Market Forecasting Quotes

Predicting the stock market is really predicting how other investors will change estimates they are now making with all their best efforts. This means that, for a market forecaster to be right, the consensus of all others must be wrong and the forecaster must determine in which direction-up or down-the market will be moved by changes in the consensus of those same active investors.

Centuries ago human knowledge increased slowly, so politics and economics changed at a leisurely pace too. Today our knowledge is increasing at breakneck speed, and theoretically we should understand the world better and better. But the very opposite is happening. Our new-found knowledge leads to faster economic, social and political changes; in an attempt to understand what is happening, we accelerate the accumulation of knowledge, which leads only to faster and greater upheavals. Consequently we are less and less able to make sense of the present or forecast the future. In 1016 it was relatively easy to predict how Europe would look in 1050. Sure, dynasties might fall, unknown raiders might invade, and natural disasters might strike; yet it was clear that in 1050 Europe would still be ruled by kings and priests, that it would be an agricultural society, that most of its inhabitants would be peasants, and that it would continue to suffer greatly from famines, plagues and wars. In contrast, in 2016 we have no idea how Europe will look in 2050. We cannot say what kind of political system it will have, how its job market will be structured, or even what kind of bodies its inhabitants will possess.

History cannot be explained deterministically and it cannot be predicted because it is chaotic. So many forces are at work and their interactions are so complex that extremely small variations in the strength of the forces and the way they interact produce huge differences in outcomes. Not only that, but history is what is called a ‘level two’ chaotic system. Chaotic systems come in two shapes. Level one chaos is chaos that does not react to predictions about it. The weather, for example, is a level one chaotic system. Though it is influenced by myriad factors, we can build computer models that take more and more of them into consideration, and produce better and better weather forecasts. Level two chaos is chaos that reacts to predictions about it, and therefore can never be predicted accurately. Markets, for example, are a level two chaotic system. What will happen if we develop a computer program that forecasts with 100 per cent accuracy the price of oil tomorrow? The price of oil will immediately react to the forecast, which would consequently fail to materialise. If the current price of oil is $90 a barrel, and the infallible computer program predicts that tomorrow it will be $100, traders will rush to buy oil so that they can profit from the predicted price rise. As a result, the price will shoot up to $100 a barrel today rather than tomorrow. Then what will happen tomorrow? Nobody knows.

Joe Louis once said, “Every fighter has a plan until they get hit.

He would joke that “stock-market forecasters exist to make astrologers look good.

By 2060, India’s economy is projected to be larger than China’s because of its greater population growth. India is forecast to produce about one-quarter of world GDP from 2040 through the rest of this century.

But do you know what happened during this period? Where do we begin ... 1.3 million Americans died while fighting nine major wars. Roughly 99.9% of all companies that were created went out of business. Four U.S. presidents were assassinated. 675,000 Americans died in a single year from a flu pandemic. 30 separate natural disasters killed at least 400 Americans each. 33 recessions lasted a cumulative 48 years. The number of forecasters who predicted any of those recessions rounds to zero. The stock market fell more than 10% from a recent high at least 102 times. Stocks lost a third of their value at least 12 times. Annual inflation exceeded 7% in 20 separate years. The words “economic pessimism” appeared in newspapers at least 29,000 times, according to Google.

The most realistic distinction between the investor and the speculator is found in their attitude toward stock-market movements. The speculator’s primary interest lies in anticipating and profiting from market fluctuations. The investor’s primary interest lies in acquiring and holding suitable securities at suitable prices. Market movements are important to him in a practical sense, because they alternately create low price levels at which he would be wise to buy and high price levels at which he certainly should refrain from buying and probably would be wise to sell. It is far from certain that the typical investor should regularly hold off buying until low market levels appear, because this may involve a long wait, very likely the loss of income, and the possible missing of investment opportunities. On the whole it may be better for the investor to do his stock buying whenever he has money to put in stocks, except when the general market level is much higher than can be justified by well-established standards of value. If he wants to be shrewd he can look for the ever-present bargain opportunities in individual securities. Aside from forecasting the movements of the general market, much effort and ability are directed on Wall Street toward selecting stocks or industrial groups that in matter of price will “do better” than the rest over a fairly short period in the future. Logical as this endeavor may seem, we do not believe it is suited to the needs or temperament of the true investor—particularly since he would be competing with a large number of stock-market traders and first-class financial analysts who are trying to do the same thing. As in all other activities that emphasize price movements first and underlying values second, the work of many intelligent minds constantly engaged in this field tends to be self-neutralizing and self-defeating over the years. The investor with a portfolio of sound stocks should expect their prices to fluctuate and should neither be concerned by sizable declines nor become excited by sizable advances. He should always remember that market quotations are there for his convenience, either to be taken advantage of or to be ignored. He should never buy a stock because it has gone up or sell one because it has gone down. He would not be far wrong if this motto read more simply: “Never buy a stock immediately after a substantial rise or sell one immediately after a substantial drop.” An

Some readers are bound to want to take the techniques we’ve introduced here and try them on the problem of forecasting the future price of securities on the stock market (or currency exchange rates, and so on). Markets have very different statistical characteristics than natural phenomena such as weather patterns. Trying to use machine learning to beat markets, when you only have access to publicly available data, is a difficult endeavor, and you’re likely to waste your time and resources with nothing to show for it. Always remember that when it comes to markets, past performance is not a good predictor of future returns—looking in the rear-view mirror is a bad way to drive. Machine learning, on the other hand, is applicable to datasets where the past is a good predictor of the future.

The fact is, nobody has the faintest idea of what is going to happen next year, next week, or even tomorrow. If you hope to get anywhere as a speculator, you must get out of the habit of listening to forecasts. It is of the utmost importance that you never take economists, market advisers, or other financial oracles seriously.

