Category:Decision theory

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Decision theory is the study of optimal actions, as determined by considering the probability and utility of different outcomes.

Pages in category "Decision theory"

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List of cognitive biases

From Dikipedia, the freer encyclopedia

A cognitive bias is a pattern of deviation in judgment that occurs in particular situations (see also cognitive distortion and the lists of thinking-related topics). Implicit in the concept of a "pattern of deviation" is a standard of comparison; this may be the judgment of people outside those particular situations, or may be a set of independently verifiable facts. The existence of some of these cognitive biases has been verified empirically in the field of psychology.

Cognitive biases are instances of evolved mental behavior. Some are presumably adaptive, for example, because they lead to more effective actions or enable faster decisions. Others presumably result from a lack of appropriate mental mechanisms, or from the misapplication of a mechanism that is adaptive under different circumstances.

Decision-making and behavioral biases

It has been suggested that Buyer decision processes#Cognitive and personal biases in decision making be merged into this article or section. (Discuss)

Many of these biases are studied for how they affect belief formation, business decisions, and scientific research.

• Bandwagon effect — the tendency to do (or believe) things because many other people do (or believe) the same. Related to groupthink and herd behaviour.
• Base rate fallacy — ignoring available statistical data in favor of particulars.
• Bias blind spot — the tendency not to compensate for one's own cognitive biases.^[1]
• Choice-supportive bias — the tendency to remember one's choices as better than they actually were.
• Confirmation bias — the tendency to search for or interpret information in a way that confirms one's preconceptions.
• Congruence bias — the tendency to test hypotheses exclusively through direct testing, in contrast to tests of possible alternative hypotheses.
• Contrast effect — the enhancement or diminishing of a weight or other measurement when compared with a recently observed contrasting object.
• Déformation professionnelle — the tendency to look at things according to the conventions of one's own profession, forgetting any broader point of view.
• Denomination effect — the tendency to spend more money when it is denominated in small amounts (e.g. coins) than large amounts (e.g. bills).^[2]
• Distinction bias — the tendency to view two options as more dissimilar when evaluating them simultaneously than when evaluating them separately.^[3]
• Endowment effect — "the fact that people often demand much more to give up an object than they would be willing to pay to acquire it".^[4]
• Experimenter's or Expectation bias — the tendency for experimenters to believe, certify, and publish data that agree with their expectations for the outcome of an experiment, and to disbelieve, discard, or downgrade the corresponding weightings for data that appear to conflict with those expectations.^[5]
• Extraordinarity bias — the tendency to value an object more than others in the same category as a result of an extraordinarity of that object that does not, in itself, change the value.^{[citation needed]}
• Focusing effect — prediction bias occurring when people place too much importance on one aspect of an event; causes error in accurately predicting the utility of a future outcome.
• Framing — Using an approach or description of the situation or issue that is too narrow. Also framing effect — drawing different conclusions based on how data is presented.
• Hyperbolic discounting — the tendency for people to have a stronger preference for more immediate payoffs relative to later payoffs, where the tendency increases the closer to the present both payoffs are.
• Illusion of control — the tendency for human beings to believe they can control or at least influence outcomes that they clearly cannot.
• Impact bias — the tendency for people to overestimate the length or the intensity of the impact of future feeling states.
• Information bias — the tendency to seek information even when it cannot affect action.
• Interloper effect — the tendency to value third party consultation as objective, confirming, and without motive. Also consultation paradox, the conclusion that solutions proposed by existing personnel within an organization are less likely to receive support than from those recruited for that purpose.
• Irrational escalation — the tendency to make irrational decisions based upon rational decisions in the past or to justify actions already taken.
• Just-world phenomenon - witnesses of an "inexplicable injustice . . . will rationalize it by searching for things that the victim might have done to deserve it"
• Loss aversion — "the disutility of giving up an object is greater than the utility associated with acquiring it".^[6] (see also sunk cost effects and Endowment effect).
• Mere exposure effect — the tendency for people to express undue liking for things merely because they are familiar with them.
• Money illusion — the tendency of people to concentrate on the nominal (face value) of money rather than its value in terms of purchasing power.
• Moral credential effect — the tendency of a track record of non-prejudice to increase subsequent prejudice.
• Need for Closure — the need to reach a verdict in important matters; to have an answer and to escape the feeling of doubt and uncertainty. The personal context (time or social pressure) might increase this bias.^[7]
• Negativity bias — phenomenon by which humans pay more attention to and give more weight to negative than positive experiences or other kinds of information.
• Neglect of probability — the tendency to completely disregard probability when making a decision under uncertainty.
• Normalcy bias — the refusal to plan for, or react to, a disaster which has never happened before.
• Not Invented Here — the tendency to ignore that a product or solution already exists, because its source is seen as an "enemy" or as "inferior".
• Omission bias — the tendency to judge harmful actions as worse, or less moral, than equally harmful omissions (inactions).
• Outcome bias — the tendency to judge a decision by its eventual outcome instead of based on the quality of the decision at the time it was made.
• Planning fallacy — the tendency to underestimate task-completion times.
• Post-purchase rationalization — the tendency to persuade oneself through rational argument that a purchase was a good value.
• Pseudocertainty effect — the tendency to make risk-averse choices if the expected outcome is positive, but make risk-seeking choices to avoid negative outcomes.
• Reactance — the urge to do the opposite of what someone wants you to do out of a need to resist a perceived attempt to constrain your freedom of choice.
• Restraint bias - the tendency to overestimate one's ability to show restraint in the face of temptation.
• Selective perception — the tendency for expectations to affect perception.
• Semmelweis reflex — the tendency to reject new evidence that contradicts an established paradigm.^[8]
• Status quo bias — the tendency for people to like things to stay relatively the same (see also loss aversion, endowment effect, and system justification).^[9]
• Von Restorff effect — the tendency for an item that "stands out like a sore thumb" to be more likely to be remembered than other items.
• Wishful thinking — the formation of beliefs and the making of decisions according to what is pleasing to imagine instead of by appeal to evidence or rationality.
• Zero-risk bias — preference for reducing a small risk to zero over a greater reduction in a larger risk.

