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.

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 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

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.

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!

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.

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.

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.

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.

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.

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.

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.

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.

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.

Once I saw this trend, the paper quickly wrote itself and was titled “Has Financial Development Made the World Riskier?” As the Wall Street Journal reported in 2009 in an article on my Jackson Hole presentation: Incentives were horribly skewed in the financial sector, with workers reaping rich rewards for making money but being only lightly penalized for losses, Mr. Rajan argued. That encouraged financial firms to invest in complex products, with potentially big payoffs, which could on occasion fail spectacularly. He pointed to “credit default swaps” which act as insurance against bond defaults. He said insurers and others were generating big returns selling these swaps with the appearance of taking on little risk, even though the pain could be immense if defaults actually occurred. Mr. Rajan also argued that because banks were holding a portion of the credit securities they created on their books, if those securities ran into trouble, the banking system itself would be at risk. Banks would lose confidence in one another, he said. “The inter-bank market could freeze up, and one could well have a full-blown financial crisis.” Two years later, that’s essentially what happened.2 Forecasting at that time did not require tremendous prescience: all I did was connect the dots using theoretical frameworks that my colleagues and I had developed. I did not, however, foresee the reaction from the normally polite conference audience. I exaggerate only a bit when I say I felt like an early Christian who had wandered into a convention of half-starved lions. As I walked away from the podium after being roundly criticized by a number of luminaries (with a few notable exceptions), I felt some unease. It was not caused by the criticism itself, for one develops a thick skin after years of lively debate in faculty seminars: if you took everything the audience said to heart, you would never publish anything. Rather it was because the critics seemed to be ignoring what was going on before their eyes.

Tough times brought on by the Gulf War were testing such assumptions, forcing us to consider our response. We needed to come up with new ideas, do more with less, make short-term gains through greater efficiency, and prepare for long-term gains. That meant cutting every dollar possible in overhead and procedures while maintaining or boosting spending in three vital competitive areas. Number one was product quality. World leadership demanded that we maintain world-class quality, and recession is generally a period when material and labor prices are lowest and room occupancies are down. So we renovated and refurbished at such normally busy properties as the Inn on the Park in London and The Pierre in New York at a time when revenue would be little affected and customers least inconvenienced. That meant we were spending when others were retrenching. We had followed that strategy in 1981-82, and the rebound from that recession had given us nine years of steady growth. I thought the odds were in our favor to score the same way again. The second area was marketing. It’s tempting during recession to cut back on consumer advertising. At the start of each of the last three recessions, the growth of spending on such advertising had slowed by an average of 27 percent. But consumer studies of those recessions had showed that companies that didn’t cut their ads had, in the recovery, captured the most market share. So we didn’t cut our ad budget. In fact, we raised it modestly to gain brand recognition, which continued advertising sustains. As studies show, it’s much easier to sustain momentum than restart it. Third, we eased the workload and reduced costs by simplifying reporting methods. We set up a new system that allowed each hotel to recalculate its forecast, with minimal input, to year’s end, then send it in electronically along with a brief monthly commentary.

Tony Plummer brilliantly pointed out in his book, Forecasting Financial Markets, the main leader of the market is price. Price is the leader of the market crowd. Traders

After our IPO in January 1997, we had to get better at predicting our numbers. … The market penalized us when we missed one quarter in ‘99 after we adopted a new manufacturing system. We said, “Look, we can’t predict what’s going on in the economy, and we have no idea what our orders will look like a year from now. … We don’t run this business by the numbers. The numbers will be doing what the numbers will be doing; we can just give you a good picture of what the next quarter will bring. So, we got away from making annual projections and started just doing quarterly forecasts. … We know our performance in the long run will be a result of just doing the right things every day.115

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?