Some people claim that they predicted a downturn, but they forecast a downturn every day, and finally, one day, they are right. Even a broken clock is right twice a day.

Nvidia discovered a vast new market for parallel processing, from computational chemistry to weather forecasting.

Cultural products will spread faster and wider when everybody can see what everybody else is doing. It suggests that the future of many hit-making markets will be fully open, radically transparent, and very, very unequal.

For a number of years, professors at Duke University conducted a survey in which the chief financial officers of large corporations estimated the returns of the Standard & Poor’s index over the following year. The Duke scholars collected 11,600 such forecasts and examined their accuracy. The conclusion was straightforward: financial officers of large corporations had no clue about the short-term future of the stock market; the correlation between their estimates and the true value was slightly less than zero!

In all sorts of markets—music, film, art, and politics—the future of popularity will be harder to predict as the broadcast power of radio and television democratizes and the channels of exposure grow.... The gatekeepers had their day. Now there are simply too many gates to keep.

The only thing you can be confident of while forecasting future stock returns is that you will probably turn out to be wrong. The only indisputable truth that the past teaches us is that the future will always surprise us—always! And the corollary to that law of financial history is that the markets will most brutally surprise the very people who are most certain that their views about the future are right. Staying humble about your forecasting powers, as Graham did, will keep you from risking too much on a view of the future that may well turn out to be wrong.

Graphene has unique mechanical and electrical properties, which promise many applications. Inspired by graphene's promise, people have figured out some considerably more efficient ways to make it! One optimistic, but maybe not crazy, study forecasts that a 100 billion market in graphene will develop over the next few years.

Over the past 30 years, approximately 300 million people have moved into China’s middle class. And according to the OECD Development Centre, the forecast is for another 200 million people to move into the middle class by 2026. This means the Asia Pacific region, which in 2009 represented 18% of the world’s middle class, will reach 66 percent by 2030. Let’s repeat that. Over the next 15 years, Asia will go from 20 percent to 66 percent of the world’s middle class. At the same time, the developed markets of North America and Europe, which held a combined 54 percent of the global middle class in 2009, are forecast to drop to only 21 percent by 2030. Basically, follow the money. Asia’s middle class consumers are the future. Learn Mandarin.

Dischord lives with us, even in the harmony of the Order. You can see the fallen buildings of Allbreaking if you look to the other side of the river. The bridge between Bankside and Paul’s shakes and stirs. The people run but never fast enough. There is no bridge between Bankside and Paul’s now, but in the streets and markets, the kids sing the old forecast, like it is still taking place, like it is always taking place. London Bridge is falling down, falling down, falling down. London Bridge is falling down, my fair Lady.

We have polluted for years, causing much damage to the environment, while the scientists currently making these complicated forecasting models were not sticking their necks out and trying to stop us from building these risks (they resemble those “risk experts” in the economic domain who fight the previous war)—these are the scientists now trying to impose the solutions on us. But the skepticism about models that I propose does not lead to the conclusions endorsed by anti-environmentalists and pro-market fundamentalists. Quite the contrary: we need to be hyper-conservationists ecologically, since we do not know what we are harming with now. That’s the sound policy under conditions of ignorance and epistemic opacity. To those who say “We have no proof that we are harming nature,” a sound response is “We have no proof that we are not harming nature, either;” the burden of the proof is not on the ecological conservationist, but on someone disrupting an old system. Furthermore we should not “try to correct” the harm done, as we may be creating another problem we do not know much about currently.

Once upon a time there was much talk of the apathy of the masses. Their silence was the crucial fact for an earlier generation. Today, however, the masses act not by deflection but by infection, tainting opinion polls and forecasts with their multifarious phantasies. Their abstention and their silence are no longer determining factors (that stage was still nihilistic); what counts now is their use of the cogs in the workings of uncertainty. Where the masses once sported with their voluntary servitude, they now sport with their involuntary incertitude. Unbeknownst to the experts who scrutinize them and the manipulators who believe they can influence them, they have grasped the fact that politics is virtually dead, and that they now have a new game to play, just as exciting as the ups and downs of the stock market. This game enables them to make audiences, charismas, levels of prestige and the market prices of images dance up and down with an intolerable facility. The masses had been deliberately demoralized and de-ideologized in order that they might become the live prey of probability theory, but now it is they who destabilize all images and play games with political truth.

For years the financial services have been making stock-market forecasts without anyone taking this activity very seriously. Like everyone else in the field they are sometimes right and sometimes wrong. Wherever possible they hedge their opinions so as to avoid the risk of being proved completely wrong. (There is a well-developed art of Delphic phrasing that adjusts itself successfully to whatever the future brings.) In our view—perhaps a prejudiced one—this segment of their work has no real significance except for the light it throws on human nature in the securities markets. Nearly everyone interested in common stocks wants to be told by someone else what he thinks the market is going to do. The demand being there, it must be supplied. Their interpretations and forecasts of business conditions, of course, are much more authoritative and informing. These are an important part of the great body of economic intelligence which is spread continuously among buyers and sellers of securities and tends to create fairly rational prices for stocks and bonds under most circumstances. Undoubtedly the material published by the financial services adds to the store of information available and fortifies the investment judgment of their clients.

One day, Carmona had an idea. Axcom had been employing various approaches to using their pricing data to trade, including relying on breakout signals. They also used simple linear regressions, a basic forecasting tool relied upon by many investors that analyzes the relationships between two sets of data or variables under the assumption those relationships will remain linear. Plot crude-oil prices on the x-axis and the price of gasoline on the y-axis, place a straight regression line through the points on the graph, extend that line, and you usually can do a pretty good job predicting prices at the pump for a given level of oil price.

