearly consider the following graph:

Ignoring the questions posed in the graph momentarily, I will focus on what the graph represents:

• The graph represents a hypothetical relationship between a certain type of risk (e.g., the risk that a particular class of antibiotics will become obsolete due to development of a newer, better class of antibiotics) and the rate of return implicit in the way a fair market sets the price of the risk.
• The points shown on the graph represent actual observations ("data") of risk and rate of return set in a fair market (e.g., actual observations of expected market rates of return across different antibiotics each with different levels of risk).
• The line running through the data represents an estimate of the fair market relationship between the type of risk and the market rate of return on the risk.

Suppose we are accountants for "YX Pharma Corporation" with "Antibiotic X" for which we have a patent expiring in 10 years.  Antibiotic X represents about 1/2 of the revenues and profits for YX Pharma, and has never been offered for sale.  So, the IP represented by the Antibiotic X patent is not traded in any market, let alone traded in a fair market.  So we don't know its fair value. 

    This means we accountants must either estimate or assume the value of the Antibiotic X patent.  Our securities law attorneys have strongly advised us against simply making up an assumption  about the value of the patent without strong supporting data and analysis.  So, we must estimate the value:  Roughly speaking we must first predict expected future marginal profits and value of the patent. Then, because the patent for Antibiotic X doesn't trade in a fair market, we must use publicly-observable data on risk measures and fair market rates of return on other similar antibiotics to estimate the market's risk-return relationship.
    With our estimate of the (equation for) the market's risk-return relationship, we can then use our measure of risk for Antibiotic X and obtain an estimate of a fair market's expected risk-adjusted return on Antibiotic X (even though, as stated, it doesn't actually trade in any market).  The estimated rate would then be used in a valuation model similar to the one shown above (the FV equation) to obtain the FV estimate of the Antibiotic X patent.

    As suggested by the questions on the graph, the scenario poses a lot of important questions we accountants must answer:

• Where exactly do we get this market rate of return data; which competitors and which antibiotics; how do we separate overall observable market rates of return on competitors' equity from the rates on the antibiotic IP?
• What is the best way to estimate the risk-return relationship; what method(s); what exactly are we estimating (i.e., the most likely relationship, the most conservative, etc.)?
• What do we do about observations that don't seem to fit the average risk-return relationship; should they be included in our analysis and estimates?

It turns out that such questions (and their answers) are precisely the domain of econometrics.  Econometricians have largely worked out the general frameworks for thinking about such problems, as well as general solutions to them, over the last 50-60 years.

    So it follows that ...

Unless professional accountants want to abrogate their responsibility for accounting measurement to those trained in econometrics, they must be trained in econometrics themselves.

It would seem strange, almost pathetic really, if accountants were to abrogate the responsibility for accounting measurement--one of the three basic aspects of accounting--to others, don't you think?  :)  QED

MMc
São Paulo

Towards a Piagetian subconscious learning theory ...

... and an explanation of why traditional lectures work (and don't work)!

Some background on educational psychology. I teach courses that most students find either boring or highly abstract; often both. The prevailing view on pedagogy in higher education seems to be that it's a Good Thing to make ideas interesting and palatable tor students. Teaching is communication and if you speak to people in a way they don't like or understand, very little communication takes place; the receiver doesn't actually receive the communication. I agree ... in principle.

Consider, however, a statement made by John Grinder, a well-known psychologist: 

I once taught a mathematics course at the University of California to ... linguistic students who had a good understanding of how language systems work, but did not have an understanding of mathematical systems. ... I made my entire approach fit their model of the world rather that demanding they have the flexibility to come to mine. ... When you do that, you certainly do them a favor in the sense that you package the material so it's quite easy for them to learn it. You also do them a disservice in the sense that you are supporting rigid patterns of learning in them. [my emphasis]

Hmmm. Very interesting. So, even if we could develop courses and instructional styles that appealed directly to our addictions to reality game shows, and even if this made learning some idea ( ... in accounting and finance :) ) really easy, maybe we shouldn't.

