How Time Erases Bad News for Investors

By PAUL J. LIM

Published: November 01, 2009

THOUGH stocks have soared more than 50 percent since the market hit bottom in March, the sentiment of individual investors is hardly euphoric.

In fact, the percentage who say they are "bullish" today is only slightly higher than it was in the summer, when the market was much lower, according to a survey by the American Association of Individual Investors.

Yet these attitudes could change soon, but not because anything has changed fundamentally in the market. It's simply that time is passing, and the quarterly performance reports sent to investors will soon no longer highlight the worst of last year's losses.

At the moment, the quarterly brokerage and 401(k) plan statements still reflect an important time lag. Open a recent statement and you're likely to find that despite their gains of late, most of your stock investments still lost money for the 12 months through the third quarter. The Standard & Poor's 500-stock index, for example, was off nearly 7 percent in the 12 months ended Sept. 30.

Fast-forward to current figures, which won't be reflected in most printed investment reports for some weeks. Even after Friday's losses, many numbers look much better. That's mainly because the market plunge of September 2008 is no longer included in them. Right now, the S.& P. 500 is up nearly 12 percent from its level 12 months ago. Through Thursday, domestic stock funds were doing even better, with gains of more than 23 percent, on average, according to Morningstar, the fund tracker.

Investors may think that the market has improved tremendously over just the last few weeks. It hasn't. It's just that by the end of October, the market was more than a year beyond the stock market swoon that followed the collapse of Lehman Brothers. During the worst of the 2008 panic, from the start of last September through Oct. 10, the market lost nearly a third of its value.

People are often told that they should invest for the long run, but Greg Schultz, a principal at Asset Allocation Advisors, a financial planning firm in Walnut Creek, Calif., said that "shorter time frames actually impact investor psychology more."

 "Investors aren't looking at 10 years," he said. "They're looking at how they've been doing over 6 months, 9 months, 12 months."

IN the last few weeks, there have been signs that small investors starting to view this rally as real, said Mike Scarborough, president of Scarborough Capital Management, a 401(k) advisory firm based in Annapolis, Md. But most of the bandwagon followers - market timers who fled stocks after last year's tumble but who now want to make a quick buck after equities have already soared - have yet to re-enter the market in droves, he said.

 Mr. Scarborough believes that this kind of market timing is ill-advised.

 "The pigs haven't shown up yet," he said. "But when they do, you'll know the market is nearing a top."

 At that point, he said, he is likely to start ratcheting down his clients' exposure to equities by around 5 to 10 percentage points. He guesses that this will take place in January and February, when investors are likely to open brokerage statements showing double-digit gains for 2009. Those statements are also likely to show modestly positive gains for most types of stock funds over the last five years.

Barring another market swoon, investor confidence is also likely to get a big boost in March. That's when the year-over-year performance figures for stocks will move beyond the sell-off that occurred in the first quarter of 2009.

If the market treads water between now and March, the one-year performance figures on March 9 will show a climb of more than 53 percent. Without further gains, of course, people may focus on the 51 percent gain that would still be needed to attain the levels of the market's last peak, which was reached in October 2007.

"Psychologically, a lot of people measure themselves off of the highs," said Ronald W. Rogé, a financial planner in Bohemia, N.Y. For now, though, the danger is that the market may be entering a period of rising optimism just as the fundamentals sour.

For example, when the rally began in March, the price-to-earnings ratio for the S.& P. 500 stood at a modest 14, based on the trailing four quarters of operating profits. Today, that P/E is about 27.

Even if you use a more conservative profit gauge - 10-year averaged earnings, a measure that smoothes out wild swings - you find that valuations have begun to soar. In March, the price-to-earnings ratio for the broad market using these "normalized" earnings was 13.3, well below the market's historical average of around 16. This P/E has since climbed to 19.5.

A surge in investor confidence could be a shot of adrenaline for a rally that's maturing. But if fundamental stock values are weakening, and investors pour money in anyway, the bears are likely to see this as a sign of a market

top.

Schwarzenegger’s secret message to the state Assembly: ‘F*ck you.’

The California Assembly and Senate recently unanimously approved Assembly Bill 1176 to help the port of San Francisco with financing issues. But Gov. Arnold Schwarzenegger (R) has decided to veto the legislation, sending a letter to the state Assembly chastising them for focusing on “unnecessary bills.” The San Francisco Bay Guardian also notes a second, more direct, message hidden in Schwarzenegger’s missive — contained in the first letter of each line:

The author of the Assembly Bill 1176 is Assemblyman Tom Ammiano, who recently shouted out “You lie!” to Schwarzenegger at a public event. Schwarzenegger’s office, however, is insisting that the letter’s coded message was just “a strange coincidence.” “When you do so many vetoes, that’s bound to happen,” said the governor’s spokesman.

Why the Economy Needs Spending, Not Tax Cuts

In This 10-Year Race, Bonds Win by a Mile

WHEN the Dow Jones industrial average climbed back to 10,000 this month, the achievement was widely noted but barely celebrated, and for good reason.

“Haven’t we done this several times before?” asked Edward Yardeni, the economist and investment strategist.

In fact, we had. The Dow had crossed 10,000 on more than 20 occasions, starting in late March 1999, when the market was so hot that stock-picking seemed to have become the national pastime. In that year, the book “Dow 36,000” confidently declared that stocks were “actually less risky than bonds” and that the Dow would more than triple in value within a few short years.

As investors know all too well, the financial history of the last decade turned out a bit differently. Stocks proved to be extremely risky. Despite the recent rally, in the 10 years through September, most stock investors lost money.

What may be less widely understood is that over that same 10 years, while the stock market’s overall returns were disappointing, the bond market produced handsome gains. Bond rallies have not generated the hoopla that the stock market customarily receives, but over the last 10 years, investors have had more reason to celebrate if they held bonds, not stocks, in their portfolios.

Calculations performed for Sunday Business by Morningstar, using data from its Ibbotson Associates subsidiary, show that the stock market underperformed important bond categories over the 10 years through September — with an annualized loss of 0.2 percent for the Standard & Poor’s 500-stock index, versus annualized gains of 8.1 percent for long-term government bonds and of 7.8 percent for long-term corporate bonds.

What’s more, the S.& P. 500 underperformed long-term government and long-term corporate bonds over the last 20 years as well. Over longer periods — 30 years, 40 years, and in an 83-year stretch from 1926 to 2009 — the Ibbotson numbers indicate that stocks did outperform bonds, sometimes by more than three percentage points, annualized. But bonds were far less volatile throughout. And the further back in history you go, the less directly comparable is the data.

Writing in the May-June issue of the “Journal of Indexes,” Robert Arnott, chief executive of the investment firm Research Affiliates in Newport Beach, Calif., declared that bonds had been neglected by the financial press and by many investors. He reviewed market returns going back 207 years, and found that stocks outperformed bonds by only 2.5 percentage points, annualized. This “2.5 percentage point advantage over two centuries compounds mightily over time,” he said. But for very long stretches, bonds have done better than stocks.

The wild ride of the last decade or so does not mean that stocks will underperform bonds in the months or years ahead. If only it were that simple.

For one thing, past returns never provide a clear guide for the future — especially when technology, innovation and government policies are changing the structure of financial markets and transforming the global economy as rapidly as they are right now.

For another, it can be argued that the recent stretch of relative stock market weakness and bond market strength is precisely why stocks are likely to do better than bonds. Jeremy J. Siegel, a finance professor at the Wharton School of the University of Pennsylvania and the author of “Stocks for the Long Run,” advocates stock holdings for people with long horizons but acknowledges that some periods have been painful for equities.

He says that the environment is auspicious again. “Historically, whenever you’ve had long periods when bonds outperform stocks, that sets up an excellent time to invest in stocks,” he said. “So looking forward, things look very favorable for stocks and not favorable for bonds, certainly not Treasury bonds.”

In part because of market intervention by the Federal Reserve, yields on long-term Treasury bonds remain extremely low, and prices, which move in the opposite direction, are high. When and if the economy recovers, bond yields are likely to rise and prices are likely to fall. Low yields, meanwhile, make it cheaper for many companies to finance their operations, which could help generate outsize profits.

Laszlo Birinyi, president of Birinyi Associates, a stock market research firm in Westport, Conn., who says he believes that we are in the middle of a vigorous bull market for stocks, has studied the long-term returns of many asset classes. He has found that from 1970 to 2008, emerging-market stocks outperformed the S.& P. 500, the bond market and alternative assets like oil, gold, real estate and diamonds.

But Mr. Birinyi recommends sticking mainly with domestic stocks and bonds, perhaps adding a sprinkling of foreign stocks that “don’t replicate your domestic stock holdings.”

“My issue with diversification beyond that,” Mr. Birinyi said, “is that an incremental or arithmetic increase in the number of decisions you make leads to a geometric increase in the degree of difficulty.”

The logic for treating domestic stocks and bonds as the two central asset classes was outlined in the 1930s by Benjamin Graham and David Dodd, the fathers of value investing. In their classic work, “Security Analysis,” they emphasized safety — favoring bonds, and only those of the highest quality, as far more suitable for small investors than stocks, which attracted “speculators.”

Value Investing Congress

David Einhorn, Greenlight Capital, “Liquor before Beer… In the Clear”

October 19, 2009

One of the nice aspects of trying to solve investment puzzles is recognizing that even though I am not always going to be right, I don’t have to be. Decent portfolio management allows for some bad luck and some bad decisions. When something does go wrong, I like to think about the bad decisions and learn from them so that hopefully I don’t repeat the same mistakes. This leaves me plenty of room to make fresh mistakes going forward. I’d like to start today by reviewing a bad decision I made and share with you what I’ve learned from that error and how I am attempting to apply the lessons to improve our funds’ prospects. At the May 2005 Ira Sohn Investment Research Conference in New York, I  recommended MDC Holdings, a homebuilder, at $67 per share. Two months later MDC reached $89 a share, a nice quick return if you timed your sale perfectly. Then the stock collapsed with the rest of the sector. Some of my MDC analysis was correct: it was less risky than its peers and would hold-up better in a down cycle because it had less leverage and held less land. But this just meant that almost half a decade later, anyone who listened to me would have lost about forty percent of his investment, instead of the seventy percent that the homebuilding sector lost.