Here are my simple rules for identifying market tops and bottoms: 1. Market tops are relatively easy to recognize. Buyers generally become overconfident and almost always believe “this time is different.” It’s usually not. 2. There’s always a surplus of relatively cheap debt capital to finance acquisitions and investments in a hot market. In some cases, lenders won’t even charge cash interest, and they often relax or suspend typical loan restrictions as well. Leverage levels escalate compared to historical averages, with borrowing sometimes reaching as high as ten times or more compared to equity. Buyers will start accepting overoptimistic accounting adjustments and financial forecasts to justify taking on high levels of debt. Unfortunately most of these forecasts tend not to materialize once the economy starts decelerating or declining. 3. Another indicator that a market is peaking is the number of people you know who start getting rich. The number of investors claiming outperformance grows with the market. Loose credit conditions and a rising tide can make it easy for individuals without any particular strategy or process to make money “accidentally.” But making money in strong markets can be short-lived. Smart investors perform well through a combination of self-discipline and sound risk assessment, even when market conditions reverse.

Basically, Graham breaks the art of investing down into two simple variables – price and value. Value is what a business is worth. Price is what you have to pay to get it. Given the stock market’s manic-depressive behavior, numerous occasions arise where a business’ market price is distinctly out of line with its true business value. In such instances, an investor may be able to purchase a dollar of value for just 50 cents. Note that there is no mention here of interest rates, economic forecasts, technical charts, market cycles, etc. The only issues are price and value. I should also note that Graham emphasizes a large margin of safety. The strategy is not to buy a dollar of value for 97 cents. Rather, the gap should be dramatic so as to absorb the effects of miscalculation and worse-than-average luck.

So these are the possibilities I see with regard to economic forecasts: Most economic forecasts are just extrapolations. Extrapolations are usually correct but not valuable. Unconventional forecasts of significant deviation from trend would be very valuable if they were correct, but usually they aren’t. Thus most forecasts of deviation from trend are incorrect and also not valuable. A few forecasts of significant deviation turn out to be correct and valuable—leading their authors to be lionized for their acumen—but it’s hard to know in advance which will be the few right ones. Since the overall batting average with regard to them is low, unconventional forecasts can’t be valuable on balance. There are forecasters who became famous for a single dramatic correct call, but the majority of their forecasts weren’t worth following.

We lack space here to discuss in detail the pros and cons of market forecasting. A great deal of brain power goes into this field, and undoubtedly some people can make money by being good stock-market analysts. But it is absurd to think that the general public can ever make money out of market forecasts. For who will buy when the general public, at a given signal, rushes to sell out at a profit? If you, the reader, expect to get rich over the years by following some system or leadership in market forecasting, you must be expecting to try to do what countless others are aiming at, and to be able to do it better than your numerous competitors in the market. There is no basis either in logic or in experience for assuming that any typical or average investor can anticipate market movements more successfully than the general public, of which he is himself a part. There is one aspect of the “timing” philosophy which seems to have escaped everyone’s notice. Timing is of great psychological importance to the speculator because he wants to make his profit in a hurry. The idea of waiting a year before his stock moves up is repugnant to him. But a waiting period, as such, is of no consequence to the investor. What advantage is there to him in having his money uninvested until he receives some (presumably) trustworthy signal that the time has come to buy? He enjoys an advantage only if by waiting he succeeds in buying later at a sufficiently lower price to offset his loss of dividend income. What this means is that timing is of no real value to the investor unless it coincides with pricing—that is, unless it enables him to repurchase his shares at substantially under his previous selling price.

Truth be known, forecasts aren’t worth very much, and most people who make them don’t make money in the markets. . . . This is because nothing is certain and when one overlays the probabilities of all of the various things that affect the future in order to make a forecast, one gets a wide array of possibilities with varying probabilities, not one highly probable outcome. . . . We believe that market movements reflect economic movements. Economic movements are reflected in economic statistics. By studying the relationships between economic statistics and market movements, we’ve developed precise rules for identifying important shifts in the economic/market environment and in turn our positions. In other words, rather than forecasting changes in the economic environment and shifting positions in anticipation of them, we pick up these changes as they’re occurring and move our money around to keep in those markets which perform best in that environment.

Short-termism also dominates in the marketplace. The market uses a discount rate of 10% per year (or more) when comparing costs now with benefits in the future. This means that a benefit that lies twenty years ahead will be valued at one-tenth of its real value. In other words, a problem twenty years in the future will be worth solving only if the cost of the solution is less than one-tenth of the value saved. It comes as no surprise to those who know economics that it is “cost efficient” to allow the world to collapse from climate damage, as long as the collapse is more than forty years into the future. The net present value of reducing emissions and saving the world is lower than the net present value of business as usual. It is cheaper to push the world over the cliff than to try to save it. The political world is not much better, given the short tenure of political appointments. Politicians can rarely spend time on agendas that yield a positive result only after the next election—which is normally less than four years away.

