Читать книгу Inside the Crystal Ball - Harris Maury - Страница 7

Because so many intelligent, well-educated economists struggle to provide forecasts that are more often right than wrong, it should be clear that forecasting is difficult. The following are among the eight most important reasons:

1. It is hard to know where you are, so it is even more difficult to know where you are going.

The economy is subject to myriad influences. At each moment, a world of inputs exerts subtle shifts on its direction and strength. It can be difficult for economists to estimate where the national economy is headed in the present, much less the future. Like a ship on the sea in the pre-GPS era, determining one's precise location at any given instant is a difficult challenge.

John Maynard Keynes – the father of Keynesian economics – taught that recessions need not automatically self-correct. Instead, turning the economy around requires reactive government fiscal policies – spending increases, tax cuts and at least temporary budget deficits. His “new economics” followers in the 1950s and 1960s took that conclusion a step further, claiming that recessions could be headed off by proactive, anticipatory countercyclical monetary and fiscal policies. But that approach assumed economists could foresee trouble down the road.

Not everyone agreed with Keynes' theories. Perhaps the most visible and influential objections were aired by University of Chicago economics professor Milton Friedman. In his classic address at the 1967 American Economic Association meeting, he argued against anticipatory macroeconomic stabilization policies.11 Why? “We simply do not know enough to be able to recognize minor disturbances when they occur or to be able to predict what their effects will be with any precision or what monetary policy is required to offset their effects,” he said.

Everyday professional practitioners of economics in the real world know the validity of Friedman's observation all too well. In Figure 1.2, for example, consider real GDP growth forecasts for a statistical quarter that were made in the third month of that quarter – after the quarter was almost over. In the current decade, such projections were 0.8 percent off from what was reported. (Note: This is judged by the mean absolute error – the absolute magnitude of an error without regard to whether the forecast was too high or too low.) Moreover, these “last minute” projections were even farther off in earlier decades.

Figure 1.2 In the Final Month of a Quarter, Forecasters' Growth Forecasts for That Quarter Can Still Err Substantially

Source: Federal Reserve Bank of Philadelphia.

Moving forward, we discuss how the various economic “weather reports” can suggest winter and summer on the same day! Let's note, too, that some of the key indicators of tomorrow's business weather are subject to substantial revisions. At times it seems like there are no reliable witnesses, because they all change their testimony under oath. In later chapters we discuss how to address these challenges.

2. History does not always repeat or even rhyme.

Forecasters address the future largely by extrapolating from the past. Consequently, prognosticators can't help but be historians. And just as the signals on current events are frequently mixed and may be subject to revision, so, too, when discussing a business or an economy, are interpretations of prior events. In subsequent chapters, we discuss how to sift through history and judge what really happened – a key step in predicting, successfully, what will happen in the future.

The initially widely acclaimed book, This Time Is Different: Eight Centuries of Financial Follies by Carmen Reinhart and Kenneth Rogoff, provides a good example of the difficulties in interpreting history in order to give advice about the future.12 Published in 2011, the book first attracted attention from global policymakers with its conclusion that, since World War II, economic growth turned negative when the government debt/GDP ratio exceeded 90 percent. Two years later, other researchers discovered calculation errors in the authors' statistical summary of economic history. Looking for repetitive historical patterns can be tricky!

3. Statistical crosscurrents make it hard to find safe footing.

Even if the past and present are clear, divining the future remains challenging when potential causal variables (e.g., the money supply and the Federal purchases of goods and services) are headed in opposite directions. However, successful and influential forecasters must avoid being hapless “two-handed economists” (i.e., “on the one hand, but on the other hand”).

Moreover, one's statistical coursework at the college and graduate level does not necessarily solve the problem of what matters most when signals diverge. Yes, there are multiple regression software packages readily available that can crank out estimated regression (i.e., response) coefficients for independent causal variables. But, alas, even the more advanced statistical courses and textbooks have yet to satisfactorily surmount the multicollinearity problem. That is when two highly correlated independent variables “compete” to claim historical credit for explaining dependent variables that must be forecast. As a professional forecaster, I have not solved this problem but have been coping with it almost every day for decades. As we proceed, you will find some helpful tips on dealing with this challenge.