The factors that determine activity on the Exchange are innumerable, with events, current or expected, often bearing no apparent relation to price variation. Beside the somewhat natural causes for variation come artificial causes: The Exchange reacts to itself, and the current trading is a function, not only of prior trading, but also of its relationship to the rest of the market. The determination of this activity depends on an infinite number of factors. It is thus impossible to hope for mathematical forecasting. Contradictory opinions about these variations are so evenly divided that at the same instant buyers expect a rise and sellers a fall. The calculus of probability can doubtless never be applied to market activity, and the dynamics of the Exchange will never be an exact science. But it is possible to study mathematically the state of the market at a given instant- that is to say, to establish the laws of probability for price variation that the market at the instant dictates. If the market, in effect, does not predict its fluctuations, it does not assess them as being more or less likely, and this likelihood can be evaluated mathematically.

Modification of firms' innovation processes to systematically search for and further develop innovations created by lead users can provide manufacturers with a better interface to the innovation process as it actually works, and so provide better performance. A natural experiment conducted at 3M illustrates this possibility. Annual sales of lead user product ideas generated by the average lead user project at 3M were conservatively forecast by management to be more than 8 times the sales forecast for new products developed in the traditional manner-$146 million versus $18 million per year. In addition, lead user projects were found to generate ideas for new product lines, while traditional market-research methods were found to produce ideas for incremental improvements to existing

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.

Consider a 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.” Ballmer’s forecast is infamous. Google “Ballmer” and “worst tech predictions”—or “Bing” it, as Ballmer would prefer—and you will see it enshrined in the forecasting hall of shame, along with such classics as the president of Digital Equipment Corporation declaring in 1977 that “there is no reason anyone would want a computer in their home.” And that seems fitting because Ballmer’s forecast looks spectacularly wrong. As the author of “The Ten Worst Tech Predictions of All Time” noted in 2013, “the iPhone commands 42% of US smartphone market share and 13.1% worldwide.”1 That’s pretty “significant.” As another journalist wrote, when Ballmer announced his departure from Microsoft in 2013, “The iPhone alone now generates more revenue than all of Microsoft.”2

And what I know about price—what has been proven to me again and again—is that oil prices have become ever more unreliable as the systems that work on it become ever more financialized. Of all the things I have studied and correctly and incorrectly forecasted in my 30+ years in the oil markets, the one unshakable truth I have held onto is that the wide ranges of prices for oil we have seen in the last 15 years have been tethered far more to unrelated financial inputs than to the underlying fundamentals of oil. It has been those financial “gremlins” inside the machine that have made oil prices go so far above any logical expectation so many times, particularly in 2007 and 2010 and equally foolishly low as in 2009 and in 2015. Those wide extremes in price have done more than make and lose fortunes in the oil world. They've affected just about everything politically and socially in the rest of the world.

Cutting budgets or slashing prices to customers are actually the easiest things that oil companies and services firms can do. For producers, cutting spending doesn't affect the bottom line immediately, as reductions in capital expenditures (capex) won’t result in production declines—and therefore profits—for months in the future. You can even claim continuing high production results despite major drops in capital expenditures, a counter-intuitive result but still at least immediately genuine. Almost all of the independent oil companies have done precisely this through their reporting up to the 4th quarter of 2014, reporting slashed spending yet increasing production forecasts. In fact, at least for the first 6 months after cutting capex, oil company executives can look like stars, chopping off the top line with little immediate effect on the bottom. Further, the projections on capital expenditures are a bit of an accountant’s dodge in that they can be adjusted several times over the year to adapt to changing market conditions. An oil company can talk about an extreme cut in spends at the start of the year, but should oil prices allow, it can still ramp spending back up later. Looking responsible using the accountant’s pen is a pretty easy way to initially react to a low price environment, and just about everyone is doing it.

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.

I have never known a great trader, with his first reputation established as a bear operator, who did not either turn bull or drop out of the market altogether.

Fourth, even these aggregate targets are mainly labeled as “forecasts” (预期性) rather than “mandatory” (约束性).