Centuries ago human knowledge increased slowly, so politics and economics changed at a leisurely pace too. Today our knowledge is increasing at breakneck speed, and theoretically we should understand the world better and better. But the very opposite is happening. Our new-found knowledge leads to faster economic, social and political changes; in an attempt to understand what is happening, we accelerate the accumulation of knowledge, which leads only to faster and greater upheavals. Consequently we are less and less able to make sense of the present or forecast the future. In 1016 it was relatively easy to predict how Europe would look in 1050. Sure, dynasties might fall, unknown raiders might invade, and natural disasters might strike; yet it was clear that in 1050 Europe would still be ruled by kings and priests, that it would be an agricultural society, that most of its inhabitants would be peasants, and that it would continue to suffer greatly from famines, plagues and wars. In contrast, in 2016 we have no idea how Europe will look in 2050. We cannot say what kind of political system it will have, how its job market will be structured, or even what kind of bodies its inhabitants will possess. A

The cheerleaders of the new data regime rarely acknowledge the impacts of digital decision-making on poor and working-class people. This myopia is not shared by those lower on the economic hierarchy, who often see themselves as targets rather than beneficiaries of these systems. For example, one day in early 2000, I sat talking to a young mother on welfare about her experiences with technology. When our conversation turned to EBT cards, Dorothy Allen said, “They’re great. Except [Social Services] uses them as a tracking device.” I must have looked shocked, because she explained that her caseworker routinely looked at her purchase records. Poor women are the test subjects for surveillance technology, Dorothy told me. Then she added, “You should pay attention to what happens to us. You’re next.” Dorothy’s insight was prescient. The kind of invasive electronic scrutiny she described has become commonplace across the class spectrum today. Digital tracking and decision-making systems have become routine in policing, political forecasting, marketing, credit reporting, criminal sentencing, business management, finance, and the administration of public programs. As these systems developed in sophistication and reach, I started to hear them described as forces for control, manipulation, and punishment

A few years ago my friend Jon Brooks supplied this great illustration of skewed interpretation at work. Here’s how investors react to events when they’re feeling good about life (which usually means the market has been rising): Strong data: economy strengthening—stocks rally Weak data: Fed likely to ease—stocks rally Data as expected: low volatility—stocks rally Banks make $4 billion: business conditions favorable—stocks rally Banks lose $4 billion: bad news out of the way—stocks rally Oil spikes: growing global economy contributing to demand—stocks rally Oil drops: more purchasing power for the consumer—stocks rally Dollar plunges: great for exporters—stocks rally Dollar strengthens: great for companies that buy from abroad—stocks rally Inflation spikes: will cause assets to appreciate—stocks rally Inflation drops: improves quality of earnings—stocks rally Of course, the same behavior also applies in the opposite direction. When psychology is negative and markets have been falling for a while, everything is capable of being interpreted negatively. Strong economic data is seen as likely to make the Fed withdraw stimulus by raising interest rates, and weak data is taken to mean companies will have trouble meeting earnings forecasts. In other words, it’s not the data or events; it’s the interpretation. And that fluctuates with swings in psychology.

This kind of speculation reached a high point with the Pentagon's initiative of creating a 'futures market in events', a stock market of prices for terrorist attacks or catastrophes. You bet on the probable occurrence of such events against those who don't believe they'll happen. This speculative market is intended to operate like the market in soya or sugar. You might speculate on the number of AIDS victims in Africa or on the probability that the San Andreas Fault will give way (the Pentagon's initiative is said to derive from the fact that they credit the free market in speculation with better forecasting powers than the secret services). Of course it is merely a step from here to insider trading: betting on the event before you cause it is still the surest way (they say Bin Laden did this, speculating on TWA shares before 11 September). It's like taking out life insurance on your wife before you murder her. There's a great difference between the event that happens (happened) in historical time and the event that happens in the real time of information. To the pure management of flows and markets under the banner of planetary deregulation, there corresponds the 'global' event- or rather the globalized non-event: the French victory in the World Cup, the year 2000, the death of Diana, The Matrix, etc. Whether or not these events are manufactured, they are orchestrated by the silent epidemic of the information networks. Fake events.

In the EPJ results, there were two statistically distinguishable groups of experts. The first failed to do better than random guessing, and in their longer-range forecasts even managed to lose to the chimp. The second group beat the chimp, though not by a wide margin, and they still had plenty of reason to be humble. Indeed, they only barely beat simple algorithms like “always predict no change” or “predict the recent rate of change.” Still, however modest their foresight was, they had some. So why did one group do better than the other? It wasn’t whether they had PhDs or access to classified information. Nor was it what they thought—whether they were liberals or conservatives, optimists or pessimists. The critical factor was how they thought. One group tended to organize their thinking around Big Ideas, although they didn’t agree on which Big Ideas were true or false. Some were environmental doomsters (“We’re running out of everything”); others were cornucopian boomsters (“We can find cost-effective substitutes for everything”). Some were socialists (who favored state control of the commanding heights of the economy); others were free-market fundamentalists (who wanted to minimize regulation). As ideologically diverse as they were, they were united by the fact that their thinking was so ideological. They sought to squeeze complex problems into the preferred cause-effect templates and treated what did not fit as irrelevant distractions. Allergic to wishy-washy answers, they kept pushing their analyses to the limit (and then some), using terms like “furthermore” and “moreover” while piling up reasons why they were right and others wrong. As a result, they were unusually confident and likelier to declare things “impossible” or “certain.” Committed to their conclusions, they were reluctant to change their minds even when their predictions clearly failed. They would tell us, “Just wait.” The other group consisted of more pragmatic experts who drew on many analytical tools, with the choice of tool hinging on the particular problem they faced. These experts gathered as much information from as many sources as they could. When thinking, they often shifted mental gears, sprinkling their speech with transition markers such as “however,” “but,” “although,” and “on the other hand.” They talked about possibilities and probabilities, not certainties. And while no one likes to say “I was wrong,” these experts more readily admitted it and changed their minds. Decades ago, the philosopher Isaiah Berlin wrote a much-acclaimed but rarely read essay that compared the styles of thinking of great authors through the ages. To organize his observations, he drew on a scrap of 2,500-year-old Greek poetry attributed to the warrior-poet Archilochus: “The fox knows many things but the hedgehog knows one big thing.” No one will ever know whether Archilochus was on the side of the fox or the hedgehog but Berlin favored foxes. I felt no need to take sides. I just liked the metaphor because it captured something deep in my data. I dubbed the Big Idea experts “hedgehogs” and the more eclectic experts “foxes.” Foxes beat hedgehogs. And the foxes didn’t just win by acting like chickens, playing it safe with 60% and 70% forecasts where hedgehogs boldly went with 90% and 100%. Foxes beat hedgehogs on both calibration and resolution. Foxes had real foresight. Hedgehogs didn’t.

