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Monthly Archives: November 2009

I recently had a discussion with a respected colleague of mine. It got me thinking about the many differing points of view in finance. My colleague accepts that regime change is a correct phenomenon in financial time series, and you will find many published articles in respected journals proposing how to deal with this regime change. I might also add the many quants accept regime change.

And here we differ. I do not accept regime change. I know from past experience building time series forecasting models that regime change is model misspecification. Let me show you 3 graphs that support my point of view. Clearly Fig 1 is a linear trend.

Figure 1: Time Series from Period 245 to Period 300

Figure 2: Time Series from Period 0 to Period 1000

However, when we expand the range of the time series to between 0 and 1,000 (Fig 2) the linear trend of Fig 1 now becomes a regime change from a level around 0 prior to period 200 to a new level of about 7 after period 350. This must be proof of regime change. No? But wait.

Figure 3: Another time series.

Figure 3 is another example of regime change. Prior to period 150 the level is around 0, between periods 170 and 270 the level is about 110, and between periods 290 and 400 the level is about 230. This would be considered an example of two regime changes.

Because I generated both data sets I can inform you that Figure 3 was generated by Normally distributed random noise and nothing more. Figures 1 & 2 were also generated by Normally distributed noise and with a trend inserted between periods 250 and 300.

First lesson. A continuous function time series can present itself as a regime change when it is not, but in real life we cannot ‘rerun’ the time series to test if regime change will recur.

Second lesson. Statistical tests will affirm that regime change did occur when no regime change was present.

Third lesson. Figure 1 is a subset of Figure 2. Therefore, our interpretation of the data depends on our perspective.

Fourth Lesson. Therefore, my experience with time series would suggest that regime change is model misspecification.

Fifth Lesson. Even though economics & finance borrows heavily from the scientific method it is still an art.

Take care,

Ben

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position.

Contact: Ben Solomon, Managing Principal, QuantumRisk

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When I was at Texas Instruments, we were told that nobody is indispensable. If Dimon leaves JP Morgan and the bank’s share drop, then he did not do a good enough a job at succession planning at the bank.

The real issue is the rumored replacing of Tim Geithner. We need to ask ourselves why the rumor. It is because unemployment has exceeded 10% and still increasing, and this can allegedly be a liability to the Obama Administration in the coming elections.

However, if we look at history, neither the Democratic nor the Republican Administrations have in the past recovered from a similar unempolyment spike at an average rate better than 0.06% per month. Therefore, what is required is a collective change to historically untried policies rather than a change in personalities.

The problem then is if Dimon cannot solve this unemployment problem (and he won’t if he does not do something radical), who are the Democrats going to blame next?

 

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position.

Contact: Ben Solomon, Managing Principal, QuantumRisk

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There is a nice story developing in the $16.7 billion Kraft bid for Cadburys’. There are possibly four players in this deal to be, Cadbury, Hershey, Kraft and Ferrero.

And there are some big time names advising the players, Akeel Sachak of Rothschild, Byron Trott of BDT Capital Partners.

In my opinion Hershey does not have what it takes to out bid Kraft so Hershey would need to develop alternative strategies. Second, Cadbury & Hershey don’t mix, not yet anyway. They tried in 2007 and failed.

This is the case of the small fish swallowing the big fish as Hershey’s market capitalization is $8.3 billion, Cadbury’s $18.1 billion and Kraft’s is $39.4 billion.  So that tells us quite a few things:

1. Hershey views the Kraft’s Cadbury bid as a threat to its long term independence. That is if Hershey does not succeed it will soon be bought out by someone else.

2. Hershey’s does not have the global reach that Kraft and for that matter even Cadbury has. Hershey generates 93% of sale in North America, while Cadbury has 16%.

3. A Kraft purchase of Cadbury would turn Cadbury from friend to foe overnight, and one with a formidable distribution network in the United States.

4. Hershey’s 93% North American sales says that Hershey cannot compete overseas. Hershey needs Cadbury more than Kraft needs Cadbury. Don’t get me wrong. This is not about distribution networks even though that is important. If, like me you have lived on both sides of the Atlantic, you know American chocolates don’t cut it when compared to European chocolates. Hershey needs the branded recipe.

Hershey needs Cadbury and will pay more.

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position.

Contact: Ben Solomon, Managing Principal, QuantumRisk

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Many unintended consequences in business & finance occur because upside (profitability) and downside (loss) risk have very different characteristics.

Background
In 2007 I sifted through 330,000 RMBS (Residential Mortgage Backed Securities) assets or loans with the purpose of developing a default & loss econometric model that would enable my then employer differentiate between good and bad RMBS bonds.

I had to do a lot of data structuring and clean up to standardize incoming data from about 20 different servicers. I should say data structuring and data scrubbing. And after that sift through this asset data set to filter out the bad data. So you can imagine the amount of work that went into this.

My strategy to identify the loss quality of a bond was to use the origination and close data to forecast future losses. Why? Because once you have purchased the bond you are stuck with whatever losses that unfold.  If this was possible we could very quickly eliminate the poor quality bonds, and use cash flow methodologies (more on this in a later blog) to evaluate the better quality bonds. We did not want to expend effort valuing poor quality bonds.

Dichotomy of Metrics
Well, at least that was the theory. What went wrong? In residential mortgages FICO scores are a major component, if not the primary driver for assessing future default behavior. I found that for residential mortgage FICO scores at origination were poor predictors of future defaults. How? Build two probability distributions of FICO scores. The first is a distribution of FICO scores of the borrowers at origination for all assets. The second is the distribution of the FICO scores at origination for defaulted assets only. You could not tell the difference between the two distributions!