Sometimes the need to adapt the forecast to the consumer can take on comical dimensions. For many years, the Weather Channel had indicated rain on their radar maps with green shading (occasionally accompanied by yellow and red for severe storms). At some point in 2001, someone in the marketing department got the bright idea to make rain blue instead—which is, after all, what we think of as the color of water. The Weather Channel was quickly besieged with phone calls from outraged—and occasionally terrified—consumers, some of whom mistook the blue blotches for some kind of heretofore unknown precipitation (plasma storms? radioactive fallout?). “That was a nuclear meltdown,” Dr. Rose told me. “Somebody wrote in and said, ‘For years you’ve been telling us that rain is green—and now it’s blue? What madness is this?

The fundamentals of the economy remain strong.' That cliche is repeated by authorities as they try to restore public confidence after every major stock market decline. They have the opportunity to say this because just about every major stock market decline appears inexplicable if one looks only at the factors that logically ought to influence stock markets. It is practically always the stock market that has changed; indeed the fundamentals haven't. How do we know that these changes could not be generated by fundamentals? If prices reflect fundamentals, they do so because those fundamentals are useful in forecasting future stock payoffs. In theory the stock prices are the predictors of the discounted value of those future income streams, in the form of future dividends or future earnings. But stock prices are much too variable. They are even much more variable than those discounted streams of dividends (or earnings) that they are trying to predict.

The dysfunctional state of the American political system is the best reason to be pessimistic about our country’s future. Our scientific and technological prowess is the best reason to be optimistic. We are an inventive people. The United States produces ridiculous numbers of patents,114 has many of the world’s best universities and research institutions, and our companies lead the market in fields ranging from pharmaceuticals to information technology. If I had a choice between a tournament of ideas and a political cage match, I know which fight I’d rather be engaging in—especially if I thought I had the right forecast.

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?).

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.

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.

The most robust evidence indicates that this wisdom-of-crowds principle holds when forecasts are made independently before being averaged together. In a true betting market (including the stock market), people can and do react to one another’s behavior.

Markets change, visions change, technologies change, teams change, settings change, relationships change… with an ever changing environment it will be naive to think that you can draw the future with a straight line.

Larry Kudlow, the president’s chief economic adviser, had been questioning the seriousness of the situation. He couldn’t square the apocalyptic forecasts with the bouyant stock market. “Is all the money dumb?” he wondered. “Everyone’s asleep at the switch? I just have a hard time believing that.”*

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

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.

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.

Put together two stock market forecasts - one predicting that prices will rise next month and one warning of a drop. Send the first mail to fifty thousand people and the second mail to a different set of fifty thousand. Suppose that after one month, the indices have fallen. Now you can send another mail, but this time only to the fifty thousand who received the correct prediction. This fifty thousand you divide into two groups: the first half learns prices will increase next month, and the second half discovers they will fall. Continue doing this. After ten months, around a hundred people will remain, all of whom you have advised impeccably. From their perspective, you are a genius. You have proven that you are truly in possession of prophetic powers. Some of these people will trust you with their money. Take it and start a new life in Brazil.

Or consider the hundreds of thousands of economists—in service of banks, think tanks, hedge funds, and governments—and all the white papers they have published from 2005 to 2007: The vast library of research reports and mathematical models. The formidable reams of comments. The polished PowerPoint presentations. The terabytes of information on Bloomberg and Reuters news services. The bacchanal dance to worship the god of information. It was all hot air. The financial crisis touched down and upended global markets, rendering the countless forecasts and comments worthless.

your brokerage costs, by trading rarely, patiently, and cheaply your ownership costs, by refusing to buy mutual funds with excessive annual expenses your expectations, by using realism, not fantasy, to forecast your returns7 your risk, by deciding how much of your total assets to put at hazard in the stock market, by diversifying, and by rebalancing your tax bills, by holding stocks for at least one year and, whenever possible, for at least five years, to lower your capital-gains liability and, most of all, your own behavior.