In an ideal world, the intelligent investor would hold stocks only when they are cheap and sell them when they become overpriced, then duck into the bunker of bonds and cash until stocks again become cheap enough to buy. From 1966 through late 2001, one study claimed, $1 held continuously in stocks would have grown to $11.71. But if you had gotten out of stocks right before the five worst days of each year, your original $1 would have grown to $987.12.1 Like most magical market ideas, this one is based on sleight of hand. How, exactly, would you (or anyone) figure out which days will be the worst days—before they arrive? On January 7, 1973, the New York Times featured an interview with one of the nation’s top financial forecasters, who urged investors to buy stocks without hesitation: “It’s very rare that you can be as unqualifiedly bullish as you can now.” That forecaster was named Alan Greenspan, and it’s very rare that anyone has ever been so unqualifiedly wrong as the future Federal Reserve chairman was that day: 1973 and 1974 turned out to be the worst years for economic growth and the stock market since the Great Depression.2 Can professionals time the market any better than Alan Green-span? “I see no reason not to think the majority of the decline is behind us,” declared Kate Leary Lee, president of the market-timing firm of R. M. Leary & Co., on December 3, 2001. “This is when you want to be in the market,” she added, predicting that stocks “look good” for the first quarter of 2002.3 Over the next three months, stocks earned a measly 0.28% return, underperforming cash by 1.5 percentage points. Leary is not alone. A study by two finance professors at Duke University found that if you had followed the recommendations of the best 10% of all market-timing newsletters, you would have earned a 12.6% annualized return from 1991 through 1995. But if you had ignored them and kept your money in a stock index fund, you would have earned 16.4%.

Was this luck, or was it more than that? Proving skill is difficult in venture investing because, as we have seen, it hinges on subjective judgment calls rather than objective or quantifiable metrics. If a distressed-debt hedge fund hires analysts and lawyers to scrutinize a bankrupt firm, it can learn precisely which bond is backed by which piece of collateral, and it can foresee how the bankruptcy judge is likely to rule; its profits are not lucky. Likewise, if an algorithmic hedge fund hires astrophysicists to look for patterns in markets, it may discover statistical signals that are reliably profitable. But when Perkins backed Tandem and Genentech, or when Valentine backed Atari, they could not muster the same certainty. They were investing in human founders with human combinations of brilliance and weakness. They were dealing with products and manufacturing processes that were untested and complex; they faced competitors whose behaviors could not be forecast; they were investing over long horizons. In consequence, quantifiable risks were multiplied by unquantifiable uncertainties; there were known unknowns and unknown unknowns; the bracing unpredictability of life could not be masked by neat financial models. Of course, in this environment, luck played its part. Kleiner Perkins lost money on six of the fourteen investments in its first fund. Its methods were not as fail-safe as Tandem’s computers. But Perkins and Valentine were not merely lucky. Just as Arthur Rock embraced methods and attitudes that put him ahead of ARD and the Small Business Investment Companies in the 1960s, so the leading figures of the 1970s had an edge over their competitors. Perkins and Valentine had been managers at leading Valley companies; they knew how to be hands-on; and their contributions to the success of their portfolio companies were obvious. It was Perkins who brought in the early consultants to eliminate the white-hot risks at Tandem, and Perkins who pressed Swanson to contract Genentech’s research out to existing laboratories. Similarly, it was Valentine who drove Atari to focus on Home Pong and to ally itself with Sears, and Valentine who arranged for Warner Communications to buy the company. Early risk elimination plus stage-by-stage financing worked wonders for all three companies. Skeptical observers have sometimes asked whether venture capitalists create innovation or whether they merely show up for it. In the case of Don Valentine and Tom Perkins, there was not much passive showing up. By force of character and intellect, they stamped their will on their portfolio companies.

Many models are constructed to account for regularly observed phenomena. By design, their direct implications are consistent with reality. But others are built up from first principles, using the profession’s preferred building blocks. They may be mathematically elegant and match up well with the prevailing modeling conventions of the day. However, this does not make them necessarily more useful, especially when their conclusions have a tenuous relationship with reality. Macroeconomists have been particularly prone to this problem. In recent decades they have put considerable effort into developing macro models that require sophisticated mathematical tools, populated by fully rational, infinitely lived individuals solving complicated dynamic optimization problems under uncertainty. These are models that are “microfounded,” in the profession’s parlance: The macro-level implications are derived from the behavior of individuals, rather than simply postulated. This is a good thing, in principle. For example, aggregate saving behavior derives from the optimization problem in which a representative consumer maximizes his consumption while adhering to a lifetime (intertemporal) budget constraint.† Keynesian models, by contrast, take a shortcut, assuming a fixed relationship between saving and national income. However, these models shed limited light on the classical questions of macroeconomics: Why are there economic booms and recessions? What generates unemployment? What roles can fiscal and monetary policy play in stabilizing the economy? In trying to render their models tractable, economists neglected many important aspects of the real world. In particular, they assumed away imperfections and frictions in markets for labor, capital, and goods. The ups and downs of the economy were ascribed to exogenous and vague “shocks” to technology and consumer preferences. The unemployed weren’t looking for jobs they couldn’t find; they represented a worker’s optimal trade-off between leisure and labor. Perhaps unsurprisingly, these models were poor forecasters of major macroeconomic variables such as inflation and growth.8 As long as the economy hummed along at a steady clip and unemployment was low, these shortcomings were not particularly evident. But their failures become more apparent and costly in the aftermath of the financial crisis of 2008–9. These newfangled models simply could not explain the magnitude and duration of the recession that followed. They needed, at the very least, to incorporate more realism about financial-market imperfections. Traditional Keynesian models, despite their lack of microfoundations, could explain how economies can get stuck with high unemployment and seemed more relevant than ever. Yet the advocates of the new models were reluctant to give up on them—not because these models did a better job of tracking reality, but because they were what models were supposed to look like. Their modeling strategy trumped the realism of conclusions. Economists’ attachment to particular modeling conventions—rational, forward-looking individuals, well-functioning markets, and so on—often leads them to overlook obvious conflicts with the world around them.

