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Category Archives: Business Strategy

Our latest CMBS product, QuantumRisk CMBS Property Risk Analytics (*1) (12 Mb Excel 2007 worksheet) will soon be available as an annual or monthly subscription or one-off purchase on the 15th of each month. 

QuantumRisk LLC invested more than $250,000 in research to develop the algorithms required to produce this product, CMBS Property Risk Analytics, on a monthly basis. For those of you who are familiar with the raw CMBS data know that this is no small feat.  

Robust,  valid and reliable market data is indispensable for actionable CMBS investment decisions and we are extremely proud to be the only one if not one of the very few companies providing this level of detail for CMBS defaults and losses. Thus providing more insightful commercial real estate business intelligence to our clients. 

On a monthly basis we analyze more than 85,000 properties backing more than 52,000 loans to report default probability, loss severity before recovery, loan to value ratio (LTV), debt service coverage ratio (DSCR), occupancy rates & change in property appraisal value for more than 400 U.S. markets, by property type, by city, by SMSA/MSA by state. Every month! 

The purpose is for sophisticated investors, investment bankers, underwriters and fund managers to know what is happening where it is happening when its happening. Even the muni bond professionals and local & state governments can use this report to figure out what is happening in their local market, as the commercial real estate market is reflective of the local business environment and therefore reflective of the local economy. 

We are also very pleased to announce that CoStar’s Watch List featured some of our May 2010 analytics in their newsletter article, Impact of CRE Distress Varies Widely Market to Market. This article received more than 10,000 reads within the first 24 hours. 

  

How Will Investors Benefit?
Because we analyze more than 85,000 CMBS properties & 52,000 loans on a monthly basis we have had to develop proprietary data algorithms to process this very large amount of data generated by the manual data entry origination-securitization-servicing business process. These CMBS Property Risk Analytics are organized into more than 420 tables for easy instant access of the data directly from your own Excel 2007 models. 

We report CMBS Property Risk Analytics by property type for cities, SMSA/MSA & states where there are 5 or more good property information in that property-geographic-statistic bucket, thereby further reducing the noise in the data. We don’t guarantee the end result is error free because we have no control over the origination-securitization-servicing business process but we do assure you that we have done our very best to give you the very best. 

Because the latest data is available on a monthly basis you get the most up to date information about what is happening across the United States in the commercial real estate world. 

Included in each monthly Excel 2007 report are 8 tutorials on how to use these analytics so that you the subscriber is benefiting from these reports within minutes of receiving them. 

  

What Questions Can Investors Answer?
1.Too much or too little capital?
Are you putting down too much capital with an LTV of 0.6, and want to know what Current (*2) LTVs are in Columbus, OH? 

Answer: You are most likely putting down too much capital as Current LTVs in Columbus OH are averaging 0.71. You could probably reduce your capital requirements by 11% by seeking other lenders. 

2. Realistic income generation?
Will the local or regional economy facilitate an income stream reflective of a DSCR of 1.2 in White Plains NY? 

Answer: Not likely as Current (*2) DSCRs in White Plains NY are averaging 1.03. A DSCR of 1.2 may be acceptable in a few years when the economy improves, but not today. If you were a muni bond professional or in local or state government the DSCRs would provide a quick & dirty indicator of whether you would need to raise taxes or not for general obligation bonds. 

3. Expected loan loss before recovery?
What is my expected loan loss in North Las Vegas, NV?   

Answer: The expected loan loss (before recovery) of North Las Vegas, NV, is 7.45% with a probability of default of 27.59% and severity of loss at 27.01%. As of May 2010 North Las Vegas is a high risk lending environment. Even though Las Vegas is high risk (2.40%, 15.79% & 15.23% respectively) it is less risky than North Las Vegas.  

4. City not found?
OK there is no data about Lewiston, ME, can I substitute with the SMSA Lewiston-Auburn, ME or the state level data? 

Answer: Yes. If a property count for a statistic is less than 5 (*3) we do not report this city, SMSA or state level statistic. 

5. Realistic occupancy?
In the past, CMBS cash flow models have generally assumed occupancies of about 99%. Is this a valid assumption especially since this Great Recession? So what would be a reasonable occupancy rate for Fort Worth TX? 

Answer: As of May 2010 the occupancy rate for Fort Worth TX is 85.97%. This rate will definitely increase as the local Fort Worth / Texas economy improves but at this time any occupancy rate much greater that 85.9% would be considered optimistic. The occupancy numbers presented in our CMBS Property Risk Analytics does not include completely vacant properties. 

