Forecast Best 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.

A hallmark of interactions on the best teams is what psychologist Jonathan Baron termed “active open-mindedness.” The best forecasters view their own ideas as hypotheses in need of testing. Their aim is not to convince their teammates of their own expertise, but to encourage their teammates to help them falsify their own notions.

This should not come as a surprise: overly optimistic forecasts of the outcome of projects are found everywhere. Amos and I coined the term planning fallacy to describe plans and forecasts that are unrealistically close to best-case scenarios could be improved by consulting the statistics of similar cases Examples of the planning fallacy abound in the experiences of individuals, governments, and businesses.

Hope is one of our central emotions, but we are often at a loss when asked to define it. Many of us confuse hope with optimism, a prevailing attitude that "things turn out for the best." But hope differs from optimism. Hope does not arise from being told to "Think Positively," or from hearing an overly rosy forecast. Hope, unlike optimism, is rooted in unalloyed reality. Although there is no uniform definition of hope, I found on that seemed to capture what my patients had taught me. Hope is the elevating feeling we experience when we see - in the mind's eye- a path to a better future. Hope acknowledges the significant obstacles and deep pitfalls along that path. True hope has no room for delusion.

Plans are best-case scenarios. Let’s avoid anchoring on plans when we forecast actual outcomes. Thinking about ways the plan could go wrong is one way to do it.

the best forecasters, it is not what they think, but how they think.

Amos and I coined the term planning fallacy to describe plans and forecasts that are unrealistically close to best-case scenarios could be improved by consulting the statistics of similar cases

The best forecasters view their own ideas as hypotheses in need of testing. Their aim is not to convince their teammates of their own expertise, but to encourage their teammates to help them falsify their own notions.

hallmark of interactions on the best teams is what psychologist Jonathan Baron termed “active open-mindedness.” The best forecasters view their own ideas as hypotheses in need of testing. Their aim is not to convince their teammates of their own expertise, but to encourage their teammates to help them falsify their own notions.

That’s where the best forecasters excelled: they were eager to think again. They saw their opinions more as hunches than as truths—as possibilities to entertain rather than facts to embrace. They questioned ideas before accepting them, and they were willing to keep questioning them even after accepting them. They were constantly seeking new information and better evidence—especially disconfirming evidence.

We have contrasted two ways of evaluating a judgment: by comparing it to an outcome and by assessing the quality of the process that led to it. Note that when the judgment is verifiable, the two ways of evaluating it may reach different conclusions in a single case. A skilled and careful forecaster using the best possible tools and techniques will often miss the correct number in making a quarterly inflation forecast. Meanwhile, in a single quarter, a dart-throwing chimpanzee will sometimes be right.

In a representative statement from 1963, he claimed, “Man does not know most of the rules on which he acts; and even what we call his intelligence is largely a system of rules which operate on him but which he does not know.”60 This deference to the precognitive or the pre-rational is what separated him from the rational choice and rational expectations models of Chicago School economists, who professed much more faith in the possibility of both formal mathematical modeling and forecasting. As he explained in his Nobel speech, Hayek saw such efforts as not only presumptuous but misleading. The best one could hope for was pattern prediction.

This embarrassing episode remains one of the most instructive experiences of my professional life. I eventually learned three lessons from it. The first was immediately apparent: I had stumbled onto a distinction between two profoundly different approaches to forecasting, which Amos and I later labeled the inside view and the outside view. The second lesson was that our initial forecasts of about two years for the completion of the project exhibited a planning fallacy. Our estimates were closer to a best-case scenario than to a realistic assessment. I was slower to accept the third lesson, which I call irrational perseverance: the folly we displayed that day in failing to abandon the project. Facing a choice, we gave up rationality rather than give up the enterprise.

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.