Dear KDP Author, Just ahead of World War II, there was a radical invention that shook the foundations of book publishing. It was the paperback book. This was a time when movie tickets cost 10 or 20 cents, and books cost $2.50. The new paperback cost 25 cents – it was ten times cheaper. Readers loved the paperback and millions of copies were sold in just the first year. With it being so inexpensive and with so many more people able to afford to buy and read books, you would think the literary establishment of the day would have celebrated the invention of the paperback, yes? Nope. Instead, they dug in and circled the wagons. They believed low cost paperbacks would destroy literary culture and harm the industry (not to mention their own bank accounts). Many bookstores refused to stock them, and the early paperback publishers had to use unconventional methods of distribution – places like newsstands and drugstores. The famous author George Orwell came out publicly and said about the new paperback format, if “publishers had any sense, they would combine against them and suppress them.” Yes, George Orwell was suggesting collusion. Well… history doesn’t repeat itself, but it does rhyme. Fast forward to today, and it’s the e-book’s turn to be opposed by the literary establishment. Amazon and Hachette – a big US publisher and part of a $10 billion media conglomerate – are in the middle of a business dispute about e-books. We want lower e-book prices. Hachette does not. Many e-books are being released at $14.99 and even $19.99. That is unjustifiably high for an e-book. With an e-book, there’s no printing, no over-printing, no need to forecast, no returns, no lost sales due to out of stock, no warehousing costs, no transportation costs, and there is no secondary market – e-books cannot be resold as used books. E-books can and should be less expensive. Perhaps channeling Orwell’s decades old suggestion, Hachette has already been caught illegally colluding with its competitors to raise e-book prices. So far those parties have paid $166 million in penalties and restitution. Colluding with its competitors to raise prices wasn’t only illegal, it was also highly disrespectful to Hachette’s readers. The fact is many established incumbents in the industry have taken the position that lower e-book prices will “devalue books” and hurt “Arts and Letters.” They’re wrong. Just as paperbacks did not destroy book culture despite being ten times cheaper, neither will e-books. On the contrary, paperbacks ended up rejuvenating the book industry and making it stronger. The same will happen with e-books. Many inside the echo-chamber of the industry often draw the box too small. They think books only compete against books. But in reality, books compete against mobile games, television, movies, Facebook, blogs, free news sites and more. If we want a healthy reading culture, we have to work hard to be sure books actually are competitive against these other media types, and a big part of that is working hard to make books less expensive. Moreover, e-books are highly price elastic. This means that when the price goes down, customers buy much more. We've quantified the price elasticity of e-books from repeated measurements across many titles. For every copy an e-book would sell at $14.99, it would sell 1.74 copies if priced at $9.99. So, for example, if customers would buy 100,000 copies of a particular e-book at $14.99, then customers would buy 174,000 copies of that same e-book at $9.99. Total revenue at $14.99 would be $1,499,000. Total revenue at $9.99 is $1,738,000. The important thing to note here is that the lower price is good for all parties involved: the customer is paying 33% less and the author is getting a royalty check 16% larger and being read by an audience that’s 74% larger. The pie is simply bigger.

History cannot be explained deterministically and it cannot be predicted because it is chaotic. So many forces are at work and their interactions are so complex that extremely small variations in the strength of the forces and the way they interact produce huge differences in outcomes. Not only that, but history is what is called a ‘level two’ chaotic system. Chaotic systems come in two shapes. Level one chaos is chaos that does not react to predictions about it. The weather, for example, is a level one chaotic system. Though it is influenced by myriad factors, we can build computer models that take more and more of them into consideration, and produce better and better weather forecasts. Level two chaos is chaos that reacts to predictions about it, and therefore can never be predicted accurately. Markets, for example, are a level two chaotic system. What will happen if we develop a computer program that forecasts with 100 per cent accuracy the price of oil tomorrow? The price of oil will immediately react to the forecast, which would consequently fail to materialise. If the current price of oil is $90 a barrel, and the infallible computer program predicts that tomorrow it will be $100, traders will rush to buy oil so that they can profit from the predicted price rise. As a result, the price will shoot up to $100 a barrel today rather than tomorrow. Then what will happen tomorrow? Nobody knows. Politics, too, is a second-order chaotic system. Many people criticise Sovietologists for failing to predict the 1989 revolutions and castigate Middle East experts for not anticipating the Arab Spring revolutions of 2011. This is unfair. Revolutions are, by definition, unpredictable. A predictable revolution never erupts.