6. An estimate of recent appraisal discounts?
What is the average reported property appraisal change (*4) in the state of Texas, over the last 15 months? 

Answer: As of May 2010, in the state of Texas the reported property appraisals are at 61.37% of appraisals done at origination. 

7. Comparative local economics?
Which city poses less commercial property risk? Pasadena CA or Beverly Hills CA? 

Answer:

State:City # Of Reported Properties in City Probability of Default Severity of Loss Expected Loss
CA:Beverly Hills 45 2.22% 2.22% 0.05%
CA:Pasadena 47 0.00% 0.00% 0.00%

With our CMBS Property Risk Analytics we can answer this question conclusively. It is Pasadena CA. 

Notes: 

(*1) “Property Risk Analytics” is the trademark of QuantumRisk LLC. 

(*2)  Current LTV and Current DSCR are calculated using the most recent appraisal values, outstanding balances, NCF DSCRs & NOI DSCRs. 

(*3) The number of properties used to determine a statistic (after processing) varies from 5 to several thousands depending on the size of the city/SMSA/state, property type and the type of statistic being reported.
(*4) Appraisal changes are not as well reported as LTVs or DSCRs. For example, there may 400 SMSA reported for defaults but only 35 for appraisal changes.

  

The Big Surprise: Multi-Property/Cross-Collateralized Loans  

Since we had all this processed data I thought I would check to see if single property loans were at a higher risk than multi-property loans, because from a loss perspective why would we put multiple properties into a single loan or even cross-collateralize a loan?  

I set up 2 pools of loans. The first pool consisted of 42,488 single property loans of all property types and the second of 2,177 multi-property & cross collateralized loans. The surprisingly results below show that multi-property & cross collateralized loans are at a higher risk of default than single property loans. So much for the assumed portfolio diversification effects. With respect to losses, for a better understanding of how portfolio diversification does or does not work see my blog post Loss Containment: Portfolios

 

Mortgage Pool  Mortgage Count  Total Mortgage Original Principal Balance ($1E6)  Mortgage Default Rate  Average Mortgage Severity of Loss without Recovery 
Multi-Property  2,177  18,356  7.49%  7.19% 
Single-Property  42,488  454,809  5.94%  5.65% 

 

Why Do We Recommend a Monthly Subscription?  

With a monthly subscription you can look at trends in the data and your decision making process is enhanced by the knowledge of the local trends. The table below the DSCRs of 3 SMSA/MSA present in the data, Denver-Boulder, CO, Atlanta,GA, and Dallas-Fort Worth, TX.   

      Denver-Boulder, CO     Atlanta,GA     Dallas-Fort Worth, TX 
   Reported Properties  Reported Current DSCRs  Reported Properties  Reported Current DSCRs  Reported Properties  Reported Current DSCRs 
2010/05  112  1.32  237  1.20  212  1.28 
2010/04  239  1.36  534  1.21  520  1.24 
% Change     -2.64%     -0.94%     3.53% 

  

In this example DSCRs, the ability to generate income to cover debt payments, is used as a proxy for business revenue and therefore local economic activity. Comparatively speaking Atlanta, GA has the worst reported DSCRs of the 3 SMSA/MSAs. We can see the different lags in the local economy even though the national economy is experienceing positive GDP growth. The Atlanta, GA, local economy is still contracting (-0.94%) but not as severely as the Denver-Boulder CO local economy (-2.64%). While the Dallas-Forth Worth, TX, local economy is expanding at 3.53%.  

These contractions and expansions will change from month to month, and a general trend will show where to or not to invest in the near term.

  

Summary 

These questions & answers presented above show that with QuantumRisk CMBS Property Risk Analytics there are many news ways to infer what is happenning in the local and state economies that can mitigate risk and reduce expenses. 

Further, we have shown how muni bond professionals, local & state governments can use this data to determine a quick & dirty assessment (and not a substitute for a thorough evaluation) of whether general obligation bonds can be issued without raising taxes and which part of a state needs further attention in terms of recession assistance or business policy matters. 

For a limited time we are making these QuantumRisk CMBS Property Risk Analytics available at a discounted price. Please contact me, Ben Solomon, for further information or to place orders.

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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. Nor is this blog post to be construed as investment advice. 

Contact: Ben Solomon, Managing Principal, QuantumRisk
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This blog is in response to many readers comments about jobs moving overseas.