The Princeton economist and wine lover Orley Ashenfelter has offered a compelling demonstration of the power of simple statistics to outdo world-renowned experts. Ashenfelter wanted to predict the future value of fine Bordeaux wines from information available in the year they are made. The question is important because fine wines take years to reach their peak quality, and the prices of mature wines from the same vineyard vary dramatically across different vintages; bottles filled only twelve months apart can differ in value by a factor of 10 or more. An ability to forecast future prices is of substantial value, because investors buy wine, like art, in the anticipation that its value will appreciate. It is generally agreed that the effect of vintage can be due only to variations in the weather during the grape-growing season. The best wines are produced when the summer is warm and dry, which makes the Bordeaux wine industry a likely beneficiary of global warming. The industry is also helped by wet springs, which increase quantity without much effect on quality. Ashenfelter converted that conventional knowledge into a statistical formula that predicts the price of a wine—for a particular property and at a particular age—by three features of the weather: the average temperature over the summer growing season, the amount of rain at harvest-time, and the total rainfall during the previous winter. His formula provides accurate price forecasts years and even decades into the future. Indeed, his formula forecasts future prices much more accurately than the current prices of young wines do. This new example of a “Meehl pattern” challenges the abilities of the experts whose opinions help shape the early price. It also challenges economic theory, according to which prices should reflect all the available information, including the weather. Ashenfelter’s formula is extremely accurate—the correlation between his predictions and actual prices is above .90.

The next day’s call would be vital. Then at 12:02 P.M., the radio came to life. “Bear at camp two, it’s Neil. All okay?” I heard the voice loud and clear. “Hungry for news,” I replied, smiling. He knew exactly what I meant. “Now listen, I’ve got a forecast and an e-mail that’s come through for you from your family. Do you want to hear the good news or the bad news first?” “Go on, then, let’s get the bad news over with,” I replied. “Well, the weather’s still lousy. The typhoon is now on the move again, and heading this way. If it’s still on course tomorrow you’ve got to get down, and fast. Sorry.” “And the good news?” I asked hopefully. “Your mother sent a message via the weather guys. She says all the animals at home are well.” Click. “Well, go on, that can’t be it. What else?” “Well, they think you’re still at base camp. Probably best that way. I’ll speak to you tomorrow.” “Thanks, buddy. Oh, and pray for change. It will be our last chance.” “Roger that, Bear. Don’t start talking to yourself. Out.” I had another twenty-four hours to wait. It was hell. Knowingly feeling my body get weaker and weaker in the vain hope of a shot at the top. I was beginning to doubt both myself and my decision to stay so high. I crept outside long before dawn. It was 4:30 A.M. I sat huddled, waiting for the sun to rise while sitting in the porch of my tent. My mind wandered to being up there--up higher on this unforgiving mountain of attrition. Would I ever get a shot at climbing in that deathly land above camp three? By 10:00 A.M. I was ready on the radio. This time, though, they called early. “Bear, your God is shining on you. It’s come!” Henry’s voice was excited. “The cyclone has spun off to the east. We’ve got a break. A small break. They say the jet-stream winds are lifting again in two days. How do you think you feel? Do you have any strength left?” “We’re rocking, yeah, good, I mean fine. I can’t believe it.” I leapt to my feet, tripped over the tent’s guy ropes, and let out a squeal of sheer joy. These last five days had been the longest of my life.

The birth of TERLS, and then VSSC, gave India the capability to design, develop and produce world-class rocket systems. India developed the capability of launching geo-synchronous, sun-synchronous and meteorology spacecraft, communication satellites and remote sensing satellites, thereby providing fast communication, weather forecasting and also locating water resources for the country.

Kisan Call Centres provide valuable and timely knowledge support to farmers and fishermen. Similar domain service provider call centres are required in the field of commerce and industry, entrepreneurial skill development and employment generation, travel and tourism, banking and insurance, meteorological forecasting, disaster warning systems, education and human resource development and healthcare.

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.

Superforecasters’ online interactions are exercises in extremely polite antagonism, disagreeing without being disagreeable. Even on a rare occasion when someone does say, “‘You’re full of beans, that doesn’t make sense to me, explain this,’” Cousins told me, “they don’t mind that.” Agreement is not what they are after; they are after aggregating perspectives, lots of them. In an impressively unsightly image, Tetlock described the very best forecasters as foxes with dragonfly eyes. Dragonfly eyes are composed of tens of thousands of lenses, each with a different perspective, which are then synthesized in the dragonfly’s brain.