Test the market with samples first, if you can, to know what is really going to sell. • If possible, don’t build inventory in large quantities and eat up cash unless the business has the orders in its hands. • Try to find strategic partners that have quick turnarounds for building inventory. • Unless you have real-time data on customer demand and have an extremely tight connection to your suppliers, you’ll never get inventory forecasting exactly right. • Err on the side of less rather than more inventory as a rule of thumb. • If you have to make a trade-off between paying more per unit in COGS to reduce the cycle time to build inventory, choose the higher COGS and reduced production time. You’ll be placing smaller orders with greater frequency, turning inventory faster and cash faster. Read this point again—it’s not very complicated (place smaller orders, more frequently), but it’s really, really important for managing your inventory.

The top ten individual use cases by score across all 5Ps were as follows: 1.​Recommend highly targeted content to users in real time (3.96) 2.​Adapt audience targeting based on behavior and look-alike analysis (3.92) 3.​Measure ROI by channel, campaign, and overall (3.91) 4.​Discover insights into top-performing content and campaigns (3.86) 5.​Create data-driven content (3.82) 6.​Predict winning creatives (e.g., digital ads, landing pages, calls to action) before launch without A/B testing (3.81) 7.​Forecast campaign results based on predictive analysis (3.80) 8.​Deliver individualized content experiences across channels (3.80) 9.​Choose keywords and topic clusters for content optimization (3.78) 10.​Optimize website content for search engines (3.77)

AI is forecasted to have trillions of dollars of impact on businesses and the economy, yet the majority of marketers struggle to understand what it is and how to apply it to their marketing.

Amazon Comprehend is a natural language processing (NLP) solution that uses machine learning to find and extract insights and relationships from documents. •​Amazon Forecast combines your historical data with other variables, such as weather, to forecast outcomes. •​Amazon Kendra is an intelligent search service powered by machine learning. •​Amazon Lex is a solution for building conversational interfaces that can understand user intent and enable humanlike interactions. •​Amazon Lookout for Metrics detects and diagnoses anomalies in business and marketing data, such as unexpected drops in sales or unusual spikes in customer churn rates. •​Amazon Personalize powers personalized recommendations using the same machine-learning technology as Amazon.com. •​Amazon Polly converts text into natural-sounding speech, enabling you to create applications that talk. •​Amazon Rekognition makes it possible to identify objects, people, text, scenes, and activities in images and videos. •​Amazon Textract automatically reads and processes scanned documents to extract text, handwriting, tables, and data. •​Amazon Transcribe converts speech to text. •​Amazon Translate uses deep-learning models to deliver accurate, natural-sounding translation.

Forecasting: Predicting business outcomes •​Pattern Recognition: Identifying patterns in data •​Personalization: Personalizing experiences •​Recommendation: Making recommendations to achieve desired outcomes

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.

After 28 years at this post, and 22 years before this in money management, I can sum up whatever wisdom I have accumulated this way: The trick is not to be the hottest stock-picker, the winningest forecaster, or the developer of the neatest model; such victories are transient. The trick is to survive! Performing that trick requires a strong stomach for being wrong because we are all going to be wrong more often then we expect. The future is not ours to know. But it helps to know that being wrong is inevitable and normal, not some terrible tragedy, not some awful failing in reasoning, not even bad luck in most instances. Being wrong comes with the franchise of an activity whose outcome depends on an unknown future . . . (Jeff Saut, “Being Wrong and Still Making Money,” Seeking Alpha, March 13, 2017, emphasis added)

While weather forecasters and handicappers get accurate and timely feedback, long-term investors don’t. Maybe one day we’ll create a simulator that provides investors the training they need to make better decisions. Of course, the result will be markets that are even harder to beat.

the late 1940s there were still only a few devices. Early in that decade IBM’s president, Thomas J. Watson, had allegedly (and notoriously) said, “I think there is a world market for about five computers.” Popular Mechanics magazine made a forecast typical of its time in 1949: “Computers in the future may have only 1000 vacuum tubes,” it argued, “and perhaps weigh only 1½ tons.” A decade after Bletchley, there were still only hundreds of computers around the world.

