Financial Forecast Quotes

Realizing the future might not look anything like the past is a special kind of skill that is not generally looked highly upon by the financial forecasting community.

Financial forecasting appears to be a science that makes astrology look respectable.

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.

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.

As Nassim Taleb pointed out in The Black Swan, our tendency to construct and believe coherent narratives of the past makes it difficult for us to accept the limits of our forecasting ability. Everything makes sense in hindsight, a fact that financial pundits exploit every evening as they offer convincing accounts of the day’s events.

Nassim Taleb writes in his book Fooled By Randomness: In Pharaonic Egypt … scribes tracked the high-water mark of the Nile and used it as an estimate for a future worst-case scenario. The same can be seen in the Fukushima nuclear reactor, which experienced a catastrophic failure in 2011 when a tsunami struck. It had been built to withstand the worst past historical earthquake, with the builders not imagining much worse—and not thinking that the worst past event had to be a surprise, as it had no precedent. This is not a failure of analysis. It’s a failure of imagination. Realizing the future might not look anything like the past is a special kind of skill that is not generally looked highly upon by the financial forecasting community. At a 2017 dinner I attended in New York, Daniel Kahneman was asked how investors should respond when our forecasts are wrong. He said: Whenever we are surprised by something, even if we admit that we made a mistake, we say, ‘Oh I’ll never make that mistake again.’ But, in fact, what you should learn when you make a mistake because you did not anticipate something is that the world is difficult to anticipate. That’s the correct lesson to learn from surprises: that the world is surprising.

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!

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.

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.

This book aims to learn from that mistake. One of its goals is to ask whether Minsky’s demand for a theory that generates the possibility of great depressions is reasonable and, if so, how economists should respond. I believe it is quite reasonable. Many mainstream economists react by arguing that crises are impossible to forecast: if they were not, they would either already have happened or been forestalled by rational agents. That is certainly a satisfying doctrine, since few mainstream economists foresaw the crisis, or even the possibility of one. For the dominant school of neoclassical economics, depressions are a result of some external (or, as economists say, ‘exogenous’) shock, not of forces generated within the system.

If a model did anything too obviously bizarre—flooded the Sahara or tripled interest rates—the programmers would revise the equations to bring the output back in line with expectation. In practice, econometric models proved dismally blind to what the future would bring, but many people who should have known better acted as though they believed in the results. Forecasts of economic growth or unemployment were put forward with an implied precision of two or three decimal places. Governments and financial institutions paid for such predictions and acted on them, perhaps out of necessity or for want of anything better. Presumably they knew that such variables as “consumer optimism” were not as nicely measurable as “humidity” and that the perfect differential equations had not yet been written for the movement of politics and fashion. But few realized how fragile was the very process of modeling flows on computers, even when the data was reasonably trustworthy and the laws were purely physical, as in weather forecasting.

At a private lunch when I recently asked one of the world’s highest-ranking international diplomats what, among all the possible scenarios for Pakistan, was the most positive vision she held, everyone around the table laughed nervously. This diplomat was surprisingly honest. She admitted that she had not one positive vision for Pakistan. She was candid about a view that leaders widely hold but seldom acknowledge: humanity is on a slippery slope of resource depletion. It is unlikely leaders can do anything about it. Hence, their job is to make sure their people will lose last. This means securing for their people enough resources from the globe’s diminishing resource pie to ensure that their nation will float even if others sink. From this vantage point, money shields a population from losing first. Leaders beholden to this view therefore embrace even more vigorously GDP growth as their key objective; the financial advantage will allow their constituency to stay just a bit further ahead of the others in the resource race to 2052.

For instance, people trust more confident financial advisers over those who are less confident even when their track records are identical. And people equate confidence and competence, which makes the forecaster who says something has a middling probability of happening less worthy of respect. As one study noted, people “took such judgments as indications the forecasters were either generally incompetent, ignorant of the facts in a given case, or lazy, unwilling to expend the effort required to gather information that would justify greater confidence.