I want to revisit this because the loss was not bad luck; it was bad analysis. I down played the importance of what was then an ongoing housing bubble. On the very same day, at the very same conference, a more experienced and wiser investor, Stanley Druckenmiller, explained in gory detail the big picture problem the country faced from a growing housing bubble fueled by a growing debt bubble. At the time, I wondered whether even if he were correct, would it be possible to convert such big picture macro-thinking into successful portfolio management? I thought this was particularly tricky since getting both the timing of big macro changes as well as the market’s recognition of them correct has proven at best a difficult proposition. Smart investors had been complaining about the housing bubble since at least 2001. I ignored Stan, rationalizing that even if he were right, there was no way to know when he would be right. This was an expensive error.

The lesson that I have learned is that it isn’t reasonable to be agnostic about the big picture. For years I had believed that I didn’t need to take a view on the market or the economy because I considered myself to be a “bottom up” investor. Having my eyes open to the big picture doesn’t mean abandoning stock picking, but it does mean managing the longshort exposure ratio more actively, worrying about what may be brewing in certain industries, and when appropriate, buying some just-in-case insurance for foreseeable macro risks even if they are hard to time. In a few minutes, I will tell you what Greenlight has done along these lines.

But first, I’d like to explain what I see as the macro risks we face. To do that I need to digress into some political science. Please humor me since my mom and dad spent a lot of money so I could be a government major, the usefulness of which has not been apparent for some time.

Winston Churchill said that, “Democracy is the worst form of government except for all the others that have been tried from time to time.” As I see it, there are two basic problems in how we have designed our government. The first is that officials favor policies with short-term impact over those in our long-term interest because they need to be popular while they are in office and they want to be reelected. In recent times, opinion tracking polls, the immediate reactions of focus groups, the 24/7 news cycle, the constant campaign, and the moment-to-moment obsession with the Dow Jones Industrial Average have magnified the political pressures to favor short-term solutions. Earlier this year, the political topic du jour was to debate whether the stimulus was working, before it had even been spent.  Paul Volcker was an unusual public official because he was willing to make unpopular decisions in the early ’80s and was disliked at the time. History, though, judges him kindly for the era of prosperity that followed.

Presently, Ben Bernanke and Tim Geithner have become the quintessential short-term decision makers. They explicitly “do whatever it takes” to “solve one problem at a time” and deal with the unintended consequences later. It is too soon for history to evaluate their work, because there hasn’t been time for the unintended consequences of the “do whatever it takes” decision-making to materialize.

The second weakness in our government is “concentrated benefit versus diffuse harm” also known as the problem of special interests. Decision makers help small groups who care about narrow issues and whose “special interests” invest substantial resources to be better heard through lobbying, public relations and campaign support. The special interests benefit while the associated costs and consequences are spread broadly through the rest of the population. With individuals bearing a comparatively small extra burden, they are less motivated or able to fight in Washington.

In the context of the recent economic crisis, a highly motivated and organized banking lobby has demonstrated enormous influence. Bankers advance ideas like, “without banks, we would have no economy.” Of course, there was a public interest in protecting the guts of the system, but the ATMs could have continued working, even with forced debt-to-equity conversions that would not have required any public funds. Instead, our leaders responded by handing over hundreds of billions of taxpayer dollars to protect the speculative investments of bank shareholders and creditors. This has been particularly remarkable, considering that most agree that these same banks had an enormous role in creating this mess which has thrown millions out of their homes and jobs.

Like teenagers with their parents away, financial institutions threw a wild party that eventually tore-up the neighborhood. With their charge arrested and put in jail to detoxify, the supervisors were faced with a decision: Do we let the party goers learn a tough lesson or 3 do we bail them out? Different parents with different philosophies might come to different decisions on this point. As you know our regulators went the bail-out route. But then the question becomes, once you bail them out, what do you do to discipline the misbehavior? Our authorities have taken the response that kids will be kids. “What? You drank beer and then vodka. Are you kidding? Didn’t I teach you, beer before liquor, never sicker, liquor before beer, in the clear! Now, get back out there and have a good time.” And for the last few months we have seen the beginning of another party, which plays nicely toward government preferences for short-term favorable news-flow while satisfying the banking special interest. It has not done much to repair the damage to the neighborhood. And the neighbors are angry, because at some level, Americans understand that the Washington-Wall Street relationship has rewarded the least deserving people and institutions at the expense of the prudent. They don’t know the particulars or how to argue against the “without banks, we have no economy” demagogues. So, they fight healthcare reform, where they have enough personal experience to equip them to argue with Congressmen at town hall meetings. As I see it, the revolt over healthcare isn’t really about healthcare, but represents a broader upset at Washington. The lack of trust over the inability to deal seriously with the party goers feeds the lack of trust over healthcare.

On the anniversary of Lehman’s failure, President Obama gave a terrific speech. He said, “Those on Wall Street cannot resume taking risks without regard for the consequences, and expect that next time, American taxpayers will be there to break the fall.” Later he advocated an end of “too big to fail.” Then he added, “For a market to function, those who invest and lend in that market must believe that their money is actually at risk.” These are good points that he should run by his policy team, because Secretary Geithner’s reform proposal does exactly the opposite.

The financial reform on the table is analogous to our response to airline terrorism by

frisking grandma and taking away everyone’s shampoo, in that it gives the appearance of

officially “doing something” and adds to our bureaucracy without really making anything

safer.

With the ensuing government bailout, we have now institutionalized the idea of toobig- to-fail and insulated investors from risk. The proper way to deal with too-big-to-fail, or too inter-connected to fail, is to make sure that no institution is too big or inter-connected to fail. The test ought to be that no institution should ever be of individual importance such that if we were faced with its demise the government would be forced to intervene. The real solution is to break up anything that fails that test.

The lesson of Lehman should not be that the government should have prevented its failure. The lesson of Lehman should be that Lehman should not have existed at a scale that allowed it to jeopardize the financial system. And the same logic applies to AIG, Fannie, Freddie, Bear Stearns, Citigroup and a couple dozen others.

Twenty-five years ago the government dismantled AT&T. Its break-up set forth decades of unbelievable progress in that industry. We can do that again here in the financial sector and we would achieve very positive social benefit with no cost that anyone can seem to explain.  The proposed reform takes us in the polar opposite direction. The cop-out response   from Washington is that it isn’t “practical.” Our leaders are so influenced by the banking special interests that they would rather declare it “impractical” than roll up their sleeves and figure out how to get the job done.

The bailouts have installed a great deal of moral hazard, which in the absence of radical change will be reinforced and thereby grant every big institution a permanent “implicit” government backstop. This creates an enormous ongoing subsidy for the too-big to- fails, as well as making it much harder for the non-too-big-to-fails to compete. In effect, we all continue to subsidize the big banks even though we keep hearing the worst of the crisis is behind us.

In addition, the now larger too-big-to-fails are beginning to take advantage of developing oligopolies. Even as the government spends trillions to subsidize mortgage rates, the resulting discount is not being passed to homeowners but is being kept by mortgage originators who are earning record profits per mortgage originated. Recently, Goldman upgraded Wells Fargo partly based on its ability to earn long-term oligopolistic mortgage origination spreads. The proposed reform does not deal with the serious risks that the recent crisis exposed. Credit Default Swaps, which create large, correlated and asymmetric risks, scared the authorities into spending hundreds of billions of taxpayer money to prevent the speculators who made bad bets from having to pay.

CDS are also highly anti-social. Bondholders who also hold CDS make a bigger return when the issuing firms fail. As a result, holders of so-called “basis packages” – a bond and a CDS – have an incentive to use their position as bondholders to force bankruptcy triggering payment on their CDS, rather than negotiate traditional out of court restructurings or covenant amendments with troubled creditors. Press accounts have noted that this dynamic has contributed to the recent bankruptcies of Abitibi-Bowater, General Growth Properties, Six Flags and even General Motors. They are a pending problem in CIT’s efforts to avoid bankruptcy.

The reform proposal to create a CDS clearing house does nothing more than maintain private profits and socialized risks by moving the counter-party risk from the private sector to a newly created too-big-to-fail entity. I think that trying to make safer CDS is like trying to make safer asbestos. How many real businesses have to fail before policy makers decide to simply ban them?

Similarly, the money markets were exposed as creating systemic risk during the crisis. Apparently, investors in these pools of lending assets that carry no reserve for loss expect to be shielded from losing money while earning a higher return than bank deposits or T-bills. Mr. Bernanke decided they needed to be bailed out to save the system. It is hard to imagine why this structure shouldn’t be fixed, either by adding them to the FDIC insurance program and subjecting them to bank regulation, or at least forcing them to stop using $1 net-asset values, which gives their customers the impression that they can’t fall in value.

The most constructive aspect of the Geithner reform plan is to separate banking from commerce. This would have the effect of forcing industrial companies to divest big finance subsidiaries, which would have to be regulated as banks. During the bubble, companies like GMAC, AIG Financial Products and GE Capital, with cheap funding supported by inaccurate credit ratings, took enormous unregulated risks. When the crisis hit, GMAC and AIG needed huge federal bailouts. The Federal Reserve set up the Commercial Paper Funding Facility to backstop GE Capital among others, and GE became the largest borrower under the FDIC’s Temporary Liquidity Guarantee Program, even though prior to the crisis it wasn’t even in the FDIC. 

In response to the Geithner proposal, GE immediately let it be known that it had “talked to a number of people in Congress” and it should not have to separate its finance subsidiary because it disingenuously asserted that it hadn’t contributed to the crisis. We will see whether the GE special interest is able to stave-off this constructive reform proposal. Rather than deal with these simple problems with simple, obvious solutions, the official reform plans are complicated, convoluted and designed to only have the veneer of reform while mostly serving the special interests. The complications serve to reduce transparency, preventing the public at large from really seeing the overwhelming influence of the banks in shaping the new regulation.