Biases in probability and belief

Many of these biases are often studied for how they affect business and economic decisions and how they affect experimental research.

• Ambiguity effect — the avoidance of options for which missing information makes the probability seem "unknown".
• Anchoring effect — the tendency to rely too heavily, or "anchor," on a past reference or on one trait or piece of information when making decisions (also called "insufficient adjustment").
• Attentional bias — neglect of relevant data when making judgments of a correlation or association.
• Authority bias — the tendency to value an ambiguous stimulus (e.g., an art performance) according to the opinion of someone who is seen as an authority on the topic.
• Availability heuristic — estimating what is more likely by what is more available in memory, which is biased toward vivid, unusual, or emotionally charged examples.
• Availability cascade — a self-reinforcing process in which a collective belief gains more and more plausibility through its increasing repetition in public discourse (or "repeat something long enough and it will become true").
• Belief bias — an effect where someone's evaluation of the logical strength of an argument is biased by the believability of the conclusion.
• Clustering illusion — the tendency to see patterns where actually none exist.
• Capability bias — The tendency to believe that the closer average performance is to a target, the tighter the distribution of the data set.
• Conjunction fallacy — the tendency to assume that specific conditions are more probable than general ones.
• Disposition effect — the tendency to sell assets that have increased in value but hold assets that have decreased in value.
• Gambler's fallacy — the tendency to think that future probabilities are altered by past events, when in reality they are unchanged. Results from an erroneous conceptualization of the Law of large numbers. For example, "I've flipped heads with this coin five times consecutively, so the chance of tails coming out on the sixth flip is much greater than heads."
• Hawthorne effect — the tendency of people to perform or perceive differently when they know that they are being observed.
• Hindsight bias — sometimes called the "I-knew-it-all-along" effect, the inclination to see past events as being predictable.
• Illusory correlation — beliefs that inaccurately suppose a relationship between a certain type of action and an effect.^[10]
• Ludic fallacy — the analysis of chance-related problems according to the belief that the unstructured randomness found in life resembles the structured randomness found in games, ignoring the non-gaussian distribution of many real-world results.
• Neglect of prior base rates effect — the tendency to neglect known odds when reevaluating odds in light of weak evidence.
• Observer-expectancy effect — when a researcher expects a given result and therefore unconsciously manipulates an experiment or misinterprets data in order to find it (see also subject-expectancy effect).
• Optimism bias — the systematic tendency to be over-optimistic about the outcome of planned actions.
• Ostrich effect — ignoring an obvious (negative) situation.
• Overconfidence effect — excessive confidence in one's own answers to questions. For example, for certain types of question, answers that people rate as "99% certain" turn out to be wrong 40% of the time.
• Positive outcome bias — a tendency in prediction to overestimate the probability of good things happening to them (see also wishful thinking, optimism bias, and valence effect).
• Pareidolia — a vague and random stimulus (often an image or sound) is perceived as significant, e.g., seeing images of animals or faces in clouds, the man in the moon, and hearing hidden messages on records played in reverse.
• Primacy effect — the tendency to weigh initial events more than subsequent events.
• Recency effect — the tendency to weigh recent events more than earlier events (see also peak-end rule).
• Disregard of regression toward the mean — the tendency to expect extreme performance to continue.
• Selection bias — a distortion of evidence or data that arises from the way that the data are collected.
• Stereotyping — expecting a member of a group to have certain characteristics without having actual information about that individual.
• Subadditivity effect — the tendency to judge probability of the whole to be less than the probabilities of the parts.
• Subjective validation — perception that something is true if a subject's belief demands it to be true. Also assigns perceived connections between coincidences.
• Telescoping effect — the effect that recent events appear to have occurred more remotely and remote events appear to have occurred more recently.
• Texas sharpshooter fallacy — the fallacy of selecting or adjusting a hypothesis after the data is collected, making it impossible to test the hypothesis fairly. Refers to the concept of firing shots at a barn door, drawing a circle around the best group, and declaring that to be the target.
• Well travelled road effect - underestimation of the duration taken to traverse oft-travelled routes and over-estimate the duration taken to traverse less familiar routes.