Threadless is a T-shirt company founded by people with expertise in information technology services, web design, and consulting. Their business model involves holding weekly design contests open to outside participants, printing only T-shirts with the most popular designs, and selling them to their large and growing customer base. Threadless doesn’t need to hire artistic talent, since skilled designers compete for prizes and prestige. It doesn’t need to do marketing, since eager designers contact their friends to solicit votes and sales. It doesn’t need to forecast sales, since voting customers have already announced what numbers they will buy. By outsourcing production, Threadless can also minimize its handling and inventory costs. Thanks to this almost frictionless model, Threadless can scale rapidly and easily, with minimal structural restrictions.

There is an even starker contrast between the two groups of countries when one compares their contributions to growth in global debt versus growth in global GDP. Emerging markets contribute far more to growth in global GDP than to the growth in global public debt. These economies accounted for 14 percent of the increase in global debt levels from 2007 to 2012. In contrast, their contribution to the increase in global GDP over this period was 70 percent. The numbers are equally stark when one examines forecasts for the subsequent five years. From 2012 to 2017, emerging markets are expected to account for about three-fifths of global GDP growth but less than one-fifth of global public debt accumulation. In other words, emerging markets are adding substantially to global GDP, whereas advanced economies are mainly adding to global public debt (see Figure A-3 in the Appendix for details). The U.S. and Japan are certainly heavy hitters when it comes to debt accumulation. These two economies are making a far greater contribution to the rise in global debt than to the rise in global GDP. The U.S. contributed 38 percent of the increase in global debt from 2007 to 2012 and is expected to account for nearly half of the anticipated increase from 2012 to 2017. Its contributions to the increases in global GDP over those two periods are 12 percent and 23 percent, respectively. Japan accounted for 25 percent of the increase in debt from 2007 to 2012 and is expected to add 9 percent from 2012 to 2017, whereas its contributions to the increase in global GDP are far more modest. One

The familiar if sad tale of Apple Computer illustrates this crucial concept. Apple has suffered of late because positive feedback has fueled the competing system offered by Microsoft and Intel. As Wintel’s share of the personal computer market grew, users found the Wintel system more and more attractive. Success begat more success, which is the essence of positive feedback. With Apple’s share continuing to decline, many computer users now worry that the Apple Macintosh will shortly become the Sony Beta of computers, orphaned and doomed to a slow death as support from software producers gradually fades away. This worry is cutting into Apple’s sales, making it a potentially self-fulfilling forecast. Failure breeds failure: this, too, is the essence of positive feedback.

Market economies are self-propelling and self-referential systems strongly driven by perceptions and expectations, and these systems routinely develop explosive amplifying feedbacks.

In India, organised chain retailers account for only 7% of the $435 billion market, a share forecast to rise to 20% by 2020.11 The discounter model is unstoppable in fragmented, unorganised markets.

High-performing companies view planning altogether differently. They want their forecasts to drive the work they actually do. To make this possible, they have to ensure that the assumptions underlying their long-term plans reflect both the real economics of their markets and the performance experience of the company relative to competitors.

There is a second type of technical analysis that neither predicts or forecasts. This type is based on reacting to price action, as trend trader Martin Estlander notes: “We identify market trends, we do not predict them. Our models are kept reactive at all times.

Ocean, the future center of global trade. Why should it not prosper? Nobody can predict the future with 100 percent certainty. I’m not convinced it will happen. But I am a possibilist and these facts convince me: it is possible. The destiny instinct makes it difficult for us to accept that Africa can catch up with the West. Africa’s progress, if it is noticed at all, is seen as an improbable stroke of good fortune, a temporary break from its impoverished and war-torn destiny. The same destiny instinct also seems to make us take continuing Western progress for granted, with the West’s current economic stagnation portrayed as a temporary accident from which it will soon recover. For years after the global crash of 2008, the International Monetary Fund continued to forecast 3 percent annual economic growth for countries on Level 4. Each year, for five years, countries on Level 4 failed to meet this forecast. Each year, for five years, the IMF said, “Next year it will get back on track.” Finally, the IMF realized that there was no “normal” to go back to, and it downgraded its future growth expectations to 2 percent. At the same time the IMF acknowledged that the fast growth (above 5 percent) during those years had instead happened in countries on Level 2, like Ghana, Nigeria, Ethiopia, and Kenya in Africa, and Bangladesh in Asia. Why does this matter? One reason is this: the IMF forecasters’ worldview had a strong influence on where your retirement funds were invested. Countries in Europe and North America were expected to experience fast and reliable growth, which made them attractive to investors. When these forecasts turned out to be wrong, and when these countries did not in fact grow fast, the retirement funds did not grow either. Supposedly low-risk/high-return countries turned out to be high-risk/low-return countries. And at the same time African countries with great growth potential were being starved of investment. Another reason it matters, if you work for a company based in the old “West,” is that you are probably missing opportunities in the largest expansion of the middle-income consumer market in history, which is taking place right now in Africa and Asia. Other, local brands are already establishing a foothold, gaining brand recognition, and spreading throughout these continents, while you are still waking up to what is going on. The Western consumer market was just a teaser for what is coming next.

Consider two investors, Sam Scared and Charlie Compounder. Suppose Sam Scared starts with $1; each time it doubles, he puts his $1 profit in a sock instead of reinvesting it. After ten doublings, Sam has a profit in the sock of $1 × 10 plus his original $1 for a total of $11. Charlie also starts with $1 and makes the same investments but lets his profit ride. His $1 becomes $2, $4, $8, et cetera, until after ten doublings he has $1,024. Sam’s wealth grows as $1, $2, $3…$11. This is called simple growth, arithmetic growth, or growth by addition. Charlie’s increases as $1, $2, $4…$1,024. This is known variously as compound, exponential, geometric, or multiplicative growth. Over a sufficiently long time, compound growth at a small rate will vastly exceed any rate of arithmetic growth, no matter how large! For instance, if Sam Scared made 100 percent a year and put it in a sock and Charlie Compounder made only 1 percent a year but reinvested it, Charlie’s wealth would eventually exceed Sam’s by as much as you please. This is true even if Sam started with far more than Charlie, even $1 billion to Charlie’s $1. Realizing this truth, Robert Malthus (1766–1834), believing that population grew geometrically and resources grew arithmetically, forecast increasingly great misery.