Less science-curious adults were like hedgehogs: they became more resistant to contrary evidence and more politically polarized as they gained subject matter knowledge. Those who were high in science curiosity bucked that trend. Their foxy hunt for information was like a literal fox’s hunt for prey: roam freely, listen carefully, and consume omnivorously. Just as Tetlock says of the best forecasters, it is not what they think, but how they think. The best forecasters are high in active open-mindedness. They are also extremely curious, and don’t merely consider contrary ideas, they proactively cross disciplines looking for them. “Depth can be inadequate without breadth,” wrote Jonathan Baron, the psychologist who developed measurements of active open-mindedness.

This chapter is about the hurdles and best practices in idea selection. To figure out how we can make fewer bad bets, I sought out skilled forecasters who have learned to avoid the risks of false positives and false negatives.

The problem is that we often use events like the Great Depression and World War II to guide our view of things like worst case scenarios when thinking about future investment returns. But those record setting events had no precedent when they occurred. So, the forecaster who assumes the worst and best events of the past will match the worst and best events of the future, is not following history. They're accidentally assuming that the history of unprecedented events doesn't apply to the future.

You will make sure your team is working on the right opportunities at the right time through your efforts to hone the team’s focus. You’ll lead the field each day with the right people in the right roles in the right places with the right tools and the right resources through your efforts to build it. Your team will consistently execute through your efforts to drive the fundamentals. You will predict the future through measuring the right KPIs and metrics engrossing your responsibility to forecast. And you will drive fun through the creation, management, and optimization of an environment where your team is intrinsically inspired, so they’ll show up, do their best, stay, and tell their friends.

FROM OTHER SOURCES Pre–race and Venue Homework Get hold of any history of past events at the venue, plus any information that the conducting club may have about weather and expected conditions. Go to the weather bureau and get history for the area. Speak to sailors from your class who have this venue as their home club or who have sailed there on a number of occasions. Boat, Sails, Gear Preparation Checklist Many times the outcome of a race is as dependent on what you have done prior to the race as to what you do out on the course. Sometimes no matter how good your tactics and strategy are a simple breakage could render all that useless. Hull – make sure that your hull is well sanded and polished, centreboard strips are in good condition, venturis if fitted are working efficiently, buoyancy tanks are dry and there are no extraneous pieces of kit in your boat which adds unwanted weight. Update any gear that looks tired or worn especially control lines. Mast, boom and poles – check that all halyards, stays and trapeze wires are not worn or damaged and that pins are secure, knots tight and that anything that can tear a sail or injure flesh is taped. Mark the full hoist position on all halyards. Deck hardware – check all cam cleats for spring tension and tape anything that may cause a sail tear or cut legs hands and arms. Check the length of all sheets and control lines and shorten anything that is too long. This not only reduces weight but also minimises clutter. Have marks on sheets and stick or draw numbers and reference scales for the jib tracks, outhaul and halyards so that you can easily duplicate settings that you know are fast in various conditions. Centreboard and rudder – ensure that all nicks and gouges are filled and sanded and the surfaces are polished and most importantly that rudder safety clips are working. Sails – select the correct battens for the day’s forecast. Write on the deck, with a china graph pencil, things like the starting sequence, courses, tide times and anything else that will remind you to sail fast. Tools and spares – carry a shackle key with screwdriver head on your person along with some spare shackles and short lengths of rope or different diameters. A tool like a Leatherman can be very useful to deal with unexpected breakages that can occur even in the best prepared boat.

It was Christmas Eve in 1971 and more than anything in the world, 17-year-old Juliane Köpcke was looking forward to seeing her father. She was travelling with her mother Maria, an ornithologist. The flight in the Lockheed Electra turboprop would take less than an hour. It would leave Lima and cross the huge wilderness of the Reserva Comunal El Sira before touching down in Pucallpa in the Amazonian rainforest where her parents ran a research station in the jungle studying wildlife. The airline, LANSA, didn’t have the best safety reputation: it had recently lost two aircraft in crashes. The weather forecast was not good. But the family desperately wanted to be together for Christmas, so they stepped on board. For the first twenty-five minutes everything was fine. Then the plane flew into heavy clouds and started shaking. Juliane’s mother was very nervous.