Social biases

Most of these biases are labeled as attributional biases.

• Actor-observer bias — the tendency for explanations of other individuals' behaviors to overemphasize the influence of their personality and underemphasize the influence of their situation (see also fundamental attribution error). However, this is coupled with the opposite tendency for the self in that explanations for our own behaviors overemphasize the influence of our situation and underemphasize the influence of our own personality.
• Egocentric bias — occurs when people claim more responsibility for themselves for the results of a joint action than an outside observer would.
• Forer effect (aka Barnum Effect) — the tendency to give high accuracy ratings to descriptions of their personality that supposedly are tailored specifically for them, but are in fact vague and general enough to apply to a wide range of people. For example, horoscopes.
• False consensus effect — the tendency for people to overestimate the degree to which others agree with them.
• Fundamental attribution error — the tendency for people to over-emphasize personality-based explanations for behaviors observed in others while under-emphasizing the role and power of situational influences on the same behavior (see also actor-observer bias, group attribution error, positivity effect, and negativity effect).
• Halo effect — the tendency for a person's positive or negative traits to "spill over" from one area of their personality to another in others' perceptions of them (see also physical attractiveness stereotype).
• Herd instinct — Common tendency to adopt the opinions and follow the behaviors of the majority to feel safer and to avoid conflict.
• Illusion of asymmetric insight — people perceive their knowledge of their peers to surpass their peers' knowledge of them.
• Illusion of transparency — people overestimate others' ability to know them, and they also overestimate their ability to know others.
• Illusory superiority — overestimating one's desirable qualities, and underestimating undesirable qualities, relative to other people. Also known as Superiority bias (also known as "Lake Wobegon effect", "better-than-average effect", "superiority bias", or Dunning-Kruger effect).
• Ingroup bias — the tendency for people to give preferential treatment to others they perceive to be members of their own groups.
• Just-world phenomenon — the tendency for people to believe that the world is just and therefore people "get what they deserve."
• Notational bias — a form of cultural bias in which the notational conventions of recording data biases the appearance of that data toward (or away from) the system upon which the notational schema is based.
• Outgroup homogeneity bias — individuals see members of their own group as being relatively more varied than members of other groups.
• Projection bias — the tendency to unconsciously assume that others share the same or similar thoughts, beliefs, values, or positions.
• Self-serving bias (also called "behavioral confirmation effect") — the tendency to claim more responsibility for successes than failures. It may also manifest itself as a tendency for people to evaluate ambiguous information in a way beneficial to their interests (see also group-serving bias).
• Self-fulfilling prophecy — the tendency to engage in behaviors that elicit results which will (consciously or not) confirm existing attitudes.^[11]
• System justification — the tendency to defend and bolster the status quo. Existing social, economic, and political arrangements tend to be preferred, and alternatives disparaged sometimes even at the expense of individual and collective self-interest. (See also status quo bias.)
• Trait ascription bias — the tendency for people to view themselves as relatively variable in terms of personality, behavior and mood while viewing others as much more predictable.
• Ultimate attribution error — Similar to the fundamental attribution error, in this error a person is likely to make an internal attribution to an entire group instead of the individuals within the group.

