Excerpts from the book, by David Dreman.

Ch 1: Planet of the Bubbles

In every bubble, once the crowd begins to realize how wildly overpriced the stocks they rushed into are, there is a scramble to escape. A horrific panic ensues as the image changes from euphoria to doom.

In Extraordinary Popular Delusions and the Madness of Crowds, by Charles Mackay (1841):

We find that whole communities suddenly fix their minds upon one object, and go mad in its pursuit; that millions of people become simultaneously impressed with one delusion, and run after it…Sober nations have all at once become desperate gamblers, and risked almost their existence upon the turn of a piece of paper…Men it has been said, think in herds…go mad in herds, while they only recover their senses slowly, and one by one."

Caution and rationality are lost in the stampede to sell. In a market collapse, terrified investors are oblivious to fundamental value, just as they were when no price was too high to pay.

Ch 2: The Perils of Affect

Images and associations are pulled into the conscious mind from past, current, and hoped-for experiences. And the more intensive our positive or negative feelings are, whether about ideas, groups of people, socks, industries, or markets, the more intensely Affect influences our decisions on them.

In periods of great anxiety and uncertainty, it is quite natural for the experiential system, often dominated by Affect, to take over.

Ways in which Affect leads us to errors in judgment:

1. Insensitivity to Probability

When a potential outcome, such as a major gain from a stock purchase, carries sharp and strong affective meaning, the actual probability of that outcome, or changes in the probability due to changing circumstances, will tend to carry very little weight…gamblers are moved by the possibility, rather than the probability, of a strong positive consequence. The result is that very small probabilities carry great weight.

2. Judgments of Risk and Benefit Are Negatively Correlated

Repeatedly, subjects answered that for many potentially dangerous or hazardous situations the greater the perceived benefit or reward, the lower the perceived risk. Conversely, the lower the perceived reward or benefit, the greater the perceived risk. In a word, modern risk theory [which argues that risk and reward are positively correlated] is turned upside down.

[Moreover:]

…people base their judgments of the risk and benefits of an activity or a technology not only on what they think about it, but also on how they feel about it. If they have an idea or concept they strongly like, they are moved to judge that the risk is low. The more they dislike an idea or concept, the higher they judge a risk.

3. The Durability Bias

…we tend to overestimate how long a positive or negative event or earnings surprise will have an impact on a stock and industry or the entire market itself.

This finding […] would seem to be helpful in explaining both the superior performance of “worst” over “best” stocks and the consistent but opposite of reaction to surprise events by these two categories.

4. Tempora Construal

This Affect characteristic causes investors to extend their views of the prospects of stocks both in and out of favor far out into the future.

…investors expect the worst from out-of-favor contrarian stocks and discount their prospects far into the future. In the second case [of technological stocks in bubbles], investors generally extend expectations for concept companies' positive results as they expand their markets rapidly too far into the future.

Personal note: I experienced the consequences of Affect during the GME mania in 2021, as I was infuriated by my inability to afford a house for my family while real-estate prices were skyrocketing, which had a big (and very negative) influence on my financial decisions.

I also wonder if the oil sector has a negative Affect attached to it for a large subset of market participants that would be reluctant to invest in it, even if they were able to find rationally interesting returns. Same question for China…

Ch 3: Treacherous Shortcuts in Decision Making

The Perils of Availability

Tversky and Kahneman: “[we] assess the frequency of a class or the probability of an event by the ease with which instances or occurrences can be brought to mind.” This is why we think deaths by shark attack are more common than deaths from pieces of airplanes falling [whereas the latter is 30 times more probably than the former].

recent, salient events often strongly influence decision making in the stock market and bond markets that can cause or exacerbate sharp price movements.

A Picture Is Not Worth a Thousand Words

…it’s a natural human tendency to draw analogies and see identical situations - where non exist…causes us to give too much emphasis to the similarities between events…reduces the importance we give to variables that are actually critical in determining and event’s probability.

The Remarkable Success of Wannabe Gunslingers

In a December 27, 1999, missive he [Jim Cramer] wrote, referring to money managers who refused to buy enormously overpriced dot-com stocks, “The losers better chance or they will lose again next year” - this less than three weeks before the dot-com market collapse.

Don’t be influenced by the short-term record or “great” market calls of a money manager, analyst, market-timer or economist, no matter how impressive they are.

