Do earnings drive stocks’ returns?

Overview

Mainstream financial media, as well as professional and retail investors, obsess about corporate earnings and their near-term prospects. The fixation is constant, but it’s most intense during “earnings season.” The media encourage investors, and investors abet the media: as a result of this feedback loop of mutual reinforcement, both groups apparently – and ardently – believe that the rate of growth of today’s profits, and expectations about tomorrow’s, propel stocks’ current and prospective returns.

On its face, this conjecture seems plausible. It’s hardly unreasonable to suppose that, all things considered, companies whose earnings are rising (or expected to rise) will be more valuable to investors – and thus generate higher returns – than those whose profits are static or falling.

Perhaps the self-evident nature of this tacit assumption and routine assertion to so many people, including prominent and influential people, explains why so few have bothered to investigate it.

In this article I ask: do actual and anticipated earnings really underpin stocks’ actual and prospective returns? I corroborate research which shows that this relationship is on average much weaker and far more erratic than investors and the media believe, and that the influence of dividends upon returns is much more significant than they realise (see also Dividends aren’t a bane – they’re a boon, 20 November 2023).

Specifically, as determinants of short-term returns short-term earnings typically don’t matter; but as determinants of long-term returns, long-term average earnings occasionally exert some influence. Even in the long term, however, earnings count much less than the crowd supposes, and far less than something that it ignores – long-term average dividends.

(Readers who’ll find tedious the descriptions of the analysis’ assumptions and results can skip the next several sections and proceed directly to the “Applying This Key Result” and “Implications” sections.)

Some Fundamental Preliminaries

“There is no way to predict the price of stocks and bonds over the next few days or weeks,” declared The Royal Swedish Academy of Sciences on 14 October 2013 in the press release that announced that year’s Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (which is universally but erroneously known as “The Nobel Prize in Economics”). 

This key point also applies over periods of at least several months and perhaps as long as 1-2 years: during these intervals, equities’ prices – and thus returns – fluctuate mostly randomly. “But,” the announcement continued, “it is quite possible to foresee the broad course of these prices over longer periods, such as the next three to five years.” These longer periods extend to 10 years. As The Wall Street Journal (“Can Stocks Surpass 2022 Highs? Yes, but the Math Looks Scarier from There,” 9 January) noted:

“How will stocks perform in 2024? The only honest answer to the obvious New Year’s question is that we don’t know, but we can have some idea how they might fare over the coming decade.”

“These findings,”the Academy’s press release elaborated, “which might seem both surprising and contradictory, were made and analyzed by this year’s Laureates, Eugene Fama, Lars Peter Hansen and Robert Shiller.”

Stocks’ prices and market indexes’ levels – and thus returns – “are nearly impossible to predict” over days, weeks, months and sometimes periods of more than one year, but they vary relatively systematically over periods ranging from several years to ten years. In the short term, noise overwhelms signal; but in the long term, the ratio of signal to noise rises substantially.

Using the longest, most valid and reliable series of monthly data – which Shiller compiled for his book Irrational Exuberance (Princeton University Press, 1^st edition, 2001) and has updated thereafter, Figure 1, Figure 2 and Figure 3 apply the insight that the Academy’s press release summarised to the Standard & Poor’s 500 Index’s short-term (12-month), medium-term (five-year) and long-term (ten-year) changes of CPI-adjusted earnings, dividends and total (that is, its dividend and capital growth) returns.

Figure 1: CPI-Adjusted Earnings, Dividends and Total Returns, CAGRs over Three Time Intervals, January 1871-September 2023

The Index’s CPI-adjusted total return over rolling 12-month periods since January 1872 has averaged 8.0%. For rolling 60-month periods since January 1876, its total return, expressed as a compound annual growth rate (CAGR), has averaged 7.1%; and for rolling 120-month periods since 1881, it’s averaged 6.9%.

Figure 2: Standard Deviations of the Index’s CPI-Adjusted Earnings, Dividends and Returns over Three Time Intervals, January 1871-September 2023

Moreover,

1. The same point applies to earnings and dividends: the longer is the interval, the lower is the CAGR.
2. Figure 1 also shows, regardless of the interval, that the average total return greatly exceeds earnings’ average CAGR; and the rate of growth of earnings, on average, modestly exceeds the growth of dividends.
3. Figure 2 shows that the longer is the interval and regardless of the variable, the lower is the standard deviation (that is, the dispersion of observations around their mean). In other words, long-term earnings, dividends and returns fluctuate much less than their short-term counterparts.