Memory errors

• Consistency bias — incorrectly remembering one's past attitudes and behaviour as resembling present attitudes and behaviour.
• Cryptomnesia — a form of misattribution where a memory is mistaken for imagination.
• Egocentric bias — recalling the past in a self-serving manner, e.g. remembering one's exam grades as being better than they were, or remembering a caught fish as being bigger than it was
• False memory — confusion of imagination with memory, or the confusion of true memories with false memories.
• Hindsight bias — filtering memory of past events through present knowledge, so that those events look more predictable than they actually were; also known as the 'I-knew-it-all-along effect'.
• Reminiscence bump — the effect that people tend to recall more personal events from adolescence and early adulthood than from other lifetime periods.
• Rosy retrospection — the tendency to rate past events more positively than they had actually rated them when the event occurred.
• Self-serving bias — perceiving oneself responsible for desirable outcomes but not responsible for undesirable ones.
• Suggestibility — a form of misattribution where ideas suggested by a questioner are mistaken for memory.

Common theoretical causes of some cognitive biases

• Attribute substitution – making a complex, difficult judgement by unconsciously substituting an easier judgement^[12]
• Attribution theory, especially:
  • Salience
• Cognitive dissonance, and related:
  • Impression management
  • Self-perception theory
• Heuristics, including:
  • Availability heuristic – estimating what is more likely by what is more available in memory, which is biased toward vivid, unusual, or emotionally charged examples^[10]
  • Representativeness heuristic – judging probabilities on the basis of resemblance^[10]
  • Affect heuristic – basing a decision on an emotional reaction rather than a calculation of risks and benefits ^[13]
• Adaptive bias
• Misinterpretations or misuse of statistics.

See also

Psychology portal

Thinking portal

• Attribution theory
• Black swan theory
• Groupthink
• List of common misconceptions
• List of fallacies
• List of memory biases
• List of topics related to public relations and propaganda
• Logical fallacy
• Media bias
• Psychological immune system
• Self-deception
• System justification
• Systematic bias

Notes

References

• Baron, Johnathan (2000), Thinking and deciding (3rd ed.), New York: Cambridge University Press, ISBN 0-521-65030-5 
• Bishop, Michael A.; J. D. Trout (2004), Epistemology and the Psychology of Human Judgment, New York: Oxford University Press, ISBN 0-19-516229-3 
• Gilovich, Thomas (1993), How We Know What Isn't So: The Fallibility of Human Reason in Everyday Life, New York: The Free Press, ISBN 0-02-911706-2 
• Gilovich, Thomas; Dale Griffin, Daniel Kahneman (2002), Heuristics and biases: The psychology of intuitive judgment, Cambridge, UK: Cambridge University Press, ISBN 0-521-79679-2 
• Greenwald, A. (1980), "The Totalitarian Ego: Fabrication and Revision of Personal History", American Psychologist (American Psychological Association) 35 (7), ISSN 0003-066X 
• Kahneman, Daniel; Paul Slovic, Amos Tversky (1982), Judgment under Uncertainty: Heuristics and Biases, Cambridge, UK: Cambridge University Press, ISBN 0-521-28414-7 
• Kahneman, Daniel; Jack L. Knetsch, Richard H. Thaler (1991), "Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias", The Journal of Economic Perspectives (American Economic Association) 5 (1): 193–206 
• Plous, Scott (1993), The Psychology of Judgment and Decision Making, New York: McGraw-Hill, ISBN 0-07-050477-6 
• Schacter, Daniel L. (1999), "The Seven Sins of Memory: Insights From Psychology and Cognitive Neuroscience", American Psychologist (American Psychological Association) 54 (3): 182–203, ISSN 0003-066X 
• Tetlock, Philip E. (2005), Expert Political Judgment: how good is it? how can we know?, Princeton: Princeton University Press, ISBN 978-0-691-12302-8 
• Virine, L.; M. Trumper (2007), Project Decisions: The Art and Science, Vienna, VA: Management Concepts, ISBN 978-1567262179

Taleb distribution

From Dikipedia, the freer encyclopedia

In economics and finance, a Taleb distribution is a probability distribution in which there is a high probability of a small gain, and a small probability of a very large loss, which more than outweighs the gains. In these situations the expected value is (very much) less than zero, but this fact is camouflaged by the appearance of low risk and steady returns. It is a combination of kurtosis risk and skewness risk: overall returns are dominated by extreme events (kurtosis), which are to the downside (skew). The corresponding situation is also known as the peso problem.