In dealing with the continued weak economy, our leaders are so determined not to repeat the perceived mistakes of the 1930s that they are risking policies with possibly far worse consequences designed by the same people at the Fed who ran policy with the shortterm view that asset bubbles don’t matter because the fallout can be managed after they pop. That view created a disaster that required unprecedented intervention for which our leaders congratulated themselves for doing whatever it took to solve. With a sense of mission accomplished, the G-20 proclaimed “it worked.”

We are now being told that the most important thing is to not remove the fiscal and monetary support too soon. Christine Romer, a top advisor to the President, argues that we made a great mistake by withdrawing stimulus in 1937.

Just to review, in 1934 GDP grew 17.0%, in 1935 it grew another 11.1%, and in 1936 it grew another 14.3%. Over the period unemployment fell by 30%. That is three years of progress. Apparently, even this would not have been enough to achieve what Larry Summers has called “exit velocity.”

Imagine, in our modern market, where we now get economic data on practically a daily basis, living through three years of favorable economic reports and deciding that it would be “premature” to withdraw the stimulus. An alternative lesson from the double dip the economy took in 1938 is that the GDP created by massive fiscal stimulus is artificial. So whenever it is eventually removed, there will be significant economic fall out. Our choice may be either to maintain large annual deficits until our creditors refuse to finance them or tolerate another leg down in our economy by accepting some measure of fiscal discipline.

This brings me to our present fiscal situation and the current investment puzzle. Over the next decade the welfare states will come to face severe demographic problems. Baby Boomers have driven the U.S. economy since they were born. It is no coincidence that we experienced an economic boom between 1980 and 2000, as the Boomers reached their peak productive years. The Boomers are now reaching retirement. The Social Security and Medicare commitments to them are astronomical. When the government calculates its debt and deficit it does so on a cash basis. This means that deficit accounting does not take into account the cost of future promises until the money goes out the door. According to shadowstats.com, if the federal government counted the cost of its future promises, the 2008 deficit was over $5 trillion and total obligations are over $60 trillion. And that was before the crisis.

Over the last couple of years we have adopted a policy of private profits and socialized risks. We are transferring many private obligations onto the national ledger. Although our leaders ought to make some serious choices, they appear too trapped in short-termism and special interests to make them. Taking no action is an action.

In the nearer-term the deficit on a cash basis is about $1.6 trillion or 11% of GDP. President Obama forecasts $1.4 trillion next year, and with an optimistic economic outlook, $9 trillion over the next decade. The American Enterprise Institute for Public Policy Research recently published a study that indicated that “by all relevant debt indicators, the U.S. fiscal scenario will soon approximate the economic scenario for countries on the verge of a sovereign debt default.”

As we sit here today, the Federal Reserve is propping up the bond market, buying long-dated assets with printed money. It cannot turn around and sell what it has just bought. There is a basic rule of liquidity. It isn’t the same for everyone. If you own 10,000 shares of Greenlight Re, you have a liquid investment. However, if I own 5 million shares it is not liquid to me, because of both the size of the position and the signal my selling would send to the market. For this reason, the Fed cannot sell its Treasuries or Agencies without destroying the market. This means that it will be challenged to shrink the monetary base if inflation actually turns up.

Further, the Federal Open Market Committee members may not recognize inflation when they see it, as looking at inflation solely through the prices of goods and services, while ignoring asset inflation, can lead to a repeat of the last policy error of holding rates too low for too long.

At the same time, the Treasury has dramatically shortened the duration of the government debt. As a result, higher rates become a fiscal issue, not just a monetary one. The Fed could reach the point where it perceives doing whatever it takes requires it to become the buyer of Treasuries of first and last resort.

Japan appears even more vulnerable, because it is even more indebted and its poor demographics are a decade ahead of ours. Japan may already be past the point of no return. When a country cannot reduce its ratio of debt to GDP over any time horizon, it means it can only refinance, but can never repay its debts. Japan has about 190% debt-to-GDP financed at an average cost of less than 2%. Even with the benefit of cheap financing the Japanese deficit is expected to be 10% of GDP this year. At some point, as American homeowners with teaser interest rates have learned, when the market refuses to refinance at cheap rates, problems quickly emerge. Imagine the fiscal impact of the market resetting Japanese borrowing costs to 5%.

Over the last few years, Japanese savers have been willing to finance their government deficit. However, with Japan’s population aging, it’s likely that the domestic savers will begin using those savings to fund their retirements. The newly elected DPJ party that favors domestic consumption might speed up this development. Should the market re-price Japanese credit risk, it is hard to see how Japan could avoid a government default or hyperinflationary currency death spiral.

The failure of Lehman meant that barring extraordinary measures, Merrill Lynch, Morgan Stanley and Goldman Sachs would have failed as the credit market realized that if the government were willing to permit failures, then the cost of financing such institutions needed to be re-priced so as to invalidate their business models. I believe there is a real possibility that the collapse of any of the major currencies could have a similar domino effect on re-assessing the credit risk of the other fiat currencies run by countries with structural deficits and large, unfunded commitments to aging populations.

I believe that the conventional view that government bonds should be "risk free" and tied to nominal GDP is at risk of changing. Periodically, high quality corporate bonds have traded at lower yields than sovereign debt. That could happen again. And, of course, these structural risks are exacerbated by the continued presence of credit rating agencies that inspire false confidence with potentially catastrophic results by over-rating the sovereign debt of the largest countries. There is no reason to believe that the rating agencies will do a better job on sovereign risk than they have done on corporate or structured finance risks.

My firm recently met with a Moody’s sovereign risk team covering twenty countries in Asia and the Middle East. They have only four professionals covering the entire region. Moody’s does not have a long-term quantitative model that incorporates changes in the population, incomes, expected tax rates, and so forth. They use a short-term outlook – only 12-18 months – to analyze data to assess countries’ abilities to finance themselves. Moody’s makes five-year medium-term qualitative assessments for each country, but does not appear to do any long-term quantitative or critical work.

Their main role, again, appears to be to tell everyone that things are fine, until a real crisis emerges at which point they will pile-on credit downgrades at the least opportune moment, making a difficult situation even more difficult for the authorities to manage. I can just envision a future Congressional Hearing so elected officials can blame the rating agencies for blowing it, as the rating agencies respond by blaming Congress. Now, the question for us as investors is how to manage some of these possible risks. Four years ago I spoke at this conference and said that I favored my Grandma Cookie’s investment style of investing in stocks like Nike, IBM, McDonalds and Walgreens over my Grandpa Ben’s style of buying gold bullion and gold stocks. He feared the economic ruin of our country through a paper money and deficit driven hyper inflation. I explained how Grandma Cookie had been right for the last thirty years and would probably be right for the next thirty as well. I subscribed to Warren Buffett’s old criticism that gold just sits there with no yield and viewed gold’s long-term value as difficult to assess.

However, the recent crisis has changed my view. The question can be flipped: how does one know what the dollar is worth given that dollars can be created out of thin air or dropped from helicopters? Just because something hasn’t happened, doesn’t mean it won’t. Yes, we should continue to buy stocks in great companies, but there is room for Grandpa Ben’s view as well.

I have seen many people debate whether gold is a bet on inflation or deflation. As I see it, it is neither. Gold does well when monetary and fiscal policies are poor and does poorly when they appear sensible. Gold did very well during the Great Depression when FDR debased the currency. It did well again in the money printing 1970s, but collapsed in response to Paul Volcker’s austerity. It ultimately made a bottom around 2001 when the excitement about our future budget surpluses peaked.

Prospectively, gold should do fine unless our leaders implement much greater fiscal and monetary restraint than appears likely. Of course, gold should do very well if there is a sovereign debt default or currency crisis. A few weeks ago, the Office of Inspector General called out the Treasury Department for misrepresenting the position of the banks last fall. The Treasury’s response was an unapologetic expression that amounted to saying that at that point “doing whatever it takes” meant pulling a Colonel Jessup: “YOU CAN’T HANDLE THE TRUTH!” At least we know what we are dealing with.

When I watch Chairman Bernanke, Secretary Geithner and Mr. Summers on TV, read speeches written by the Fed Governors, observe the “stimulus” black hole, and think about our short-termism and lack of fiscal discipline and political will, my instinct is to want to short the dollar. But then I look at the other major currencies. The Euro, the Yen, and the British Pound might be worse. So, I conclude that picking one these currencies is like choosing my favorite dental procedure. And I decide holding gold is better than holding cash, especially now, where both earn no yield.

Along these same lines, we have bought long-dated options on much higher U.S. and Japanese interest rates. The options in Japan are particularly cheap because the historical volatility is so low. I prefer options to simply shorting government bonds, because there remains a possibility of a further government bond rally in response to the economy rolling over again. With options, I can clearly limit how much I am willing to lose, while creating a lot of leverage to a possible rate spiral.

For years, the discussion has been that our deficit spending will pass the costs onto “our grandchildren.” I believe that this is no longer the case and that the consequences will be seen during the lifetime of the leaders who have pursued short-term popularity over our solvency. The recent economic crisis and our response has brought forward the eventual reconciliation into a window that is near enough that it makes sense for investors to buy some insurance to protect themselves from a possible systemic event. To slightly modify Alexis de Tocqueville: Events can move from the impossible to the inevitable without ever stopping at the probable.  As investors, we can’t change the course of events, but we can attempt to protect capital in the face of foreseeable risks. Of course, just like MDC, there remains the possibility that I am completely wrong. And, personally, I hope I am. I wonder what Stan Druckenmiller thinks.

22 Years, Two Lessons

Today is the 22nd anniversary of the 1987 stock market crash — a plunge that first terrified investors and then, after stocks recovered, persuaded them that stocks were always great investments.

That got me to wondering how a buy-and-hold investor would have done over those 22 years. I assume that lucky investor bought the Standard & Poor’s 500 at the close on Oct. 19, 1987, and held on thereafter, reinvesting dividends and changing the portfolio only to reflect index changes. I also assume no taxes and no transaction costs, assumptions that may strike some people as a tad optimistic.