Finally, Figure 3 plots the co-efficient of variation (CV), i.e., the ratio of standard deviation to mean, of the Index’s short-term, medium-term and long-term earnings, dividends and total returns. For example, the mean return for rolling 12-month periods is 8.0% (Figure 1) and its standard deviation is 15.6% (Figure 2); hence its CV is 15.6% ÷ 8.0% = 1.95, and so on for the other intervals and variables.

Figure 3: Coefficients of Variation, S&P 500 Index’s CPI-Adjusted Earnings, Dividends and Total Returns, Three Intervals, January 1871-September 2023

In plain English, the higher is the CV, the greater is the ratio of noise to signal. For each variable, the ratio is highest in the short-term and lowest in the long-term. Accordingly,

1. If we wish better to understand the S&P 500 Index’s CPI-adjusted total returns, it makes sense to analyse its long-term (rolling ten-year) total returns. That’s because its ratio of noise to signal is lowest.
2. As factors explaining short-term total returns, short-term changes of earnings and dividends are useless – they contain much more noise than signal.
3. As a candidate explaining long-term total returns, dividends make at least as much sense as earnings.

Clarifying Some Key Assumptions

Do companies’ earnings drive the S&P 500 Index’s returns? It’s a simple question, yet some complex assumptions underlie it, and it’s vital to clarify and justify them. Let’s start with what’s indisputable and obvious: every month, as the newest observation of the Index’s earnings, dividend and total return enter the rolling 12-month, 60-month and 120-month series and the oldest one leaves it, these series’ means, CAGRs, etc., update. Table 1, which enumerates the Index’s CPI-adjusted earnings during two randomly-selected rolling 12-month intervals, provides a simple example – and clarifies an important distinction.

Table 1: the S&P 500 Index’s CPI-Adjusted Earnings and Varying Summary Statistics, Two Consecutive 12-Month Intervals

The two intervals are November 1952-October 1953 and December 1952-November 1953. The “Actual” column enumerates the Index’s actual CPI-adjusted earnings during each month of the relevant period; the entries in “12-month mean” compute average earnings for the year ending in a given month. For example, earnings averaged $28.29 in November 1952-October 1953; that quantity appears both as the mean of the “Actual” column and as observation #12 (bold font) in the “12-month mean” column. The mean for October 1952-September 1953, $28.16, appears as observation #11 in the “12-month mean column, and so on for that column’s other observations, and so on for the interval from December 1952 to November 1953.

Which earnings drive the Index’s returns: the actual monthly earnings or the rolling 12-month means? The later remove (some of the) the random month-to-month variation of earnings; accordingly, their standard deviations are one-third to one-half as large as those of the former.

The rolling 12-month means provide a slower-moving target – one that investors, to the very limited extent that they can discern current and forecast prospective earnings, will hit more easily than the actual observations. That’s why these data – specifically the CAGRs, which allow direct comparisons of 12-month, 60-month and 120-month series – underpin my subsequent analysis.

From one 12-month interval to the next, the oldest observation from the previous interval (#1) exits the series and the newest one (#12) enters it. Consequently, if the correlation of a given series of earnings with another corresponding series (say, total CPI-adjusted returns from November 1952 to October 1953) is perfect, during each of an interval’s 12 (or 60 or 120 as appropriate) months the change from one month to the next (i.e., from observation 1 to 2, etc.) of the earnings series begets an equivalent magnitude of change of the corresponding observations in the return series.

Under these conditions, by buying or selling the Index as appropriate, and thus adjusting the total return series’ CAGR to change in lock-step to the earnings series’ CAGR, investors respond instantly and perfectly accurately to each month’s earnings.

More generally, the more quickly and accurately investors react – by buying or selling the Index and thereby adjusting its total return – to new information about earnings throughout a given (12, 60 or 120-month) interval, the higher will be the two series’ correlation during that interval; conversely, the less quickly and accurately they respond, the lower will be their correlation.

Do Earnings Drive Returns?

Adjusted for CPI and expressed as CAGRs, and over one-year, five-year and 10-year rolling intervals, the Index’s growth of earnings and total returns are generally positive (I very much doubt that’s controversial, so for the sake of brevity I’ll skip several graphs that substantiate this point) but vary greatly (recall Figure 1 and Figure 2). But are they correlated?