The term is therefore increasingly used in the financial markets to describe dangerous or flawed trading strategies. The Taleb distribution is named for Nassim Taleb, based on ideas outlined in his Fooled by Randomness.^[1]

Criticism of trading strategies

Pursuing a trading strategy with a Taleb distribution yields a high probability of steady returns for a time, but with a near certainty of eventual ruin. This is done consciously by some as a risky trading strategy, while some critics argue that it is done either unconsciously by some, unaware of the hazards ("innocent fraud"), or consciously by others, particularly in hedge funds.

Risky strategy

If done consciously, with one's own capital or openly disclosed to investors, this is a risky strategy, but appeals to some: one will want to exit the trade before the rare event happens. This occurs for instance in a speculative bubble, where one purchases an asset in the expectation that it will likely go up, but may plummet, and hopes to sell the asset before the bubble bursts.

This has also been referred to as "picking up pennies in front of a steamroller".^[2]

"Innocent fraud"

John Kay has likened securities trading to bad driving, as both are characterized by Taleb distributions.^[3] Drivers can make many small gains in time by taking risks such as overtaking on the inside and tailgating, however, they are then at risk of experiencing a very large loss in the form of a serious traffic accident. Kay has described Taleb Distributions as the basis of the carry trade and has claimed that along with mark-to-market accounting and other practices, constitute part of what JK Galbraith has called "innocent fraud".^[4]

Moral hazard

Some critics of the hedge fund industry claim that the compensation structure generate high fees for investment strategies that follow a Taleb distribution, creating moral hazard.^[5] In such a scenario, the fund can claim high asset management and performance fees until they suddenly 'blow up', losing the investor significant sums of money and wiping out all the gains to the investor generated in previous periods; however, the fund manager keeps all fees earned prior to the losses being incurred – and ends up enriching himself in the long run because he does not pay for his losses.

Risks

Taleb distributions pose several fundamental problems, all possibly leading to risk being overlooked:

presence of extreme adverse events

The very presence or possibility of adverse events may pose a problem per se, which is ignored by only looking at the average case – a decision may be good in expectation (in the aggregate, in the long term), but a single rare event may ruin the investor: one is courting disaster.

unobserved events

This is Taleb's central contention, which he calls black swans – because extreme events are rare, they have often not been observed yet, and thus are not included in scenario analysis or stress testing.

hard-to-compute expectation

A subtler issue is that expectation is very sensitive to assumptions about probability: a trade with a $1 gain 99.9% of the time and a $500 loss 0.1% of the time has positive expected value; while if the $500 loss occurs 0.2% of the time it has approximately 0 expected value; and if the $500 loss occurs 0.3% of the time it has negative expected value. This is exacerbated by the difficulty of estimating the probability of rare events (in this example one would need to observe thousands of trials to estimate the probability with confidence), and by the use of financial leverage: mistaking a small loss for a small gain and magnifying by leverage yields a hidden large loss.

More formally, while the risks for a known distribution can be calculated, in practice one does not know the distribution: one is operating under uncertainty, in economics called Knightian uncertainty.

Mitigants

A number of mitigants have been proposed, by Taleb and others.  These include:

not exposing oneself to large losses

For instance, only buying options (so one can at most lose the premium), not selling them.

performing sensitivity analysis on assumptions

This does not eliminate the risk, but identifies which assumptions are key to conclusions, and thus meriting close scrutiny.

scenario analysis and stress testing

Widely used in industry, they do not include unforeseen events but emphasize various possibilities and what one stands to lose, so one is not blinded by absence of losses thus far.

using non-probabilistic decision techniques

While most classical decision theory is based on probabilistic techniques of expected value or expected utility, alternatives exist which do not require assumptions about the probabilities of various outcomes, and are thus robust. These include minimax, minimax regret, and info-gap decision theory.