Not only were the best forecasters foxy as individuals, they had qualities that made them particularly effective collaborators—partners in sharing information and discussing predictions. Every team member still had to make individual predictions, but the team was scored by collective performance. On average, forecasters on the small superteams became 50 percent more accurate in their individual predictions. Superteams beat the wisdom of much larger crowds—in which the predictions of a large group of people are averaged—and they also beat prediction markets, where forecasters “trade” the outcomes of future events like stocks, and the market price represents the crowd prediction. It might seem like the complexity of predicting geopolitical and economic events would necessitate a group of narrow specialists, each bringing to the team extreme depth in one area. But it was actually the opposite. As with comic book creators and inventors patenting new technologies, in the face of uncertainty, individual breadth was critical. The foxiest forecasters were impressive alone, but together they exemplified the most lofty ideal of teams: they became more than the sum of their parts. A lot more.

Economists, market advisers, political oracles, and clairvoyants all know this basic rule by heart: if you can’t forecast right, forecast often.

Strategy usually begins with an assessment of your industry. Your choice of strategic style should begin there as well. Although many industry factors will play into the strategy you actually formulate, you can narrow down your options by considering just two critical factors: predictability (How far into the future and how accurately can you confidently forecast demand, corporate performance, competitive dynamics, and market expectations?) and malleability (To what extent can you or your competitors influence those factors?).

Level two chaos is chaos that reacts to predictions about it, and therefore can never be predicted accurately. Markets, for example, are a level two chaotic system. What will happen if we develop a computer program that forecasts with 100 per cent accuracy the price of oil tomorrow? The price of oil will immediately react to the forecast, which would consequently fail to materialise. If the current price of oil is $90 a barrel, and the infallible computer program predicts that tomorrow it will be $100, traders will rush to buy oil so that they can profit from the predicted price rise. As a result, the price will shoot up to $100 a barrel today rather than tomorrow. Then what will happen tomorrow? Nobody knows.

The official chronicler of business cycles in the United States, the National Bureau of Economic Research, a not-for-profit group founded in 1920, would declare, though many months later, that a recession had set in that August. But in September, no one was aware of it. There were the odd signs of economic slowdown, especially in some of the more interest-rate-sensitive sectors - automobile sales had peaked and construction had been down all year, but most short-term indicators, for example, steel production or railroad freight car loadings, remained exceptionally strong. By the middle of the month, the market was back at its highs and Babson's forecast of a crash had been thoroughly discredited.

Chaotic systems come in two shapes. Level one chaos is chaos that does not react to predictions about it. The weather, for example, is a level one chaotic system. Though it is influenced by myriad factors, we can build computer models that take more and more of them into consideration, and product better and better weather forecasts. Level two chaos is chaos that reacts to predictions about it and therefore can never be predicted accurately. Markets, for example, are a level two chaotic system. What will happen if we develop a computer program that forecasts with 100 per cent accuracy the price of oil tomorrow? The price of oil will immediately react to the forecast, which would consequently fail to materialize.

In 1997, money manager David Leinweber wondered which statistics would have best predicted the performance of the U.S. stock market from 1981 through 1993. He sifted through thousands of publicly available numbers until he found one that had forecast U.S. stock returns with 75% accuracy: the total volume of butter produced each year in Bangladesh. Leinweber was able to improve the accuracy of his forecasting “model” by adding a couple of other variables, including the number of sheep in the United States. Abracadabra! He could now predict past stock returns with 99% accuracy. Leinweber meant his exercise as satire, but his point was serious: Financial marketers have such an immense volume of data to slice and dice that they can “prove” anything.

On January 7, 1973, the New York Times featured an interview with one of the nation’s top financial forecasters, who urged investors to buy stocks without hesitation: “It’s very rare that you can be as unqualifiedly bullish as you can now.” That forecaster was named Alan Greenspan, and it’s very rare that anyone has ever been so unqualifiedly wrong as the future Federal Reserve chairman was that day: 1973 and 1974 turned out to be the worst years for economic growth and the stock market since the Great Depression.

So many people were basing decisions on Granville’s forecasts in the early 1980s that when he said something was going to happen, it happened because they believed it would. That is, when he said the market would go down, the prediction scared buyers out of the market – and lo, it went down. This happened early in 1981, when Granville told his disciples to sell everything. The day after this famous warning was issued, the stock market fell out of bed – 23 points on the Dow. All of Wall Street said ooh and ah. What a powerful prophet was this Granville! The plunge was brief but impressive while it lasted.

forecast Steve Ballmer made in 2007, when he was CEO of Microsoft: “There’s no chance that the iPhone is going to get any significant market share. No chance.

Are we expanding our sales force appropriately to match needed sales growth and market penetration?   2. Are our reps properly trained, and what is the lag time between training and an effective rep?   3. Is our compensation package and awards program sufficient to attract and retain high performers?   4. Is our field sales forecasting system functioning properly to anticipate negative trends?   5. Can we continue to leverage the sales expense line without damaging sales?   6. Is our expense budget tracking system effective?   7. Are we accurately monitoring sales force morale?   8. Is our pay schedule competitive?