If, from one month to the next throughout a given interval, the earnings and total return series’CAGRs changed by an identical amount and in the same direction (but not necessary by the same magnitude each month), then the two series would be perfectly positively correlated and their correlation coefficient (r) would attain its maximum value of 1.0.

Conversely, if during each month one series increased by some amount and the other fell by an equivalent magnitude, then the two series would be perfectly negatively correlated and the coefficient would attain its minimum of -1.0. Finally, if the change of one series from one month to the next bears to relation to the change in the other, then their correlation is 0.0 and the PE ratio fluctuates randomly.

Figure 4: Correlationsof S&P 500’s CPI-Adjusted Earnings and Total Return, 12-Month Rolling Intervals, January 1872-September 2023

Figure 4 plots the correlation coefficient (r) of CPI-adjusted earnings and total returns, expressed as CAGRs, for each rolling 12-month interval since January 1872. The average coefficient is 0.105. “Variation explained,” i.e., r × r = r², measures the percentage by which our bivariate model reduces (“explains”) total return’s variation compared to a univariate model. On average, then, the change of the Index’s CPI-adjusted earnings during a 12-month interval explains a mere 0.105 × 0.105 = 1.1% of the change of its CPI-adjusted total return.

The most charitable interpretation of these results: on average over the past 150 years, short-term noise has completely overwhelmed short-term signal. The most reasonable reading is that investors as a whole have responded hopelessly inaccurately to new earnings information. In the short-term, earnings simply don’t drive stocks’ returns.

What about medium and long terms? Their graphs are almost as squiggly as Figure 4, and so for the sake of brevity I’ve omitted them. Far more informative results emerged when I rank-ordered the correlation coefficients, divided them into five groups of equal (net of rounding) numbers of observations and noted various attributes about each quintile; Table 2 details the results for intervals of 12, 60 and 120 months.

Table 2: Correlation Coefficients, S&P 500’s CPI-adjusted Earnings and Total Returns, by Quintile, Three Intervals, January 1872-September 2023

In the median quintile, earnings explain, on average, little more than one-twentieth of the Index’s total 12-month return – and almost one-fifth of its total five-year return and almost one-quarter of its ten-year total return.

The longer is the interval, in other words, the stronger is the influence of earnings upon total return.

Approximately 40% of the time (Quintiles 1 and 2), the short-term relationship is nonsensical from the mainstream’s point of view: an increase of earnings is associated with a lower or negative total return. Another 20% of the time, and on average (Quintile 3), earnings and returns aren’t correlated. Accordingly, in 40% of the 12-month intervals (Quintiles 4 and 5) is an increase of earnings associated with a higher total return (and a decrease of earnings associated with a lower total return).

To a significant extent and on average,short-term resultsdisconfirm the mainstream’s expectation, but long-term results largely corroborate them. Only 20% of the time (Quintile 1) does the correlation have the wrong sign, but its average is much lower than in the short term. Further, moderate or strong positive relationships occur most (60%) of the time (Quintile 3-5).

Do earnings influence stocks’ returns? My analysis thus far implies that over 12-month intervals over the past 150 years they typically haven’t (but regularly have), and in the long term they’ve usually exerted minor influence. By buying or selling the Index as appropriate, have investors responded instantly and perfectly accurately to each month’s earnings? Table 2’s bottom row is unequivocal: they haven’t.

Expected Rather than Current Earnings Growth

Mainstream investors will likely discountthe analysis and results in the previous section. Investors, they believe, don’t respond primarily to today’s actual earnings, but rather to next year’s estimated (“forward” or “prospective”) earnings. “Perhaps,” reckons Nicolas Rabener (“Myth-Busting: Earnings Don’t Matter Much for Stock Returns,” CFA Institute, 22 March 2022), “the lack of correlation between stock returns and earnings growth is because investors focus on expected rather than current growth … We tested this hypothesis by focusing on earnings growth for the next 12 months and assume investors are perfect forecasters of the earnings of U.S. stocks. We treat them as superinvestors.”

I test the assumption that they’re super-duper investors: I assume not merely that today’s investors can (a) precisely divine the CAGR of next year’s earnings, but also that their prescience extends far further into the future: specifically, (b) they perfectly prophesy the CAGR of the next five years’ and ten years’ earnings. Moreover, (c) by buying and selling the Index as appropriate, in the present they react instantly and accurately to their unerring prediction of the medium-term and long-term future.