An increasing number of innovative baby care product launches to address consumers’ growing awareness about baby health and safety concerns are expected to boost the growth of South Africa Baby Care Products Market during the forecast period between 2024 and 2030.

BlueWeave Consulting, a leading strategic consulting and market research firm, in its recent study, estimated the South Africa Baby Care Products Market size at USD 626.75 million in 2023. During the forecast period between 2024 and 2030, BlueWeave expects the South Africa Baby Care Products Market size to expand at a CAGR of 5.93% reaching a value of USD 885.41 million by 2030

Scott Huettel, a neuroeconomist at Duke University, has shown that the ACC reacts roughly three times more vigorously if a pattern reverses after eight repetitions than it does after a three-in-a-row pattern is broken. The stock market provides uncanny real-world proof of Huettel’s laboratory findings: The more times in a row a company has exceeded Wall Street’s expectations, the worse its stock gets whacked when it finally misses the analysts’ forecasts. While a shortfall after a run of three good earnings reports trims just 3.4% off the price of the typical growth stock, a miss after a streak of eight positive quarters hacks off 7.9%. So

I describe my forecasting model as “good enough.” I’m confident people will solve problems and become more productive over time. I’m confident markets will allocate the rewards of that productivity to investors over time. I’m confident in other people’s overconfidence, so I know there will be mistakes and accidents and booms and busts along the way. It’s not detailed, but it’s good enough.

Antrich wasn’t looking for a new trading technique when he first met with Tom Nesmith. He just wanted information. There was one critical piece missing in Koch Energy Trading’s intelligence network. Koch Industries didn’t own any power plants, so it didn’t have access to the kind of inside information that made its energy trading desks so successful. Antrich was on a quest for such information, and he tried to get it by forming information-sharing systems with utility companies that owned the plants. Antrich approached one such utility outside California: Public Service Company of New Mexico, or PNM, as most people called it. The company owned a power plant in Arizona that sold electricity into California. This meant that PNM could sell into the coveted ISO market. Antrich wanted PNM to sign a deal that would give Koch’s traders access to PNM’s inside information, such as information on plant outages, its own weather forecasts, and other data that could give Koch a head start on responding to changes in the market. In return, PNM would get access to Koch’s trading analysis, its secret in-house weather projections, and its forecasts on natural gas markets, among other things.

Validated learning is the process of demonstrating empirically that a team has discovered valuable truths about a startup’s present and future business prospects. It is more concrete, more accurate, and faster than market forecasting or classical business planning. It is the principal antidote to the lethal problem of achieving failure: successfully executing a plan that leads nowhere.

Or take the stock market. The valuation of every company is simply a number from today multiplied by a story about tomorrow. Some companies are incredibly good at telling stories, and during some eras investors become captivated by the wildest ideas of what the future might bring. If you’re trying to figure out where something is going next, you have to understand more than its technical possibilities. You have to understand the stories everyone tells themselves about those possibilities, because it’s such a big part of the forecasting equation.

Creating an algorithmic trading system should be every trader's goal. Yet, developing a trading system can be overwhelming since it involves several moving parts. Another challenge is that today's markets require an algorithm that adapts to different market conditions. In "Algorithmic Trading 101" Jacinta Chan sets you up by starting with the basics and walking you through the process, step-by-step. She touches on all aspects of a trading system. After going through the entire process detailed in the book, the trader will be ready to develop a customized trading system that follows the principles of professional traders." Jayanthi Gopalakrishnan,  Director, Site Content  StockCharts.com

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.

the Motley Fool’s goofy ratio couldn’t possibly be causing stock prices to rise, the only sensible conclusion was that its predictive power was an illusion. The Foolish Four portfolio made investors feel like idiots when it lost 14% in the year 2000 alone. Meanwhile, after six years of underperforming the market by nearly two percentage points annually, the Harry Dent–inspired mutual fund shut down in mid-2005 with the Dow mired about 31,000 points below his forecast.