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.

SUMMARY A vast array of additional statistical methods exists. In this concluding chapter, we summarized some of these methods (path analysis, survival analysis, and factor analysis) and briefly mentioned other related techniques. This chapter can help managers and analysts become familiar with these additional techniques and increase their access to research literature in which these techniques are used. Managers and analysts who would like more information about these techniques will likely consult other texts or on-line sources. In many instances, managers will need only simple approaches to calculate the means of their variables, produce a few good graphs that tell the story, make simple forecasts, and test for significant differences among a few groups. Why, then, bother with these more advanced techniques? They are part of the analytical world in which managers operate. Through research and consulting, managers cannot help but come in contact with them. It is hoped that this chapter whets the appetite and provides a useful reference for managers and students alike. KEY TERMS   Endogenous variables Exogenous variables Factor analysis Indirect effects Loading Path analysis Recursive models Survival analysis Notes 1. Two types of feedback loops are illustrated as follows: 2. When feedback loops are present, error terms for the different models will be correlated with exogenous variables, violating an error term assumption for such models. Then, alternative estimation methodologies are necessary, such as two-stage least squares and others discussed later in this chapter. 3. Some models may show double-headed arrows among error terms. These show the correlation between error terms, which is of no importance in estimating the beta coefficients. 4. In SPSS, survival analysis is available through the add-on module in SPSS Advanced Models. 5. The functions used to estimate probabilities are rather complex. They are so-called Weibull distributions, which are defined as h(t) = αλ(λt)a–1, where a and 1 are chosen to best fit the data. 6. Hence, the SSL is greater than the squared loadings reported. For example, because the loadings of variables in groups B and C are not shown for factor 1, the SSL of shown loadings is 3.27 rather than the reported 4.084. If one assumes the other loadings are each .25, then the SSL of the not reported loadings is [12*.252 =] .75, bringing the SSL of factor 1 to [3.27 + .75 =] 4.02, which is very close to the 4.084 value reported in the table. 7. Readers who are interested in multinomial logistic regression can consult on-line sources or the SPSS manual, Regression Models 10.0 or higher. The statistics of discriminant analysis are very dissimilar from those of logistic regression, and readers are advised to consult a separate text on that topic. Discriminant analysis is not often used in public

It was eleven forty-five, overcast, fourteen degrees above zero, and damp. Cold for any month of the year, even January. The forecast called for a major snowstorm. They said it was moving in from Chicago. Ten to fifteen inches by nightfall. It was not the best day to be standing outside, but the congressman from Arizona was oblivious to the weather.

the use of statistical process control tools to evaluate variation, correlate root cause, forecast capacity, and anticipate throughput barriers. By measuring incidence of preventable venous

The best weather prediction for the present moment is to look out of the window!

TheSpanish government, which faces an election in the autumn, was buoyed by news that the economy grew by 1.4% last year, the most since 2007. Some forecasters think Spain will be the best-performing economy among the larger euro-zone countries this year. Spending and investment have increased after cuts to both income and corporate taxes.

Your best estimates for the future will not match up to the actual numbers for several reasons. First, even if your information sources are impeccable, you must convert raw information into forecasts, and any mistakes that you make at this stage will cause estimation error. Next, the path that you envision for a firm can prove to be hopelessly off. The firm may do much better or much worse than you expected it to perform, and the resulting earnings and cash flows will be different from your estimates; consider this firm-specific uncertainty. When valuing Cisco in 2001, for instance, we seriously underestimated how difficult it would be for the company to maintain its acquisition-driven growth in the future, and we overvalued the company as a consequence. Finally, even if a firm evolves exactly the way you expected it to, the macroeconomic environment can change in unpredictable ways. Interest rates can go up or down, and the economy can do much better or worse than expected. Our valuation of Marriott from November 2019 looks hopelessly optimistic, in hindsight, because we did not foresee the global pandemic in 2020 and the economic consequences for the hospitality business.

The single most important driver of forecasters’ success was how often they updated their beliefs. The best forecasters went through more rethinking cycles. They had the confident humility to doubt their judgments and the curiosity to discover new information that led them to revise their predictions.

