Archive for May, 2011

Date: May 4th, 2011
Cate: Finance, Risk & Stability, Uncategorized

Gold and Forex ETF sensitivity to Presidential decree?

The cheapest lessons are provided by history, sadly they are often ignored as irrelevant or untimely. In 1971, Nixon took the US off the gold standard and also put an immediate 10% tax on import goods including the use of price and wage freezes. He blamed speculators and wrapped the message up in “worker support” speak and anti-elitist tones.

Many Americans would now believe these types of actions to be the sole activity of banana republics. Things can change in an instant. This post isn’t about a party, policy or president, merely a pointer to a past that isn’t really that far past. If you are young read more history, if you are old, don’t forget.

Here is a transcript of the video I had produced for $1 on Mechanical turk:

The third indispensable element in building the new prosperity is closely related to creating new jobs and halting inflation. We must protect the position of the American dollar as a pillar of monetary stability around the world. In the past seven years there has been an average of one international monetary crisis every year.

Now who gains from these crises? Not the working man, not the investor, not the real producers of wealth. The gainers are the international money speculators. Because they thrive on crises, they help to create them. In recent weeks the speculators have been waging an all out war on the American dollar.

The strength of a nation’s currency is based on the strength of that nation’s economy, and the American economy is by far the strongest in the world. Accordingly, I have directed the Secretary of the Treasury to take the action necessary to defend the dollar against the speculators. I directed Secretary Connally to suspend temporarily the convertibility of the dollar into gold or other reserve assets except in amounts and conditions determined to be in the interest of monetary stability and in the best interest of the United States.

Now what does this action, which is very technical, what does it mean for you? Let me lay to rest the bugaboo of what is called devaluation. If you want to buy a foreign car or take a trip abroad, market conditions may cause your dollar to buy slightly less. But if you are among the overwhelming majority of Americans who buy American-made products in America, your dollar will be worth just as much tomorrow as it is today. The effect of this action in other words will be to stabilize the dollar. Now this action will not win us any friends among the international money traders, but our primary concern is with the American workers and with fair competition around the world.

To our friends abroad including the many responsible members of the international banking community who are dedicated to stability in the flow of trade, I give this assurance: The United States has always been, and will continue to be, a forward-looking and trustworthy trading partner. In full cooperation with the International Monetary Fund and those who trade with us we will press for the necessary reforms to set up an urgently needed new international monetary system.

Stability and equal treatment is in everybody’s best interest. I am determined that the American dollar must never again be a hostage in the hands of international speculators. I am taking one further step to protect the dollar, to improve our balance of payments, and to increase jobs for Americans.

As a temporary measure I am today imposing an additional tax of ten percent on goods imported into the United States. This is a better solution for international trade than direct controls on the amount of imports. This import tax is a temporary action. It isn’t directed against any other country. It’s an action to make certain that American products will not be at a disadvantage because of unfair exchange rates.

When the unfair treatment is ended, the import tax will end as well. As a result of these actions the product of American labor will be more competitive and the unfair edge that some of our foreign competition has will be removed. This is a major reason why our trade balance has eroded over the past 15 years.

Date: May 3rd, 2011
Cate: Finance, Risk & Stability, Systems

Flash Crash 1yr anniversary of the $1 trillion accident (feedback loop).

According to the SEC Flash Crash report 86% of securities traded 10% lower during the May 6th,2010 flash crash with some shares trading down to $.01 (see page 26 of SEC report).  The US Equity market is valued at roughly $15 trillion.  The Flash crash was a temporary $1.0 trillion accident.

1. In complex systems “more” is different

A single share’s transaction & quote volume is now measured in thousands of bid/ask quotes per second.   To visualize this, imagine that a share now has the equivalent of 10,000 people offering bid/ask quotes every 6 seconds all day long vs 10-20 people a few years ago.  Change the rate, number or nature of components in a system and th systems behavioral rules change. Nanex has pictures to help to visualize this micro-second world of market makers Nanex

2. Positive feedback loops grow exponentially

A microphone amplifying the loudspeaker leading to high pitch screech is an example of an audio positive feedback loop.
The flash crash occurred when one exchanges system clock got overloaded and was slightly mis-aligning bid/ask times stamps.  The HFT traders interpreted the mis-stamped quotes as an arbitrage opportunity sending more messages and activity into the system further mis-aligning the time stamp and increasing the perceived opportunity. see Nanex report

3. Normal accidents are caused by common errors

in accident and systems theory terms the flash crash was a “normal” accident.  The timing mismatch wasn’t a large error in and of itself, but because of the HFT equity trading system structure (tightly coupled & brittle) the normal error caused a large accident.

Summary & Flash Crash 2.0

The poorly understood system dynamics of HFT mean that another Flash Crash is likely to be caused by a previously innocuous error.  Greater risks may be associated with the sovereign debt markets and VaR  related feedback loops as High Frequency Trading expands its reach.  The debate is one of speed (Liquidity) vs. safety (system integrity).

Explanatory pictures and video:

Date: May 3rd, 2011
Cate: Finance, Risk & Stability, Systems

Tarp Bailout was closer to $12.8 trillion in money movement

 

Watch the full episode. See more Need To Know.

HT: Barry ritholtz for this

Pretty amazing video given how big the amounts are and how little investigative work has been done here.  The amounts involved dwarf anything else discussed to date.  The $12.8 trillion has a significant amount of “back stop” money that wasn’t called upon but was effectively guaranteed and with a lot of swap lines etc.