Like all decisions in life, the decision is based on a value judgment. That is, what value or objective are we trying to maximize? Consider, what precisely is the objective of higher education in accounting and finance? Is it to learn how to use the finance functions in Microsoft Excel? Or is it to learn the fundamental concepts, principles, theories, and application methods underlying the spreadsheet functions? I've asked the question many times and here is the empirical result: Employers and, hence, students want to know how to use the finance functions ("on the job"). Professors believe it best for students and employers if students understand the underlying concepts, principles, theories, and methods. After all, just because we understand how to use Excel finance functions like =IRR to determine internal rate of return, doesn't even mean we understand what it is. Although there is no doubt some compromise, it seems clear--at least to me--that the professors have it about right since people only spend about 2-4 years studying accounting and finance to prepare them for the entire careers.
 
With that as background and the premise that the objective of accounting and finance education is to prepare people for their entire careers, we can proceed to the main topic.

A theory of subconscious learning.  Now a story: When I first began teaching I believed, as I had been instructed, that it was necessary to "engage" with (hopefully) all the students in the class. That is, I believed it was necessary to have their active attention as I lectured. Some would say that lecturing was a mistake in the first place: what's needed is a roll-up-your-sleeves, get-out-into-the-class, get-in-their-faces mode of interaction that Makes Them Engage.
 
Unfortunately, I happen to believe in free will: Why shouldn't students be free to choose to actively attend, inactively attend, or skip lecture altogether? Isn't it presumptuous and unreasonable to expect anywhere near the majority of a class of 50 students to pay rapt attention and interact throughout a lecture? After all, in the corporate world it's my experience that peoples' attention spans are generally less than 10 minutes. Most students spend about 15 hours a week in lectures. And, anyway, is it not even a little unfair to make students learn when there are essentially enforced grade distributions (i.e., is it fair to make all students learn at a high level--say an A- or A level--and then grade them in such a way suggesting half are below average?). After solving this philosophical problem to my satisfaction by allowing students to engage and learn the way they believed best, I moved on to focusing on other aspects of teaching ...
 
Over a period of about two or three years I noticed that students generally fell clearly into several groups, arranged in the order that I preferred teaching to:

Group 1  Students who always came to class but never really took notes, and generally alternated between paying close attention, spacing out, or intermittently dozing off. These students tended to ask the most meaningful questions.
Group 2  Students who always came to class, furiously took notes trying to write down every single word, symbol, or diagram I spoke about or wrote. These students generally did not ask any meaningful questions.
Group 3  Students who came to class but generally took no notes and just dozed-off most of the time. No questions here, needless to say.
Group 4  Students who rarely came to class. Plenty of questions here, but only about exams and exam dates and locations!

Now then, Esteemed Reader, what do you suppose the ranking of the groups was in terms of their average performance on examinations? As it turned out, and as it continues to turn out, the groups fall clearly into the following performance ranking:

Rank 1 ... is dominated by Group 1 students.
Rank 2 ... is dominated by Group 3 students.
Rank 3 ... is dominated by Group 2 students.
Rank 4 ... is dominated by Group 4 students.

Interesting, no? What possibly could explain this? By what we've all been led to believe, the Group 2 students should have clearly dominated all the other groups in terms of learning performance. They have done as instructed: They have become active learners by pay rapt attention, using diligent note-taking as a means of staying focused on what they are learning.
 
Also interesting is that Group 2 students are not only furious at note-taking, they are furious when they don't earn an A in the courses. In contrast, the Group 1 and Group 3 students are often amusedly surprised at how well they did in the courses (with actually relatively little effort). No wonder the Group 2 students are furious. Unfortunately, they always take their revenge on said instructor, but that's another story ... .

So, what is going on?! The careful reader will see just what I did after about two years: Both Group 1 and 3 students were (i) present in class, and (ii) not actively attending to what was going on in class; the were often half-asleep in fact. Not the kind of bug-eyed engagement necessary, right?  Well, consider the following subconscious learning hypothesis:

When presented with ideas students consider abstract, boring, and irrelevant, learning is maximized when students' conscious minds are not engaged in the learning process; rather, it is maximized when their conscious minds are substantially not present, thus allowing their subconscious minds to simply record, absorb, and assimilate the ideas into their existing mental models.

But wouldn't active learning be best? Well, yes, if students don't detest (1) the subject matter, (2) the way it is presented , or (3) the instructor! In my experience, getting past these three prerequisites for the efficacy of active learning are almost insurmountable in classes meeting only about 45 hours in a semester. Moreover, in many cases it's not even reasonably possible to adjust (1), (2), or (3).