There are a few things I dismiss and a few I believe in thoroughly. The former include economic forecasts, which I think don’t add value, and the list of the latter starts with cycles and the need to prepare for them. “Hey, ” you might say, “that’s contradictory. The best way to prepare for cycles is to predict them, and you just said it can’t be done.” That's absolutely true, but in my opinion by no means debilitating. All of investing consists of dealing with the future [...] and the future is something we can’t know much about. But the limits on our foreknowledge needn't doom us to failure as long as we acknowledge them and act accordingly. In my opinion, the key to dealing with the future lies in knowing where you are, even if you can’t know precisely where you're going. Knowing where you are in a cycle and what that implies for the future is different from predicting the timing, extent and shape of the cyclical move.

Four years to the day after Fairchild's 1908 gift of the trees to Washington's schools, on March 27, 1912, Mrs. Taft broke dirt during the private ceremony in West Potomac Park near the banks of the Potomac River. The wife of the Japanese ambassador was invited to plant the second tree. Eliza Scidmore and David Fairchild took shovels not long after. The 3,020 trees were more than could fit around the tidal basin. Gardeners planted extras on the White House grounds, in Rock Creek Park, and near the corner of Seventeenth and B streets close to the new headquarters of the American Red Cross. It took only two springs for the trees to become universally adored, at least enough for the American government to feel the itch to reciprocate. No American tree could rival the delicate glamour of the sakura, but officials decided to offer Japan the next best thing, a shipment of flowering dogwoods, native to the United States, with bright white blooms. Meanwhile, the cherry blossoms in Washington would endure over one hundred years, each tree replaced by clones and cuttings every quarter century to keep them spry. As the trees grew, so did a cottage industry around them: an elite group of gardeners, a team to manage their public relations, and weather-monitoring officials to forecast "peak bloom"---an occasion around which tourists would be encouraged to plan their visits. Eventually, cuttings from the original Washington, D.C, trees would also make their way to other American cities with hospitable climates. Denver, Colorado; Birmingham, Alabama; Saint Paul, Minnesota.

But there were plenty of examples of international negotiation failures, exits from international agreements, and forced currency conversions that allowed the best forecasters to ground themselves in what usually happens without focusing narrowly on all the unique details of the present situation. Starting with the details—the inside view—is dangerous. Hedgehog experts have more than enough knowledge about the minutiae of an issue in their specialty to do just what Dan Kahan suggested: cherry-pick details that fit their all-encompassing theories. Their deep knowledge works against them. Skillful forecasters depart from the problem at hand to consider completely unrelated events with structural commonalities rather than relying on intuition based on personal experience or a single area of expertise.

The researcher who led that work went on to study thousands of businesses. She found that the most effective leaders and organizations had range; they were, in effect, paradoxical. They could be demanding and nurturing, orderly and entrepreneurial, even hierarchical and individualistic all at once. A level of ambiguity, it seemed, was not harmful. In decision making, it can broaden an organization’s toolbox in a way that is uniquely valuable. Philip Tetlock and Barbara Mellers showed that thinkers who tolerate ambiguity make the best forecasts; one of Tetlock’s former graduate students, University of Texas professor Shefali Patil, spearheaded a project with them to show that cultures can build in a form of ambiguity that forces decision makers to use more than one tool, and to become more flexible and learn more readily.

Investment advisory services, earnings forecasts, and chart patterns are useless.

Consider Jerry. His prospecting is inconsistent at best. Several of the deals he was counting on and put into his forecast pushed off decisions to next quarter or were lost to a competitor. Because of this, he has only a handful of viable opportunities left in his pipeline. Now, with the end of the quarter looming, Jerry is under tremendous pressure. He desperately needs one of these deals to close. As Jerry becomes more desperate to close anything, he comes face to face with a cruel reality: Desperation magnifies and