The freeze up in the money markets was obviously looming as a cascading failure.  To learn about  large scale failure in complex systems check out the excellent and fun to read book ubiquity: why catastrophes happen.

Well done to Bloomberg to rock the boat and dig a bit into this.  Please note that the brave ladies and lads at CNBC, Fox business etc. who are busy posing as business journalists really haven’t even touched these issues, much less challenged the fed with an FOIA request.  Investigative or real journalism stops at the edge of the newsdesk and echo chamber for the TV folks.  Please: Reuters, WSJ pick up this torch there is probably Pulitzer material here.

The fact remains that a huge opaque system, the Fed is at work trying to sort out a mess that may be growing quietly larger.  The low rates are helping balance sheets, but it is a race between free FED spread and loan deteriorations.

Only history will correctly identify whether the stealthy approach has prevented a broader crisis of faith in the system in 2008 averting a temporary liquidity issue or whether the game is only perpetuating a problem under the popular extend and pretend sobriquet.

Que sera sera. read when money dies

Disclosure: Long N. Krone, Gold

Date: May 3rd, 2011
Cate: Finance, Risk & Stability, Systems
3 msgs

Process and systems thinking about risk

Risk management is understanding and controlling exposure to bad outcomes. Risk managers often focus on extreme events that may overwhelm a system. Scenarios simulate extreme events and expected price outcomes. In non-natural disasters, the worst outcomes often occur when the inputs and processes seem to operate normally, but wrong processes are in place. Wrong process risk is undermanaged.

Risk exposure occurs when a manager exposes cash or the balance sheet to a (return generating) system such as an equity, bond, business line or some other system with an expected net positive cash flow.  This exposure is to a system made up of inputs, processes and outputs.

Value investors are famous for discussing the capricious nature of price as an occasionally flawed output relative to value.  The value thesis separates inputs and asset value creating process from the output of short-term market price.  The argument being that “value” shows up in price over a time and short-term price may be a “wrong output”. The return generating system needs time to create value, which will eventually be reflected in the “right” price.

Risk managers overly focus on system outputs, usually price

Many risk tools model price outputs. Fewer risk management approaches focus on monitoring return generating inputs and processes.  Price output fixation is likely due to the fact that sources of return generation are often poorly understood, differ significantly across exposures and are difficult to quantify.

System outputs such as price and its variance become easy proxies for an exposure’s risk.  True risk exposure may be better managed by looking at the exposure as a system to be monitored including the inputs and return generating processes.

The bigger Flash Crash issue: market integrity

An example of overly focusing on price is the recent Flash Crash and lesser known ongoing mini-crashes[1].  The Flash Crash is commonly misunderstood relative to its real market system risk impact.  Price is easily discussed, but masks the more germane point of market system integrity as often the crashed shares revert to near pre-crash levels.

The Flash crash and ongoing mini-crashes reflect a change in the underlying equity market process.  Price may vary so quickly from value that it causes market risk of a more subtle but profound type.   This risk is market integrity risk relating to the primary economic function of an equity market.

The primary role of an equity market is to act as a system for price discovery and efficient transactions.  Ongoing flash crashes send signals to participants that the equity market system may be malfunctioning in its price discovery role.   This perceived failure could lead to less “real” liquidity, exacerbating current flash crash inducing processes.

Publicly traded firms trade at a premium to privately held firms due to the faith in the liquidity and fair pricing of the public market.  If the public market system is believed to have an increased risk component, the equity premium of all participants could suffer.  It remains to be seen if micro-second messaging and execution is a fair trade off for even slight declines in the overall public market equity premium.  These points are raised to indicate an example of over focus on an event and price outcome such as the flash crash and its outputs that may miss the bigger point, i.e. the integrity of the system process.  Maybe high frequency trading is the wrong process for the public equity market system.

Managing risk involves a system’s inputs, processes and outputs

The risk manager is told that an exposure or line of business is changing and new risk management systems need to be put in place, these systems typically measure an amount of exposure and ongoing price.  The systems are stress tested using scenarios to determine that they are fit for purpose.

The exposure and systems are brought on line and the system churns away, hopefully with the desired positive cash flow output.  Inputs and return generating processes may not be revisited as long as the outputs of the system are looking “good” i.e. within tolerances and generating positive returns.

Systems failure: Event based & wrong process

Risk managers are familiar with operational or price event based failures. Event based failure involves a process overwhelmed by an input or the process breaking on its own.

Example: our payment clearing system was designed to handle 4 million TPS (transactions per second) but we had 7 million…

Price & operational risk management is typically about choosing event based scenarios to determine an acceptable stress/failure point for the exposure.   A business case is created based on scenarios describing input, process and expected outputs during a stress event.

Credit card example of business case and scenario Exposure sought: Personal credit card industry

  • Input:We are allocating $3 billion and appropriate resources to credit cards
  • Process:Credit spreads indicate 10.0% annual return on capital for 7 years
  • Output:We anticipate an +$300m annual cash flow

Risk Scenario:

  • Input:A -5% GDP & -300 bps spread contraction and behavior shifts…
  • Process:Charge off rates increase +20% from baseline…
  • Output:The business could temporarily show $200m/year losses

Wrong process failures are larger than extreme events

Wrong process failure occurs when everything is working correctly relative to inputs and processes but outputs are undesirable, the wrong process is being used.  Wrong model selection or utilization is a form of Wrong process.