Charitably, these assumptions are extremely heroic; frankly, they’re absurd: as I’ve already demonstrated elsewhere (see in particular How experts’ earnings forecasts harm investors, 11 July 2022), the estimates of prospective earnings 12 months hence are, because they’re so inaccurate and unreliable, effectively worthless. For that very reason, however, these assumptions are massively favourable to the mainstream.

Table 3 outlines the basic logic. “Current earnings” refers to CPI-adjusted earnings during each monthof the 12-month interval from November 1952 to November 1953; “12-month prospective earnings” refers to earnings during each month of the interval from November 1953 to November 1954 and “current returns” refers to the CPI-adjusted total return Index during each month of the 12-month interval from November 1952 to November 1953.

Table 3: the S&P 500 Index’s CPI-Adjusted Earnings and Varying Summary Statistics, Three Consecutive 12-Month Intervals

The assumption is that (a) in Observation #1 (November 1952) investors will exactly predict earnings one year hence ($28.72, versus the current earnings of $27.36); moreover, (2) with this foreknowledge they’ll instantly and accurately adjust the Index’s CPI-adjusted total current return; finally, (3) they’ll do likewise during each of the next 12 months.

(Remember, I’m analysing the CAGRs of rolling 12-month, five-year and ten-year means. The 4^th and 7^th columns of Table 1 provided examples, but I’ve omitted these data to avoid cluttering Table 3. I’m therefore assuming that investors will unerringly predict the CAGR of 12-month forward average earnings, and automatically adjust the CAGR of the Index’s return.)

In this example, investors exhibited keen foresight: the correlation of prospective earnings to current total returns is very strong (0.92). The crucial question, however, is: how good, on average, has been their foresight during all 12-month intervals? During all five-year and ten-year intervals?

Once again, I rank-ordered the correlation coefficients for all 12-month intervals, divided them into five groups of equal (net of rounding) numbers of observations and noted various attributes about each quintile. I then repeated the exercise for 60-month and 120-month intervals; Table 4 details the results.

Table 4: Correlations of S&P 500’s Correlations, S&P 500’s CPI-adjusted Prospective Earnings and Total Returns,by Quintile, Three Intervals, Jan 1872-Sep 2023

Focusing upon the 12-month intervals and comparing Table 2 and Table 3, it’s possible that in a key respect the mainstream is correct: investors focus more upon the next year than upon the previous 12 months. The average 12-month correlation in Table 3 is 0.235 versus 0.102 in Table 2 and the variances explained are 5.6% and 1.0% respectively.

But if so, then in another key sense the mainstream is clearly mistaken: investors’ ability accurately to predict next year’s earnings, and to buy and sell the Index accordingly, is abysmal.

Investors’ ability to divine the medium-term and long-term future, and to act accordingly today, is even worse: the mean the variances explained is 2.8% for prospective five-year intervals and 2.6% for prospective ten-year periods. For all practical purposes, these percentages are effectively 0%.

Does investors’ ability to anticipate earnings drive today’s returns? Investors are unable accurately to predict earnings; logically, therefore, their inability can’t influence their returns.

On average in the short term, expectations about prospective earnings impart a minimal influence upon returns. Only by putting resultsin the most charitable lightand ignoring the nonsensical negative correlations in Quintiles 1 and 2 is it possible to infer that theyregularly might (Quintile 4 and 5). In the medium and long terms, investors’ ability to anticipate earnings generally doesn’t but occasionally (Quintile 5) might affect returns.

If Earnings Generally Don’t Drive Returns, Then What Might?

The foregoing analysis allows us to answer this question concisely and conclusively. Its short answer – in the long term – is “dividends.” I repeated the exercise summarised in Table 2 for the Index’s dividends – specifically, the CAGRs of dividends’ running averages. Table 5 details the results.

Table 5: Correlations of S&P 500’s CPI-adjusted Dividends and Total Returns,by Quintile, Three Intervals, Jan 1872-Sep 2023

Over the past 150 years, the rate of change (CAGR) of dividends’ CPI-adjusted rolling 12-month average has exerted, on average, no influence upon the Index’s total 12-month return (rolling 12-month average CAGR). But over ten-year periods, dividends have exerted a substantial influence uponreturns.

On average, dividends explain almost 30% of returns’ long-term variation. Some of the time (Quintile 1) they exert no influence, and another 20% of the time (Quintile 2) their influence is relatively weak; but most (60%) of the time (Quintiles 3-5), they explain at least one-half of the variation.