Example 1: A friend losing his job.
We have a friend here in Colorado who works for a services company. He has been asked by his American management to train his replacement in the Philippines and when that is completed he will be laid off. The conventional perspective is that many jobs are moving to India & China, but in this example my friend’s job is moving to the Philippines.

Lesson 1: The problem is with us. This example illustrates that the real problem is not the BRIC or other countries. It is that many American managers have found it easier to remain profitable by procuring cheaper overseas talent than expensive local talent.

Example 2: ICL in the 1970s.
Let me give another example. In the 70s, ICL was a very big computer company in the UK. They were losing money and brought in someone from Texas Instruments to turnaround the company. I forget his name. He did a good job and increased the profitability of the company, but in doing so he dropped ICL’s internally developed chip and replaced it with a Fujitsu chip.

After completion of the turnaround ICL shareholders wanted to sell the company. They found that even though the company was very profitable nobody but Fujitsu would consider buying the company. Why? Because ICL was dependent on Fujitsu chips, and from the perspective of value creation it was no long an independent company but an extension of Fujitsu.

Lesson 2: We are handicapping the value of our business: So all this outsourcing of talent is in reality working against the long term advantage of the shareholders, because who wants to buy a company with many expensive senior managers, many technology boxes and most of the talent 6,000 miles away? This in my opinion is short sightedness. But people have the right to make mistakes. Unfortunately these mistakes can be very expensive for other people, their employees.

Lesson 3: We cannot legislate good business decisions.

Example 3: Texas Instruments (TI) Daily Factory Starts (DFS) team in the 1990s.
I was the architect of Texas Instruments’ Daily Factory Starts (DFS) factory scheduling system. This program was TI’s worldwide top ranked project for 1991 and 1992. This system, built from scratch on a theoretical basis not found in other ERP systems, revolutionized capacity allocation in a 3,000 SKU factory capable of producing up to 6,000 SKUs. It reduced work-in-progress from 5 day to 3 days or a savings of $8.2 million for TI Malaysia and TI Philippines.

What happened? The only three people who designed, developed and implemented this project left within 12 months of completion. Imagine that! All 3 of us left. Needless to say even TI made some mistakes. TI did not sufficiently recognize any of us for the ingenuity & work we had put in. And because we reported to a third country management team, corporate had little say in the matter. I am told that the project was folded in 1994.

Lesson 4: Program discontinuity risk: One very substantially increases the risk of program discontinuity when talent is procured from third party companies, and even more so when procured from overseas. Unless you are a big company like Microsoft, GE, or IBM who can have multiple teams concurrently working on similar programs, procuring talent from outside the company is something you have to think through very carefully.

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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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QuantumRisk is having a one-day event:

Event: Answer Your Business Questions
Topics: Strategy, Re-engineering, Financial & Risk Modeling
Price: No Charge
Date: December 14, 2009
Time: 9:00 am to 5:00 pm Mountain Standard Time
Venue: Over The Phone 303-618-2800

Email me (benjamin.t.solomon@QuantumRisk.com) your contact information and a brief summary prior to your call so that I have some idea of who you are and why you are calling.

If you don’t get through (lines are busy) and you have emailed your contact information, and I will call back on the next available business day.

We are doing this to celebrate the end of the recession, Christmas and New Year.

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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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The Department of Labor statistics shows that the long term (1970-2008) annual unemployment is about 6.1%. Not good. 

USEconomicStatistics

Figure 1: Annual Unemployment & Annual GDP Growth. 

 

When Will Unemployment Return To Normal?
Using 6.1% as a bench mark this data show that the unemployment recovery rate after a peak, averages at 0.06%/month. From a May 2009 of 9.4% unemployment, it will take until November 2013 for unemployment to come down to 6.1%. Today’s (10/02/09) news Jobless Report Is Worse Than Expected; Rate Rises to 9.8% or 15.1 million jobless, suggest that it is more likely to be June 2014. Not good. Definitely not good.

 

What Does This Mean?
The US economy is 70% dependent on consumer spending, so we can expect GDP growth to much lower when this recession is over. The recession is expected to be over possibly this quarter 4Q 2009 or next quarter 1Q 2010. Then the “real” economic growth i.e. the job growth (job growth is what really counts) becomes the main driver and indicator of this economy. Without the job growths, mortgages are hampered, and banks, are constrained  and . . .