Most structured finance was related to wrong process failure. The wrong risk assessment processes were being used. The dis-aggregation of information and diligence responsibilities in the securitization process lead downstream participants to believe they had quality assets. Downstream securitization due diligence processes as implemented worked well.  Unfortunately the choice of process involving shallow diligence with minimal discovery was flawed.

Wrong process risk is often more dangerous than extreme event risk. Wrong processes create the illusion of normally functioning systems with successful outputs (profits) in the short or medium term.  The false appearance of normal operational “profitable” behavior means that processes aren’t inspected or deeply questioned and challenged.  In finance, like businesses, the only axiom more dangerous than, “If it ain’t broke don’t fix it.”, is probably, “if it is making money, don’t question it.”  It is occasionally forgotten that humility and vigilance are often the price for excellence in finance.

The most insidious wrong processes take longer to show bad outcomes.  Unquestionable faith in an exposure often correlates to an exposures recent profitability.  The longer something is profitable, the more money and participants crowd in with similar systems.  At the same time each new participant questions the fundamental inputs and processes less and less. Group behavior and faith that “someone must be paying attention”, has the perverse effect of reducing input & process risk analysis at exactly the time that aggregate exposure risk is growing.

Systemically threatening Wrong process is difficult to root out of short term output focused organizational cultures.

Actionable Systems Risk

Actionable systems risk involves placing ongoing equal weighting and importance on a systems inputs, process and outcomes.  Every system has capacity constraints in inputs, processes and outputs.

The first indicator of trouble in a system occurs when the outputs vary significantly from the inputs. This is the law of the conservation of risk, risk doesn’t disappear, it is shifted sideways to someone else or forward in time.  If risk seems to disappear from a system, it may be indicative of a flawed understanding of the system.

Securitization is an example of this, a bundle of debt based payment streams is sliced (tranched) and presented in such a way that aggregate risk appears less than original risk.

Trading strategies or alternative asset classes requiring increased leverage or more input relative to an expected rate of return are another example of an exposure that may have shifted due to overcrowding.  These environments are highly unstable as the crowded process becomes more vulnerable to collapse.

Complexity & innovation in finance are antithetical to Actionable Systems Risk Thinking

In finance complexity is often seen to represent sophistication and innovation.  This sophistication backed by math models, ratings and or years of apparent success can lead to large problems. Complexity masks a system’s inputs, processes and/or outputs.

In the case of mortgage securitization, complexity masked inputs.  As profits rolled into the securitization sector, inputs and processes were questioned less.  Over time this lead to an almost blind faith in ratings downstream. Faith in the ratings system meant ignoring system inputs, namely the deteriorating quality of underwritten mortgages.

In alternative asset investments, risk management is mostly performed at the output and input level with processes such as security selection obscured.  Initial and ongoing due diligence and portfolio monitoring may allow the alternative asset risk manager to assess the inputs to a system (manager exposure).  Principal component analysis, peer benchmarking and other output-based analytics may be used as soft proxies for retrospective allocation process management.  The reality is that many alternative exposures by definition have idiosyncratic processes limiting risk management.

War games: The Wrong input and Wrong process audit

Due to the danger of wrong inputs and processes, it is may be time for the actionable systems risk manager to audit many exposures inputs and processes.  Independent, internal benchmarking of inputs, processes, capacities and systemic assumptions on a periodic basis would most likely be a worthwhile activity for many risk managers.

War games test expected behaviors and system responses by fully questioning the integrity of inputs and processes. Performing an annual or semi-annual wrong process and wrong input audit may make sense for many lines of business as a way of testing input and process assumptions.  Cross checking of inputs, stressing of processes and questioning those assumptions that appear “most” obvious may prove valuable.  At a minimum performing a wrong input, wrong process audit forces the manager to pose new questions and reframe old beliefs.


[1] According to Nanex, more than 549 mini-flash crashes affecting multiple equities on many days have occurred in 2010.

Disclosure: “No Positions”

Date: May 3rd, 2011
Cate: Risk & Stability, Systems

Nuclear risk management for New York and Connecticut

The Indian Point reactor near New York city may be at risk of de-commissioning if the 1:10,000 year geological failure risk is correctly presented as a 1:200 risk of reactor failure over the systems life. For Entergy (ETR) decomissioning could cost $1b based on comparative costs.

The Indian Point reactor provides up to 30% of New York city and surrounding area electricity. De-commissioning would mean increased demand on other resources and likely capex of $1-2b for a replacement generating source and transmission facilities if Indian Point is de-commissioned.

Measuring risk correctly

We respond to risk based on our perception of it. Geological nuclear risk is mis-perceived and could be costly for investors. Thinking about risk using a technique called actionable systems thinking can help.

Systems thinking involves looking at risks or value creating processes as whole systems. This technique simplifies and clarifies. Many risk managers get carried away with complex tools and piles of data. These tools and data are used to as the basis for complicated models with costly and sometimes tragic consequences.

Events in Japan raise concerns about US nuclear risk. The NRC (Nuclear Regulatory Commission) released the risks associated with, “an earthquake that would cause damage to a reactor’s core releasing radiation”. The information as released mis-represents the risks.

The flawed risk unit known as the year

Risk is often expressed as the likelihood of an event occurring within a period of time such as a year. The time period is arbitrary. Like the useless financial Value at Risk metrics used by banks, co-variances and other non-sense these misrepresentations lead to bad choices.

Natural event risk is usually represented as an event happening every X number of years. This presentation of data is misleading. A more useful presentation is to use the unit of the system lifecycle.