Do long-term dividends drive stocks’ long-term returns? Their influence is usually significant, often substantial – and much stronger than the influence of earnings.

Applying This Key Result

Figure 5 plots the 10-year CAGRs of the S&P 500 Index’s 10-year rolling average total CPI-adjusted return. They’re heavily mean-regressing: the average return is 6.5% per year, and the higher above this mean a given return rises, the higher is the likelihood that it subsequent falls (“regresses”) towards its mean. Equally, the lower it falls below its mean, the higher is the probability that it subsequently recovers.

Figure 5: 10-Year Average CAGRs, S&P 500 Index’s CPI-Adjusted Total Return, January 1900-September 2023

“Analysts,” “experts” and others pontificate relentlessly about the S&P 500’s (and the S&P/ASX 200’s, etc.) return during the next year.

That’s a mug’s game and a fool’s errand: as we’ve seen, the Index’s short-term returns are so variable that they’re effectively unpredictable. If, however, the long-term past is prelude then we can more confidently infer that during the next decade its average total return, expressed as a CPI-adjusted CAGR, will be approximately 6.5%.

Figure 6: 10-Year Average CAGRs, S&P 500’s CPI-Adjusted Dividend, January 1900-September 2023

Figure 6 plots the 10-year CAGRs of the S&P 500 Index’s 10-year rolling average CPI-adjusted dividend. Their average is 1.3% per year and they’re also mean-regressing (albeit less strongly so than the CAGRs in Figure 5). Since early-2021, dividends’ long-term rate of growth has exceeded 5% per year, and since January 2022 they’ve surpassed 5.5%. That’s the fastest rate of growth in more than 120 years.

Assuming that series continues to mean-regress, it’s reasonable to infer than during the next decade dividends’ 10-year rate of growth will decelerate towards their overall mean. Given the relatively strong long-term positive correlation of dividends’ and returns’ CAGRs, it’s also reasonable to consider the possibility that the Index’s CPI-adjusted long-term return, too, will decelerate.

Implications

“In sum,” stated Robert Shiller in Irrational Exuberance, “stock prices clearly have a life of their own; they are not simply responding to (changes of) earnings or dividends. Nor does it appear that they are determined ... by (expectations about) about future earnings or dividends. In seeking explanations of stock price movements, we must look elsewhere.”  

Equally, analysis in The Review of Financial Studies by Shiller and one of his colleagues, John Campbell, concluded that “27% of the annual return volatility of the U.S. stock market might be justified in terms of information ... about dividends.” My results in Table 5 closely corroborate it.

Earnings Are Subjective – and Thus Subject to Regular and Occasionally Hefty Revision

Why, by and large, don’t earnings influence stocks’ returns? A big part of the answer is that net profit after tax calculated according to Generally Accepted Accounting Principles are to an appreciable degree subjective – and thus prone to revision.

Earnings are estimates – that is, educated guesses – of competent and dispassionate people who’re implementing mostly coherent and sensible rules. But the people aren’t infallible, nor are the rules perfect; hence earnings are to a significant extent a matter of judgment – and judgments are subject to regular, often unexpected and occasionally drastic revision.

Why is the bottom line always an imperfect – and sometimes a poor – measure of a business’s results during a given period? “Because,” says Steve Hanke (“Where’s the Cash?” Cato Institute Commentary, 4 April 2004), “so many of the numbers … in the profitand‐ loss statement are subjective … Depreciation isn’t the only fuzzy number. There are several ways to account for long‐​term contracts, in which payments can run ahead of or behind the work. How should a fluctuating derivative contract be evaluated on the books? In liquid markets you can mark it to recent market value. In illiquid ones you have to guess.”

Hanke continues: “the valuation of foreign assets involves assumptions about exchange rates. The treatment of stock options can give the best accountants a headache. Goodwill is yet another accounting issue subject to widely varying opinion. Impairment of long‐​lived assets involves write-downs, but when? Yet another judgment call.”

Individually, these judgements needn’t significantly affect a company’s earnings; collectively, however, they can and often do.

An extreme example occurred during the GFC: the S&P 500’s CPI-adjusted earnings plummeted from $124.77 in June 2007 to just $9.87 in March 2009: that’s a collapse of -91.8% and a CAGR of -76.2%. Plunges also occurred during the Dot Com Bust and COVID-19 panic (Figure 7). In sharp contrast, during those intervals dividends hardly budged.