In 2013, on the auspicious date of April 1, I received an email from Tetlock inviting me to join what he described as “a major new research program funded in part by Intelligence Advanced Research Projects Activity, an agency within the U.S. intelligence community.” The core of the program, which had been running since 2011, was a collection of quantifiable forecasts much like Tetlock’s long-running study. The forecasts would be of economic and geopolitical events, “real and pressing matters of the sort that concern the intelligence community—whether Greece will default, whether there will be a military strike on Iran, etc.” These forecasts took the form of a tournament with thousands of contestants; the tournament ran for four annual seasons. “You would simply log on to a website,” Tetlock’s email continued, “give your best judgment about matters you may be following anyway, and update that judgment if and when you feel it should be. When time passes and forecasts are judged, you could compare your results with those of others.” I did not participate. I told myself I was too busy; perhaps I was too much of a coward as well. But the truth is that I did not participate because, largely thanks to Tetlock’s work, I had concluded that the forecasting task was impossible. Still, more than 20,000 people embraced the idea. Some could reasonably be described as having some professional standing, with experience in intelligence analysis, think tanks, or academia. Others were pure amateurs. Tetlock and two other psychologists, Barbara Mellers (Mellers and Tetlock are married) and Don Moore, ran experiments with the cooperation of this army of volunteers. Some were given training in some basic statistical techniques (more on this in a moment); some were assembled into teams; some were given information about other forecasts; and others operated in isolation. The entire exercise was given the name Good Judgment Project, and the aim was to find better ways to see into the future. This vast project has produced a number of insights, but the most striking is that there was a select group of people whose forecasts, while they were by no means perfect, were vastly better than the dart-throwing-chimp standard reached by the typical prognosticator. What is more, they got better over time rather than fading away as their luck changed. Tetlock, with an uncharacteristic touch of hyperbole, called this group “superforecasters.” The cynics were too hasty: it is possible to see into the future after all. What makes a superforecaster? Not subject-matter expertise: professors were no better than well-informed amateurs. Nor was it a matter of intelligence; otherwise Irving Fisher would have been just fine. But there were a few common traits among the better forecasters.

Regression: This is a well-understood technique from the field of statistics. The goal is to find a best fitting curve through the many data points. The best fitting curve is that which minimizes the (error) distance between the actual data points and the values predicted by the curve.  Regression models can be projected into the future for prediction and forecasting purposes.

It’s worth pausing for a moment to meditate on what Tesla had accomplished. Musk had set out to make an electric car that did not suffer from any compromises. He did that. Then, using a form of entrepreneurial judo, he upended the decades of criticisms against electric cars. The Model S was not just the best electric car; it was best car, period, and the car people desired. America had not seen a successful car company since Chrysler emerged in 1925. Silicon Valley had done little of note in the automotive industry. Musk had never run a car factory before and was considered arrogant and amateurish by Detroit. Yet, one year after the Model S went on sale, Tesla had posted a profit, hit $562 million in quarterly revenue, raised its sales forecast, and become as valuable as Mazda Motor. Elon Musk had built the automotive equivalent of the iPhone. And car executives in Detroit, Japan, and Germany had only their crappy ads to watch as they pondered how such a thing had occurred.

As we mentioned in chapter 4, any accounting change that is “material” to the bottom line must be footnoted in this manner. But who decides what is material and what isn’t? You guessed it: the accountants. In fact, it could very well be that recognizing 75 percent up front presents a more accurate picture of the software division’s reality. But was the change in accounting method due to good financial analysis, or did it reflect the need to make the earnings forecast? Could there be a bias lurking in here? Remember, accounting is the art of using limited data to come as close as possible to an accurate description of how well a company is performing. Revenue on the income statement is an estimate, a best guess. This example shows how estimates can introduce bias.