Buying a house on a flood plain vulnerable to a once in a hundred year flood (1:100 years) may feel fairly safe. If you plan on owning the house for 33 years (its functional system life period), you have a 33% chance of disaster. People think differently when risk is expressed in system lifecycles.

The Indian Point nuclear facility near New York city is reported to have a one in 10,000 year risk of geological activity that could breach the core leading to radioactive material escape. This sounds safe until one considers the plant as a system. Systems have functional lives. Many nuclear reactors are re-licensed for 10 or more year increments. A 50 year functional life isn’t extraordinary.

Systems thinking risk applied to the Indian Point reactor puts failure odds at 1:200

When viewed as a 50 year system the Indian Point nuclear facility has a 1:200 chance of earthquake risk breaching the core and spilling radiation during its life. 50×1:10,000= 1:200 If during the design and permitting phase someone presented such a low probability high impact risk with that figure it would most likely be un-acceptable.

On the other side of the coin using the 1:10,000 year figure means on any given day the odds are 1:3,652,000 which many may say is acceptable. In the actionable systems risk framework, the correct metric to use is the systems life indicating The nuclear system has a 1:200 chance of geologically induced failure.

Each of the 104 reactors in the US operates independently, but combined can be considered as the US nuclear system. Using NRC data aggregating the US nuclear geological system risk one gets annual odds of 1:480 for a failure in the system. If one assumes each reactor is licensed and operational for 50 years, the risk horizon for a geological event in the US nuclear system is 10.42% or roughly 1:10 over a 50 year lifetime. 50×1:480 =50:480

This seems high for just one dimension of risk, namely geological. I am a fan of nuclear as a “clean” energy but only when risk is designed and priced correctly. Most likely some reactors should be shut down or moved if geographic and other risk vectors were presented using a systems risk perspective.

Nuclear operator’s liabilities are capped under the Price act at $560 million but the potential national cost for such an incident could exceed $500 billion. (see article link below).

The nuclear and finance industries needs to measure risk using systems thinking and systems frameworks to better engineer in safety. The higher risk operators in the Spreadsheet attached to this article may face material cost impacts from shut-down or redesigns of reactors.

Even NASA gets it wrong

NASA got risk wrong with the space Shuttle. NASA estimated the space shuttle system to be over 99.9999% safe. Nobel prize wining physicist Richard Feynman brilliantly described his role on the Challenger Blue ribbon panel in his book “What do you care what other people think?”. Feynman calculated probability of shuttle failure as 1:96. NASA organizationally saw risk and reported it the way it wanted to, not the way it was. Bankers and Utility companies may have the same behavioral risk drives.

The utility companies listed below may have margins shrink or costs increase if risks are correctly interpreted using a systems thinking perspective. This could be short term expensive for a few, but better for society in the long run.

In my day job I help banks, family offices and hedge funds understand risk and opportunity. This task often starts by getting rid of all price based models like VaR, volatility, beta, BIS standards and Modern Portfolio Theory. Losing these frames of belief causes distress at first until the Systems Thinking approach is brought in. Letting go of familiar but wrong metrics to replace them unfamiliar metrics that may bear bad news is rarely easy or popular.

Systems thinking mostly ignores price

Price reflects two opposing opinions expressed at a single point in time. 99% of investors can’t beat a buy and hold index. It stands to reason 99% of the opinions creating price are probably wrong when considering the correct measurement of value and risk.

participants symbol list: GE (General Electric), HIT (Hitachi), EXC (Excelon), AEE (Ameren), CEP (Constellation energy), DUK (Duke energy), D (Dominion Energy), private (Energy Northwest), FE (First Energy), FPL (Florida Light and Power), private (Nebraska Public Power District), NU (Northeast Utilities), NMC (Nuclear Management Company), NA (Omaha Public Power District), PCG (Pacific Gas and Electric), PGN (Progress Energy), SO (Southern Company), TVE (Tennessee Valley Authority), TXU (TXU energy), XCL (Xcel Energy)

Geological risk table:

Nuclear facility and geological risk an event compromising the reactor. Yearly rate Risk as % Systems rate @ 50 years Systems Risk as %
1. Indian Point 3, Buchanan, N.Y.: 1 in 10,000 chance each year. Old estimate: 1 in 17,241. Change in risk: 72 percent. 10,000 0.0001 200 0.50%
2. Pilgrim 1, Plymouth, Mass.: 1 in 14,493 chance each year. Old estimate: 1 in 125,000. Change in risk: 763 percent. 14,493 6.89988E-05 289.86 0.34%
3. Limerick 1, Limerick, Pa.: 1 in 18,868 chance each year. Old estimate: 1 in 45,455. Change in risk: 141 percent. 18,868 5.29998E-05 377.36 0.26%
3. Limerick 2, Limerick, Pa.: 1 in 18,868 chance each year. Old estimate: 1 in 45,455. Change in risk: 141 percent. 18,868 5.29998E-05 377.36 0.26%
5. Sequoyah 1, Soddy-Daisy, Tenn.: 1 in 19,608 chance each year. Old estimate: 1 in 102,041. Change in risk: 420 percent. 19,608 5.09996E-05 392.16 0.25%
5. Sequoyah 2, Soddy-Daisy, Tenn.: 1 in 19,608 chance each year. Old estimate: 1 in 102,041. Change in risk: 420 percent. 19,608 5.09996E-05 392.16 0.25%
7. Beaver Valley 1, Shippingport, Pa.: 1 in 20,833 chance each year. Old estimate: 1 in 76,923. Change in risk: 269 percent. 20,833 4.80008E-05 416.66 0.24%
8. Saint Lucie 1, Jensen Beach, Fla.: 1 in 21,739 chance each year. Old estimate: N/A. Change in risk: N/A. 21,739 4.60003E-05 434.78 0.23%
8. Saint Lucie 2, Jensen Beach, Fla.: 1 in 21,739 chance each year. Old estimate: N/A. Change in risk: N/A. 21,739 4.60003E-05 434.78 0.23%
10. North Anna 1, Louisa, Va.: 1 in 22,727 chance each year. Old estimate: 1 in 31,250. Change in risk: 38 percent. 31,250 0.000032 625 0.16%
10. North Anna 2, Louisa, Va.: 1 in 22,727 chance each year. Old estimate: 1 in 31,250. Change in risk: 38 percent. 31,250 0.000032 625 0.16%
12. Oconee 1, Seneca, S.C.: 1 in 23,256 chance each year. Old estimate: 1 in 100,000. Change in risk: 330 percent. 23,256 4.29997E-05 465.12 0.21%
12. Oconee 2, Seneca, S.C.: 1 in 23,256 chance each year. Old estimate: 1 in 100,000. Change in risk: 330 percent. 23,256 4.29997E-05 465.12 0.21%
12. Oconee 3, Seneca, S.C.: 1 in 23,256 chance each year. Old estimate: 1 in 100,000. Change in risk: 330 percent. 23,256 4.29997E-05 465.12 0.21%
15. Diablo Canyon 1, Avila Beach, Calif.: 1 in 23,810 chance each year. Old estimate: N/A. Change in risk: N/A. 23,810 4.19992E-05 476.2 0.21%
15. Diablo Canyon 2, Avila Beach, Calif.: 1 in 23,810 chance each year. Old estimate: N/A. Change in risk: N/A. 23,810 4.19992E-05 476.2 0.21%
17. Three Mile Island 1, Middletown, Pa.: 1 in 25,000 chance each year. Old estimate: 1 in 45,455. Change in risk: 82 percent. 25,000 0.00004 500 0.20%
18. Palo Verde 1, Wintersburg, Ariz.: 1 in 26,316 chance each year. Old estimate: N/A. Change in risk: N/A. 26,316 3.79997E-05 526.32 0.19%
18. Palo Verde 2, Wintersburg, Ariz.: 1 in 26,316 chance each year. Old estimate: N/A. Change in risk: N/A. 26,316 3.79997E-05 526.32 0.19%
18. Palo Verde 3, Wintersburg, Ariz.: 1 in 26,316 chance each year. Old estimate: N/A. Change in risk: N/A. 26,316 3.79997E-05 526.32 0.19%
18. Summer, Jenkensville, S.C.: 1 in 26,316 chance each year. Old estimate: 1 in 138,889. Change in risk: 428 percent. 26,316 3.79997E-05 526.32 0.19%
22. Catawba 1, York, S.C.: 1 in 27,027 chance each year. Old estimate: 1 in 33,333. Change in risk: 23 percent. 27,027 3.7E-05 540.54 0.19%
22. Catawba 2, York, S.C.: 1 in 27,027 chance each year. Old estimate: 1 in 33,333. Change in risk: 23 percent. 27,027 3.7E-05 540.54 0.19%
24. Watts Bar 1, Spring City, Tenn.: 1 in 27,778 chance each year. Old estimate: 1 in 178,571. Change in risk: 543 percent. 27,778 3.59997E-05 555.56 0.18%
25. Indian Point 2, Buchanan, N.Y.: 1 in 30,303 chance each year. Old estimate: 1 in 71,429. Change in risk: 136 percent. 30,303 3.3E-05 606.06 0.17%
26. Duane Arnold, Palo, Iowa: 1 in 31,250 chance each year. Old estimate: N/A. Change in risk: N/A. 31,250 0.000032 625 0.16%
27. McGuire 1, Huntersville, N.C.: 1 in 32,258 chance each year. Old estimate: 1 in 35,714. Change in risk: 11 percent. 32,258 3.10001E-05 645.16 0.16%
27. McGuire 2, Huntersville, N.C.: 1 in 32,258 chance each year. Old estimate: 1 in 35,714. Change in risk: 11 percent. 32,258 3.10001E-05 645.16 0.16%
29. Farley 1, Columbia, Ala.: 1 in 35,714 chance each year. Old estimate: 1 in 263,158. Change in risk: 637 percent. 35,714 2.80002E-05 714.28 0.14%
29. Farley 2, Columbia, Ala.: 1 in 35,714 chance each year. Old estimate: 1 in 263,158. Change in risk: 637 percent. 35,714 2.80002E-05 714.28 0.14%
31. Quad Cities 1, Cordova, Ill.: 1 in 37,037 chance each year. Old estimate: 1 in 71,429. Change in risk: 93 percent. 37,037 2.7E-05 740.74 0.14%
31. Quad Cities 2, Cordova, Ill.: 1 in 37,037 chance each year. Old estimate: 1 in 71,429. Change in risk: 93 percent. 37,037 2.7E-05 740.74 0.14%
33. River Bend 1, St. Francisville, La.: 1 in 40,000 chance each year. Old estimate: 1 in 370,370. Change in risk: 826 percent. 40,000 0.000025 800 0.13%
34. Peach Bottom 2, Delta, Pa.: 1 in 41,667 chance each year. Old estimate: 1 in 120,482. Change in risk: 189 percent. 41,667 2.39998E-05 833.34 0.12%
34. Peach Bottom 3, Delta, Pa.: 1 in 41,667 chance each year. Old estimate: 1 in 120,482. Change in risk: 189 percent. 41,667 2.39998E-05 833.34 0.12%