So, why is it important that the conscious mind not be engaged in the learning process? I think the answer hinges on Piaget's learning theory:

Intellectual growth involves three fundamental processes: assimilation, accommodation, and equilibration. Assimilation involves the incorporation of new events into preexisting cognitive structures. Accommodation means existing structures change to accommodate to the new information. This dual process, assimilation-accommodation, enables the child to form schema. Equilibration involves the person striking a balance between himself and the environment, between assimilation and accommodation. When a child experiences a new event, disequilibrium sets in until he is able to assimilate and accommodate the new information and thus attain equilibrium. There are many types of equilibrium between assimilation and accommodation that vary with the levels of development and the problems to be solved. For Piaget, equilibration is the major factor in explaining why some children advance more quickly in the development of logical intelligence than do others (Lavatelli, 40; see http://www.sk.com.br/sk-piage.html).

While Piaget vehemently endorsed active engagement and learning, he was implicitly assuming it was possible to adjust the prerequisites for active learning I discuss above.  In my experience and that of other colleagues I've had discussions with, these adjustments are very difficult in practice.

In the words of Piaget, the process of equilibration involves an active choice by a person about whether they should assimilate information into their existing mental models.  So, here's the key: Groups 1 and 3 are perhaps best at learning what they believe to be boring, abstract, irrelevant ideas because their conscious minds are not constantly telling them to disregard the ideas because they don't like the subject, presentation, or instructor. 

The efficacy of traditional lectures. A corollary on why traditional lectures have served humanity quite well over the last 2,000 years or so follows very naturally:

Traditional dry, boring lectures are optimal for teaching dry, boring material because it does not require distorting the subject matter, presentation, or instructor (ethos, pathos, logos, mannerisms or physical attractiveness) because it has the strong tendency to shut down students' conscious minds and all the ideas are presented directly to the subconscious mind, thus bypassing the (Piagetian) conscious equilibration process.

So, there it is. A basic applied theory of subconscious learning. I've run the idea by several educational psychologists and clinical psychiatrists and they are extremely skeptical. But I believe the theory is logically consistent with other accepted learning theories (e.g., Piaget) and I actually have a lot of empirical evidence that is strongly consistent with the theory!

And now the good news: Yes, if you're bored in class, just come to class  anyway and doze off. You'll be better off for it in several ways ...
 

MMc
São Paulo

A healthcare case for (and against) Wall Street ...

Enquiring minds want to know, or so we're led to believe. If they do, then perhaps they want to know some reasons why the US government seems fixated on making sure Wall Street, the stock market, is stable and successful. Of course, we all think we know the primary reason: People get panicky when their investment portfolio values--particularly those held in their retirement funds--head South. And these people vote in a way that's highly correlated with their investment portfolio growth. Alternatively, the conspiracy theorists among us believe there's some dark collusion between the US government and the investment banks. It seems, however, there might be other critically important reasons as well. Read on ...

The Medicare program is essentially heath care insurance covering most all US citizens over age 64; part of the US' long-term trend towards socialized medicine. Medicare spending is the third largest component of US federal government spending, representing approximately 16% of total expenditures. Suffice it to say, it's important to understand what drives this component of US government spending. I have come to believe that an important factor driving Medicare spending is closely related to the health of Wall Street, so to speak.

Microeconomic theory suggests Medicare spending is a function of:

• time, since prices tend to increase because of monetary inflation;
• population over age 64, since more people requires more expenditure; and
  
• societal wealth, since societies with more wealth tend to provide higher levels of government-funded social services).

If we are to estimate average effects of each of the factors on Medicare spending, it's necessary to develop an econometric model that can be estimated using observable data. While time and population are observable, societal wealth is not; at least it's not easily observable. There are good theoretical arguments, however, suggesting US equity market values are highly correlated with societal wealth. This in turn suggests using an observable index of equity market values, such as the S&P 500 Index, as a proxy for societal wealth in an econometric model. Putting this all together, stating the relationships in terms of annual percentage changes, and adding quadratic terms as (hopefully) an approximation of more general forms of non-linearities, results in a model like this:

I chose to model the growth rate in Medicare spending on physician and clinical services--an important subset of total Medicare spending--because I thought it was the component most directly associated with the over age 64 population.