Always expect the unexpected. Never get too when things are going well, because otherwise the fall will be a lot harder. dinosaurs: triceratops and stegosaurus. Weather forecasters are like prison visitors. Nice people but usually misguided. The answer was yes, no, and maybe all rolled in one. She added that she hoped she might see him again. Not if I catch sight of you first, he thought. But like anything in life, you can never quite tell. People you know always have the ability to shock you. The label said it was "just like the mama used to cook" but if that was the case mama had obviously long since been banned from the kitchen. He wasn't work-shy. He was work-allergic. The problem these days is that gangsters, whether they be small time drug dealers with guns and attitude or wannabe urban godfathers like Nicholas Tyndall, have no qualms about using serious violence and the treat of it to get what they want, because they know that neither the judicial system nor the police service have the wherewithal or the powers to protect those who speak out against them. English prisons are roughly on a par with English traffic, English weather and English hospitals. In other words, fucking terrible. The striation marks on a bullet are the microscopic scratches caused by imperfections on the surface of the interior of a gun's barrel that are unique to each individual firearm, and act as its calling card.The same striation marks will appear on a bullet every time a particular gun is fired. 'The last time I spent quality time with you was Heathrow last week and five people ended up shot' The thing with me is that I am pessimist who's constantly trying to be optimistic, but can't quite manage it. Experience gained through years of policework doesn't allow for that sort of naivety. They say its a grand life if you don't weaken and for so long I've tried to live my life like that, but at that moment in time, weakness felt so tempting that I almost open my arms to greet it. 'And the whole time I couldn't wait to leave. And you know what, thy were the best years of my life.

Every team should be required to use the same reconciled demand, pricing, and master data (as well as any other relevant information sources). A supply chain cannot allow two teams to use different historical figures to populate forecasts.24

15.2.1 Short history of machine-learning models Harder, Better, Faster, Stronger —Daft Punk

She nods, changes lanes. “I was going to take you down to Raglan, but the surf forecast for Piha is awesome, and it’s not so far.” “Crazy how you always know now, isn’t it?” “Right? Just call up the reports, and Bob’s your uncle.” I laugh. “What did you just say?” “Bob’s your uncle, mate.” She laughs. “Best slang in the world, right here.

For seven months each year, the subarctic environment is transformed by a gift (or perhaps some would say a curse) of the weather. This, of course, is snow. By midwinter the land is covered by soft powder lying two to six feet deep in the forest, hardened to dunelike drifts on the broad lakes and rivers, creating a nivean world of its own. The coming of snow is forecast by many signs… When the sky is bright orange at sunrise there will be snow, "usually two mornings later." Perhaps the best sign of snow is a moondog, a luminous circle around a bright winter moon. When the Koyukon speak of it, they say, "the moon pulls his (parka] ruff around his face," as if he is telling them that snow is coming soon. The Koyukon people regard snow as an elemental part of their world, much like the river, the air, or the sun. It can be a great inconvenience at times, but mostly it is a benefit. Without snow, the ease and freedom of winter travel would be lost, the movements of animals would not be faithfully recorded, the winter darkness would be far deeper, and the quintessential beauty of the world would be lessened. I never heard Koyukon people complain about snow, even when it stubbornly refused to melt away in late spring.

2015, ECMWF’s scientists had squeezed out another day from the future, which meant the six-day forecast was now as good as the two-day forecast in 1975. Then they moved the goalposts: By 2025, ECMWF wants to have a model capable of predicting high-impact events two weeks ahead. (It predicted Sandy eight days ahead.) This is the truly remarkable thing about the place: not merely that ECMWF had the best global weather model in the world but that it had been constantly improved, for forty straight years.

the senior inventory managers typically lock themselves in a room and find a Band-Aid tool that satisfies the immediate request. Inevitably, the Band-Aid comes loose and those people uninvolved and underutilized in the decision-making process were then overworked trying to force the plan to work. But this time it was different. The entire inventory management team had just signed up for the 30-Day Challenge and selected the Debate Maker discipline for their work. This time, when the urgent request came from senior management, the group prepared for a thorough debate to find a sustainable solution. They brought in senior planners and the IT group (who usually had to scramble after the fact), who could give practical input to the feasibility of any suggested solution. They framed the issues and set ground rules for debate, including no barriers to the thinking. The team challenged their assumptions and in the end developed a means of in-season forecasting that served the new demands. The solution they arrived at started as a wild idea, but with input from IT, it became a plausible reality.