Bewildering. Was there an analogy in other areas of finance? Yes. Company pro forma profitability analysis are good predictors of future profitability, but they are poor predictors of severe losses or bankruptcy risk. To get a handle on future company bankruptcy risk one requires a tool like Altman’s Z-Scores.

Here we observe a dichotomy in forecasting characteristics. On the one hand pro forma informs us of future profitability and on the other hand Altman’s Z-Scores informs on bankruptcy risk. We concluded that FICO scores behave like pro forma profitability, that they are good predictors of success but poor predictors of defaults.

I must add that unlike investment bankers and fund managers who are focused on upside risk or profitability, my career in financial services has been focused on downside risk. Therefore I did not test if FICO scores are good predictors of success.

When we recognized this dichotomy we abandoned RMBS bonds as a viable investment vehicle. But this story does not end here. I have an extensive business reengineering background and investigated how FICO scores are generated.

How Credit Cards Drive FICO Scores
From a business process perspective recent FICO scores are generated by your credit card issuer. Really. You the card holder use your card to transact purchases. In so doing you create debt. How much of this debt you pay down, how quickly, etc., is reported to the rating agencies. This reporting provides an insight into your debt payment characteristics which the rating agencies convert into FICO scores. FICO scores appear to predict defaults because they are adjusted downwards as your credit card payments deteriorate. That is, in my opinion, FICO scores have a limited window of about 90-days within which they work, but by then it is too late for the card issuer. Or no credit card visibility no FICO adjustments.

Here is the weak link in this whole business process. Without your credit card spending and payments reported by your card issuer, the rating agencies would not have a clue what your recent credit rating should be.

Take 2 people John & Jay. John has house and car payments and uses his credit card for all his purchasing transactions. Jay also has house and car payments, but he only uses cash for his purchasing transactions.

Now both have suffered a partial loss of income. They both continue to make house and car payments. Therefore the ratings agencies cannot tell from these payment that there has been a partial loss of income. Both reduce their spending to manage within their new realities. But John has now slowed his credit card payments. The rating agencies see this and are able to adjust John’s FICO scores accordingly but not Jay’s.

The use or not of credit cards provides asymmetric information to rating agencies. Now guess what happens as credit card companies ramp up interest rates, consumers pay down their card debt and stop using their cards.

Unintended Consequences
All manner of unintended consequences occur.

1. The banking industry loses it recent FICO information for a much larger proportion of the population.

2. Consumers do get penalized with lower FICO scores for not having recent payment histories.

3. First time home buyers will face higher rates because they don’t have sufficient recent history.

4. FICO scores are no longer as “effective” as they were “supposed” to be.

5. Because of this additional “FICO risk” for the same “risk” bonds with credit card debt should trade at a discount to bonds with residential mortgages which in turn should trade at a discount to CMBS bonds.

6. As I had stated in my Feb/02/09 post Risk-Reward is Non-Linear, increased rates will increase defaults. The unintended consequence of this is that banks that have rushed to increase rates to 30% before the new laws come into effect, will have locked themselves into a population demographics that experiences a higher default rate. Therefore we can expect to see an increase in credit card losses.

Summary
I’m sure there are a lot of very smart people in the credit card and rating industries, but… This situation is not dissimilar to what I observed as a management consultant reengineering manufacturing companies – one may have the best, the brightest and the most experienced managers but surprisingly when they come together at their management meetings they do strange things.

There needs to be a better way to address bank losses on credit cards and a better way to treat customers. Remember your customers are not your enemies.

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position.
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Are banks panicking? I ask this question because the New York Times article, A Squeeze on Customers Ahead of New Rules, points to several bewildering measures (quoted directly from the article) banks seem to be taking:

1. Practices outlawed by Congress: ‘A study by the Pew Charitable Trusts, released late last month, concluded that the 12 largest banks, issuing more than 80 percent of the credit cards, were continuing to use practices that the Fed concluded were “unfair or deceptive” and that in many instances had been outlawed by Congress’.

This is unfortunate as the banks are bring more negative attention to themselves. This is only going to increase the determination of our elected officials to tighted the regulatory environment.

2. Banks are becoming aggressive: ‘As banks have become more aggressive in making changes, lawmakers have accused them of trying to impose rate increases before many of the new rules take effect in February’.

Chasing diminishing profits in this manner is the fastest way to alienate your customers, and ensure sustained negative growth in the years to come. There are many other competitive instruments that can replace credit cards, e.g. debit cards, electronic checks, bill pay, ecommerce services like PayPal. Oh! I forgot the humble paper check.

My bet is that PayPal and similar ecommerce sites will grow substantially from these banking missteps. This will pose further risks if the new ecommerce sites that take advantage of these banking missteps, are based offshore.

3. We don’t sell sweaters: “We sell credit; we don’t sell sweaters,” said Kenneth J. Clayton, senior vice president for card policy at the American Bankers Association. “The only way to manage your return is through the price of the product or the availability.”

I don’t understand Kenneth Clayton’s comments about sweaters. Everything is supply and demand. Maybe he is thinking about it from the perspective of the algorithms. Credit card debt is based on mathematical/statistical algorithms and sweaters are not? If that is the case then credit cards are like sweaters when compared to quant trading strategies. Anyone have any idea what Kenneth Clayton was trying to express?

I must say that having worked in financial services for many years I know that financial services companies did attract the brightest and the best, but the recent banking industry responses to the changing regulatory and economic environment bewilders me.

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Disclosure: I’m a capitalist too, and my musings & opinions on this blog are for informational/educational purposes and part of my efforts to learn from the mistakes of other people. Hope you do, too. These musings are not to be taken as financial advise, and are based on data that is assumed to be correct. Therefore, my opinions are subject to change without notice. This blog is not intended to either negate or advocate any persons, entity, product, services or political position.
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