36. Crystal River 3, Crystal River, Fla.: 1 in 45,455 chance each year. Old estimate: 1 in 192,308. Change in risk: 323 percent. 45,455 2.19998E-05 909.1 0.11%
36. Seabrook 1, Seabrook, N.H.: 1 in 45,455 chance each year. Old estimate: 1 in 114,943. Change in risk: 153 percent. 45,455 2.19998E-05 909.1 0.11%
36. Beaver Valley 2, Shippingport, Pa.: 1 in 45,455 chance each year. Old estimate: 1 in 188,679. Change in risk: 315 percent. 45,455 2.19998E-05 909.1 0.11%
39. Perry 1, Perry, Ohio: 1 in 47,619 chance each year. Old estimate: 1 in 1,176,471. Change in risk: 2371 percent. 47,619 2.1E-05 952.38 0.11%
39. Columbia 1, Richland, Wash.: 1 in 47,619 chance each year. Old estimate: N/A. Change in risk: N/A. 47,619 2.1E-05 952.38 0.11%
41. Waterford 3, Killona, La.: 1 in 50,000 chance each year. Old estimate: 1 in 833,333. Change in risk: 1567 percent. 50,000 0.00002 1000 0.10%
42. Dresden 2, Morris, Ill.: 1 in 52,632 chance each year. Old estimate: 1 in 434,783. Change in risk: 726 percent. 52,632 1.89998E-05 1052.64 0.09%
42. Dresden 3, Morris, Ill.: 1 in 52,632 chance each year. Old estimate: 1 in 434,783. Change in risk: 726 percent. 52,532 1.9036E-05 1050.64 0.10%
42. Monticello, Monticello, Minn.: 1 in 52,632 chance each year. Old estimate: 1 in 38,462. Change in risk: -27 percent. 52,632 1.89998E-05 1052.64 0.09%
45. Wolf Creek 1, Burlington, Kansas: 1 in 55,556 chance each year. Old estimate: 1 in 400,000. Change in risk: 620 percent. 55,556 1.79999E-05 1111.12 0.09%
46. San Onofre 2, San Clemente, Calif.: 1 in 58,824 chance each year. Old estimate: N/A. Change in risk: N/A. 58,824 1.69999E-05 1176.48 0.08%
46. San Onofre 3, San Clemente, Calif.: 1 in 58,824 chance each year. Old estimate: N/A. Change in risk: N/A. 58,824 1.69999E-05 1176.48 0.08%
48. Millstone 3, Waterford, Conn.: 1 in 66,667 chance each year. Old estimate: 1 in 100,000. Change in risk: 50 percent. 66,667 1.49999E-05 1333.34 0.07%
48. Brunswick 1, Southport, N.C.: 1 in 66,667 chance each year. Old estimate: 1 in 263,158. Change in risk: 295 percent. 66,667 1.49999E-05 1333.34 0.07%
48. Brunswick 2, Southport, N.C.: 1 in 66,667 chance each year. Old estimate: 1 in 263,158. Change in risk: 295 percent. 66,667 1.49999E-05 1333.34 0.07%
48. Robinson 2, Hartsville, S.C.: 1 in 66,667 chance each year. Old estimate: 1 in 370,370. Change in risk: 456 percent. 66,667 1.49999E-05 1333.34 0.07%
52. Oyster Creek, Forked River, N.J.: 1 in 71,429 chance each year. Old estimate: 1 in 126,582. Change in risk: 77 percent. 71,429 1.39999E-05 1428.58 0.07%
53. Fort Calhoun, Fort Calhoun, Neb.: 1 in 76,923 chance each year. Old estimate: N/A. Change in risk: N/A. 76,923 1.3E-05 1538.46 0.07%
53. Ginna, Ontario, N.Y.: 1 in 76,923 chance each year. Old estimate: 1 in 238,095. Change in risk: 210 percent. 76,923 1.3E-05 1538.46 0.07%
53. Susquehanna 1, Salem Township, Pa.: 1 in 76,923 chance each year. Old estimate: 1 in 416,667. Change in risk: 442 percent. 76,923 1.3E-05 1538.46 0.07%
53. Susquehanna 2, Salem Township, Pa.: 1 in 76,923 chance each year. Old estimate: 1 in 416,667. Change in risk: 442 percent. 76,923 1.3E-05 1538.46 0.07%
57. Calvert Cliffs 2, Lusby, Md.: 1 in 83,333 chance each year. Old estimate: 1 in 116,279. Change in risk: 40 percent. 83,333 1.2E-05 1666.66 0.06%
57. D.C. Cook 1, Bridgman, Mich.: 1 in 83,333 chance each year. Old estimate: N/A. Change in risk: N/A. 83,333 1.2E-05 1666.66 0.06%
57. D.C. Cook 2, Bridgman, Mich.: 1 in 83,333 chance each year. Old estimate: N/A. Change in risk: N/A. 83,333 1.2E-05 1666.66 0.06%
57. Grand Gulf 1, Port Gibson, Miss.: 1 in 83,333 chance each year. Old estimate: 1 in 106,383. Change in risk: 28 percent. 83,333 1.2E-05 1666.66 0.06%
57. Kewaunee, Kewaunee, Wis.: 1 in 83,333 chance each year. Old estimate: 1 in 71,429. Change in risk: -14 percent. 83,333 1.2E-05 1666.66 0.06%
62. Millstone 2, Waterford, Conn.: 1 in 90,909 chance each year. Old estimate: 1 in 156,250. Change in risk: 72 percent. 90,909 1.1E-05 1818.18 0.06%
62. Salem 1, Hancocks Bridge, N.J.: 1 in 90,909 chance each year. Old estimate: 1 in 172,414. Change in risk: 90 percent. 90,909 1.1E-05 1818.18 0.06%
62. Salem 2, Hancocks Bridge, N.J.: 1 in 90,909 chance each year. Old estimate: 1 in 172,414. Change in risk: 90 percent. 90,909 1.1E-05 1818.18 0.06%
62. Point Beach 1, Two Rivers, Wis.: 1 in 90,909 chance each year. Old estimate: 1 in 76,923. Change in risk: -15 percent. 90,909 1.1E-05 1818.18 0.06%
62. Point Beach 2, Two Rivers, Wis.: 1 in 90,909 chance each year. Old estimate: 1 in 76,923. Change in risk: -15 percent. 90,909 1.1E-05 1818.18 0.06%
67. Turkey Point 3, Homestead, Fla.: 1 in 100,000 chance each year. Old estimate: N/A. Change in risk: N/A. 100,000 0.00001 2000 0.05%
67. Turkey Point 4, Homestead, Fla.: 1 in 100,000 chance each year. Old estimate: N/A. Change in risk: N/A. 100,000 0.00001 2000 0.05%
67. Calvert Cliffs 1, Lusby, Md.: 1 in 100,000 chance each year. Old estimate: 1 in 142,857. Change in risk: 43 percent. 100,000 0.00001 2000 0.05%
70. Vermont Yankee, Vernon, Vt.: 1 in 123,457 chance each year. Old estimate: 1 in 434,783. Change in risk: 252 percent. 123,457 8.09999E-06 2469.14 0.04%