Doing Heuristic Math

“If the stock is so good, why doesn’t it go up?” Don’t expect that the strategy you adopt will prove a quick success in the market; give it a reasonable time to work out.

Anchoring and Hindsight Biases

…we choose some natural starting point where we think the stock is a good buy or sale and make adjustments from there. The adjustments are typically insufficient.

Hindsight bias seriously impairs proper assessment of past errors and significantly limits what can be learned from experience. As a result, we think mistakes are easy to see and are confident we won’t make them again - until we do.

Ch 4: Conquistadors in Tweed Jackets

…the efficient-market hypothesis, which holds that competition between sophisticated, knowledgeable investors keeps stock prices where they should be. This happens because all facts that determine stock prices are analyzed by large numbers of intelligent and rational investors. Prices may not always be right, but they are unbiased, so if they are wrong, they are just as likely to be too high as too low.l

A key premise of the efficient-market hypothesis is that the market reacts almost instantaneously (and correctly) to new information, so investors cannot benefit.

The professors assumed that investors were emotionless and as efficient as the computers they used to generate their theory.

Ch 5: Only a Flesh Wound

The 1987 Stock Market Crash

A crucial assumption of EMH, that there is always sufficient liquidity in markets, was disproved…The lack of understanding by the academics and the portfolio insurance managers of liquidity was a primary cause of the 1987 crash.

The argument that rational investors keep stock prices in line with their value, another core assumption of EMH, was seriously challenged. When was the market efficient? When professional and other sophisticated investors took the S&P500 down (31 percent) in five trading days or when it recovered all of its losses nineteen months later, with only minor changes in the underlying fundamentals?

The 1998 Long-Term Capital Management Debacle

LTCM was convinced that it could calculate precisely the odds of what the best, average, and worst days would return, as well as the odds that the portfolio could take serious or mortal losses.

“Risk is a function of volatility. These things are quantifiable.” - Peter Rosenthal, LTCM’s press spokesman.

So focused were they on volatility, and so strong was their belief that volatility was risk, that leverage and liquidity as independent risk elements were considered inconsequential.

Merton was taking an enormous chance. EMH theory, the Black-Scholes model, and Merton’s model say only that a stock’s volatility is consistent over time. They do not say in what time period securities or derivatives will revert to the mean. It’s like crossing a river that’s four feet deep on average, carrying a fifty-pound backpack; it might be three feet deep in some places and fifteen feet deep in others.

As a result of the firm’s gigantic margin calls and its inability to sell most major positions because they were very illiquid, the fund’s leverage shot up to 100 to 1, which meant that even a 1 percent drop in assets would bankrupt it.

Although Merton admitted that the fund’s risk measurements hadn’t worked, he stated that the principles it had followed were right. What was needed, he argued, was more sophisticated modeling. He went back to teaching at Harvard and, perhaps ironically, was hired by J.P. Morgan as a risk consultant.

The 2006-2008 Housing Bubble and Market Crash

As for Professor Fama and his colleagues, did this experience shake their belief that volatility is the only measure of risk or that the market is efficient? Apparently not. In 2007, with mortgage-backed security prices already dropping rapidly, Fama said in an interview, “The word ‘bubble’ drives me nuts,” and went on to explain why people could trust housing market values. “Housing markets are less liquid, but people are very careful when they buy houses…The bidding process is very detailed.”

Ch 6: Efficient Markets and Ptolemaic Epicycles

…the Ptolemaic system met two major criteria of a useful scientific hypothesis: it was “predictive” in correctly forecasting where various celestial bodies would be at future points in time, and it was “explanatory” because it codified a system of planetary motion…I liken the continued belief in EMH to the unyielding acceptance of the Ptolemaic system after Galileo had shown that the new sun does not revolve around the sun.

How did leading EMH academics know that investors measured risk strictly by the volatility of the stock? They didn’t…The academics simply declared it as a fact. Importantly, this definition of risk was easy to use to build complex computer finance models, and that’s what the professors wanted to do.

Morningstar’s five stars, its top ranking, widely followed and much sought after, uses Fama’s three-factor model, which is dubious at best, as part of its risk measurement…These models [three-, four-, and five-factor models] all attempt to show that higher volatility provides higher return and that lower volatility provides lower return.