 

How Can Your Company Help?
1. Don’t downsize your jobs: Downsize your pay & benefits (temporarily) starting from the top.

2. Pay down your debt: This helps banks with their recovery.

3. Don’t ship jobs overseas: This is probably the single most important lesson from history. Jobs follow markets and markets follow jobs. Of course there are some jobs where the domestic-foreign pay diffference is so great that companies have no choice but to out-source overseas.

4. Invest, invest, invest: Good investments lead to better run companies. A good approach to becoming competitive with overseas costs is to invest in technology or capex that substantially increases productivity.

 

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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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Our GDP contracted very severely this recession. To understand the severity of this contraction the Federal Reserve of St. Louis data shows that the Annual Percentage Change in GDP has not been negative this last 50 years, until 2009, but the Quarter to Quarter Percentage Change in GDP has 4 times.

 

Economic Outlook
There is some good news though. It is not all bad. Figure 1 shows that the updated moving average of Home Price Growth will likely turn positive 4Q 2009, which is about 1Q earlier than my original forecast 2 months ago. That is Home Prices will bottom in 4Q 2009. Lets hope.

HomePricesNONREVNS

Figure 1: Home Price (Composite-10 CSXR) Growth & Total Non-Revolving Credit Outstanding

However, the Total Non Revolving Credit is still contracting, but the lag to Home Prices appears to be around 2-years, or we can expect Total Non Revolving Credit to bottom in 4Q 2011. This is much better news than my preivous forecast. But we are not out of the woods yet.

 

What to do?
The necessity for caution is reinforced by the lack of near term job recovery & non-revolving credit in my forecast. Here are several suggestions on how to reduce business risk:

1. Manage your cashflow: Cash flow is what pays the bills, not profits, so watch your cash flow.

2. Be careful: Cost cutting is temporary and will unravel the moment you stop wacthing and it can affect what little revenue you are generating.

3. Manage labor wisely: Reduce your man hours but not your manpower. Seek voluntary temporary pay cuts with the biggest cuts at the top and reducing amounts to the lower ranks. This will allow you to bounce back quickly when your sub-segment of the economy turns around because the human asset of your company is still intact. Remember it takes 3 years to bring a fresh graduate engineer up to par. Therefore though it looks cheaper, it will take that much longer to recoup any costs or savings.

4. Reduce dependence on debt: Non Revolving Credit Outstanding shows that banks will continue to have difficulty lending for at least until 4Q 2010 and more likely until 4Q 2011. Pay down your debt as much as you can because it reduces your costs and it gives banks more breathing room to lend to someone else and thereby hastening the turnaround of the Non Revolving Credit Outstanding and the unemployment situation.

5. Watch your A/R: A/R is how your customers borrow while you pay the interest on their credit. A/R is also reflective of your downstream customers, so take care. I have seen companies go under because they did not manage their A/R until it was too late.

6. The Goldman Sachs mini case study shows that we can change the how and why we do things to be more successful, and that we don’t have to wait for eveyone else before we make internal improvements. If we wait we still incur the cost but have lost the competitive advantage.

 7. Slope of the Pay Cuts: Biggest proportion at the top and smallest proportion at the bottom. Why? First, that is how risk-returns (or risk-reward) works there are no two ways about this. Second, large pay cuts at the top of the company have a bigger impact on saving the company but small impact on consumer spending. Pay cuts at the bottom of the company add up to real slow down in consumer spending, save the compnay in the short term but generally leads to negative future effects. There was a study done some time in the 70’s or 80’s (I forget the name and year) that showed that companies that retrench/layoff labor on a regular basis generally don’t last very long.  

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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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Goldman’s Profits
Goldman Sachs 2Q 2009 profits of $3.44 billion made the news. Its trading activities was the primary earnings driver with wider profit margins on the buying and selling of securities while everyone else did not make as much. There are news reports where Goldman Sachs denies substantial profits from trading but in this post I analyze the available public data, and necessarily infer that Goldman Sachs increased profits by changing their algorithms.

First full disclosure. I have no connections with Goldman Sachs (GS), don’t know what specifically they do, how they do it or why they do it, and as of today I don’t know anyone at GS.

Lets lay down some facts.