Because such relationships tend to be non-stationary over time, I estimated the model using FGLS estimation with heteroscedasticity-robust standard errors and publicly-available data that I downloaded from www.freelunch.com with the following parameter estimate and statistical significance results:

Decimal numbers below the parameter estimates are p-values, showing the estimated probability the parameter is actually zero in the real world (i.e., the probability the factor has no effect on Medicare spending) given the sample data and that necessary assumptions underlying the model hold.

So what, you ask?  Consider the estimated marginal effects of a 20% change in S&P 500 returns on Medicare spending:

The econometric results suggest a 20% increase or decrease in the S&P 500 return results in an increase in Medicare spending for physician and clinical services, which at 2008 levels results in an estimated $14.5 billion increase in Medicare spending.  Hmmm ... that's a lot of money.


How often do 20% increases or decreases in S&P 500 returns occur?  Consider  the following histogram:

The histogram shows that 20% or greater absolute changes in the S&P 500 return from the prior year return occurred 14 times in the 41 year period ended 2008; so, about 34% of the time.  Ouch.  Moreover, consider what happens in the really volatile years:

Ouch, that hurts even more ... .  

Do the results make sense?  If humans (and politicians too) are into pain avoidance, one might suspect they would do their best to avoid volatile stock returns ... because apparently there are a lot more people seeing physicians, getting lab tests, etc. when the stock market goes crazy.  Considering (i) the high level of stress most people are under in developed, highly competitive  economies, and (ii) how many people obsess daily on their investment portfolio market values (e.g., even I go to Google finance daily to check on the markets and I don't even have any investments!), this seems entirely reasonable to me: Stock market volatility sends people over their health tipping points due to the  psychosomatic effects of the incremental stress.

So ... as promised by the title, here is the health care case for, and against, Wall Street:

Theory and evidence presented above, skeletal though it may be, suggest that (1) to the extent Wall Street firms promote stability in the financial markets, they are good for US health care, and (2) to the extent Wall Street firms promote instability in the financial markets, they are bad for US health care.

It's not completely clear, at least to me, whether Wall Street firms promote stability or instability in the financial markets.  My guess is that they benefit most, and most immediately, by promoting instability: Wall Street makes money from trading.  But perhaps if the conspiracy theorists are correct and the US government is trying to protect Wall Street firms from competition of various types, then lessening the pressure to produce trading profits might lead the firms to promote more stable financial markets.  But at this point, it seems the only thing to do is reserve judgment on whether Wall Street is good or bad for US health care.

MMc
São Paulo

Selling skills caused the recent US recession ...

Provocative title, no? But it's really not so far-fetched considering credit rationing theory ...

Ignorance of credit rationing theory caused the recent US recession, I assert.   In my professional life, when I say the word "theory" in the presence of other professionals the reaction is often the same: they roll their eyes, make some vague statement about "ivory tower types", and continue the conversation while completely ignoring whatever I said in connection with the dreaded word: theory. These are people, by the way, who scoff at the stupidity of Wall Street quantitative trading strategies while--in the same breath--talk about "alphas" and "betas" on stocks; alluding to the not-very-well-accepted theory, the Capital Asset Pricing Model. And these are the people who--because they choose to ignore what theories like the CAPM actually say (and don't say)--make mistakes.  Nonetheless, I will once again use the dreaded word discussing the relationship between credit rationing theory and the mortgage loan crisis, which many reasonably believe is the proximate cause of the recent US economic recession. It seems once again that ignoring theory led people to make mistakes ...

The basic idea underlying credit rationing theory is lenders intentionally limit credit supply such that loan demand exceeds loan supply. This seems strange from a Microeconomics 201 perspective because we expect prices (and, so, interest rates) to adjust to equilibrate loan supply and demand. So why would lenders intentionally limit supply when they could just increase interest rates, make the loans, and generate additional profit? The theory suggests lenders cannot simply increase interest rates to make it profitable to increase loan supply because (1) increasing rates above a certain level attract borrowers that are too risky (i.e., increased rates will not compensate for increased risk, so the marginal expected profit is negative) and (2) lenders are often unable to estimate such risks a priori due to information asymmetries. So here's the proposition:

Lenders systematically bought mortgages--with interest rates at levels representing rates well in excess of historical interest rate spreads over costs of funds--where the expected profit was negative due to excessive credit default risk; thus leading to widespread mortgage defaults, the related credit crunch feedback cycle, and the recent US recession.