The results are not bad. For the entire period, that investor would have earned an annual return of 9.9 percent per year before considering inflation. After inflation, the return is a still good annual rate of 6.8 percent.

But, as you well know, the stock market returns were not evenly spread over that period. During the first 11 years, the average return was 18.6 percent per year before inflation, and 14.6 percent after.

During the next 11 years, the returns averaged 2 percent per year, not enough to offset inflation. The investor lost 0.5 percent per year when inflation is taken into account.

THE LAST DAYS OF THE POLYMATH

People who know a lot about a lot have long been an exclusive club, but now they are an endangered species. Edward Carr tracks some down ...

From INTELLIGENT LIFE Magazine, Autumn 2009

His wife, Diane Middlebrook, thought it was a ridiculous idea. She was also a professor—of literature. “She admired the fact that I was a scientist who also wrote,” Djerassi says. He remembers her telling him, “‘You’ve been writing about a world that writers know little about. You’re writing the real truth inside of almost a closed tribe. But there are tens of thousands, hundreds of thousands of people who know more about writing than you do. I advise you not to do this.’ ”

Even at 85, slight and snowy-haired, Djerassi is a det­ermined man. You sense his need to prove that he can, he will prevail. Sitting in his London flat, he leans forward to fix me with his hazel eyes. “I said, ‘ok. I’m not going to show it to you till I finish. And if I find a publisher then I’ll give it to you.’ ”

Eventually Djerassi got the bound galleys of his book. “We were leaving San Francisco for London for our usual summer and I said ‘Look, would you read this now?’ She said, ‘Sure, on the plane.’ So my wife sits next to me and of course I sit and look over. And I still remember, I had a Trollope, 700 pages long, and I couldn’t read anything because I wanted to see her expression.”

Diane Middlebrook died of cancer in 2007 and, as Djerassi speaks, her presence grows stronger. By the end it is as if there are three of us in the room. “She was always a fantastic reader,” he says. “She read fast and continuously. And suddenly you hear the snap of the book closing, like a thunder clap. And I looked at her, and she then looked at me.  She always used to call me, not ‘Carl’ or ‘Darling’, she used to call me ‘Chemist’ in a dear, affectionate sort of way. It was always ‘Chemist’. And she said, ‘Chemist, this is good’.”

Carl Djerassi is a polymath. Strictly speaking that means he is someone who knows a lot about a lot. But Djerassi also passes a sterner test: he can do a lot, too. As a chemist (synthesising cortisone and helping invent the Pill); an art collector (he assembled one of the world’s largest collections of works by Paul Klee); and an author (19 books and plays), he has accomplished more than enough for one lifetime.

His latest book, “Four Jews on Parnassus”, is an ima­gined series of debates between Theodor Adorno, Arnold Schönberg, Walter Benjamin and Gershom Scholem, which touches on art, music, philosophy and Jewish identity. In itself, the book is an exercise in polymathy. At a reading in the Austrian Cultural Forum in London this summer, complete with Schönberg’s songs and four actors, including Djerassi himself, it drew a good crowd and bewitched them for an hour and a half. Sitting down with the book the next day, I found it sharp, funny, mannered and dazzlingly erudite—sometimes, like a bumptious student, too erudite for its own good. I enjoy Djerassi’s writing, though not everyone will. But even his critics would admit that he really is more than “a scientist who writes”.

The word “polymath” teeters somewhere between Leo­nardo da Vinci and Stephen Fry. Embracing both one of history’s great intellects and a brainy actor, writer, director and TV personality, it is at once presumptuous and banal. Djerassi doesn’t want much to do with it. “Nowadays people that are called polymaths are dabblers—are dabblers in many different areas,” he says. “I aspire to be an intellectual polygamist. And I deliberately use that metaphor to provoke with its sexual allusion and to point out the real difference to me between polygamy and promiscuity."

“To me, promiscuity is a way of flitting around. Polygamy, serious polygamy, is where you have various marriages and each of them is important. And in the ideal polygamy I suspect there’s no number one wife and no number six wife. You have a deep connection with each person.”

Djerassi is right to be suspicious of flitting. We all know a gifted person who cannot stick at anything. In his book “Casanova: A Study in Self-Portraiture” Stefan Zweig describes an extreme case:

[Casanova] excelled in mathematics no less than in philosophy. He was a competent theologian, preaching his first sermon in a Venetian church when he was not yet 16 years old. As a violinist, he earned his daily bread for a whole year in the San Samuele theatre. When he was 18 he became doctor of laws at the University of Padua—though down to the present day the Casanovists are still disputing whether the degree was genuine or spurious...He was well informed in chemistry, medicine, history, philosophy, literature, and, above all, in the more lucrative (because perplexing) sciences of astrology and alchemy...As universal dilettante, indeed, he was perfect, knowing an incredible amount of all the arts and all the sciences; but he lacked one thing, and this lack made it impossible for him to become truly productive. He lacked will, resolution, patience.

Mindful of that sort of promiscuity, I asked my colleagues to suggest living polymaths of the polygamous sort—doers, not dabblers. One test I imposed was breadth. A scientist who composes operas and writes novels is more of a polymath than a novelist who can turn out a play or a painter who can sculpt. For Djerassi, influence is essential too. “It means that your polymath activities have passed a certain quality control that is exerted within each field by the competition. If they accept you at their level, then I think you have reached that state rather than just dabbling.” They mentioned a score of names—Djerassi was prominent among them. Others included Jared Diamond, Noam Chomsky, Umberto Eco, Brian Eno, Michael Frayn and Oliver Sacks.

It is an impressive list, by anyone’s standards. You can find scientists, writers, actors, artists—the whole range of human creativity. Even so, what struck me most strongly was how poorly today’s polymaths compare with the polymaths of the past.

In the first half of 1802 a physician and scientist called Thomas Young gave a series of 50 lectures at London’s new Royal Institution, arranged into subjects like “Mechanics” and “Hydro­dynamics”. By the end, says Young’s biographer Andrew Robinson, he had pretty much laid out the sum of scientific knowledge. Robinson called his book “The Last Man Who Knew Everything”.

Young’s achievements are staggering. He smashed Newtonian orthodoxy by showing that light is a wave, not just a particle; he described how the eye can vary its focus; and he proposed the three-colour theory of vision. In materials science, engineers dealing with elasticity still talk about Young’s modulus; in linguistics, Young studied the grammar and voc­abulary of 400 or so languages and coined the term “Indo-European”; in Egyptology, Jean-François Champollion drew on his work to decode the Rosetta stone. Young even tinkered around with life insurance.

When Young was alive the world contained about a billion people. Few of them were literate and fewer still had the chance to experiment on the nature of light or to examine the Rosetta stone. Today the planet teems with 6.7 billion minds. Never have so many been taught to read and write and think, and then been free to choose what they would do with their lives. The electronic age has broken the shackles of knowledge. Never has it been easier to find something out, or to get someone to explain it to you.

Yet as human learning has flowered, the man or woman who does great things in many fields has become a rare species. Young was hardly Aristotle, but his capacity to do important work in such a range of fields startled his contemporaries and today seems quite bewildering. The dead cast a large shadow but, even allowing for that, the 21st century has no one to match Michelangelo, who was a poet as well as a sculptor, an architect and a painter. It has no Alexander von Humboldt, who towered over early-19th-century geography and science. And no Leibniz, who invented calculus at the same time as Newton and also wrote on technology, philosophy, biology, politics and just about everything else.

Although you may be able to think of a few living polymaths who rival the breadth of Young’s knowledge, not one of them beg­ins to rival the breadth of his achievements. Over the past 200 years the nature of intellectual endeavour has changed profoundly. The polymaths of old were one-brain universities. These days you count as a polymath if you excel at one thing and go on to write a decent book about another.

Young was just 29 when he gave his lectures at the Royal Institution. Back in the early 19th century you could grasp a field with a little reading and a ready wit. But the distinction between the dabbling and doing is more demanding these days, because breaking new ground is so much harder. There is so much further to trek through other researchers’ territory before you can find a patch of unploughed earth of your own.

Even the best scientists have to make that journey. Benjamin Jones, of the Kellogg School of Management near Chicago, looked at the careers of Nobel laureates. Slightly under half of them did their path-breaking work in their 30s, a smattering in their 20s—Einstein, at 26, was unusually precocious. Yet when the laureates of 1998 did their seminal research, they were typically six years older than the laureates of 1873 had been. It was the same with great inventors.

Once you have reached the vanguard, you have to work harder to stay there, especially in the sciences. So many scientists are publishing research in each specialism that merely to keep up with the reading is a full-time job. “The frontier of knowledge is getting longer,” says Professor Martin Rees, the president of the Royal Society, where Young was a leading light for over three decades. “It is impossible now for anyone to focus on more than one part at a time.”

Specialisation is hard on polymaths. Every moment devoted to one area is a moment less to give over to something else. Researchers are focused on narrower areas of work. In the sciences this means that you often need to put together a team to do anything useful. Most scientific papers have more than one author; papers in some disciplines have 20 or 30. Only a fool sets out to cure cancer, Rees says. You need to concentrate on some detail—while remembering the big question you are ultimately trying to answer. “These days”, he says, “no scientist makes a unique contribution.”

It is not only the explosion of knowledge that puts polymaths at a disadvantage, but also the vast increase in the number of specialists and experts in every field. This is because the learning that creates would-be polymaths creates monomaths too and in overwhelming numbers. If you have a multitude who give their lives to a specialism, their combined knowledge will drown out even a gifted generalist. And while the polymath tries to take possession of a second expertise in some distant discipline, his or her first expertise is being colonised by someone else.

The arts are more forgiving than the sciences. Rees is reminded of a remark by Peter Medawar, the zoologist, who pointed out that, after finishing a draft of “Siegfried” in 1857, Wagner was able to put the opera aside for 12 years before setting out to complete his Ring Cycle with “Götterdämmerung”. A scientist would have had to worry about a rival stealing his thunder. But nobody else was about to compose the destruction of Valhalla.