Experts’ Knowledge
A. Use the Artificial Intelligence definition of experts. An expert is that 5% of the population that has 95% of the knowledge of a field, and that the other 95% of this population are non-experts as they have substantially less than 95% of this knowledge. Given that many people have passed through Goldman Sachs we can infer some possibilities:

A1. This trading knowledge (TK) has not left Goldman Sachs. The experts are still at Goldman Sachs and the people who left are knowledgeable but not experts. Or,

A2. This knowledge has left GS. That some of these TK experts at Goldman Sachs are now employed with other companies.

Outcome A1 provides us with little room to infer Goldman Sachs’ TK other than they are very good at holding on to their experts. Outcome A2 then raises some interesting possibilities.


Knowledge Dispersion
B. Second fact. We know from recent news reports that only Goldman Sachs made a ton of profits, and nobody else, or substantially so. Therefore we can infer that the experts who left Goldman Sachs were unable to reproduce Goldman Sachs’ successes, because:

B1. They could not reproduce GS’s knowledge capabilities.

B2. They could reproduce GS’s knowledge capabilities but given the recession were constrained from executing these trading strategies.

Outcome B2 is of little value to us. It would also suggest that the other banks having invested so much in technology, people & processes did not have the confidence in their own people. That would not make sense. So we are left with outcome B1. So what does Goldman Sachs have that nobody else seems to have?


Goldman Changed Their Algorithms
C. Third fact, from a statistical perspective there is only one way to make sustained profits, and this can be divided into 3 steps:

C1. To make a profit on a trade, say some distribution P(x,y)

C2. To make a loss on a trade, say some distribution L(a,b)

C3. To make sure you have more profits than losses in a series of trades or E(P) > E(L)

I must admit here that I am assuming that Goldman Sachs’ TK profitability is sustainable, i.e. that Goldman Sachs has achieved E(P) > E(L).

According to the reported data Goldman Sachs increased their VaR (Value at Risk an industry standard for measuring financial risk) from $240 million (1Q 2009) to $245 million (2Q 2009) or 2%. This is a marginal increase in risk recognition compared to the increase in profits from about $1.84 billion (1Q 2009) to $3.44 billion (2Q 2009) or 87%. But they increased their VaR by 33% from a year ago. The general consensus reported in the news is that Goldman Sachs substantially increased their trading risk.

Having worked real numbers with VaR and CVaR over many, many years I would put forward a different opinion. Given the Wall St. crash of 2008, Goldman Sachs substantially changed their VaR methodology to recognize the underestimation of their trading risk in prior years. This can be seen in their historical data. Between 3Q 2002 and 1Q 2008 VaR normalized for asset size ranged between 0.0122% (2Q 2005) and 0.0189% (2Q 2006). Between 2Q 2008 and 4Q 2008 VaR was increased from 0.0206% to 0.0253%. VaR was again increased to 0.0311% (1Q 2009) and 0.0299% (2Q 2009).

Compare the 2009 VaRs to the last time when the Dow was in the 8,000 to 9,500 range or 3Q 2002 to 4Q 2003. In 2002/2003 Goldman Sachs VaR was 0.0132% (4Q 2002) to 0.0175% (3Q 2003). However, in 2009 Goldman Sachs VaR was between 0.0299% and 0.0311%, or double that in 2003. See Figure 1.

GoldmanSachsHistoricalVaR

Therefore my experience working with VaR and CVaR suggest that Goldman Sachs changed their methodology in 3 stages (2Q 2008, 4Q 2008 & 1Q 2009) but did not alter the riskiness of their asset classes.


Industry Misconception: Probability is a Sufficient Criterion
D. So we can assume that Goldman Sachs’ TK has figured out how to ensure that E(P) > E(L). But if you buy into coherent measures of risk, R, and Black Swans, BS, as I do, you would also add some additional constraints to their trading strategies:

D1. First constraint, E(P) > E(L). That is, it is not sufficient that the probability of profit P(P) be greater than the probability of loss P(L) or P(P) > P(L) is an insufficient condition, because the shape of the return distribution’s tail can significantly alter outcomes. We should note here that on an industry-wide basis quants use this P(P) > P(L) as a sufficient criterion.

D2. Second constraint, the sum of profits S(P) generated from past profitable trades must be significantly greater than the sum of losses S(L) generated from past losing trades or S(P) > S(L). Therefore, E(P) > E(L) versus P(P) > P(L) is a subtle but significant finding.

D3. Third constraint, the 98% loss CVaR or R(L,98%) must be not substantially large or R(L,98%) << 100%.

D4. Fourth constraint, Goldman Sachs does not trade when Black Swans are substantially large or BS(L) >> 0.