Supervised learning algorithms typically require stationary features. The reason is that we need to map a previously unseen (unlabeled) observation to a collection of labeled examples, and infer from them the label of that new observation. If the features are not stationary, we cannot map the new observation to a large number of known examples. But stationary does not ensure predictive power. Stationarity is a necessary, non-sufficient condition for the high performance of an ML algorithm. The problem is, there is a trade-off between stationarity and memory. We can always make a series more stationary through differentiation, but it will be at the cost of erasing some memory, which will defeat the forecasting purpose of the ML algorithm.

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.

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.

While the research and applications are in its early days, many experts see probabilistic programming as an alternative approach in areas where deep learning performs poorly, such as concept formulation using sparse or medium-sized data. Probabilistic programs have been used successfully in applications such as medical imaging, machine perception, financial predictions, and econometric and atmospheric forecasting.

The only thing that we know about financial predictions of startups is that 100 percent of them are wrong. If you can predict the future accurately, we have a few suggestions for other things you could be doing besides starting a risky early stage company. Furthermore, the earlier stage the startup, the less accurate any predications will be. While we know you can't predict your revenue with any degree of accuracy (although we are always very pleased in that rare case where revenue starts earlier and grows faster than expected), the expense side of your financial plan is very instructive as to how you think about the business. You can't predict your revenue with any level of precision, but you should be able to manage your expenses exactly to plan. Your financials will mean different things to different investors. In our case, we focus on two things: (1) the assumptions underlying the revenue forecast (which we don't need a spreadsheet for—we'd rather just talk about them) and (2) the monthly burn rate or cash consumption of the business. Since your revenue forecast will be wrong, your cash flow forecast will be wrong. However, if you are an effective manager, you'll know how to budget for this by focusing on lagging your increase in cash spend behind your expected growth in revenue.

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

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

Since the proper test of a theory is not the realism of its assumptions but the acceptability of its implications, and since these assumptions imply equilibrium conditions which form a major part of classical financial doctrine, it is far from clear that this formulation should be rejected—especially in view of the dearth of alternative models leading to similar results.

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.

Larry Kudlow hosted a business talk show on CNBC and is a widely published pundit, but he got his start as an economist in the Reagan administration and later worked with Art Laffer, the economist whose theories were the cornerstone of Ronald Reagan’s economic policies. Kudlow’s one Big Idea is supply-side economics. When President George W. Bush followed the supply-side prescription by enacting substantial tax cuts, Kudlow was certain an economic boom of equal magnitude would follow. He dubbed it “the Bush boom.” Reality fell short: growth and job creation were positive but somewhat disappointing relative to the long-term average and particularly in comparison to that of the Clinton era, which began with a substantial tax hike. But Kudlow stuck to his guns and insisted, year after year, that the “Bush boom” was happening as forecast, even if commentators hadn’t noticed. He called it “the biggest story never told.” In December 2007, months after the first rumblings of the financial crisis had been felt, the economy looked shaky, and many observers worried a recession was coming, or had even arrived, Kudlow was optimistic. “There is no recession,” he wrote. “In fact, we are about to enter the seventh consecutive year of the Bush boom.”19 The National Bureau of Economic Research later designated December 2007 as the official start of the Great Recession of 2007–9. As the months passed, the economy weakened and worries grew, but Kudlow did not budge. There is no recession and there will be no recession, he insisted. When the White House said the same in April 2008, Kudlow wrote, “President George W. Bush may turn out to be the top economic forecaster in the country.”20 Through the spring and into summer, the economy worsened but Kudlow denied it. “We are in a mental recession, not an actual recession,”21 he wrote, a theme he kept repeating until September 15, when Lehman Brothers filed for bankruptcy, Wall Street was thrown into chaos, the global financial system froze, and people the world over felt like passengers in a plunging jet, eyes wide, fingers digging into armrests. How could Kudlow be so consistently wrong? Like all of us, hedgehog forecasters first see things from the tip-of-your-nose perspective. That’s natural enough. But the hedgehog also “knows one big thing,” the Big Idea he uses over and over when trying to figure out what will happen next. Think of that Big Idea like a pair of glasses that the hedgehog never takes off. The hedgehog sees everything through those glasses. And they aren’t ordinary glasses. They’re green-tinted glasses—like the glasses that visitors to the Emerald City were required to wear in L. Frank Baum’s The Wonderful Wizard of Oz. Now, wearing green-tinted glasses may sometimes be helpful, in that they accentuate something real that might otherwise be overlooked. Maybe there is just a trace of green in a tablecloth that a naked eye might miss, or a subtle shade of green in running water. But far more often, green-tinted glasses distort reality. Everywhere you look, you see green, whether it’s there or not. And very often, it’s not. The Emerald City wasn’t even emerald in the fable. People only thought it was because they were forced to wear green-tinted glasses! So the hedgehog’s one Big Idea doesn’t improve his foresight. It distorts it. And more information doesn’t help because it’s all seen through the same tinted glasses. It may increase the hedgehog’s confidence, but not his accuracy. That’s a bad combination.