Perhaps that explains why would-be polymaths these days so often turn to writing books. Yet, as Richard Posner has discovered, even that is often enemy territory.

Unlike France, America and Britain don’t tend to encourage public intellectuals. But if they did, Richard Posner would be their standard-bearer. Posner’s day job is as an appeals-court judge in Chicago—a career founded upon his reputation as America’s pre-eminent thinker on anti-trust law. But Posner is not just a lawyer. In his spare time he has written on sex, security, politics, Hegel, Homeric society, medieval Iceland and a whole lot more. The Wall Street Journal once called him a “one-man think-tank”.

Posner thinks like a polymath. “I’m impatient and I’m restless,” he says, in a matter-of-fact way. “After I graduated from law school, I worked first in government for six years. I enjoyed it but I didn’t really want to make a career of that. I went into teaching without any great sense of commitment, but I couldn’t think of anything else. But gradually I lost int­erest, as the 1970s wore on, I became involved in consulting. So when the judgeship came along in 1981—quite out of the blue—I was happy to take that. I just kind of slid into law. It is sort of the default career choice in the United States.”

Posner first made his name as a monomath. “I had a very big intellectual commitment for many years to anti-trust law. I wrote a lot about that.” Eventually, though, the polymath rose to the surface and he put anti-trust behind him. “I just got bored with it, I think the field slowed down—it happens with fields,” he says. These days most people cling to their expertise; Posner talks about it as if he were trading in an old car.

After he became immersed in the intellectual life of the University of Chicago, Posner started to apply insights from economics to a broad range of subjects. In his book “Sex and Reason”, written in 1990, he used economics to explain a part of life that specialist lawyers and economists had tended to think was beyond their reach. To take a simple example, the AIDS epidemic made gay sex unavoidably more costly, either because of the risk of disease or of switching to safe sex. It therefore reduced the amount of gay sex—and, by the same mechanism, cut the number of illegitimate births and inc­reased the number of legitimate ones.

The book was a success because Posner had the field pretty much to himself. “Sometimes one goes into a new area and there hasn’t been much done in it and then you are a little ahead of the curve,” he says. Even then, the monomaths were in hot pursuit. “After a while there is so much in it that you don’t know what you’re going to do. Since 1990 the field has become extremely crowded because of specialisation and not very attractive.” Time to move on.

The monomaths do not only swarm over a specialism, they also play dirty. In each new area that Posner picks—policy or science—the experts start to erect barricades. “Even in relatively soft fields, specialists tend to develop a specialised vocabulary which creates barriers to entry,” Posner says with his economic hat pulled down over his head. “Specialists want to fend off the generalists. They may also want to convince themselves that what they are doing is really very difficult and challenging. One of the ways they do that is to develop what they regard a rigorous methodology—often mathematical.

“The specialist will always be able to nail the generalists by pointing out that they don’t use the vocabulary quite right and they make mistakes that an insider would never make. It’s a defence mechanism. They don’t like people invading their turf, especially outsiders criticising insiders. So if I make mistakes about this economic situation, it doesn’t really bother me tremendously. It’s not my field. I can make mistakes. On the other hand for me to be criticising someone whose whole career is committed to a particular outlook and method and so on, that is very painful.”

For a polymath, the charge of dabbling never lies far below the surface. “With the amount of information that’s around, if you really want to understand your topic thoroughly then, yes, you have to specialise,” says Chris Leek, the chairman of British Mensa, a club for people who score well on IQ tests. “And if you want to speak with authority, then it’s important to be seen to specialise.”

That is why modern institutions tend to exclude polymaths, he says. “It’s very hard to show yourself as a polymath in the current academic climate. If you’ve got someone interested in going across departments, spending part of the time in physics and part of the time elsewhere, their colleagues are going to kick them out. They’re not contributing fully to any single department. OK, every so often you’re going to get a huge benefit, but from day to day, where the universities are making appointments, they want the focus in one field.”

Britain goes out of its way to create monomaths, by asking students aged 15 to choose just three or four subjects to study at A-level. Djerassi thinks this is a mistake. “There’ll be students here at age 16 or 17 who are much better than many Americans at French or maths or something, but abysmally ignorant in another area,” he says. “We really preach intellectual monogamy more and more in this day and age. That’s by necessity, but we’re overdoing it. And what we really ought to do is start with intellectual polygamy.”

Djerassi has also suffered in his own work because of monomaths’ hostility, especially as a playwright. “They always keep crying out ‘the co-inventor, father, the mother of the Pill’,” he growls. “Without having any knowledge about the play, they start with it. As if it’s got anything to do with it.” Djerassi thinks that this means he has to work harder to promote his work. “No agent has ever been interested in me. They want 29-year-old Irish playwrights, not 86-year-old expatriates.” A trace of bitterness creeps into his voice, but he concedes: “If I were an agent I’d feel the same way.”

Overwhelmed by specialists and attacked by experts as dilettantes, it is amazing that there are any polymaths at all. How do they manage?

Alexander McCall Smith is a natural writer. “I just have to do it,” he says. “I suppose I write four novels a year now, which I don’t have to do. In one sense, that is breaking all the rules in publishing: you’re only meant to write one, but I write four, sometimes five. But I just feel that I have got to do it and I enjoy it greatly. I suppose I am very fortunate. The way I work is I go into a trance and write. I don’t have to sit there and think: it happens. It just comes, so I am very, very lucky.”

These days McCall Smith is best-known as the man behind “The No. 1 Ladies’ Detective Agency”. But his first career, as a university professor, was eminent in its own right. “My interest was medical law. That, I suppose, was cross-disciplinary. You had to be able to understand the scientific issues and the medical issues, but you just had to have a sound lay understanding of them. So, for example, I worked as a member of the Human Genetics Commission for a while. And that meant I had to go off and make sure that I understood what the issues in genetics were.”

He is also musical—though in a dabbling way. “I play wind instruments, but I don’t play them very well,” he says. “My wife and I set up an orchestra, which is called the Really Terrible Orchestra, and indeed that is absolutely accurate. Virtually everybody I know is better at music than I am.”

McCall Smith is a polymath by necessity. He wrote while he was an academic, producing fiction, about 30 children’s books, short stories and plays for radio. He paid a price. “I probably would have made more of my academic career had I not had another interest, I think, yes. Academia requires a lot of commitment, so I suppose I could have done more.” But, speaking to him, I don’t think he had a choice.

Circumstance also played its part. McCall Smith was able to write because university life allowed it. “It would have been different had I been somebody who practised commercial law in a law firm, for instance. That wouldn’t be compatible with doing anything else. If you were a futures trader or something like that—there are some jobs where the pressure is so intense that it must be very difficult to have any energy by the time you come home at night.”

Posner could become a polymath because he has a unifying set of ideas. “A lot of this work is economic theory in new areas. So applying a method to a new field is not the same thing as mastering multiple fields. To achieve mastery in unrelated areas in an age of specialisation is exceedingly difficult. On the other hand, to take a technique that can be applied to a variety of substantive fields is not as difficult. So if I write about the economics of old age and the economics of sex and the economics of the national security and intelligence services, I am not mastering the field. I am not becoming a sociologist, or a psychiatrist or what have you.”

Djerassi could become a polymath because he has had two careers, one after the other—he did his science and, having made a fortune, he concentrated on his writing. He was helped by his wife. “She was a very sophisticated writer and an extremely tough critic and she managed to divorce affection from criticism. She thought ‘this is terrible’ or ‘this is clichéd’.” He also has ambition and the willpower of someone on borrowed time. At 62 he was diagnosed with cancer. “Suddenly, from one day to another, I didn’t even know what my life expectancy would be before I got the pathology back after the operation. And I remember being very depressed and afterwards I didn’t want to talk to anyone.” He said to himself, “‘Gee, now if I’d known five years earlier it would come out that I’d have cancer and be told I’d live for another few years, would I live a different life?’ And I said, ‘Absolutely’.”

Not all polymaths find their way. Andrew Robinson, Young’s biographer, gives the example of Michael Ventris, who died aged 34, having tried to satisfy both his urge to be an architect and also his fascination with codes. Ventris was the first to make sense of Linear B, an early Greek script, but he could not apply himself as successfully to architecture.

“With Michael Ventris, the polymathy gradually des­troyed him,” Robinson says. “He was famous for cracking Linear B, but I believe he was depressed. Architecture was not enough. He was a logician. Linear B took him over. He couldn’t reach the standard he had set in another field, he couldn’t do justice to his own gifts, he couldn’t let it all go and give it up.”

Robinson thinks that Young also ran up against his limits. “Young understood after 1814 that he couldn’t carry on with serious medicine. He could have pursued it but even then it was clear that he wouldn’t be taken seriously. People love a sole genius with tunnel vision—a focus,” Robinson says. Darwin spent several years thinking about barnacles. But because Young’s work was in so many different fields, he was accused of being a dilettante. “Polymaths are disconcerting,” Robinson says. “People feel they are trespassing.”

Even Leonardo warned against being spread thin. The other day Robinson came across one of his late notebooks, in which he had written, “Like a kingdom divided, which rushes to its doom, the mind that engages in subjects of too great variety becomes confused and weakened.”

In an age of specialists, does it matter that generalists no longer thrive? The world is hardly short of knowledge. Countless books are written, canvases painted and songs recorded. A torrent of research is pouring out. A new orthodoxy, popularised by Malcolm Gladwell, sees obsessive focus as the key that unlocks genius.

Just knowing about a lot of things has never been easier. Never before have dabblers been so free to paddle along the shore and dip into the first rock pool that catches the eye. If you have an urge to take off your shoes and test the water, countless specialists are ready to hold your hand.