Week 1: Build an Arsenal of Ideas Day 1: Predict the Future Day 2: Learn How Money Grows on Trees Day 3: Brainstorm, Borrow, or Steal Ideas Day 4: Weigh the Obstacles and Opportunities of Each Idea Day 5: Forecast Your Profit on the Back of a Napkin Week 2: Select Your Best Idea Day 6: Use the Side Hustle Selector to Compare Ideas Day 7: Become a Detective Day 8: Have Imaginary Coffee with Your Ideal Customer Day 9: Transform Your Idea into an Offer Day 10: Create Your Origins Story Week 3: Prepare for Liftoff Day 11: Assemble the Nuts and Bolts Day 12: Decide How to Price Your Offer Day 13: Create a Side Hustle Shopping List Day 14: Set Up a Way to Get Paid Day 15: Design Your First Workflow Day 16: Spend 10 Percent More Time on the Most Important Tasks Week 4: Launch Your Idea to the Right People Day 17: Publish Your Offer! Day 18: Sell Like a Girl Scout Day 19: Ask Ten People for Help Day 20: Test, Test, and Test Again Day 21: Burn Down the Furniture Store Day 22: Frame Your First Dollar Week 5: Regroup and Refine Day 23: Track Your Progress and Decide on Next Steps Day 24: Grow What Works, Let Go of What Doesn’t Day 25: Look for Money Lying Under a Rock Day 26: Get It Out of Your Head Day 27: Back to the Future

The single most important driver of forecasters’29 success was how often they updated their beliefs. The best forecasters went through more rethinking cycles.

The task of wealth generation for the nation has to be woven around national competencies. The Technology Information, Forecasting and Assessment Council (TIFAC) task team has identified core areas that will spearhead our march towards becoming a knowledge society. The areas are: information technology, biotechnology, space technology, weather forecasting, disaster management, tele-medicine and tele-education, technologies utilizing traditional knowledge, service sector and infotainment which is the emerging area resulting from the convergence of information and entertainment.

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.

A forecaster who doesn’t adjust her views in light of new information won’t capture the value of that information, while a forecaster who is so impressed by the new information that he bases his forecast entirely on it will lose the value of the old information that underpinned his prior forecast. But the forecaster who carefully balances old and new captures the value in both—and puts it into her new forecast. The best way to do that is by updating often but bit by bit.

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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%.

Ideally, perhaps, the security analyst should pick out the three or four companies whose future he thinks he knows the best, and concentrate his own and his clients’ interest on what he forecasts for them. Unfortunately, it appears to be almost impossible to distinguish in advance between those individual forecasts which can be relied upon and those which are subject to a large chance of error. At bottom, this is the reason for the wide diversification practiced by the investment funds. For it is undoubtedly better to concentrate on one stock that you know is going to prove highly profitable, rather than dilute your results to a mediocre figure, merely for diversification’s sake. But this is not done, because it cannot be done dependably. 4 The prevalence of wide diversification is in itself a pragmatic repudiation of the fetish of “selectivity,” to which Wall Street constantly pays lip service.*

Technological innovations that produced certain major components of the United States military cannot be understood as resulting from a qualitative arms race. Those involved in decisions about new military technologies for the U.S. Army and Air Force simply do not appear to have had access to good intelligence about the Soviet military technological developments. How, then, were decisions made as to technologies to develop? Military research and development decisions are made amid great uncertainties. In an ideal world, such decisions would be managed by estimating the future costs of alternative programs and their prospective military values, and then pursuing the program with the best ratio of cost to value. But...there are tremendous difficulties in forecasting the real value and costs of weapons development programs. These uncertainties, combined with the empirical difficulty American technology managers had in collecting intelligence on the Soviet Union, meant that research and development strategies in the real world tended to become strategies for managing uncertainties. At least two such strategies are conceivable. One of the most politically important can be called, for want of a better phrase, "let the scientists choose." [This approach should be] compared with the theoretical and practical arguments for a strategy that concentrates on low-cot hedges against various forms of uncertainty.

His favoured objects of contemplation were economic facts, usually in statistical form. He used to say that his best ideas came to him from ‘messing about with figures and seeing what they must mean’. Yet he was famously sceptical about econometrics – the use of statistical methods for forecasting purposes. He championed the cause of better statistics, not to provide material for the regression coefficient, but for the intuition of the economist to play on.

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Ideas are easy, but reality is this. We have separate lives. Right people, wrong time. We will love each other in the best ways we can, while each following our own dreams. It’s messy, it’s painful, but that’s what we have.