See also

• Kurtosis risk
• Skewness risk

References

1. ^ Martin Wolf, Why today’s hedge fund industry may not survive, Financial Times, 18 March 2008
2. ^ Taleb, p. 19
3. ^ John Kay "A strategy for hedge funds and dangerous drivers", Financial Times, 16 January 2003.
4. ^ John Kay "Banks got burned by their own ‘innocent fraud’", Financial Times, 15 October 2008.
5. ^ Are hedge funds a scam? Naked Capitalism/Financial Times, March 2008.

Privatizing profits and socializing losses

From Dikipedia, the freer encyclopedia

In political discourse, the term privatizing profits and socializing losses refers to the alleged tendency of some firms to benefit (privately) from profits, but not suffer from losses, instead pushing the losses onto society at large, particularly via the government.

History

The notion that banks privatize profits and socialize losses dates at least to the 19th century, as in this 1834 quote of Andrew Jackson:

I have had men watching you for a long time and I am convinced that you have used the funds of the bank to speculate in the breadstuffs of the country. When you won, you divided the profits amongst you, and when you lost, you charged it to the Bank. ... You are a den of vipers and thieves.

—Andrew Jackson, 1834, on closing the Second Bank of the United States;
(unabridged form, extended citation)

Examples

Large firms and banks have been accused of this, as a form of crony capitalism and corporate welfare, and some bailouts are cited as examples of this: a bailout socializes a company's losses.

It has been argued that in the current economic system, especially in the U.S., large corporations and wealthy parts of society policies can commonize costs and privatize profits, with the effect of a further concentration of wealth. In particular, government sponsoring and bailouts such as the federal takeover of Fannie Mae and Freddie Mac and the proposed bailout of the U.S. financial system in the economic crisis of 2008 have frequently been referred to in the U.S. as “private gains and public losses”^[1] or “privatization of profits and socialization of losses”.^[2][3][4] Economic policies which favor such concentration of capital have frequently been criticized as socialism for the rich and capitalism for the poor.^[5]

Interpretations

In game theory, this is formalized as the CC–PP game.

In the financial language of options, "socializing losses" corresponds to private firms having a put option from the government: if they lose, the government will cover their losses. The most famous example of this is the Greenspan put.

In the black swan theory of Nassim Nicholas Taleb, he criticizes this as one of his Ten Principles for a Black Swan Robust World,^[6] writing as his second principle:

2. No socialisation of losses and privatisation of gains.

Support

While the term is generally used to critique, some have argued that socializing losses, while politically unpopular, is in fact economically desirable in the case of a financial crisis:

What is ineluctably needed involves socializing the losses of a banking system – both conventional banking and shadow banking – after the spectacular winnings of the Forward Minsky Journey were privatized.

—Paul McCulley^[7]

See also

• Bailout
• Corporate welfare
• Crony capitalism
• Externality
• Greenspan put
• Lender of last resort
• Moral hazard in finance
• Private good
• Put option
• Too Big to Fail policy

Related concepts

• CC–PP game
• Lemon socialism
• Socialism for the rich and capitalism for the poor

References

External links

• Who benefits, who pays?, an extract from Filters Against Folly

Too Big to Fail

From Dikipedia, the freer encyclopedia

The examples and perspective in this article deal primarily with the United States and do not represent a worldwide view of the subject. Please improve this article and discuss the issue on the talk page.

"Too Big to Fail" is a phrase referring to the idea that in economic regulation, the largest and most interconnected businesses are so large that a government cannot allow them to declare bankruptcy because said failure would have a disastrous effect on the overall economy.

This results in reckless behavior of the institution since the government will intervene (e.g. by bailing out the company) in the event the institution is going to fail.^[1] The phrase has also been more broadly applied to refer to a government's policy to bail out any corporation. It raises the issue of moral hazard in business operations.

The term is back to centre stage since the start of the financial meltdown. One of the biggest US companies referred to as too big to fail is American International Group (AIG).