Figure 7: S&P 500’s CPI-Adjusted Earnings and Dividends, Peak-to-Trough CAGRs during Three Crises

Dividends Are Objective – and Never Subject to Revision

Why do long-term changes of dividends’ rate of growth exert significant influence upon stocks’ long-term total returns? 

Unlike earnings, dividends are objective: “earnings are opinion; cash (in shareholders’ hands) is fact.” First recorded in the 1890s, this adage should be tattooed onto every male investor’s forearm.

“Dividends historically represent the dominant part of the average (total) return one gets from holding stocks,” added Shiller. In The Wall Street Journal (21 August 2001), Jeremy Siegel contributed a basic but crucial point: “by and large, companies that are paying (cash) dividends have to have (genuine) earnings. They can’t do that with smoke and mirrors.” 

Shiller concluded: “the reliable return attributable to dividends, not the less predictable portion arising from capital gains, is the main reason stocks have on average been such good investments historically.”

In The Theory of Investment Value (1938), John Burr Williams, a pioneer of valuation whom Buffett has lauded, wrote: “earnings are only a means to an end, and the means should not be mistaken for the end. Therefore, we must say that a stock derives its value from its dividends, not its earnings” (see also Dividends aren’t a bane – they’re a boon, 20 November 2023).

Yet Another Reason to Disbelieve and Ignore the Mainstream Media

Today’s corporate earnings, and expectations about tomorrow’s, the mainstream media believes (or it reckons that most investors believe), underpin stocks’ current and near-term prospective returns. Babble about earnings, together with prattle about macro-economic conditions and prospects, comprises the lion’s share of the media’s coverage.

As I’ve shown, however (and as Shiller and others demonstrated decades ago), short-term changes of earnings don’t drive short-term returns; the implication is that – in this key respect, as well as many others – investors should disbelieve and ignore the mainstream media’s obsession.

Warren Buffett agrees. In his letter to Berkshire’s shareholders (2014), he observed:

“... listening to macro or market (or earnings) predictions … is a waste of time. Indeed, it is dangerous because it may blur your vision of the facts that are truly important. When I hear TV commentators glibly opine on what the market will do next, I am reminded of (American baseballer) Mickey Mantle’s scathing comment: ‘You don't know how easy this game is until you get into that broadcasting booth.’”

Shiller also agrees. “Unknown to most investors,” he observes, “is the troubling lack of credibility in the quality of research being done on the stock market, to say nothing of the clarity and accuracy with which it is communicated to the public. Some of this so-called research often seems no more rigorous than the reading of the tea leaves.”

“The headlines,” he continues, “reflect the news media’s constant attention to trivial factoids and ‘celebrity’ opinions about the market’s price level. Driven as their authors are by competition for readers, listeners and viewers, media accounts tend to be superficial and thus to encourage basic misconceptions about the market.” Among the most fundamental of these misconceptions: companies’ short-term earnings drive markets’ short-term returns.

Shiller concludes: “many individual investors think that institutional investors ... have sophisticated models to understand prices – superior knowledge. Little do they know that most institutional investors are, by and large, equally clueless about the level of the market. In short, the price level is driven to a certain extent by a self-fulfilling prophesy based on similar hunches held by ... large and small investors and reinforced by a news media that ... ratify this investor-induced conventional wisdom.”

The mainstream media credulously repeat what “analysts” and “experts” pretend to know, but nobody really can know: stocks’ earnings and returns during the next year. Unable to impart reliable knowledge, journalists relentlessly parrot guesses.

Financial “news” about companies’ and markets’ prospective earnings and returns is, to its consumers, pointless and valueless. To its suppliers, however, it serves the useful purpose of masking self-interest: forecasters are strongly incentivised to issue bullish forecasts. The Wall Street Journal (“Why Market Forecasts Are So Bad,” 20 December 2013), which quoted Citigroup’s chief U.S. equity analyst, Tobias Levkovich, encapsulated their motives:

“If you’re a bull and you’re wrong, you’re forgiven. If you’re a bull and you’re right (your boss, clients, readers, etc.), love you. If you’re a bear and you're right, you’re respected. If you’re a bear and you’re wrong, you’re fired.”

Heads they win, tails you lose. Is it any wonder that the vast majority of “analysts,” “experts” and “strategists” – cheerleaders is a more apt description – are mindless perma-bulls fervently talking their book?

Click LIKE so that Livewire knows that you want more of this type of content. Click FOLLOW on my profile for notification when my next wire appears.