The reader may ask what is the significance of D3 or D4? If a large extreme loss is realizable, then it only takes one such trade to eliminate past profits. D1 tells us that for a specific set of trades Goldman Sachs had figured out the statistical long run outcomes. D2 tells us that Goldman Sachs is keeping track of their trading history within their algorithms. And D3 & D4 tells us that they are selective in what they trade.

Many people have suggested that Goldman Sachs use super computers to effect latency differences, but I have heard this since the 70s. So assuming that Goldman Sachs is using super computers, I tend to discount that this is the real reason for super computers. I would think that Goldman Sachs uses super computers to evaluate D1, and some form of D3, on the fly in real time. 


The Ability to Make Money is not the same as Having Money to Make Money
I received a lot of comments from a lot of people. These comments can be summarized into 4 points, Cheap Funds, Insider Information/Conspiracy Theory, Organization, and Market Efficiency. Goldman Sachs did fail and had to be rescued, but the question remains why did they make a ton of profits that made headlines while others did not? Looking at each of the 4 suggestions here are my opinions:

E1. Organization: Goldman’s need for rescue shows that they weren’t organizationally better than any of the other banks.

E2. Market Efficiency: Under severe stress of 2008/2009 markets would not have been efficient, but that would not exclude Goldman Sachs losing money like the other banks did.

E3. Insider Information/Conspiracy Theory: First, that is a very big risk to take especially if you get caught. However, would not the other big banks have had the same ‘advantage’ just by virtue of their size? In my opinion this is foolishness and I am sure GS employees would agree with me. Second, this is an asymmetric problem. You hear of insiders getting caught because they made a good profit from their inside information, but not when the lost money. In general I believe that inside information is over rated

E4. Cheap Funds: To use the army term, cheap funds are a force multiplier. You have to have the ability to make profits before you can amplify those gains. That is why VCs are picky and still they don’t always succeed because they too don’t always get the ‘make’ part right.

Conclusion
My inference is that Goldman Sachs had a trading knowledge that enabled them to make those trading profits. This must have been fairly recent (2008 & 2009) for that knowledge not to have dispersed into the rest of the industry, and the historical data tends to agree with this timing. This mini case study illustrated two very important points, that it is possible to reduce business risk if you can get it right, and that there still are hidden misconceptions that need to be identified and resolved even in a sophisticated environment  like quant based trading.

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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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Your Business Model in 1-Hour! In 1-hr you will definitely learn to quickly fix your strategy problems. Register here for Aug 17, 18 & 19 2009. This is critical  knowledge in a recession. 2 presentation slides are shown below.
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Feedback of previous related seminars:
1. Great, very informative (DM),
2. Very well thought out, effective and practical (RS)

Premium Webinar: Your Business Model in 1-Hour!

Benefit: You will immediately learn,
1. If your business model works.
2. How to recognize useful industry metrics.
3. Determine what strategies work for your company.
4. Take away some strategy maps / frameworks.   .   .   .   .   .   .    Register here.

This is a practical short course on how to recognize the real strategies your business needs and how to recognize the industry metrics for you to stay on track. It demystifies the role of financial statements and gets you back on track with real industry data to manage your strategy. This seminar concludes with a set of quick checks for your business model.

 

Who Should Attend: CxOs, Corporate Managers, Consultants & Investors.

About Benjamin T Solomon: Managing Principal of QuantumRisk LLC, he has 28+ years working in multinational, national and regional companies (Texas Instruments, West Port, Capmark, Coopers & Lybrand …) developing IT systems, streamlining business processes, designing and implementing strategies, and extensive econometric & economic capital modeling in the CMBS/RMBS industry.

Registration: This will be a 1 hour webinar limited to 20 participants per session. Register here.

Dates: August 17, 18 & 19, 2009.

Notes:
1. You will be emailed a meeting key to join, and webinar link.
2. You will be provided a pdf copy of the slides, 7 days after the seminar.
3. Last Refund Date: No refunds after Aug 14 2009.
4. Refund Policy, here.

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2 Presentation Slides:

Slide32

An Example of a Process Framework

 

Slide33

An Example of a Format Framework

The Opportunity:
What would you pay to know the Black Swan of your CMBS deal risk?

Reverse that question!

What would a deal manager pay you to know the Black Swan of his CMBS deal?