…a considerable body of literature demonstrates that contrarian strategies have produced significantly better returns than the market over many decades. The explanation for this explicitly contradicts the central tenet of EMH - that people behave with almost omniscient rationality in markets. Conversely, the tenet that no group of investors and no strategies should consistently underperform in an efficient market is another rock that EMH flounders on. Below-market performance has been turned in for decades by people who buy favorite stocks…Another significant underperformance finding is the research that shows that IOPs have been dogs in the marketplace for forty years.

Another major premise of EMH is the hypothesis that all new information is analyzed almost immediately and accurately reflected in stock prices…we’ll often find that the researchers mistakenly take any market reaction to new information as the correct one…A number of other studies have shown that the market is slow to digest new information.

Ch 7: Wall Street’s Addiction to Forecasting

When information-processing requirements are large and complex analysis is necessary to integrate it, the rational system, which is deliberative and analytical, is often subtly overridden without the professional’s knowledge by the experiential system.

…ingesting large amounts of investment information can lead to worse rather than better decisions because the Affect working with other cognitive heuristics, such as representativeness and availability, takes over.

…only a marginal improvement in accuracy occurs as increasing amounts of new information are heaped on.

…when a problem is relatively simple to diagnose, experts are realistic about their ability to solve it. When the problem becomes more complex, however, and the solution depends on a number of hard to quantify factors, they became overconfident in their ability to reach a solution.

In-depth information does not translate into in-depth profits.

Ch 8: How Big a Long Shot Will You Play?

Experienced analysts make between $700,000 and $800,000 a year; standouts receive more. There there is the million-dollar-a-year club, which includes several dozen of the Street’s outstanding oracles. Income-wise, they are in a class with popular entertainers and professional athletes.

E-mails and other documents showed that many analysts had been pressured into giving favorable ratings to weak companies, a good number of which were wobbly internet firms with almost no business plans, revenues, or viable platforms.

Jack Grubman was among the masters. Grubman was always negative on AT&T, but Citigroup CEO Sandy Weill “asked” him to take a fresh look at his rating of the company, which he had previously never recommended. “Asked”, in this case, implied funneling millions of extra bonus dollars to Grubman if he went along. Rumor at the time had it that AT&T chairman would not let Citigroup participate in a major forthcoming underwriting unless Grubman, who carried enormous weight in the communications sector, upgraded the stock. Grubman, under Weill’s watchful eye, upgraded the stock to a buy near its peak in 1999. Shortly thereafter. Citigroup earned $63 million in underwriting fees when AT&T spun off its wireless unit.

Citigroup was estimated to have made $1 billion in fees generated by Grubman from its investment banking subsidiaries, while shareholders lost $2 trillion in the telecom scandal alone.

Grubman resigned under suspicion in August 2002. He received $30 million in severance pay from Citigroup’s brokerage subsidiary, and by mutual agreement, Citi continued to pay his legal bills.

Analysts' forecast errors are high (40% on average) in all stages of the cycle (expansion or recession). Second, and more important, analysts have a strong optimistic bias in their forecasts.

“The ranking of seven factors determining an analyst’s compensation places ‘accuracy of forecasts’ dead last”. What is most important is how the analyst is rated by the brokerage firm’s sales force […] primarily on how much commission business they can drum up.

Recommending a sell, even when the analyst proves to be dead-on, can be costly. In the late 1980s, an analyst at Janney Montgomery Scott issued a sell recommendation on one of Atlantic City casinos owned by Donald Trump. Trump went bananas and insisted that the analyst be fired for his lack of knowledge. Shortly thereafter, he was fired - but naturally, said the brokerage firm, “for other reasons.” The analyst proved right, and the casino went into Chapter 11 bankruptcy.

A large number of studies in cognitive psychology indicate that human judgment is often predictably incorrect. Nor is overconfidence unique to analysts. People in situations of uncertainty are generally overconfident on the basis of the information available to them; they usually believe they are right much more often than they are. Researchers have also shown that people can maintain a high degree of confidence in their answers, even when they know the “hit rate” is not very high.

Cognitive psychologists note that there are two distinct methods of forecasting. The first is called the “inside view.” The analyst or stock forecaster focuses entirely on the stock and related aspects such as growth rates, market share, product development, the general market, the economic outlook, and a host of other variables. Forecasters are “excessively prone” to treating each problem as unique, paying no attention to history. The “outside view”, on the other hand, ignores the multitude of factors that go into making the individual forecast and focuses instead on the group of cases believed to be the most similar. The basic difference is that with the outside view, the problem is treated not as unique but as an instance of a number of similar problems. According to Daniel Kahneman, “It should be obvious that when both methods are applied with intelligence and skill the outside view is much more likely to yield a realistic estimate. In general, the future of long and complex undertakings is simply not foreseeable in detail.” The number of possible outcomes when dozens or hundreds of factors interact in the marketplace is, for all practical purposes, infinite.