71. Braidwood 1, Braceville, Ill.: 1 in 136,986 chance each year. Old estimate: 1 in 1,785,714. Change in risk: 1204 percent. 136,986 7.30002E-06 2739.72 0.04%
71. Braidwood 2, Braceville, Ill.: 1 in 136,986 chance each year. Old estimate: 1 in 1,785,714. Change in risk: 1204 percent. 136,986 7.30002E-06 2739.72 0.04%
73. Vogtle 1, Waynesboro, Ga.: 1 in 140,845 chance each year. Old estimate: 1 in 384,615. Change in risk: 173 percent. 140,845 7.1E-06 2816.9 0.04%
73. Vogtle 2, Waynesboro, Ga.: 1 in 140,845 chance each year. Old estimate: 1 in 384,615. Change in risk: 173 percent. 140,845 7.1E-06 2816.9 0.04%
75. Cooper, Brownville, Neb.: 1 in 142,857 chance each year. Old estimate: N/A. Change in risk: N/A. 142,857 7.00001E-06 2857.14 0.04%
76. Davis-Besse, Oak Harbor, Ohio: 1 in 149,254 chance each year. Old estimate: 1 in 625,000. Change in risk: 319 percent. 149,254 6.69999E-06 2985.08 0.03%
77. Palisades, Covert, Mich.: 1 in 156,250 chance each year. Old estimate: N/A. Change in risk: N/A. 156,250 0.0000064 3125 0.03%
78. South Texas 1, Bay City, Texas: 1 in 158,730 chance each year. Old estimate: 1 in 1,298,701. Change in risk: 718 percent. 158,730 6.30001E-06 3174.6 0.03%
78. South Texas 2, Bay City, Texas: 1 in 158,730 chance each year. Old estimate: 1 in 1,298,701. Change in risk: 718 percent. 158,730 6.30001E-06 3174.6 0.03%
80. FitzPatrick, Scriba, N.Y.: 1 in 163,934 chance each year. Old estimate: 1 in 833,333. Change in risk: 408 percent. 163,934 6.10002E-06 3278.68 0.03%
81. Byron 1, Byron, Ill.: 1 in 172,414 chance each year. Old estimate: 1 in 1,470,588. Change in risk: 753 percent. 172,414 5.79999E-06 3448.28 0.03%
81. Byron 2, Byron, Ill.: 1 in 172,414 chance each year. Old estimate: 1 in 1,470,588. Change in risk: 753 percent. 172,414 5.79999E-06 3448.28 0.03%
83. Surry 1, Surry, Va.: 1 in 175,439 chance each year. Old estimate: 1 in 123,457. Change in risk: -30 percent. 175,439 5.69999E-06 3508.78 0.03%
83. Surry 2, Surry, Va.: 1 in 175,439 chance each year. Old estimate: 1 in 123,457. Change in risk: -30 percent. 175,439 5.69999E-06 3508.78 0.03%
85. Nine Mile Point 2, Scriba, N.Y.: 1 in 178,571 chance each year. Old estimate: 1 in 1,000,000. Change in risk: 460 percent. 178,571 5.60001E-06 3571.42 0.03%
86. Browns Ferry 2, Athens, Ala.: 1 in 185,185 chance each year. Old estimate: 1 in 625,000. Change in risk: 238 percent. 185,185 5.40001E-06 3703.7 0.03%
86. Browns Ferry 3, Athens, Ala.: 1 in 185,185 chance each year. Old estimate: 1 in 625,000. Change in risk: 238 percent. 185,185 5.40001E-06 3703.7 0.03%
88. Nine Mile Point 1, Scriba, N.Y.: 1 in 238,095 chance each year. Old estimate: 1 in 1,724,138. Change in risk: 624 percent. 238,095 4.2E-06 4761.9 0.02%
88. Fermi 2, Monroe, Mich.: 1 in 238,095 chance each year. Old estimate: 1 in 625,000. Change in risk: 163 percent. 238,095 4.2E-06 4761.9 0.02%
90. Arkansas Nuclear 1, London, Ark.: 1 in 243,902 chance each year. Old estimate: 1 in 1,063,830. Change in risk: 336 percent. 243,902 4.10001E-06 4878.04 0.02%
90. Arkansas Nuclear 2, London, Ark.: 1 in 243,902 chance each year. Old estimate: 1 in 1,063,830. Change in risk: 336 percent. 243,902 4.10001E-06 4878.04 0.02%
92. Comanche Peak 1, Glen Rose, Texas: 1 in 250,000 chance each year. Old estimate: 1 in 833,333. Change in risk: 233 percent. 250,000 0.000004 5000 0.02%
92. Comanche Peak 2, Glen Rose, Texas: 1 in 250,000 chance each year. Old estimate: 1 in 833,333. Change in risk: 233 percent. 250,000 0.000004 5000 0.02%
94. Browns Ferry 1, Athens, Ala.: 1 in 270,270 chance each year. Old estimate: 1 in 1,000,000. Change in risk: 270 percent. 270,270 3.7E-06 5405.4 0.02%
95. Prairie Island 1, Welch, Minn.: 1 in 333,333 chance each year. Old estimate: 1 in 714,286. Change in risk: 114 percent. 333,333 3E-06 6666.66 0.02%
95. Prairie Island 2, Welch, Minn.: 1 in 333,333 chance each year. Old estimate: 1 in 714,286. Change in risk: 114 percent. 333,333 3E-06 6666.66 0.02%
97. La Salle 1, Marseilles, Ill.: 1 in 357,143 chance each year. Old estimate: 1 in 1,851,852. Change in risk: 419 percent. 357,143 2.8E-06 7142.86 0.01%
97. La Salle 2, Marseilles, Ill.: 1 in 357,143 chance each year. Old estimate: 1 in 1,851,852. Change in risk: 419 percent. 357,143 2.8E-06 7142.86 0.01%
97. Hope Creek 1, Hancocks Bridge, N.J.: 1 in 357,143 chance each year. Old estimate: 1 in 909,091. Change in risk: 155 percent. 357,143 2.8E-06 7142.86 0.01%
100. Clinton, Clinton, Ill.: 1 in 400,000 chance each year. Old estimate: 1 in 370,370. Change in risk: -7 percent. 400,000 0.0000025 8000 0.01%
101. Shearon Harris 1, New Hill, N.C.: 1 in 434,783 chance each year. Old estimate: 1 in 277,778. Change in risk: -36 percent. 434,783 2.3E-06 8695.66 0.01%
102. Hatch 1, Baxley, Ga.: 1 in 454,545 chance each year. Old estimate: 1 in 1,351,351. Change in risk: 197 percent. 454,545 2.2E-06 9090.9 0.01%
102. Hatch 2, Baxley, Ga.: 1 in 454,545 chance each year. Old estimate: 1 in 1,351,351. Change in risk: 197 percent. 454,545 2.2E-06 9090.9 0.01%
104. Callaway, Fulton, Mo.: 1 in 500,000 chance each year. Old estimate: N/A. Change in risk: N/A. 500,000 0.000002 10000 0.01%
www.msnbc.msn.com/id/42103936/ns/world_n…/ 0.21% 10.42%