And yet you will never get very deep. Depth is for monomaths—which is why experts so often seem to miss what really matters. Specialisation has made the study of English so sterile that students lose much of the joy in reading great literature for its own sake. A generation of mathematically inclined economists neglected many of  Keynes’s insights about the Depression because he put them into words. For decades economists sweated over fiendish mathematical equations, only to be brought down to earth by the credit crunch: Keynes’s well-turned phrases had come back to life.

Part of my regret at the scarcity of polymaths is sentimental. Polymaths were the product of a particular time, when great learning was a mark of distinction and few people had money and leisure. Their moment has passed, like great houses or the horse-drawn carriage. The world may well be a better place for the specialisation that has come along instead. The pity is that progress has to come at a price. Civilisation has put up fences that people can no longer leap across; a certain type of mind is worth less. The choices modern life imposes are duller, more cramped.

The question is whether their loss has affected the course of human thought. Polymaths possess something that monomaths do not. Time and again, innovations come from a fresh eye or from another discipline. Most scientists devote their careers to solving the everyday problems in their specialism. Everyone knows what they are and it takes ingenuity and perseverance to crack them. But breakthroughs—the sort of idea that opens up whole sets of new problems—often come from other fields. The work in the early 20th century that showed how nerves work and, later, how DNA is structured originally came from a marriage of physics and biology. Today, Einstein’s old employer, the Institute for Advanced Study at Princeton, is laid out especially so that different disciplines rub shoulders. I suspect that it is a poor substitute.

Isaiah Berlin once divided thinkers into two types. Foxes, he wrote, know many things; whereas hedgehogs know one big thing. The foxes used to roam free across the hills. Today the hedgehogs rule. 

Abstract
Yen carry trades have traditionally been viewed in narrow terms purely
as a foreign exchange transaction. We argue that the carry trade should instead be viewed in the broader context of global credit conditions. We show that the volume of Yen funding that is channeled for use outside Japan is mirrored by fluctuations in the size of US broker-dealer balance sheets. Differences in short-term interest rates across currencies help to explain the incidence of the carry trade, as does the measure of implied equity risk given by the VIX index. The conjunction of deteriorating credit conditions in the US and the weakness of the US Dollar against the Yen in the early stages of the credit crisis of 2007/8 can thus be seen as two sides of the same coin. Both can be seen as consequences of nancial sector deleveraging in
the US.

Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the "logic of science"; it is the instrument of risk-taking; it is the applied tools of epistemology; you can't be a modern intellectual and not think probabilistically—but... let's not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now. It can even bankrupt the system (let's face it: use of probabilistic methods for the estimation of risks did just blow up the banking system).

THE FOURTH QUADRANT: A MAP OF THE LIMITS OF STATISTICS [9.15.08]
By Nassim Nicholas Taleb

An Edge Original Essay

Introduction

When Nassim Taleb talks about the limits of statistics, he becomes outraged. "My outrage," he says, "is aimed at the scientist-charlatan putting society at risk using statistical methods. This is similar to iatrogenics, the study of the doctor putting the patient at risk." As a researcher in probability, he has some credibility. In 2006, using FNMA and bank risk managers as his prime perpetrators, he wrote the following:

The government-sponsored institution Fannie Mae, when I look at its risks, seems to be sitting on a barrel of dynamite, vulnerable to the slightest hiccup. But not to worry: their large staff of scientists deemed these events "unlikely."

In the following Edge original essay, Taleb continues his examination of Black Swans, the highly improbable and unpredictable events that have massive impact. He claims that those who are putting society at risk are "no true statisticians", merely people using statistics either without understanding them, or in a self-serving manner. "The current subprime crisis did wonders to help me drill my point about the limits of statistically driven claims," he says.

Taleb, looking at the cataclysmic situation facing financial institutions today, points out that "the banking system, betting against Black Swans, has lost over 1 Trillion dollars (so far), more than was ever made in the history of banking".

But, as he points out, there is also good news.

We can identify where the danger zone is located, which I call "the fourth quadrant", and show it on a map with more or less clear boundaries. A map is a useful thing because you know where you are safe and where your knowledge is questionable. So I drew for the Edge readers a tableau showing the boundaries where statistics works well and where it is questionable or unreliable. Now once you identify where the danger zone is, where your knowledge is no longer valid, you can easily make some policy rules: how to conduct yourself in that fourth quadrant; what to avoid.

—John Brockman

NASSIM NICHOLAS TALEB, essayist and former mathematical trader, is Distinguished Professor of Risk Engineering at New York University’s Polytechnic Institute. He is the author of Fooled by Randomness and the international bestseller The Black Swan.

Nassim Taleb's Edge Bio Page

REALITY CLUB: Jaron Lanier, George Dyson

BLOGWATCH

THE FOURTH QUADRANT: A MAP OF THE LIMITS OF STATISTICS

Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the "logic of science"; it is the instrument of risk-taking; it is the applied tools of epistemology; you can't be a modern intellectual and not think probabilistically—but... let's not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now. It can even bankrupt the system (let's face it: use of probabilistic methods for the estimation of risks did just blow up the banking system).

The current subprime crisis has been doing wonders for the reception of any ideas about probability-driven claims in science, particularly in social science, economics, and "econometrics" (quantitative economics).  Clearly, with current International Monetary Fund estimates of the costs of the 2007-2008 subprime crisis,  the banking system seems to have lost more on risk taking (from the failures of quantitative risk management) than every penny banks ever earned taking risks. But it was easy to see from the past that the pilot did not have the qualifications to fly the plane and was using the wrong navigation tools: The same happened in 1983 with money center banks losing cumulatively every penny ever made, and in 1991-1992 when the Savings and Loans industry became history.

It appears that financial institutions earn money on transactions (say fees on your mother-in-law's checking account) and lose everything taking risks they don't understand. I want this to stop, and stop now— the current patching by the banking establishment worldwide is akin to using the same doctor to cure the patient when the doctor has a track record of systematically killing them. And this is not limited to banking—I generalize to an entire class of random variables that do not have the structure we thing they have, in which we can be suckers.

And we are beyond suckers: not only, for socio-economic and other nonlinear, complicated variables, we are riding in a bus driven a blindfolded driver, but we refuse to acknowledge it in spite of the evidence, which to me is a pathological problem with academia. After 1998, when a "Nobel-crowned" collection of people (and the crème de la crème of the financial economics establishment) blew up Long Term Capital Management, a hedge fund, because the "scientific" methods they used misestimated the role of the rare event, such methodologies and such claims on understanding risks of rare events should have been discredited. Yet the Fed helped their bailout and exposure to rare events (and model error) patently increased exponentially (as we can see from banks' swelling portfolios of derivatives that we do not understand).

Are we using models of uncertainty to produce certainties?

This masquerade does not seem to come from statisticians—but from the commoditized, "me-too" users of the products. Professional statisticians can be remarkably introspective and self-critical. Recently, the American Statistical Association had a special panel session on the "black swan" concept at the annual Joint Statistical Meeting in Denver last August. They insistently made a distinction between the "statisticians" (those who deal with the subject itself and design the tools and methods) and those in other fields who pick up statistical tools from textbooks without really understanding them. For them it is a problem with statistical education and half-baked expertise. Alas, this category of blind users includes regulators and risk managers, whom I accuse of creating more risk than they reduce. 

So the good news is that we can identify where the danger zone is located, which I call "the fourth quadrant", and show it on a map with more or less clear boundaries.  A map is a useful thing because you know where you are safe and where your knowledge is questionable. So I drew for the Edge readers a tableau showing the boundaries where statistics works well and where it is questionable or unreliable.  Now once you identify where the danger zone is, where your knowledge is no longer valid, you can easily make some policy rules: how to conduct yourself in that fourth quadrant; what to avoid.

So the principal value of the map is that it allows for policy making. Indeed, I am moving on: my new project is about methods on how to domesticate the unknown, exploit randomness, figure out how to live in a world we don't understand very well. While most human thought (particularly since the enlightenment) has focused us on how to turn knowledge into decisions, my new mission is to build methods to turn lack of information, lack of understanding, and lack of "knowledge" into decisions—how, as we will see, not to be a "turkey".

This piece has a technical appendix that presents mathematical points and empirical evidence. (See link below.) It includes a battery of tests showing that no known conventional tool can allow us to make precise statistical claims in the Fourth Quadrant. While in the past I limited myself to citing research papers, and evidence compiled  by others (a less risky trade), here I got hold of more than 20 million pieces of data (includes 98% of the corresponding macroeconomics values of transacted daily, weekly, and monthly variables for the last 40 years) and redid a systematic analysis that includes recent years.

What Is Fundamentally Different About Real Life

My anger with "empirical" claims in risk management does not come from research. It comes from spending twenty tense (but entertaining) years taking risky decisions in the real world managing portfolios of complex derivatives, with payoffs that depend on higher order statistical properties —and you quickly realize that a certain class of relationships that "look good" in research papers almost never replicate in real life (in spite of the papers making some claims with a "p" close to infallible). But that is not the main problem with research.

For us the world is vastly simpler in some sense than the academy, vastly more complicated in another. So the central lesson from decision-making (as opposed to working with data on a computer or bickering about logical constructions) is the following: it is the exposure (or payoff) that creates the complexity —and the opportunities and dangers— not so much the knowledge ( i.e., statistical distribution, model representation, etc.). In some situations, you can be extremely wrong and be fine, in others you can be slightly wrong and explode. If you are leveraged, errors blow you up; if you are not, you can enjoy life.

So knowledge (i.e., if some statement is "true" or "false") matters little, very little in many situations. In the real world, there are very few situations where what you do and your belief if some statement is true or false naively map into each other. Some decisions require vastly more caution than others—or highly more drastic confidence intervals. For instance you do not "need evidence" that the water is poisonous to not drink from it. You do not need "evidence" that a gun is loaded to avoid playing Russian roulette, or evidence that a thief a on the lookout to lock your door. You need evidence of safety—not evidence of lack of safety— a central asymmetry that affects us with rare events. This asymmetry in skepticism makes it easy to draw a map of danger spots.