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.

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

Assuming the total error was approximately normally distributed (the Gaussian or bell-shaped curve), we needed the standard deviation (a measure of uncertainty) for the error of prediction around the actual outcome to be sixteen pockets (0.42 revolution) or less to get an edge. We achieved the tighter estimate of ten pockets, or 0.26 revolution. This gave us the enormous average profit of 44 percent of the amount we bet on the forecast number. If we spread our bet over the two closest numbers on each side, for a total of five numbers in all, we cut risk and still had a 43 percent advantage.

Chambers et al. conclude that Keynes had no skill as a market timer. By then, however, the man who had started out as a top-down speculator relying upon his “superior knowledge” to forecast the macroeconomic climate, was behaving more like a bottom-up, fundamental investor who sought solid, dividend-paying stocks with good long-term prospects. His gains came from taking large positions in those securities that had financial statement sheets he could understand, and sold products or services he believed he could assess objectively.

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.

Investors should avoid any urge to forecast the stock market. Forecasts, even forecasts by recognized “experts,” are unlikely to be better than random guesses. “It will fluctuate,” declared J. P. Morgan when asked about his expectation for the stock market. He was right. All other market forecasts—usually estimating the overall direction of the stock market—are historically about 50 percent right and 50 percent wrong. You wouldn’t bet much money on a coin toss, so don’t even think of acting on stock market forecasts.

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

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.

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.

Lukasz Gogolewski is a product manager who provides the help in marketing and forecasting currently lives in New York.

Accenture, the management consultancy, forecasts the wearable technology market, currently worth $1bn-$3bn a year, will rise to $18bn by 2018.

forecasts, far short of the 1billion once expected. Consulting firm eMarketer suggests that Twitter's user growth will plateau in major developed markets within five years.

Good news is extrapolated into strong market expectations which are often not realized. As important, investor expectations are negatively correlated with model-based expected returns derived from dividend/price, consumption patterns and market valuation.  Investors, no matter what the level of experience, do not seem to use the models that provide useful information on expected returns. Put differently, when expected return models forecast higher returns, they are usually correct. When the expectations of returns are high from surveys, the actual returns are low. These market expectations are correlated with mutual fund flows. The surveys show expectation that investors actually use, albeit incorrectly.

Human civilization has in the past absorbed similar technology-driven shocks to the economy, turning hundreds of millions of farmers into factory workers over the nineteenth and twentieth centuries. But none of these changes ever arrived as quickly as AI. Based on the current trends in technology advancement and adoption, I predict that within fifteen years, artificial intelligence will technically be able to replace around 40 to 50 percent of jobs in the United States. Actual job losses may end up lagging those technical capabilities by an additional decade, but I forecast that the disruption to job markets will be very real, very large, and coming soon.

In an interview with Business Wire in November 2011, Buffett said, “If you understand chapters 8 and 20 of The Intelligent Investor (Benjamin Graham, 1949) and chapter 12 of The General Theory (John Maynard Keynes, 1936), you don’t need to read anything else and you can turn off your TV.”2 This advice from Buffett references two classics from the field of investing and economics. Chapter 8 of Graham’s book talks about not letting the mood swings of Mr. Market coax us into speculating, selling in panic, or trying to time the market. Chapter 20 explains that, after careful analysis of a company’s ongoing business and its prospects for future earnings, we should consider buying only if its current price implies a large margin of safety. In chapter 12 of The General Theory of Employment, Interest, and Money (“The State of Long-Term Expectation”), Keynes remarks that most professional investors and speculators were “largely concerned, not with making superior long-term forecasts of the probable yield of an investment over

United Kingdom-based growth strategy and research firm Frost and Sullivan forecasts the smallsat launch market will generate a whopping $69 billion in revenue by 2030, with new satellites, constellations, and replacement missions accounting for nearly 12,000 launches.