4. Behavioral sciences are inevitably limited.

There have been quantum leaps in the science of public opinion polling since the fiasco of 1948, when President Truman's reelection stunned pollsters. Nevertheless, there continue to be plenty of surprises (“upsets”) on election night. Are there innate limits to humans' ability to understand and predict the behavior of other humans? That was what the well-known conservative economist Henry Hazlitt observed in reaction to all of the hand wringing about “scientific polling” in the aftermath of the 1948 debacle. Writing in the November 22, 1948, issue of Newsweek, Hazlitt noted: “The economic future, like the political future, will be determined by future human behavior and decisions. That is why it is uncertain. And in spite of the enormous and constantly growing literature on business cycles, business forecasting will never, any more than opinion polls, become an exact science.”13

In other words, forecast success or failure can reflect “what we don't know that we don't know” (generalized uncertainty) more than “what we know” (risk).

5. The most important determinants may not be measureable.

Statistics are all about measurement. But what if you cannot measure what matters? Statisticians often approach this stumbling block with a dummy variable. It is assigned a zero or one in each examined historical period (year, quarter, month, or week) according to whether the statistician believes that the unmeasurable variable was active or dormant in that period. (For example, when explaining U.S. inflation history with a regression model, a dummy variable might be used to identify periods when there were price controls.) If the dummy variable in an estimated multiple regression equation achieves statistical significance, the statistician can then claim that it reflects the influence of the unmeasured, hypothesized causal factor.

The problem, though, is that a statistically significant dummy variable can be credited for anything that cannot be otherwise accounted for. The label attached to the dummy variable may not be a true causal factor useful in forecasting. In other words, there can be a naming contest for a dummy variable that is statistically sweeping up what other variables cannot explain. There are some common sense approaches to addressing this problem, and we discuss them later.

6. There can be conflicts between the goal of accuracy and the goal of pleasing a forecaster's everyday workplace environment.

Many of the most publicly visible and influential forecasters – especially securities analysts and investment bank economists – have job-related considerations that can influence their advice about the future. It is ironic that financial analysts and economists whose good work has earned them national recognition can find pressures at the top that complicate their ability to give good advice once the internal and external audience enlarges.

Many Wall Street economists, for instance, are employed by fixed-income or currency trading desks. Huge amounts of their firms' and their clients' money are positioned before key economic statistics are reported. This knowledge might understandably make a forecaster reluctant to go against the consensus. And, as we discuss shortly, there can be other work-related pressures not to go against the grain as well.

Are trading desks' economists' forecasts sometimes made to assist their employers' business?

It is hard, if not impossible, to gauge how much and how frequently forecasts are conditioned by an employer's business interests. However, it can be observed that certain types of behavior are consistent with the hypothesis that forecasts are being affected in this manner. For instance, the economist Takatoshi Ito at the University of Tokyo has authored research suggesting that foreign exchange rate projections are systematically biased toward scenarios that would benefit the forecaster's employer. He has attached the label “wishful expectations” to such forecasts.14

What is the effect of the sell-side working environment on stock analysts' performance?

In order to be successful, sell-side securities analysts at brokerage houses and investment banks must, in addition to performing their analytical research, spend time and effort marketing their research to their firms' clients. In buy-side organizations, such as pension funds, mutual funds, and hedge funds, analysts generally do not have these marketing responsibilities. Do the two different work environments make a difference in performance? The evidence is inconclusive.

For instance, one study funded by the Division of Research at the Harvard Business School examined the July 1997 to December 2004 period and reached the following conclusions: “Sell-side firm analysts make more optimistic and less accurate earnings forecasts than their buy-side counterparts. In addition, abnormal returns from investing in their Strong Buy/Buy recommendations are negative and under-perform comparable sell-side recommendations.”15

There is a wide range of performance results within the sell-side analyst universe. For example, one study concluded that sell-side securities analysts ranked well by buy-side users of sell-side research out-performed lesser ranked sell-side analysts.16 (Note: This study, which was sponsored by the William E. Simon Graduate School of Business Administration, reviewed performance results from 1991 to 2000.)

How does media exposure affect forecasters?