Ch 9: Nasty Surprises and Neuroeconomics

Do surprises affect favored and unfavored stocks in the same way? …all surprises (positive and negative) helped unpopular stocks and hurt popular ones…Surprise significantly benefits unfavored low-P/E stocks and works against the high-P/E group, while it has a nominal effect on stocks in the middle group. [Same conclusion if we use price-to-cash flow or price-to-book].

The prices of out-of-favor stocks do not just move up in the quarter of the [positive] surprise and then drop back again, as do those of the favorites. Instead, they continue to move steadily higher relative to the market in the year following the surprise.

Just as apparent was the sharp underperformance of the winners, the top 20 percent of stocks, as measured by the price-to-earnings or price-to-cash flow ratio when the analysts' estimates were too optimistic.

When we recall that money managers are considered “stars” if they outperform markets by 2 percent or 3percent annually over a five-year period, the 3.4 percent annual outperformance of the “worst” stocks after all surprises, coupled with the 3.6 percent underperformance of the “best”, or 7.0 percent total outperformance of low-P/E over high-P/E stocks, is enormous.

Analysts' estimates are slow to adjust to earnings surprises. Whether the estimate was too high or too low, analysts do not revise it accurately immediately but take as long as three quarters after the surprise to do so. If for example, investors are taken aback by a negative earnings surprise on a favorite stock and more negative surprises occur in the following quarters (as a result of analysts' not revising their earnings estimates down enough), people’s increasingly poor reappraisal of the company pushes the stock even lower. The same is true for a series of positive surprises on an out-of-favor company.

Reinforcing events, on the other hand (positive surprises on favored stocks and negative surprises on out-of-favor stocks) have a negligible 0.6 percent impact on prices after one year.

When investors receive unexpectedly higher earnings on out-of-favor stocks, their dopamine is also likely to fire up almost instantaneously and strongly - studies show from three to forty times a second. Schultz and Anthony Dickinson, in the 2000 Annual Review of Neuroscience, wrote, “In summary, the reward responses depend on the difference between the occurrence and the prediction of reward (dopamine response = reward occurred - reward predicted)”.

Ch 10: A Powerful Contrarian Approach to Profits

Favored stocks underperform the market, while out-of-favor companies outperform the market, but the reappraisal often happens slowly, even glacially.

Most investors do not recognize the immense difficulty of predicting earnings and economic events, and when forecasting methods fail, a predictable reaction occurs. Here we confront the main irony: one of the most obvious and consistent variables that can be harnessed into a workable investment strategy is the continual overreaction of people to companies they consider to have excellent or mundane prospects.

Buy solid companies currently out of market favor, as measured by their low price-to-earnings, low price-to-cash-flow, or low price-to-book ratios.

Though the statistics drag us toward the value camp, our emotions just as surely tug us the other way. People are captivated by exciting new concepts. The lure of hitting a home run on a hot new idea overwhelms caution. Investors just as surely want to stay well away from companies whose outlooks seem poor.

There are, of course, strong stocks that justify their price-earnings ratios and others that deserve the slimmest of multiples. But, as the evidence indicates, the “best” are relatively few in number, and the chances of recognizing them are very small.

Contrarian strategies succeed because investors don’t know their limitations as forecasters. As long as investors believe they can pinpoint the future of favored and out-of-favor stocks, you should be able to make good returns on contrarian strategies.

Ch 11: Profiting from Investors' Overreactions

The investor overreaction hypothesis states that it is far safer to project a continuation of investor overreaction than to attempt to project the visibility of stocks or other investments themselves.

A part of the evidence used to build the IOH was considered to be a series of aberrations by efficient market believers and dismissed as anomalies.

High levels of leverage and liquidity have a long-standing record of creating serious downturns, often accompanied by panic. IOH would thus caution against high leverage and massive illiquidity, because of the dangers of a major overreaction.

Diversification is essential (for contrarian stock selection). The returns of individual issues vary widely, so it is dangerous to rely on only a few companies or industries.