Some critics see the policy as wrong and counterproductive. They think big banks should be left to fail if their risk management was not effective.^[2][3] For example, in the international context, the "too big to fail" policy has been explicitly refuted in the People's Republic of China.^[4]

Regulatory basis

Before 1950, U.S. federal bank regulators had essentially two options for resolving an insolvent institution: closure, with liquidation of assets and payouts for insured depositors, or purchase and assumption, encouraging the acquisition of assets and assumption of liabilities by another firm. A third option was made available by the Federal Deposit Insurance Act of 1950: providing assistance, the power to support an institution through loans or direct federal acquisition of assets, until it could recover from its distress. The statute limited the "assistance" option to cases where "continued operation of the bank is essential to provide adequate banking service." Regulators shunned this third option for many years, fearing that if regionally- or nationally-important banks were thought to be generally immune to liquidation, markets in their shares would be distorted. Thus the assistance option was never employed during the period 1950-1969, and very seldom thereafter.^[5]

Continental Illinois case

Distress

The Continental Illinois National Bank and Trust Company experienced a fall in its overall asset quality during the early 1980s. Tight money, Mexico's default and plunging oil prices followed a period when the bank had aggressively pursued commercial lending business, Latin American syndicated loan business, and loan participations in the energy sector. Complicating matters further, the bank's funding mix was heavily dependent on large CDs and foreign money markets, which meant its depositors were more risk-averse than average retail depositors in the US.

Payments crisis

The bank held significant participation in highly-speculative oil and gas loans of Oklahoma's Penn Square Bank. When Penn Square failed in July 1982, the Continental's distress became acute, culminating with press rumors of failure and an investor-and-depositor run in early May 1984. In the first week of the run, the Fed permitted the Continental Illinois discount window credits on the order of $3.6 billion. Still in significant distress, the management obtained a further $4.5 billion in credits from a syndicate of money center banks the following week. These measures failed to stop the run, and regulators were confronted with a crisis.

Regulatory crisis

The seventh-largest bank in the nation by deposits would very shortly be unable to meet its obligations. Regulators faced a tough decision about how to resolve the matter. Of the three options available, only two were seriously considered. Even banks much smaller than the Continental were deemed unsuitable for resolution by liquidation, owing to the disruptions this would have inevitably caused. The normal course would be to seek a purchaser (and indeed press accounts that such a search was underway contributed to Continental depositors' fears in 1984). However, in the tight-money financial climate of the early 1980s, no purchaser was forthcoming.

Besides generic concerns of size, contagion of depositor panic and bank distress, regulators feared the significant disruption of national payment and settlement systems. Of special concern was the wide network of correspondent banks with high percentages of their capital invested in the Continental Illinois. Essentially, the bank was deemed "too big to fail," and the "provide assistance" option was reluctantly taken. The dilemma now became, how to provide assistance without significantly unbalancing the nation's banking system?

Stopping the run

To prevent immediate failure, the Federal Reserve announced categorically that it would meet any liquidity needs the Continental might have, while FDIC gave depositors and general creditors a full guarantee (not subject to the $100,000 FDIC deposit-insurance limit) and provided direct assistance of $2 billion (including participations). Money center banks assembled an additional $5.3 billion unsecured facility pending a resolution and resumption of more-normal business. These measures slowed, but did not stop, the outflow of deposits.

Controversy

In a United States Senate hearing afterwards, the then Comptroller of the Currency C. T. Conover defended his position by admitting the regulators will not let the largest 11 banks fail^[6]. Regulatory agencies (FDIC, Office of the Comptroller of the Currency, the Fed, etc.) feared this may cause widespread financial complications and a major bank run that may easily spread by financial contagion. The implicit guarantee of too-big-to-fail has been criticized by many since then for its preferential treatment of large banks^{[citation needed]}. Simultaneously, the perception of too-big-to-fail may diminish healthy market discipline, and may have influenced the decisions behind insolvency of the Washington Mutual in 2008. For example, large depositors in banks not covered by the policy tend to have a strong incentive to monitor the bank's financial condition, and/or withdraw in case the bank's policies exposes them to high risks, since FDIC guarantees have an upper limit. However, large depositors in a "too big to fail" bank would have less incentive, since they'd expect to be bailed out in the event of failure.