Having run the proposition by some professionals who gave me the usual eye-rolling response, along with statements similar to "Well, that's just obvious.". When asked the question, Well then, why did the lenders systematically--pretty much across the entire economy--buy the mortgages? To this they answered, "They were just plain stupid." Hmmm. I know quite a few people in the mortgage lending industry and I wouldn't characterize any of them as stupid. Moreover, it seems patently ridiculous to suggest mortgage lending professionals were systematically stupid across the entire industry. So, contrary to their belief the proposition is not exactly obvious, and I don't think they were plain stupid; they understood Microeconomics 201 but, unfortunately, not credit rationing theory.

In any case I thought it best to make the proposition more provocative by making more of a story of it and do my best to avoid any mention of the word theory. Here goes ...

Mortgage broker selling skills caused the recent US recession, I assert.  Consider the following highly simplified representation of the mortgage lending process in the US over about the last 15 years or so:

Many mortgage loans, and perhaps most sub-prime mortgage loans, have been originated by mortgage brokers. That is to say, mortgage brokers somehow find potential borrowers, obtain information on the proposed borrowers' credit and property values, and then "sell" the proposed mortgage loans to mortgage lenders, usually banks and other types of financial institutions. The loans are then bundled together in the form of securities ("mortgage-backed securities", MBSs) with presumably well-understood credit risks, and then sold by investment banks to investors. The loans underlying the MBSs are then serviced, again most often by banks and other financial institutions involved in mortgage lending.

So, what exactly is the relationship between mortgage broker selling skills and the recent US recession? I will first state the proposition in dramatic terms, then present the argument after:

The excellent selling skills of mortgage brokers, in conjunction with the incentives provided to them to exercise the skills, caused the recent US credit crunch and recession.

The proposition might seem a bit of a stretch but systematic micro-motives often result in macro-behaviors (i.e., pervasive incentives provided to individuals have large scale economic effects; see Schelling's book of a similar title), so it's really not so unreasonable. Let me explain.  Here's what mortgage loan brokers do (or at least it is what they did before the credit crunch):

  ::  speak and meet with prospective borrowers;
  ::  obtain prospective borrowers' credit and property value information;
  :: determine whether borrowers "qualify" (i.e., meet lender underwriting standards);
  ::  negotiate contracts so borrowers qualify and broker fees are maximized; and
  ::  sell the qualified contracts and related information to lenders.

Notice selling skills factor prominently in both activities shown in bold: Negotiating terms with borrowers in a way that ensures they meet mortgage lender underwriting standards while, at the same time, maximizing the broker's fees for originating the loans often requires considerable selling skills. To see this, consider the following annotated graph:

The graph summarizes certain problematic selling activities of mortgage brokers in the context of two factors addressed in credit rationing theory: credit default risk and interest rate. It also shows the main prediction of credit rationing theory: Lenders generally set a fixed interest rate for borrowers of observably-acceptable credit risk (usually around 3.0% over funds cost in mortgage lending) so as not to attract unacceptable credit risk borrowers. The problematic selling activities:

Interest rate up-selling -- Convincing prospective borrowers that they represent higher credit risks than they actually are, thus justifying higher than expected interest rates. (Mortgage broker: "Hey, I'm doing my best for you guys, but lenders are requiring a higher rate to compensate for the risk; you guys have some late payments on your credit history ... ." Late payments on a credit history can be pure gold for a mortgage broker with good selling skills.)

Credit risk up-selling -- Convincing loan underwriters employed by mortgage lenders that proposed mortgage contracts being offered for sale to the lenders have acceptable credit default risk (given acceptable collateral risk), even when they perhaps represent unacceptable risk. (This is how mortgage brokers can "help their customers": by misrepresenting facts and circumstances relevant to a mortgage lender's assessment of a borrower's credit risk, and "getting them loans".)