Such companies (medium or large-sized stocks on the NYSE, the NASDAQ, or the AMEX) are usually subject to less accounting gimmickry than smaller ones, and this difference provides some added measure of protection. Finally, larger companies have more “staying power”; their failure rate is substantially lower than that of smaller and start-up companies.

Should we consider abandoning security analysis entirely? The evidence we’ve seen certainly shows that it doesn’t help much. However, I would not go quite this far. I believe parts of it can be valuable within a contrarian framework.

Indicator 1. A strong financial position (current assets vs current liabilities debt as a percentage of capital structure, interest coverage, etc). A strong financial position will enable a company to sail unimpaired through periods of operating difficulties. And of course, in a liquidity crisis, it is often the difference between survival and insolvency.

Indicator 3. A higher rate of earnings growth than the S&P500 in the immediate past and the likelihood that it will not plummet in the near future.

Indicator 4. Earnings estimate should always lean to the conservative side.

Strikeouts (Freddie and Fannie), important lessons:

  • never buy a company that is losing money. Losses are an early warning that all is not well. Most good companies do turn around, but when they don’t you get stung.

  • never, never believe senior officials when they say all is well in trying times for a company or an industry. In most cases that’s the time to sell.

It’s important to realize that investing using contrarian strategies is a long-term game. One roll of the dice or a single hand at blackjack is meaningless to a casino owner. He knows there will be hot streaks that will cost him a night’s, a week’s, sometimes even a month’s revenues. He may grumble when he loses, but he doesn’t shut down the casino. He knows he’ll get the money back.

Even though a strategy works most of the time and generates excellent returns, no strategy works consistently.

In order to win you have to stay with the game, but for many people that is difficult to impossible. Though the strategies are simple and easy to use, the influence of immediate events is very powerful.

Ch 12: Contrarian Strategies Within Industries

The results of investing in the most out-of-favor stocks within industries are similar to those of buying the most unpopular stocks overall; however, the industry returns are somewhat lower. The returns of the laggards continue to outperform the market but fall off more rapidly against it over longer periods of time than do absolute contrarian strategies. It is best to rebalance portfolios annually when this strateg is used.

The advantage of the contrarian industry strategy is that you have far more diversification by industry than you do the original contrarian strategies. This diversification should protect you from the underperformance that occurs when the most out-of-favor stocks and industries in the market are taboo. Thus, if industries such as […] are headed for the starts, you will not feel left out. You will also not be positioned only in the most disliked groups, which may underperform for months or sometimes years.

But remember that averages are deceptive. Even if contrarian stocks provide better returns in most bear markets, they don’t do so for all bear markets. Similarly, though contrarian strategies provide far-above-average returns over time, they don’t do so every time.

Ch 13: Investing in a New, Alien World

Although precise figures are elusive, some trade sources estimated that by mid-2009, high-frequency trading accounted for almost 75% of the volume on all U.S. markets, up from 33% in 2006.

We also know that the HFT firms fire off enormous numbers of bids or offers for futures and stocks in milliseconds and cancel them a few milliseconds later. One reason in a panicky market might be to drive prices even lower. If a flotilla of HFT firms are attempting to drive prices down and a large tidal wave of flash sell orders show up on the screen, canceled in milliseconds, other investors can easily be fooled into thinking that heavy potential waves of selling will hit the floor in seconds, resulting in investors and traders selling immediately to escape the deluge.

The same strategy may apply in a market which is rising substantially…HFT firms thus win on both the upside and the downside. All they require is enormous volatility in either case, which from the evidence available, they help to create. The net result is that volatility is far higher than it should be and all too many people abandon markets because of their casino-like behavior.

In the fall of 2008, fear shot up like the flames of a roaring forest fire. Some investors tried to protect their blue-chip stocks by buying puts, which gave them the right to see a stock at its then-current, highly depressed price if the market went lower. The cost to do so can be exorbitantly large with ultrahigh volatility, premiums sometimes rising to 20 to 35 percent of the entire principal for 90 to 100 days. For a year, if the put premiums remained relatively unchanged, the cost increased to 80 to 85 percent annually. In effect, the insurance provided by buying a put would have almost wiped out any blue-chip portfolio in little more than a year. […] in periods of crisis, using options clearly does not work. Volatility, unfortunately, is something we just have to live through.

The bottom line here is not to buy puts or calls unless you are highly experienced with them. Even then, the odds are against most people.