The Federal Deposit Insurance Corporation Improvement Act was passed in 1991, giving the FDIC the responsibility to rescue an insolvent bank by the least costly method. The Act had the implicit goal of eliminating the widespread belief among depositors that a loss of depositors and bondholders will be prevented for large banks. However, the Act included an exception in cases of systemic risk, subject to the approval of two-thirds of the FDIC Board of Directors, the Federal Reserve Board of Governors, and the Treasury Secretary.^[7]

Effect on banks' cost of capital

Since the full amount of the deposits and debts of "too big to fail" banks are effectively guaranteed by the government, large depositors view deposits with these banks as a safer investment than deposits with smaller banks. Therefore, large banks are able to pay lower interest rates to depositors than small banks are obliged to pay. In October 2009, Sheila Bair, the current Chairperson of the FDIC, commented that "'Too big to fail' has become worse. It's become explicit when it was implicit before. It creates competitive disparities between large and small institutions, because everybody knows small institutions can fail. So it's more expensive for them to raise capital and secure funding.".^[8] A study conducted by the Center for Economic and Policy Research found that the difference between the cost of funds for banks with more than $100 billion in assets and the cost of funds for smaller banks widened dramatically after the formalization of the "too big to fail" policy in the U.S. in the fourth quarter of 2008.^[9] This shift in the large banks' cost of funds gave an indirect "too big to fail" subsidy of $34.1 billion per year to the 18 U.S. banks with more than $100 billion in assets.

"Too big to fail is too big"

Mervyn King, the governor of the Bank of England, called for banks that are "too big to fail" to be cut down to size, as a solution to the problem of banks having taxpayer-funded guarantees for their speculative investment banking activities. "If some banks are thought to be too big to fail, then, in the words of a distinguished American economist, they are too big. It is not sensible to allow large banks to combine high street retail banking with risky investment banking or funding strategies, and then provide an implicit state guarantee against failure."^[10]

However, Alastair Darling disagreed; "Many people talk about how to deal with the big banks – banks so important to the financial system that they cannot be allowed to fail. But the solution is not as simple, as some have suggested, as restricting the size of the banks".^[10]

Too big to fail tax

Willem Buiter proposes a tax to internalize the massive external costs inflicted by "too big to fail" institution. "When size creates externalities, do what you would do with any negative externality: tax it. The other way to limit size is to tax size. This can be done through capital requirements that are progressive in the size of the business (as measured by value added, the size of the balance sheet or some other metric). Such measures for preventing the New Darwinism of the survival of the fattest and the politically best connected should be distinguished from regulatory interventions based on the narrow leverage ratio aimed at regulating risk (regardless of size, except for a de minimis lower limit)."^[11]

See also

• Lender of last resort
• List of largest U.S. bank failures
• 2008-2009 bank failures in the United States
• List of acquired or bankrupt United States banks in the late 2000s financial crisis

Notes

Further reading

• "Carping about the TARP: Congress wrangles over how best to avoid financial Armageddon". The Economist. 2008-09-23. http://www.economist.com/finance/displayStory.cfm?story_id=12286340&source=features_box1. 
• Kaufman, George G. (1990). "Are Some Banks Too Large to Fail? Myth and Reality". Contemporary Economic Policy 8 (4): 1–14. doi:10.1111/j.1465-7287.1990.tb00298.x. 
• Mishkin, Frederic S. (2006). "How Big a Problem is Too Big to Fail? A Review of Gary Stern and Ron Feldman's Too Big to Fail: The Hazards of Bank Bailouts". Journal of Economic Literature 44 (4): 988–1004. doi:10.1257/002205106779436233. 
• O'Hara, Maureen; Shaw, Wayne (1990). "Deposit Insurance and Wealth Effects: The Value of Being ‘Too Big to Fail’". Journal of Finance 45 (5): 1587–1600. doi:10.2307/2328751. 
• Stern, Gary H.; Feldman, Ron J. (2004). Too Big to Fail: The Hazards of Bank Bailouts. Washington, DC: Brookings Institution Press. ISBN 0815781520. 
• Moseley, Fred (January/February 2009). "Time for Permanent Nationalization". Dollars & Sense. http://www.dollarsandsense.org/archives/2009/0309moseley.html. Retrieved 2009-02-25. 

External links

• http://www.usbudgetwatch.org/stimulus/
• http://www.fdic.gov/bank/historical/history/235_258.pdf
• http://www.rbnz.govt.nz/research/workshops/27apr2004/27apr04_kaufman1.pdf
• http://www.fdic.gov/deposit/deposits/international/bibliography/1999/toobig.pdf
• http://www.docstoc.com/docs/5606575/Some-Comments-on-Too-Big-to-Fail---The-Hazards-of-Bank-Bailouts