It turns out neither selling activity is easy. Mortgage lenders were, in my experience, reasonably diligent in credit risk analysis and loan underwriting. It often took brokers considerable skill to convince borrowers they had to pay a higher interest rate because they were a higher credit risks than they actually were, while at the same time convincing lenders that borrowers were lower credit risks than they were. That, as they say, is why mortgage brokers "got paid the big bucks".  But, of course, enquiring minds naturally want answers to questions about this idea of "up-selling":

Why were both borrowers and lenders willing to accept such "up-selling"?  They accept it largely unknowingly because of bi-lateral information asymmetry: In general, borrowers don't actually know what lenders think and say to mortgage brokers (even on average; many borrowers are quite naive when it comes to common lending practices), and mortgage brokers can withhold relevant credit risk information from lenders (e.g., the mortgage broker might realize a two income household will lose one income forthwith because of a new child and maternity leave, but choose not to disclose this to the lender since it might suggest higher, unacceptable credit default risk). 

Why, exactly would mortgage brokers up-sell borrower credit risk to lenders?  Two reasons: Strong monetary incentives and a decision horizon that is too short for a mortgage broker's reputation to matter. The basic monetary incentives are related to origination fees, which generally range from .5% to 2.0% (though there are perhaps additional fees as well). So that's between $500 and $2,000 on a hypothetical $100,000 mortgage. 

Why, exactly, would mortgage brokers up-sell interest rates?  Among other factors commonly listed in adverts for sales positions like being an Aggressive, Self-Starting, Team Player, the prominent reason brokers up-sell interest rates is very strong monetary incentives. To give one a rough idea of the incentives, consider a $100,000 mortgage loan offered by a broker to a lender at an annual interest rate of ...

... with daily interest compounding. It turns out that at the height of the US mortgage lending boom in about 2003, a certain mortgage lender/securitiser in the Western US would pay a mortgage broker an additional $250 for the daily interest compounding provision (as opposed to monthly or quarterly compounding) and an additional $2,000 for the .50% above the lender's normal interest rate spread.

So, adding a basic 1.0% origination fee of $1,000 to the additional 2,250 fees for daily interest compounding and the up-sold-by-.5% interest rate, the broker receives $3,250 on a $100,000 mortgage. One could easily say monetary incentives for credit risk and interest rate up-selling are "strong". (Kaaaa Ching. "Money talks ... and it's persuasive." Elvis Costello.)

Why, exactly, is interest rate up-selling a bad thing?  Two reasons: Interest rates in excess of normal interest rate spreads (about 3.0%-3.5%) tend to attract higher-than-normal-risk borrowers since normal-risk borrowers can, and on average do, borrower at normal rates. Higher interest rates, holding all else equal, make it more likely borrowers will default on loans if--for whatever reason--their incomes decrease. (I recall having an interesting discussion in the mid-1980s with David Cates, a highly-regarded bank industry consultant, where he mentioned that rapid loan growth and increased interest rate spreads were the two primary predictors of credit quality problems in banks. It seems the half-life of knowledge in the lending industry is quite short or ... perhaps there is systematic ignorance of credit rationing theory.)

But wait. Isn't it a little unreasonable to pin the whole mortgage loan crisis on the mortgage brokers? Weren't others in the mortgage lending industry, and investors, equally at fault? It's true that factors leading to the US mortgage loan crisis were systematic. So, mortgage broker behavior selling skills and activities alone did not cause the problems: In any exchange there is a buyer and seller and both have an obligation to themselves, and perhaps others, not to do anything stupid. Lenders, investment bankers, and investors all had the obligation to properly value and price the mortgage contracts and related securities to account for the expected risk. Moreover, the lenders, investment bankers, and investors each played a non-trivial role in providing the mortgage brokers with the strong monetary incentives for up-selling.

Nonetheless ...

Without mortgage brokers' on-average-excellent selling skills that allowed them to strategically and convincingly misrepresent credit risk factors to both borrowers and lenders, they would not have been able to sell many billions of US dollars worth of improperly priced mortgage loans with excessive credit risk, which essentially caused a near-collapse of the US credit markets and represented the proximate cause of the recent US recession.

I believe we can now reasonably say, quod erat demonstrandum: Notwithstanding mortgage brokers' excellent selling skills, had lenders and investors not ignored credit rationing theory they would not have made critical errors that led to the recent US credit crunch and recession. :)

MMc
São Paulo
 

Postscript: The term "credit risk up-selling" is not one I've actually heard in practice; I've just defined it here as a convenient nominalisation of a mortgage broker over-selling the expected credit risk associated with a mortgage loan. Also, I've personally known 5 commercial mortgage loan brokers who were highly ethical when dealing with matters discussed above. None I knew would misrepresent credit default risk to lenders, or jack-up interest rates to borrowers, since they were more interested in developing long-term relationships with both lenders and borrowers. MMc

Understanding financial accounting ...