When Should You Sell?

Psychological forces misdirect most sell decisions, often disastrously. I have seen many a money manager set stringent sell targets […] But as the stock moved rapidly toward the preset price, more and more good news usually accompanied its rise […] a manager would often bump the sell price higher. This frequently resulted in the manager’s taking the “Round trip”: riding a stock all the way up, only to ride it all the way down again.

Given what we know, it seems that the safest approach, once again, is to rely on mechanical guidelines [e.g. when its P/E ratio (or other contrarian indicator) approaches that of the overall market], which filter out much of the emotional content of the decision.

The first guideline, then, is simple: pick a sell point when you buy a stock. If it reaches that point, grit your teeth, brace yourself, and get rid of it. You will probably be unhappy because the issue will often go higher. But why be greedy? You’ve made a good gain, and that’s what the game is all about.

Another question is how long you should hold a stock that has not worked out. Investors all too often fall in love with their holdings. I have seen portfolios loaded with dozens of companies that look good on paper but have been dogs in the market, resulting in poor returns. Again, there are many partial answers to this problem, but I think two and a half to three years is an adequate waiting period. If after that time the stock still disappoints, sell it.

Another important rule is to sell a stock immediately if its long-term fundamentals deteriorate significantly. I’m talking not about a poor quarter or a temporary surprise that a stock will snap back from but about major changes that weaken a company’s prospects. Under these conditions, I have found that taking your lumps immediately and moving on usually results in the smallest loss.

Ch 14: Toward a Better Theory of Risk

Huxley on creationist thinking: “The great tragedy of Science [is] ?- the slaying of a beautiful hypothesis by an ugly fact”.

The damage we are primarily concerned with in this section occurs in periods of abundant monetary availability.

Their endings have all been remarkably similar: At some point, the band and other financial institutions woke up to the fact that projects would not be nearly as profitable as originally projected.

…there is […] a good deal of evidence that in periods of severe strain, liquidity dries up, in the face of sharp drops in price.

In the past, we did not focus on inflation, as rising prices have been pretty much a nonevent for centuries…Inflation was a minuscule 0.1% from 1802 to 1870 and 0.6% from 1871 to 1925.

Inflation caught investors […] flatfooted after World War II.

When we adjust for inflation, supposedly risky assets such as stocks become far safer [than T-Bills].

Inflation and taxes sharply reduce your risk of owning stocks over longer periods of time.

Ch 15: They’re Gambling with Your Money

Greenspan at this pinnacle was widely considered to be the most able central banker who ever walked the face of the earth.

If his speeches were opaque, his actions as Fed chairman for almost twenty years certainly were not. And it was those actions that played an important role in the worst financial crisis in history.

He had an unshakable belief that companies, regardless of their size, industry, or circumstances, would, in their own self-interest, regulate themselves well.

Greenspan was also the “leading proponent of the deregulation of derivatives.”

During the 2008 crash, in a speech at George-town University, Greenspan said that the problem was not the derivatives but the people using them - who got “greedy”.

As obstinate as Chairman Greenspan was about derivatives, he was even more so about not taking any action to curb the excesses of the housing bubble.

By the time the Fed realized the enormity of the problem, it was far too late. In 2007, even after the bubble was already imploding, both Greenspan and Bernanke continued to issue reassuring statements that all was well. Bernanke said in late March 2007, “At this juncture…the impact on the broader economy and financial markets of the problems in the subprime market seems to be contained.” Three months before the collapse of Lehman Brothers, he stated, “The danger that the economy has fallen into a substantial downturn appears to have waned.”

He [Greenspan] expected and believed that the financial firms would protect their interests because they are rational companies …so they would not take risks that would threaten their very existence. Where he went wrong, according to Kahneman, was that there was a huge gulf in the goals between the firms and their managers' (their agents') interests. The firm takes the long view of profitability over time. The agents take a much shorter-term view, basing decisions on possible promotions, large salaries, and bonuses…the executives did not commit suicide by taking such risks. They walked away unscathed. It was the corporations they managed that were crippled or committed suicide.

The three top credit-rating agencies became enormously prosperous, not unlike the mortgage lenders…The ratings were a very lucrative business for the credit-raring agencies, costing upward of $50,000 for plain-vanilla slices to $1 million or more for supercomplex, multilayered collateralized debt obligations.


Until next time, stay cool & stay invested!