Their forecasts lure most investors into overconfidence – and thus losses. If you recognise experts’ biases, you can grasp opportunities.
Preview
A week, it’s often been said, is a long time in politics. If so, a couple of days can be an eternity in financial markets! On 2 August, a journalist in The Australian (“Markets find ‘sweet spot’ in policy, outlook”) wrote: “… the risk appetite has turned positive after strong gains in stocks (including an all-time high of the S&P/ASX 200) this week … the Australian market in particular has come a long way on a favourable monetary policy and economic outlook. There are no cracks in the bullish narrative at this point …”
What underlay the bullish narrative? Many forecasters had recently become convinced that the Federal Reserve, prompted by receding consumer price inflation, would shortly begin to reduce its policy rate – which, they believed, would support the continuation of equity markets’ strong rise. The Australian cited one bull: “have we hit a sweet spot again for risk assets with Fed cuts all but assured, U.S. labour markets cooling but not collapsing, inflation glacially falling in line and solid company EPS growth?”
Yet bulls overlooked a wide crack – indeed, a chasm – which gravely weakened their narrative: “a scenario where the Fed gradually lowers rates … would have vastly different market implications to one where the Fed was forced to slash rates … to stop a recession …”
The latter scenario, market participants have suddenly realised, is much more likely than they’d previously assumed. Two developments in the U.S. have prompted this reassessment. On 1 August, a closely-watched indicator, the ISM Manufacturing Purchasing Managers’ Index, plunged to a four-year low. Apart from its nadir during the COVID-10 panic, it’s now reached its lowest point since the GFC.
And on 2 August a very poor jobs report triggered an indicator of recession. The “Sahm Rule” (Claudia Sahm, formerly a Federal Reserve and White House economist, modified and popularised it) takes the average rate of unemployment during the last three months and subtracts minimum rate over the past 12 months; if the difference is 50 basis points (0.50 percentage points) or more – which it now is – then a recession has commenced. Albeit with a couple of “false positive” readings, his indicator has detected every American recession since the 1950s.
“What we are seeing,” reckoned The Australian Financial Review (“Recession panic is gripping markets,” 5 August), is “a perfect storm … fear about a recession exacerbated by the fact that no one is remotely ready for it.” In reaction, equity markets have plunged and measures of volatility have soared more than at any time since the COVID-19 panic. Last year, many forecasters predicted a recession that didn’t eventuate; now, the “sweet spot in policy and outlook” has – within a week! – disintegrated into the strong possibility of a recession that few forecasters foresaw.
Reality, it seems, has once again – this time much more quickly and severely than usual – shocked investors by upending experts’ prophesies.
Overview
In most respects, Leithner & Company disregard analysts’ forecasts of companies’ earnings and markets’ prospects; we also ignore economists’ and the Reserve Bank of Australia’s predictions of macroeconomic phenomena such as consumer price inflation (CPI), gross domestic product (GDP), the RBA’s overnight cash rate (OCR), etc. (see also Why we mostly ignore market sentiment, 29 April and Stock tips are for patsies – are you a patsy? 12 February).
That’s because analysts and economists are poor seers. It’s hardly a sophisticated criticism; it’s a deduction from simple logic and undeniable observation. Analysts and economists are human beings; humans are unable to predict the future accurately; hence analysts and economists are unable to prophesy reliably.
I don’t criticise analysts and economists because they predict so poorly; I disparage some of them (and the journalists who obsessively and gullibly cite them) because, despite their amply and repeatedly demonstrated inability to foresee the future, they nonetheless continue the charade that they can.
I do, however, pay great attention to two key aspects of predictions: the magnitude and timing of their errors. Analysts and economists aren’t merely usually incorrect; occasionally they’re egregiously wrong and on rare but repeated occasions have been catastrophically mistaken.
Yet economic forecasters seldom err randomly; instead, and crucially, they do so consistently and even systematically.
In this article, I analyse data which the RBA released in May. These data, which summarise economists’ predictions of various macroeconomic variables since February 2001, allow us to address key questions such as:
- Over the past quarter-century, how accurate, on average, have been economists’ forecasts of consumer price inflation (CPI) and the RBA’s overnight cash rate (OCR)? Have these predictions been biased? If so, in what way?