What does it mean to understand financial accounting?  Many surprisingly learned people spend the better part of a lifetime developing an understanding of accounting but, in fact, perhaps 80% of such accounting knowledge--the knowledge used by practicing accountants routinely, everyday--can be summarized as follows ...

(1) The fundamental structure of financial accounting:

... where ASSETS represents the accounting value (see (2), (3), and (4) below) of economic resources controlled by the firm; LIABILITIES represent actual and estimated non-ownership claims against the firm's assets; EQUITY represents owners' residual claims to (net) assets; EQUITY TRANSACTIONS basically represent payments to and from owners in exchange for changes in owners' equity claims; REV represents the accounting value of products and services sold to customers in ordinary operating activities; EXP represents the accounting value consumed in ordinary operating activities; and G<L> represents non-operating gains (lossses) resulting from any change in net assets (A(t) - L(t)) not associated with ET, REV, or EXP.
 

(2) The historical exchange price principle:

Economic exchanges--between unrelated parties who are not compelled to enter the exchange--are measured at the clearer (i.e., more accurate and precise) of the estimated fair market value of consideration (i.e., something of value) given or of consideration received; and such values are assumed to be equal:

The value is formally called the historical exchange price (HEP).

(3) The revenue recognition principle:

Setting aside accounting period technicalities, revenue recognized in the financial statements for a product or sales contract through time t is determined by the following revenue recognition function:

... where it is important to recognize that each of the three factors on the right-hand side of the expression are almost always either estimates (preferably) or assumptions (less preferably).

(4) The expense recognition principle:

Again setting aside accounting period technicalities, expense recognized in the financial statements for a particular resource or set of resources through time t is determined by the following expense recognition function:

... where it is important to recognize that each of the two factors on the right-hand side of the expression are almost always either estimates (preferably) or assumptions (less preferably).

(5) The disclosure principle:

Accounting principles and methods used by the firm—as well as descriptions and details of financial statement components, and other financial matters relevant to users—are fully disclosed in the financial statements.

So, understanding basically comes down to understanding the fundamental concepts and structure of financial accounting, and four fundamental principles.  There are, of course, other "principles" such as the "conservatism principle", but these have been omitted because they are not universally applicable in all countries and are not even consistently applied across accounting standards within individual countries.

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No doubt the reader finds the above outline quite kōan-like.  This is intentional: It really is as simple as outlined above, but it does require thought to understand accounting this way; examples are good but words seem to just get in the way. Here's an accounting koan of sorts, mentioned in a previous posting, and a semi-cryptic discussion of it:

If my company pays $100 cash for an ordinary pen that can easily be purchased for $1 at any office supply shop in the area, how should my company record the exchange?  

Most accountants will simply answer the question by saying the acquired pen should be recorded at the amount paid for it.  When asked why the exchange should be recorded in this way, most accountants answer that it is a simple application of the "historical cost principle".  It turns out that neither answer is correct:  The proper accounting measurement of the pen depends on certain economic characteristics of the exchange and of the buyer and seller, and the “historical cost principle” actually does not suggest the pen be measured at $100 for accounting purposes.  So, the accountant (or manager) knows how to record the exchange given that the measurement is correct, but actually does not know how to determine the proper measurement.

This is but one simple example of how understanding accounting mechanics—so-called, “debits and credits”—does not provide accountants or managers with an adequate understanding of accounting concepts and principles, which is in fact a necessary prerequisite for understanding accounting methods in general.

I'll give examples applying each of the principles--examples of accounting methods and mechanics--in future postings ...

MMc

São Paulo

The credit crunch/recession and why it's likely to continue ...

I often speak with entrepreneurs and managers and, even though unit sales volumes and revenues in their businesses have decreased by between 20% and 50% from 2007 levels (they're mostly in durable  products manufacturing), almost all of them say they're expecting a recovery to begin in the next 3 to 6 months.  The irony is they've now been saying just this for at least the last one and a half years!  So much for the economic theory of rational expectations.  Hope or ignorance springs eternal.  Enquiring minds would, no doubt, like to know which it is.  Let's see ...