Preparation of Financial Statements - A Simple Summary •AR-C 70 is applicable when the accountant is engaged to prepare financial statements and is not applicable when the accountant is engaged to perform a compilation or if the accountant is merely assisting with bookkeeping •The objective of the accountant is to prepare financial statements in accordance with the chosen reporting framework •The financial statements can be prepared in accordance with GAAP or a special purpose reporting framework •The financial statements can be distributed to third parties (and not just management) •The accountant must either: ·State on each financial statement page that “no assurance is provided,” or ·Provide a disclaimer •Documentation requirements include: ·The engagement letter, and ·The financial statements •An engagement letter must be signed by: ·The accountant or the accountant’s firm, and ·Management or those charged with governance •No report (e.g., compilation report) is attached to the financial statements •Consideration of independence is not required •Substantially all disclosures can be omitted •The omission of substantially all disclosures should be: ·Disclosed on the face of the financial statements, or ·In a note •Selected disclosures can be provided •Departures from the applicable financial reporting framework should be: ·Disclosed on the face of the financial statements, or ·In a note •A preparation engagement may be applied to historical financial statements and to: ·Historical information (e.g., specified items of a financial statement) and ·Prospective information, including: ·Budgets ·Forecasts, or ·Projections •A preparation engagement can be performed in relation to prescribed forms (e.g., bank personal financial statements) •Mark draft financial statements with appropriate wording (e.g., Draft Financial Statements)

Futurists who are thinking about the businesses of the future forecast that many more of us will become entrepreneurs. They see employee healthcare and financial benefits, pension plans and retirement packages, all disappearing in the future for most employees of most companies. Everybody’s going to be a free agent, and everybody’s going to be an entrepreneur. You’re going to broker your skills and negotiate your own contracts for everything. Now it may not reach 100% of companies, but it certainly is an interesting future to think about, and it’s an interesting concept to be aware of on the path to becoming an entrepreneur.

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

China will be the world’s largest economy when its per capita income reaches 25 percent of that of the United States, which is forecast to occur around 2016.

Proformas rarely perform; missed projections are more often the norm. Still, we skew them up high, we miss but we try, for proformas which rarely perform.

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The only thing I cannot predict is the future

The Fed's economic models, and economic forecasting models in general, do a poor job of incorporating the economic effects of financial instability, in part because financial crisis are (fortunately) rare enough that relevant data are scarce.

Take, for instance, a parody project that begins by subverting the anti-Black logics embedded in new high-tech approaches to crime prevention (Figure 5.2). Instead of using predictive policing techniques to forecast street crime, the White-Collar Early Warning System flips the script by creating a heat map that flags city blocks where financial crimes are likely to occur.

Suppose You Were Asked To....?' It would help to give the person a cultural shock by forecasting a change in job profile. For example, if you are interviewing a Chartered Accountant for a senior financial post, ask him, "What if we ask you to head the marketing department in a year's time?" The answer will tell you his ability for 'change management'. The more a person is adaptable to change, the more the benefit for both the parties.