The Need:
Inspite the bad publicity surrounding CDOs and structured finance the tranche strucutre is one of the most efficient methods of creating differentiated classes of assets, as The Committee on the Global Financial System explains:

A key goal of the tranching process is to create at least one class of securities whose rating is higher than the average rating of the underlying collateral pool or to create rated securities from a pool of unrated assets. This is accomplished through the use of credit support (enhancement), such as prioritisation of payments to the different tranches.

However, what really caused the market to substatially undervalue these assets was that the default rate and the severity of the loss was much greater than even the complex rating processes had estimated them to be.  Have you noticed that some (many?) of the AAA tranches had losses?

The Solution:
The answer is an econometric assessment of the default and loss distributions of CMBS assets. Note, not a single point value but the whole distribution. This distribution will provide the CMBS Deal’s 95% or 98% VaR and CVaR loss estimates.

From this distribution we can then recalculate the loss estimates for each tranche, and be pretty certain what the loss characteristics of these tranches are independently of the ratings assigned to the tranches. A backup second opinion, if you would.

Why would this provide better answers?
1. We know the assigned ratings did not cut it.
2. We know the vintage or triangular matrix method provided incorrect default and loss curves. I was the first & only person to correctly identify that a major tool, vinatges/triangular matrix method, used in the mortgage idustry was providing incorrect results. The link provides access to an Excel worksheet that allows you to confirm this for yourself. To understand the magnitude of this finding one only need to look at the vast array of mortgage analyses, from the Esaki-Snyder reports to the Wachovia 2008 CMBS Loss Study, that use this tool.
3. The DSCR loss model used in the CMBS industry does not match the historical data. I discovered this weakness in the method through extensive testing against the historical data. Do you know of anyone who has personally tested these tool against the historical data?

What would the solution look like?

The graph below is a simulated long term VaR & CVaR loss outcome for a simulated CMBS deal.

CMBSPartnership

This graph is based on a set of CMBS loss & default distributions, and provides a second method to evaluating bond pricing. After all at the end of the day, isn’t cashflow analysis and DSCR’s about bond pricing? Notice how VaR (green dash) consistently underestimates CVaR (purple dash). We can add in Black Swans into this report. Note that my initial assessement was that CMBS Black Swans are on the order of 20% to 80%.

Partnership:
I am seeking partnerships with banks/investment funds/ratings companies to fund the development of this econometric CMBS business, which I expect to be transferrable to the RMBS sub-industry.

Call me 303-618-2800 or email me at benjamin . t . solomon AT QuantumRisk . com if you are interested in this business.

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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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Is Your Business Model Sustainable? In 1-hr you will definitely learn to quickly fix your strategy problems. Register at Events.
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I had a very successful webinar series Strategy In A Recession? in July. 

Feedback for this webinar:
1. Great, very informative (DM),
2. Very well thought out, effective and practical (RS), &
3. Interesting ideas about strategy (JH)

August Premium Webinar:
I will be conducting another premium webinar Is Your Business Model Sustainable?

Benefit: 
You will learn,
1. How to determine if your business model is sustainable.
2. How to determine useful industry metrics.
3. Determine what business forces / strategies work for your company.
4. Take away some strategy maps / frameworks.

This is a practical short course on how to recognize the real strategies your business needs and how to determine the industry metrics required to keep on track. It demystifies the role of financial statements and gets you back on track with real industry data that should be used to manage strategy. This seminar concludes with a set of easy checks that enable you to determine if you should need to change your business model.

Who Should Attend:
CxOs, Corporate Managers, Consultants & Investors.

About Benjamin T Solomon: Managing Principal of QuantumRisk LLC, he has 28+ years working in multinational, national and regional companies (Texas Instruments, West Port, Capmark, Coopers & Lybrand …) developing IT systems, streamlining business processes, designing and implementing strategies, and extensive econometric & economic capital modeling in the CMBS/RMBS industry.

 Schedule & Payments:
This will be a 1+ hour (>30 slides) $97 fee based webinar limited to 20+ participants per session. Schedule and payments here.

Dates:
August 17, 18 & 19, 2009. 

Notes:
1.You will be emailed a meeting key to join, and webinar link.
2. You will be provided a pdf copy of the slides, Aug 18.
3. Last Refund Date: No refunds after Aug 14 2009.
5. Refund Policy, here.

 

Ben Solomon

P.S. I changed the title of the webinar from Determining the Status of Your Company to Is Your Business Model Sustainable? after I received feedback that the subject was a lot more interesting than the previous title had implied.