The Dangers Of Bogus Math

I start with my old crusade against "quants" (people like me who do mathematical work in finance), economists, and bank risk managers, my prime perpetrators of iatrogenic risks (the healer killing the patient). Why iatrogenic risks? Because, not only have economists been unable to prove that their models work, but no one managed to prove that  the use of a model that does not work is neutral, that it does not increase blind risk taking, hence the accumulation of hidden risks.

Figure 1 My classical metaphor: A Turkey is fed for a 1000 days—every days confirms to its statistical department that the human race cares about its welfare "with increased statistical significance". On the 1001^st day, the turkey has a surprise.

Figure 2 The graph above shows the fate of close to 1000 financial institutions (includes busts such as FNMA, Bear Stearns, Northern Rock, Lehman Brothers, etc.). The banking system (betting AGAINST rare events) just lost > 1 Trillion dollars (so far) on a single error, more than was ever earned in the history of banking. Yet bankers kept their previous bonuses and it looks like citizens have to foot the bills. And one Professor Ben Bernanke pronounced right before the blowup that we live in an era of stability and "great moderation" (he is now piloting a plane and we all are passengers on it).

Figure 3 The graph shows the daily variations a derivatives portfolio exposed to U.K. interest rates between 1988 and 2008. Close to 99% of the variations, over the span of 20 years, will be represented in 1 single day—the day the European Monetary System collapsed. As I show in the appendix, this is typical with ANY socio-economic variable (commodity prices, currencies, inflation numbers, GDP, company performance, etc. ). No known econometric statistical method can capture the probability of the event with any remotely acceptable accuracy (except, of course, in hindsight, and "on paper"). Also note that this applies to surges on electricity grids and all manner of modern-day phenomena.

Figures 1 and 2 show you the classical problem of the turkey making statements on the risks based on past history (mixed with some theorizing that happens to narrate well with the data). A friend of mine was sold a package of subprime loans (leveraged) on grounds that "30 years of history show that the trade is safe." He found the argument unassailable "empirically". And the unusual dominance of the rare event shown in Figure 3 is not unique: it affects all macroeconomic data—if you look long enough almost all the contribution in some classes of variables will come from rare events (I looked in the appendix at 98% of trade-weighted data).

Now let me tell you what worries me. Imagine that the Turkey can be the most powerful man in world economics, managing our economic fates. How? A then-Princeton economist called Ben Bernanke made a pronouncement in late 2004 about the "new moderation" in economic life: the world getting more and more stable—before becoming the Chairman of the Federal Reserve. Yet the system was getting riskier and riskier as we were turkey-style sitting on more and more barrels of dynamite—and Prof. Bernanke's predecessor the former Federal Reserve Chairman Alan Greenspan was systematically increasing the hidden risks in the system, making us all more vulnerable to blowups.

By the "narrative fallacy" the turkey economics department will always manage to state, before thanksgivings that "we are in a new era of safety", and back-it up with thorough and "rigorous" analysis. And Professor Bernanke indeed found plenty of economic explanations—what I call the narrative fallacy—with graphs, jargon, curves, the kind of facade-of-knowledge that you find in economics textbooks. (This is the find of glib, snake-oil facade of knowledge—even more dangerous because of the mathematics—that made me, before accepting the new position in NYU's engineering department, verify that there was not a single economist in the building. I have nothing against economists: you should let them entertain each others with their theories and elegant mathematics, and help keep college students inside buildings. But beware: they can be plain wrong, yet frame things in a way to make you feel stupid arguing with them. So make sure you do not give any of them risk-management responsibilities.)

Bottom Line: The Map

Things are made simple by the following. There are two distinct types of decisions, and two distinct classes of randomness.

Decisions: The first type of decisions is simple, "binary", i.e. you just care if something is true or false. Very true or very false does not matter. Someone is either pregnant or not pregnant. A statement is "true" or "false" with some confidence interval. (I call these M0 as, more technically, they depend on the zero^th moment, namely just on probability of events, and not their magnitude —you just care about "raw" probability). A biological experiment in the laboratory or a bet with a friend about the outcome of a soccer game belong to this category.

The second type of decisions is more complex. You do not just care of the frequency—but of the impact as well, or, even more complex, some function of the impact. So there is another layer of uncertainty of impact. (I call these M1+, as they depend on higher moments of the distribution). When you invest you do not care how many times you make or lose, you care about the expectation: how many times you make or lose times the amount made or lost.

Probability structures: There are two classes of probability domains—very distinct qualitatively and quantitatively. The first, thin-tailed: Mediocristan", the second, thick tailed Extremistan. Before I get into the details, take the literary distinction as follows:

In Mediocristan, exceptions occur but don't carry large consequences. Add the heaviest person on the planet to a sample of 1000. The total weight would barely change. In Extremistan, exceptions can be everything (they will eventually, in time, represent everything). Add Bill Gates to your sample: the wealth will  jump by a factor of >100,000. So, in Mediocristan, large deviations occur but they are not consequential—unlike Extremistan.

Mediocristan corresponds to "random walk" style randomness that you tend to find in regular textbooks (and in popular books on randomness). Extremistan corresponds to a "random jump" one. The first kind I can call "Gaussian-Poisson", the second "fractal" or Mandelbrotian (after the works of the great Benoit Mandelbrot linking it to the geometry of nature). But note here an epistemological question: there is a category of "I don't know" that I also bundle in Extremistan for the sake of decision making—simply because I don't know much about the probabilistic structure or the role of large events.

The Map

Now it lets see where the traps are:

First Quadrant: Simple binary decisions, in Mediocristan: Statistics does wonders. These situations are, unfortunately, more common in academia, laboratories, and games than real life—what I call the "ludic fallacy". In other words, these are the situations in casinos, games, dice, and we tend to study them because we are successful in modeling them.

Second Quadrant: Simple decisions, in Extremistan: some well known problem studied in the literature. Except of course that there are not many simple decisions in Extremistan.

Third Quadrant: Complex decisions in Mediocristan: Statistical methods work surprisingly well.

Fourth Quadrant: Complex decisions in Extremistan: Welcome to the Black Swan domain. Here is where your limits are. Do not base your decisions on statistically based claims. Or, alternatively, try to move your exposure type to make it third-quadrant style ("clipping tails").

Let me refine the analysis. The passage from theory to the real world presents two distinct difficulties: "inverse problems" and  "pre-asymptotics".

Inverse Problems. It is the greatest epistemological difficulty I know. In real life we do not observe probability distributions (not even in Soviet Russia, not even the French government). We just observe events. So we do not know the statistical properties—until, of course, after the fact. Given a set of observations, plenty of statistical distributions can correspond to the exact same realizations—each would extrapolate differently outside the set of events on which it was derived. The inverse problem is more acute when more theories, more distributions can fit a set a data.

This inverse problem is compounded by the small sample properties of rare events as these will be naturally rare in a past sample. It is also acute in the presence of nonlinearities as the families of possible models/parametrization explode in numbers.

Pre-asymptotics. Theories are, of course, bad, but they can be worse in some situations when they were derived in idealized situations, the asymptote, but are used outside the asymptote (its limit, say infinity or the infinitesimal). Some asymptotic properties do work well preasymptotically (Mediocristan), which is why casinos do well, but others do not, particularly when it comes to Extremistan.

Most statistical education is based on these asymptotic, Platonic properties—yet we live in the real world that rarely resembles the asymptote.  Furthermore, this compounds the ludic fallacy: most of what students of statistics do is assume a structure, typically with a known probability. Yet the problem we have is not so much making computations once you know the probabilities, but finding the true distribution.

The Inverse Problem Of The Rare Events

Let us start with the inverse problem of rare events and proceed with a simple, nonmathematical argument. In August 2007, The Wall Street Journal published a statement by one financial economist, expressing his surprise that financial markets experienced a string of events that "would happen once in 10,000 years". A portrait of the gentleman accompanying the article revealed that he was considerably  younger than 10,000 years; it is therefore fair to assume that he was not drawing his inference from his own empirical experience (and not from history at large), but from some theoretical model that produces the risk of rare events, or what he perceived to be rare events.

Alas, the rarer the event, the more theory you need (since we don't observe it). So the rarer the event, the worse its inverse problem. And theories are fragile (just think of Doctor Bernanke).

The tragedy is as follows. Suppose that you are deriving probabilities of future occurrences from the data, assuming (generously) that the past is representative of the future. Now, say that you estimate that an event happens every 1,000 days. You will need a lot more data than 1,000 days to ascertain its frequency, say 3,000 days. Now, what if the event happens once every 5,000 days? The estimation of this probability requires some larger number, 15,000 or more. The smaller the probability, the more observations you need, and the greater the estimation error for a set number of observations. Therefore, to estimate a rare event you need a sample that is larger and larger in inverse proportion to the occurrence of the event.

If small probability events carry large impacts, and (at the same time) these small probability events are more difficult to compute from past data itself, then: our empirical knowledge about the potential contribution—or role—of rare events (probability × consequence) is inversely proportional to their impact. This is why we should worry in the fourth quadrant!

For rare events, the confirmation bias (the tendency, Bernanke-style, of finding samples that confirm your opinion, not those that disconfirm it) is very costly and very distorting. Why? Most of histories of Black Swan prone events is going to be Black Swan free! Most samples will not reveal the black swans—except after if you are hit with them, in which case you will not be in a position to discuss them. Indeed I show with 40 years of data that past Black Swans do not predict future Black Swans in socio-economic life.

Figure 4 The Confirmation Bias At Work. For left-tailed fat-tailed distributions, we do not see much of negative outcomes for surviving entities AND we have a small sample in the left tail. This is why we tend to see a better past for a certain class of time series than warranted.

Fallacy Of The Single Event Probability

Let us look at events in Mediocristan. In a developed country a newborn female is expected to die at around 79, according to insurance tables. When she reaches her 79th birthday, her life expectancy, assuming that she is in typical health, is another 10 years. At the age of 90, she should have another 4.7 years to go. So if you are told that a person is older than 100, you can estimate that he is 102.5 and conditional on the person being older than 140 you can estimate that he is 140 plus a few minutes. The conditional expectation of additional life drops as a person gets older.