Spreadsheet risk Data: from NRC Geological nuclear risk.XLS

http://www.msnbc.msn.com/id/42103936/ns/world_news-asia-pacific/

http://www.aolnews.com/2011/03/18/would-fund-protect-us-taxpayers-from-nuke-disaster-here/

Date: May 3rd, 2011
Cate: Finance, Risk & Stability, Systems
3 msgs

Judging aggression levels of accountants, CEO’s & CFO’s

Howard Schilit’s book, Accounting Shenanigans is a classic in terms of clear simple explanation of games CFO’s play.

In buying into a company as a shareholder, you are purchasing a piece of a value creation process.  The sustainability, duration and effectiveness of that process are the businesses realities.  Those realities are converted into Cash Flow, Balance Sheet and Income statements.  The assumptions used are management’s numeric story of the value creation process.  It is important to consider how the prism of accounting policy ranging from conservative to agressive is being used to present that value creating or destroying systems reality.

For fun I built a widget to naively weight the accounting policies of a company.  See screenshot above.  The excel file shown above and checklist is available just send me an e-mail.

I am put together a few simple checklists based on Schilit’s classic book Accounting Shenanigans.  Accounting won’t tell one why value shows up, but it will tell one what form it appears to be taking and a bit about how your management tells its tale.

Here are some of the tools you should use according to Schilit.

Documents used to find shenanigans & what to look for:

Auditors report

  • Absence of opinion
  • Qualified report
  • Reputation of the auditor

Proxy statement

  • Litigation
  • Executive compensation
  • Related party transactions

Footnotes from 10-k and 10-Q’s

  • Accounting policies / changes
  • Related party transactions
  • Contingencies or commitments

President’s letter and transcripts

  • Forthrightness and history of honesty

MD&A (part of the 10-K)

  • Specific concise disclosures
  • Consistent with Footnote disclosure

Form 8-K

  • Disagreements over accounting policies

Registration statement (for IPO’s)

  • Past performance
  • Quality of management and directors