- Since 2001, economists have fed more timely data into their models and deployed far more powerful computers; they’ve also developed more arcane models. As a result, have their forecasts become more accurate?
- Over time, the number of economists making predictions has risen. Consequently, have “consensus” forecasts become more reliable?
- Are forecasters able to foresee crucial turning points such as (a) the sharp and sudden rise of consumer price inflation to 40-year highs in the wake of the COVID-19 panic and crisis and (b) the RBA’s campaign, beginning in May 2022, to lift the OCR from its all-time low of 0.1%?
From my analysis of the RBA’s data I draw four key conclusions:
- Economists’ forecasts of CPI and OCR have been so erroneous and variable that they’re useless for purposes of accurate prediction.
- These errors have, however, been systematically biased. In the crucial sense that “runs” of negative and positive errors have persisted in the face of repeatedly confounding reality, forecasts have been overconfident.
- In three respects, predictions haven’t become more accurate and reliable. Firstly, in more recent years errors haven’t been smaller than they were 20 years ago. Secondly, attempts to foresee CPI and OCR within 12 months’ time aren’t significantly more reliable than those attempting to predict it 1-2 years hence. Finally, the rising number of forecasters and forecasts has neither reduced predictions’ variability nor improved their accuracy.
- Economists are utterly unable to foresee – even a few months in advance! – crucial turning points such as sudden and considerable rise of consumer price inflation to 40-year highs in the wake of the COVID-19 panic and crisis. And throughout the 12 years from August 2008 to November 2020, as the RBA slashed OCR from 7.25% to an all-time low of just 0.1%, forecasts consistently underestimated the extent and pace of the reduction.
In short, Australian forecasters of CPI and OCR have long been inaccurate, unreliable and overconfident. Moreover, their forecasts have usually been biased, and occasionally vulnerable to severe and even catastrophic failure.
These results have important implications for conservative value investors. Macroeconomic shocks don’t surprise Leithner & Co. That’s NOT because we foresee them; it’s because we don’t make short-term predictions. Such prophesy is so prone to error that it’s pointless (see, however, Farewell low “inflation” and interest rates? 20 February 2023, Why Jerome Powell’s speech shouldn’t have surprised you, 31 August 2022 and Why inflation is and will remain high, 15 August 2022).
Our analysis, which I detail below, confirms our general view: “experts” are overconfident; hence their predictions are systematically biased; and their biased forecasts lure investors into errors and losses (see also Why you’re probably overconfident – and what you can do about it, 14 February 2022).
Downward plunges of equities’ prices, which occur when forecasts disappoint, surprise and even shock investors – and provide opportunities to Leithner & Co. It’s hugely ironic: forecasters can’t predict accurately and reliably; yet their errors form patterns, these patterns lead to surprises, and surprises have roughly foreseeable consequences!
By recognising forecasters’ biases and the volatility they foment, Leithner & Co. avoids the surprises and losses which occur when reality confounds the expectations of the overconfident consensus. We thereby abate losses, grasp opportunities and augment our long-term returns.
Consumer Price Inflation and the Overnight Cash Rate
Using quarterly data collated by the RBA (file G1), Figure 1 plots annualised percentage changes of the Consumer Price Index since June 1923. CPI’s average percentage change over the past century is 4.0%, and its average since August 2005 is 2.7%. The most recent (year to March 2024) change is 3.6%; that’s below the average since 1923 but above the mean since 2001 – as well as the RBA’s target range of 2-3%.
For my purposes, three developments are most apparent: first, from its generational high of 17.7% in the year to March 1975 to its low of -0.4% in the year to June 2020 (which was the lowest since September 1997 and among the lowest since the early-1960s), consumer price inflation trended downward. Secondly, from June 2020 to December 2022 its annualised rate of increase vaulted to 7.8% (the highest since the 8.7% in the year to March 1990 and among the highest since the 1980s). Thirdly, since December 2022 the annualised rate of increase has abated to 3.6% in the year to March 2024.
Figure 1a: Consumer Price Index, Annualised Percentage Changes, June 1923-June 2024
Using monthly data collated by the RBA (file F13), Figure 1b plots the RBA’s OCS since June 1990. Over this interval it has averaged 4.6%; currently (June 2024), it’s 4.35%. For my purposes, three developments are most important. First, from its all-time high of 17.5% in 1990, during the next seven years it fell steadily and drastically to 5.0%.