The Problem. Most of us know at some level that the overall economic downturn experienced in the US is somehow related to "the credit crunch".  Many of those I've spoken with suggest all that's needed to obtain an economic recovery is to solve the credit crunch problem: "If credit was available, people would be more confident and start spending again.  Then everything would be ok."  There is obviously some truth to this and it seems apparent this is the major premise upon which the Federal Reserve is operating.  But if it were really just a matter of pumping money into the US economy, the US should be in the strongest, most robust recovery in the history of the World.  Sadly, this isn't the case.

So what is the problem?  The problem is that the credit crunch is comprised of two inter-related feedback cycles; the consumer demand feedback cycle and the capital cost and credit default cycle.  Some feedback cycles are good (negative feedback cycles, which tend to be self-correcting) and some are bad (positive feedback cycles, which tend to be self-reinforcing).  The two feedback cycles comprising the credit crunch feedback cycle are the latter kind.; the Bad Kind.  Let me explain.  Consider the consumer demand feedback cycle:

Consumer demand feedback cycle

I think the cycle diagram is fairly self-explanatory, but there are two important aspects to it: (1) trouble in any component of the cycle leads to trouble in the rest of the cycle, and (2) once trouble starts, only some exogenous factor --i.e., something outside the cycle--that influences a component in a beneficial way has the possibility of stopping the self-reinforcing cycle.  So, for example, the Federal Reserve might manipulate the money supply to (artificially) increase reserves in the banking system; hopefully, stimulating lending, reducing consumer credit delinquencies and defaults, increasing consumers' willingness and ability to spend, etc.

So far, so good; but is this likely to work?  It depends on whether all components of the feedback cycle are improved by the Fed's actions.  It turns out that increased producer demand for labor, and the resulting improvements throughout the cycle, might not occur.  Consider (what might be called) the capital cost and credit default feedback cycle:

The capital cost and credit default feedback cycle

The diagram shows the close, positive feedback relationship between banks and producers.  Importantly, it shows that as banks' financial conditions weaken, the availability of credit required by producers decreases, capital costs increase (for banks and producers, and in fact consumers too), producer profits decrease, all leading to an increase in producer credit delinquencies and defaults; thus further damaging banks' financial conditions.  Here too the Fed attempted to intervene in the feedback cycle by (in substance) buying "toxic assets" from the banks, thus improving bank capital ratios and increasing bank lending capacity.

Again, so far, so good.  But why is the US economy (seemingly?) still so weak; long after all the Fed's interventions in the money supply and banks' toxic asset problems?  Consider now a more complete representation of the problem, which I'll call the credit crunch feedback cycle, where the two feedback cycles discussed above interact:

The credit crunch feedback cycle

The diagram just fits the two previous diagrams together by linking (1) consumer credit delinquencies and defaults to decreased bank profits, and (2) decreased consumer demand to decreased capital availability to, and increased capital costs and decreased profits of, producers.  In short, once something in the feedback cycle goes bad, it makes a lot of things go bad. 

I think one begins to see the knotty, entangled nature of the problem here: Basically, to solve the problem in the near term of 2-3 years, it's necessary to positively influence all components of the cycle.  In principle, influencing just a few important components will set everything right in the long-run.  (This is the real problem: In the word of Keynes, "In the long-run we are all dead ...", which is why he advocated manipulating the economy.)  But given real world complexities and frictions, solving the problem(s) in the short-run is, as they say, difficult.

So, which is it?  Is it hope or ignorance that the US economy will begin to recover in the next 3-6 month?  I will let the reader be the judge.

Econometric analysis.  How might the credit crunch feed cycle theory, presented in very skeletal form above, be tested?  That is, how do we reasonably know whether it explains and predicts (part of) what's happening in the US economy?  A fairly simple seemingly unrelated regression model (Zellner 1963) based on the feedback cycle presented above might look something like this:

... where deltas are interpreted as percentage changes, D is demand, S is supply, CDD is credit delinquencies and defaults, M is money supply, and u represents effects of unobservable/unmodeled factors.  Of course, the model would need to be estimated, tested, and probably modified quite a bit to get  a model that might explain and predict well.  Sounds fun, don't you think?  Hmmm ... I thought so.

MMc

São Paulo