In Extremistan things work differently and the conditional expectation of an increase in a random variable does not drop as the variable gets larger. In the real world, say with stock returns (and all economic variable), conditional on a loss being worse than the 5 units, to use a conventional unit of measure units, it will be around 8 units. Conditional that a move is more than 50 STD it should be around 80 units, and if we go all the way until the sample is depleted, the average move worse than 100 units is 250 units! This extends all the way to areas in which we have sufficient sample.

This tells us that there is "no typical" failure and "no typical" success.  You may be able to predict the occurrence of a war, but you will not be able to gauge its effect! Conditional on a war killing more than 5 million people, it should kill around 10 (or more). Conditional on it killing more than 500 million, it would kill a billion (or more, we don't know).  You may correctly predict a skilled person getting "rich", but he can make a million, ten million, a billion, ten billion—there is no typical number. We have data, for instance, for predictions of drug sales, conditional on getting things right. Sales estimates are totally uncorrelated to actual sales—some drugs that were correctly predicted to be successful had their sales underestimated by up to 22 times!

This absence of "typical" event in Extremistan is what makes prediction markets ludicrous, as they make events look binary. "A war" is meaningless: you need to estimate its damage—and no damage is typical. Many predicted that the First War would occur—but nobody predicted its magnitude. Of the reasons economics does not work is that the literature is almost completely blind to the point.

A Simple Proof Of Unpredictability In The Fourth Quadrant

I show elsewhere that if you don't know what a "typical" event is, fractal power laws are the most effective way to discuss the extremes mathematically. It does not mean that the real world generator is actually a power law—it means you don't understand the structure of the external events it delivers and need a tool of analysis so you do not become a turkey. Also, fractals simplify the mathematical discussions because all you need is play with one parameter (I call it "alpha") and it increases or decreases the role of the rare event in the total properties.

For instance, you move alpha from 2.3 to 2 in the publishing business, and the sales of books in excess of 1 million copies triple!  Before meeting Benoit Mandelbrot, I used to play with combinations of scenarios with series of probabilities and series of payoffs filling spreadsheets with clumsy simulations; learning to use fractals made such analyses immediate. Now all I do is change the alpha and see what's going on.

Now the problem: Parametrizing a power law lends itself to monstrous estimation errors (I said that heavy tails have horrible inverse problems). Small changes in the "alpha" main parameter used by power laws leads to monstrously large effects in the tails. Monstrous.

And we don't observe the "alpha. Figure 5 shows more than 40 thousand computations of the tail exponent "alpha" from different samples of different economic variables (data for which it is impossible to refute fractal power laws). We clearly have problems figuring it what the "alpha" is: our results are marred with errors. Clearly the mean absolute error is in excess of 1 (i.e. between alpha=2 and alpha=3). Numerous papers in econophysics found an "average" alpha between 2 and 3—but if you process the >20 million pieces of data analyzed in the literature, you find that the variations between single variables are extremely significant.

Figure 5—Estimation error in "alpha" from 40 thousand economic variables. I thank Pallop Angsupun for data.

Now this mean error has massive consequences. Figure 6 shows the effect: the expected value of your losses in excess of a certain amount(called "shortfall") is multiplied by >10 from a small change in the "alpha" that is less than its mean error! These are the losses banks were talking about with confident precision!

Figure 6—The value of the expected shortfall (expected losses in excess of a certain threshold) in response to changes in tail exponent "alpha". We can see it explode by an order of magnitude.

What if the distribution is not a power law? This is a question I used to get once a day. Let me repeat it: my argument would not change—it would take longer to phrase it.

Many researchers, such as Philip Tetlock, have looked into the incapacity of social scientists in forecasting (economists, political scientists). It is thus evident that while the forecasters might be just "empty suits", the forecast errors are dominated by rare events, and we are limited in our ability to track them. The "wisdom of crowds" might work in the first three quadrant; but it certainly fails (and has failed) in the fourth.

Living In The Fourth Quadrant

Beware the Charlatan. When I was a quant-trader in complex derivatives, people mistaking my profession used to ask me for "stock tips" which put me in a state of rage: a charlatan is someone likely (statistically) to give you positive advice, of the "how to" variety.

Go to a bookstore, and look at the business shelves: you will find plenty of books telling you how to make your first million, or your first quarter-billion, etc. You will not be likely to find a book on "how I failed in business and in life"—though the second type of advice is vastly more informational, and typically less charlatanic. Indeed, the only popular such finance book I found that was not quacky in nature—on how someone lost his fortune—was both self-published and out of print. Even in academia, there is little room for promotion by publishing negative results—though these, are vastly informational and less marred with statistical biases of the kind we call data snooping. So all I am saying is "what is it that we don't know", and my advice is what to avoid, no more.

You can live longer if you avoid death, get better if you avoid bankruptcy, and become prosperous if you avoid blowups in the fourth quadrant.

Now you would think that people would buy my arguments about lack of knowledge and accept unpredictability. But many kept asking me "now that you say that our measures are wrong, do you have anything better?" 

I used to give the same mathematical finance lectures for both graduate students and practitioners before giving up on academic students and grade-seekers. Students cannot understand the value of "this is what we don't know"—they think it is not information, that they are learning nothing. Practitioners on the other hand value it immensely. Likewise with statisticians: I never had a disagreement with statisticians (who build the field)—only with users of statistical methods.

Spyros Makridakis and I are editors of a special issue of a decision science journal, The International Journal of Forecasting. The issue is about "What to do in an environment of low predictability". We received tons of papers, but guess what? Very few addressed the point: they mostly focused on showing us that they predict better (on paper).  This convinced me to engage in my new project: "how to live in a world we don't understand".

So for now I can produce phronetic rules (in the Aristotelian sense of phronesis, decision-making wisdom). Here are a few, to conclude.

Phronetic Rules: What Is Wise To Do (Or Not Do) In The Fourth Quadrant

1) Avoid Optimization, Learn to Love Redundancy. Psychologists tell us that getting rich does not bring happiness—if you spend it. But if you hide it under the mattress, you are less vulnerable to a black swan. Only fools (such as Banks) optimize, not realizing that a simple model error can blow through their capital (as it just did). In one day in August 2007, Goldman Sachs experienced 24 x the average daily transaction volume—would 29 times have blown up the system? The only weak point I know of financial markets is their ability to drive people & companies to "efficiency" (to please a stock analyst’s earnings target) against risks of extreme events.

Indeed some systems tend to optimize—therefore become more fragile. Electricity grids for example optimize to the point of not coping with unexpected surges—Albert-Lazlo Barabasi warned us of the possibility of a NYC blackout like the one we had in August 2003. Quite prophetic, the fellow. Yet energy supply kept getting more and more efficient since. Commodity prices can double on a short burst in demand (oil, copper, wheat) —we no longer have any slack.  Almost everyone who talks about "flat earth" does not realize that it is overoptimized to the point of maximal vulnerability.

Biological systems—those that survived millions of years—include huge redundancies. Just consider why we like sexual encounters (so redundant to do it so often!). Historically populations tended to produced around 4-12 children to get to the historical average of ~2 survivors to adulthood.

Option-theoretic analysis: redundancy is like long an option. You certainly pay for it, but it may be necessary for survival.

2) Avoid prediction of remote payoffs—though not necessarily ordinary ones. Payoffs from remote parts of the distribution are more difficult to predict than closer parts.

A general principle is that, while in the first three quadrants you can use the best model you can find, this is dangerous in the fourth quadrant: no model should be better than just any model.

3) Beware the "atypicality" of remote events. There is a sucker's method called "scenario analysis" and "stress testing"—usually based on the past (or some "make sense" theory). Yet I show in the appendix how past shortfalls that do not predict subsequent shortfalls. Likewise, "prediction markets" are for fools. They might work for a binary election, but not in the Fourth Quadrant. Recall the very definition of events is complicated: success might mean one million in the bank ...or five billions!

4) Time. It takes much, much longer for a times series in the Fourth Quadrant to reveal its property. At the worst, we don't know how long. Yet compensation for bank executives is done on a short term window, causing a mismatch between observation window and necessary window. They get rich in spite of negative returns. But we can have a pretty clear idea if the "Black Swan" can hit on the left (losses) or on the right (profits).

The point can be used in climatic analysis. Things that have worked for a long time are preferable—they are more likely to have reached their ergodic states.

5) Beware Moral Hazard. Is optimal to make series of bonuses betting on hidden risks in the Fourth Quadrant, then blow up and write a thank you letter. Fannie Mae and Freddie Mac's Chairmen will in all likelihood keep their previous bonuses (as in all previous cases) and even get close to 15 million of severance pay each.

6) Metrics. Conventional metrics based on type 1 randomness don't work. Words like "standard deviation" are not stable and does not measure anything in the Fourth Quadrant. So does "linear regression" (the errors are in the fourth quadrant), "Sharpe ratio", Markowitz optimal portfolio, ANOVA shmnamova, Least square, etc. Literally anything mechanistically pulled out of a statistical textbook.

My problem is that people can both accept the role of rare events, agree with me, and still use these metrics, which is leading me to test if this is a psychological disorder.  

The technical appendix shows why these metrics fail: they are based on "variance"/"standard deviation" and terms invented years ago when we had no computers. One way I can prove that anything linked to standard deviation is a facade of knowledge: There is a measure called Kurtosis that indicates departure from "Normality". It is very, very unstable and marred with huge sampling error: 70-90% of the Kurtosis in Oil, SP500, Silver, UK interest rates, Nikkei, US deposit rates, sugar, and the dollar/yet currency rate come from 1 day in the past 40 years, reminiscent of figure 3. This means that no sample will ever deliver the true variance. It also tells us anyone using "variance" or "standard deviation" (or worse making models that make us take decisions based on it) in the fourth quadrant is incompetent.

7) Where is the skewness?  Clearly the Fourth Quadrant can present left or right skewness. If we suspect right-skewness, the true mean is more likely to be underestimated by measurement of past realizations, and the