Figure 1b: RBA’s Overnight Cash Rate, Monthly Observations, June 1990-June 2024
Secondly, during the next 10 years the OCR trended upwards and reached 7.25% in March 2008. Then came the GFC. The RBA slashed the OCR to 3.0% in April 2009, lifted it as high as 4.5% in May 2010, and then commenced a campaign of cutting that culminated in the reduction of the OCR to an all-time low of just 0.1% in November 2020. There it stayed until May 2022. Thirdly, since May 20322 the RBA has increased its OCR at its steepest pace since at least 1990. As a result its present level (4.35%) isn’t far below its long-term (since 1990) average.
The RBA’s Newly-Released Data
How well have economists anticipated longer-term trends of CPI and OCR? How well have they forecast shorter-term fluctuations? On 10 May, the RBA added a new statistical table (J1, “Market Economists’ Forecasts”) to its website. It provides “summary statistics from the RBA’s quarterly survey of market economists’ forecasts (since February 2001). The statistical table will be updated quarterly, on the Friday following the RBA’s Statement on Monetary Policy publication.”
This data set’s “surveyed variables,” the RBA elaborates, “are headline (consumer price) inflation, underlying (trimmed mean, etc.) inflation, real gross domestic product (GDP) growth, real domestic final demand growth, wage price index growth, unemployment rate, (overnight) cash rate (increasingly dubbed the “RBA’s policy rate” or “the official interest rate”), (the $A/$US) exchange rate, net exports, terms of trade, medium-to-long term inflation expectations, potential GDP growth, non-accelerating inflation rate of unemployment (NAIRU), nominal neutral interest rate and the output gap.”
The RBA releases its quarterly Statement on Monetary Policy in February, May, August and November of each year. Each calendar year’s first Statement (February) includes forecasts of macroeconomic variables for the half-year to the previous December, the half-year to June of the current year, the half-year to December of the current year, and the three half-years thereafter. That’s a total of six successive half-years. The Statement in May contains forecasts for exactly the same half-years as the one in February.
The Statements in August and November also contain predictions for the same half-years: the six months to the preceding June, the six months to the next December, and the four subsequent half-yearly periods. (In 2003-2005, the Statements also contained half-years to March and September.)
For this reason, and also because forecasts are updated every three months, forecasts pertaining to a given half-year are made at 10 preceding points in time. The RBA published evolving forecasts for the half-year to December 2023, for example, in August and November 2021, February, May, August and November 2022, and February, May, August and November 2023.
As a result, 10 lengths of time exist between the date a forecast is first made (“forecast date”) and the half-year to which it refers (“forecast reference period”). The typical sequence of forecast intervals 28, 25, 22, 19, 16, 13, 10, 7, 4 months and 1 month before the last month of the forecast reference period. In 2008-2010, the RBA added an additional (seventh) half-year; as a result, the dataset includes a handful of 31-month intervals.
Figure 2: Number of Forecast Periods, by Number of Months between Forecast Date and Reference Period, February 2001-May 2024
Figure 2 stratifies the total number (394) of half-year forecast periods in the RBA’s J1 dataset by the number of months between the forecast date and the forecast reference period (the slightly larger number of 10-month intervals is the result of the RBA’s short-lived (2003-2005) addition of March and September half-years.)
Apart from minor and temporary anomalies (March and September half-years, and the addition of an additional half-year of forecasts in 2008-2010), the distribution of intervals is approximately equal. We thus have (a) an extremely short (one-month) forecast periods; (b) three forecast periods of more than one month and less than one year; and (b) three periods between 10 and 19 months; and (d) four periods of at least 22 months.
Figure 3: Number of Predictions, by Length (Number of Months) of Prediction, February 2001-May 2024
Figure 3 plots another key attribute of these data: the number of economists’ predictions of consumer price inflation per interval varies according to the number of months between the date of the forecast and half-year to which the forecast refers. Since February 2001, the RBA has collated a total of 5,925 predictions of consumer price inflation. The smaller is this number of months, the greater is the number of predictions. One-month intervals contain 711 predictions; 28-month intervals have just half as many (346). (Much the same applies to the OCR; for reasons of brevity, I’ve omitted a summary. The same point applies to Figure 3, Figure 4 and Figure 5.)