To see how the working environment can affect the quality of advice, look at Wall Street's emphasis on “instant analysis.” Wall Street economists often devote considerable time and care to preparing economic-indicator forecasts. However, within seconds – literally, seconds – after data are reported at the normal 8:30 a. m. time, economists are called on to determine the implications of an economics report and announce them to clients.

Investment banks and trading firms want their analysts to offer good advice. But they also want publicity. They're happy to offer their analysts to the cameras for the instant analysis prized by the media. The awareness that a huge national television audience is watching and will know if they err can be stressful to the generally studious and usually thorough persons often attracted to the field of economics. Keep this in mind when deciding whether the televised advice of an investment bank analyst is a useful input for decision making. (Note: Securities firms in the current, more regulation-conscious decade generally scrutinize analysts' published reports, which should make the reports more reliable than televised sound bites.)

7. Audiences may condition forecasters' perceptions of professional risks.

John Maynard Keynes famously said: “Practical men, who believe themselves to be quite exempt from any intellectual influences, are usually the slaves of some defunct economist.” Forecasters subconsciously or consciously risk becoming the slaves of their intended audience of colleagues, employers, and clients. In other words, seers often fret about the reaction of their audience, especially if their proffered advice is errant. How the forecaster frames these risks is known as the loss function.

In some situations, such pressures can be constructive. The first trader I met on my first day working as a Wall Street economist had this greeting: “I like bulls and I like bears but I don't like chickens.” The message was clear: No one wants to hear anything from a two-handed economist. That was constructive pressure for a young forecaster embarking on a career.

That said, audience pressures might not be so benign. Yet they are inescapable. The ability to deal with them in a field in which periodic costly errors are inevitable is the key to a long, successful career for anyone giving advice about the future.

8. Statistics courses are not enough. It takes both math and experience to succeed.

To be sure, many dedicated statistics educators are also scholars working to advance the science of statistics. However, teaching and its attendant focus on academic research inevitably leaves less time for building a considerable body of practical experience.

No amount of schooling could have prepared me for what I experienced during my first week as a Wall Street economist in 1980. Neither a PhD in economics from Columbia University nor a half-dozen years as an economist at the Federal Reserve Bank of New York and the Bank for International Settlements in Basel, Switzerland had given me the slightest clue as to how to handle my duties as PaineWebber's Chief Money Market Economist.

At the New York Fed, my ability to digest freshly released labor market statistics, and to write a report about them before the close of business, helped trigger an early promotion for me. But on PaineWebber's New York fixed-income trading floor, I was expected to digest and opine on those same very important monthly data no more than five minutes after they hit the tape at 8:30 a. m.

There were other surprises as well. In graduate school, for example, macroeconomics courses usually skipped national income accounting and measurement. These topics were regarded as simply descriptive and too elementary for a graduate level academic curriculum. Instead, courses focused on the mathematical properties of macroeconomic mechanics and econometrics as the arbiters of economic “truth.” On Wall Street, however, the ability to understand and explain the accounting that underlies any important government or company data report is key to earning credibility with a firm's professional investor clients. In graduate school we did study more advanced statistical techniques. But they were mainly applied to testing hypotheses and studying statistical economic history, not forecasting per se.

In short, when I first peered into my crystal ball, I was behind the eight ball! As in the game of pool, survival would depend on bank shots that combined skill, nerve, and good luck. Fortunately, experience pays: More seasoned forecasters generally do better. (See Figure 1.3. The methodology for calculating the illustrated forecaster scores is discussed in Chapter 2.)

Figure 1.3 More Experienced Forecasters Usually Fare Better

^*Number of surveys in which forecaster participated.Source: Andy Bauer, Robert A. Eisenbeis, Daniel F. Waggoner, and Tao Zha, “Forecast Evaluation with Cross-Sectional Data: The Blue Chip Survey,” Federal Reserve Bank of Atlanta, Second Quarter, 2003.

In summation, then, it is difficult to be prescient because:

• Behavioral sciences are inevitably limited.

• Interpreting current events and history is challenging.

• Important causal factors may not be quantifiable.

• Work environments and audiences can bias forecasts.

• Experience counts more than statistical courses.