Mark Allin and Richard Burton started Capstone, their book-publishing venture, with high hopes. False modesty aside, they knew they were excellent editors, with a great track record at two publishing giants. I could vouch for Mark Allin’s profit-making abilities, since he gave me the idea for writing The 80/20 Principle, my bestselling book. Richard and Mark envisaged Capstone as a star venture, the leader in a new category of ‘funky business books’. They convinced me that this idea was plausible and I became their financial backer. I reckoned that I had an ‘each-way bet’ - either their star business would materialise, or, at worst, they would pick a few great winners, making Capstone highly profitable. The business appeared to start well. They commissioned a stream of trendy books from interesting authors. The product looked great, with distinctive trendy designs. Mark and Richard were full of ideas and enthusiasm, confidently projecting sales that would give us good profits. The only thing was, the forecasts never materialised. Whenever we looked at the numbers we were constantly disappointed. I kept injecting cash, and it kept vanishing. To this day I don’t know why their books didn’t sell in quantities we could reasonably expect.The favoured explanation was the weakness of the sales force - inevitably, it was difficult to acquire distribution muscle from scratch. Maybe they just had bad luck in not commissioning any smash hits. Whatever the reason, Capstone was a financial black hole. I remember a rather difficult meeting at my home in Richmond some three years after the start. Richard and Mark asked for a further loan to commission new books. I had to say no. We had to face facts. Capstone was not a star; the category of ‘funky business books’ had not established itself. Capstone was a rather weak follower in the business-books arena. Capstone had none of the financial attributes of a star. If it looked like a dog, behaved like a dog and barked like a dog, it probably was a dog.

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.

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.

As a result, tax revenues and state budgets shrink, at least in relative terms per capita. National debt inevitably grows in order to at least partially cover the shortfall. Of course, it grew enormously after governments bailed out the banks in the wake of the financial crash. The British government did so to the tune of 136.6bn and has admitted that it will never recoup at least £27bn of that amount. In the US the bailout cost at least $14.4 trillion.[56] At the start of of 2019, the US’s national debt stood at nearly $22 trillion, having increased by 10% since Trump took office two years earlier. Under his predecessor Barack Obama, the national debt increased 100%, from $10 trillion to $20 trillion. National debt has to be repaid to the government’s creditors: bondholders, ie people, companies and foreign governments; international organisations such as the World Bank; and private financial institutions. If debt is not or cannot be repaid it becomes increasingly difficult to attract creditors. US national debt when the Great Depression kicked off stood at 16% of GDP and rose to 44% when the depression ended at the end of World War Two. Before the The Great Recession it stood at 65% and by 2013 had exploded to over 100%.[57] Gross national debt and household debt have been at record highs at the same time for the first time ever. Austerity, the socialisation of national debt, therefore becomes an economic necessity, not simply an unfair and immoral ‘political choice’, as is claimed by democratic socialists. That public spending as a share of national income in Britain in 2017 (39.6%) was at the same level as in 2007 (39.6%) after seven years of debt servicing via savage cuts to state welfare and public services suggests national income must have fallen per capita. Indeed, official forecasts suggest that GDP per adult in 2022 will be 18% lower than it would have been had it grown by 2% a year since 2008 – it has averaged 1.1% – broadly the expected rate of growth at that time.

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.

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.

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%.

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.

All of this financial chaos can be traced back to the termination of Bretton Woods. Hyper-financialization and interventionist monetary policies are not just expressions of risk aversion, they reinforce it. Superficially, there seems to be no issue here given that the past five decades have seen enormous gains in stock values. But these stock gains haven’t translated into productivity gains. After 1971, the divergence between hyper-financialized markets and the real economy accelerated, ushering in an age of artificial wealth and value fueled by exploding debt and leverage. For example, the United States’ national debt increased from $427 million in 1972 to more than $32 trillion in mid-2023. If interest rates and deficits don’t decline, it’s estimated that the cost of servicing America’s existing debt could reach $1 trillion by 2029, or 3.1 percent of projected GDP—more than will be spent on defense. And this is one of the more optimistic forecasts. 40