Figure 4 plots the average number of predictions of consumer price inflation per forecast interval. As the interval’s length increases, the average number of forecasters decreases: intervals of 1-13 months contain an average of 16 or more predictions; intervals of 25 or more months contain 12 or fewer. The longer is the interval, it seems, the less willing are economists to predict.
Figure 4: Average Number of Predictions per Prediction Interval
Finally, Figure 5 plots the total number of forecasts of consumer price inflation per year. The number of forecasts per year has doubled. Although a given economist (or entity) can and presumably will make more than one forecast of consumer price inflation per year, the RBA allows no individual economist or entity to make more than one forecast per quarter; accordingly, dividing the figures in Figure 5 by four provides an estimate of the number of economists or organisations contributing predictions during each quarter. That number has risen from ca. 14 in 2001 to 36 in 2024.
Figure 5: Number of Forecasts, by Year, 2001-2024
To the data in the RBA’s file J1, I’ve merged a subset of the data from G1 (Consumer Price Inflation) and F13: namely the Consumer Price Index (calculated by the Australian Bureau of Statistics) which I used to compute the CPI’s annualised percentage change in Figure 1a, and the OCR data in Figure 1b.
This merged J1-F13-G1 dataset enables us to compare (1) economists’ forecasts of CPI and OCR at various points in time with reference to a subsequent point in time to (2) actual CPI and OCR at those points.
A Brief Digression: Forecasts’ Accuracy, Bias and Reliability
My analysis distinguishes between the accuracy and the reliability of forecasts. For my purposes, accuracy refers to the proximity of the central tendency of economists’ forecasts of CPI and OCR to their true or target values (actual CPI and OCR). Reliability refers to the dispersion of forecasts around their central tendency. The closer forecasts’ central tendency approximates their true value, the greater is their accuracy and thus the lower is systematic error (bias); the greater is the forecasts’ random error, on the other hand, the lower is their reliability.
Archers shooting arrows at a target provide an analogy. The closer, on average, archers’ (forecasters’) arrows (forecasts) cluster around a target (true value), the more accurate are the archers; the more their arrows disperse around the target, on the other hand, the less reliable they are.
Shots can thus be very accurate (tend exactly towards their target) and reliable (tightly clustered around it). It can also be accurate but unreliable (missing the target but scattered randomly around it); inaccurate (tending towards some spot far from the target) but reliable (tightly clustered around this spot), and both inaccurate and unreliable.
Results
Since February 2001, the RBA has collected a total of 5,925 predictions of “headline” CPI (hereafter “CPI”); since August 2005, it’s also collected 5,671 predictions of OCR. In each forecast period since these starting points (393 and 350 respectively), for each variable it’s computed economists’ mean and median forecasts. I’ve analysed both (as well as variations such as “underlying” and “trimmed mean” CPI), and will summarise my analysis of median forecasts (those of mean forecasts don’t differ significantly).
During forecast periods since February 2001, CPI has averaged 2.72%. Forecasts of CPI have averaged 2.60%; accordingly, forecasters’ error has averaged -0.12%, i.e., -12 basis points (Figure 6a). On average over the past quarter-century, forecasters have slightly underestimated actual consumer price inflation. During the forecast periods since August 2005, on the other hand, OCR has averaged 2.87% and forecasts have averaged 3.17%; accordingly, forecasters’ error has averaged +30 basis points (Figure 6b). Over the past 20 years, forecasters have thus overestimated the overnight cash rate.
Figure 6a: Summary Statistics, Consumer Price Inflation, Average Forecast Errors over All Intervals (Basis Points), February 2001-June 2024
At first glance, CPI’s average forecast error seems quite small: expressing it as a percentage of CPI’s mean, we have -0.12% ÷ 2.72% = -4.4%. In contrast, OCR’s average forecast error is bigger: 0.30% ÷ 2.87% = 10.5%.
However, for several reasons economists and the investors and journalists who heed their forecasts shouldn’t celebrate: the average forecast errors in Figure 6a and Figure 6b greatly understate the actual errors.
Figure 6b: Summary Statistics, Overnight Cash Rate, Average Forecast Errors over All Intervals (Basis Points), August 2005-June 2024