Abstract
We test whether analysts display multiple biases in forecasting the Institute for Supply Management’s manufacturing purchasing manager’s index (PMI). We adopt a test that does not require knowledge of the forecaster’s prior information set and is robust to rational clustering, correlated forecast errors and outliers. We find that analysts forecast the PMI poorly and display multiple biases when forecasting. In particular, forecasters anti-herd and anti-anchor. Anti-herding supports a reputation-based notion that forecasters are rewarded not only for forecast accuracy but also for being the best forecast at a single point in time. Anti-anchoring is consistent with forecasters overreacting to private information. The two biases show a strong positive correlation suggesting that the incentives that elicit anti-herding also elicit anti-anchoring behavior. Both biases result in larger absolute errors, although the effect is stronger for anti-herding.
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Notes
Loungani (2002) states “the record of failure to predict recessions is virtually unblemished.” This conclusion was based on the finding that only two of the 60 recessions that occurred around the world during the 1990s were predicted by forecasters a year in advance. About 40 of the 60 impending recessions remained undetected seven months before they occurred.
Baumohl (2013 p. 184) writes that a PMI reading of 50 is “believed to be consistent with real GDP growth of about 2.5%. Every full point in the index above 50 can add another 0.3 percentage points or so of growth every year.”
The ISM Web site cites Joseph E. Stiglitz, former chairman of President Clinton’s Council of Economic Advisors, as saying, “The [ISM] Manufacturing [index]...has one of the shortest reporting lags of any macro-economic series and gives an important early look at the economy. It also measures some concepts (such as lead times and delivery lags) that can be found nowhere else. It makes an important contribution to the American statistical system and to economic policy.” Lahiri and Monokroussos (2013) add that “..because of their nature as survey responses, ISM data are typically subject to small revisions at most. As such, they preserve most of the real-time nature that is crucial in many estimation and forecasting exercises...”
When we asked an award-winning analyst what the incentive was to turn in a forecast to Bloomberg, he replied, “Pride, publicity, and career advancement via name recognition from the Bloomberg posting.”
They use the term herding to denote the tendency to produce a range of forecasts which is narrower than that which would likely be observed if the forecasts were produced on a strictly independent basis because a forecaster takes the previous consensus mean into account.
The backlog index compares current month unfilled orders with the prior month. The inventory index compares current month units on hand, not the dollar value, with the prior month.
Specifically, five types of choices are offered as follows: for new orders, production and exports, the choices are better/same/worse; for employment, inventories, prices and imports, the choices are higher/same/lower; for supplier deliveries, the choices are slower/same/faster; for customer inventories, the choices are too high/about right/too low; and for order backlogs, the choices are greater/same/less.
While consensus forecasts are available going back to 1992, individual analyst forecasts are only available from June 1998.
We also examined the impact of forecast number on forecast bias. We collected all the first forecasts of firms, then all the second forecasts and so on. We truncated the sample where we had minimally 10 analysts/firms, i.e., at 168 forecasts. We found pervasive anti-herding.
Pierdzioch et al. (2016) examined herding to the previous period’s consensus on grounds that expectation formation shows features of adaptive learning. Although we disagree with this approach to testing for herding, we reexamined the overall sample results for herding to the previous period’s median forecast and found pervasive anti-herding.
References
Amir E, Ganzach Y (1998) Overreaction and underreaction in analysts’ forecasts. J Econ Behav Organ 37:333–347
Ashiya M, Doi T (2001) Herd behavior of Japanese economists. J Econ Behav Organ 46(3):343–346
Bachman D (2010) The information content of the ISM purchasing managers’ survey. Working paper, U.S. Department of Commerce, August 2010
Baker SR, Bloom N, Davis SJ (2013) Measuring economic policy uncertainty. http://www.policyuncertainty.com/media/BakerBloomDavis.pdf
Baumohl B (2013) The secrets of economic indicators: hidden clues to future economic trends and investment opportunities. FT Press, Upper Saddle River
Bernhardt D, Campello M, Kutsoati E (2006) Who herds? J Financ Econ 80:657–675
Bewley R, Fiebig DG (2002) On the herding instinct of interest rate forecasters. Empir Econo 27:403–425
Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. J Polit Econ 100:992–1026
Campbell S, Sharpe S (2009) Anchoring bias in consensus forecasts and its effect on market prices. J Financ Quant Anal 44:369–390
Cen L, Hillary G, Wei KCJ (2013) The role of anchoring bias in the equity market: evidence from analysts’ earnings forecasts and stock returns. J Financ Quant Anal 48:47–76
Clement MB, Tse SY (2005) Financial analyst characteristics and herding behavior in forecasting. J Financ 60(1):307–341
De Bondt W, Forbes W (1999) Herding in analyst earnings forecasts: evidence from the United Kingdom. Eur Financ Manag 5:143–163
Effinger MR, Polborn MK (2001) Herding and anti-herding: a model of reputational differentiation. Eur Econ Rev 45:385–403
Gallo GM, Granger CWJ, Jeon Y (2002) Copycats and common swings: the impact of the use of forecasts in information sets. IMF Staff Pap 49(1):4–21
Gilbert T, Scotti C, Strasser G, Vega C (2015) Is the intrinsic value of macroeconomic news announcements related to their asset price impact? Finance and economics discussion series 2015-046. Board of Governors of the Federal Reserve System, Washington. doi:10.17016/FEDS.2015.046
Hess D, Orbe S (2013) Irrationality or efficiency of macroeconomic survey forecasts? Implications from the anchoring bias test. Rev Financ 17:2097–2131
Hong H, Kubik JD, Solomon A (2000) Security analysts’ career concerns and herding of earnings forecasts. RAND J Econ 31(1):121–144
Kim CF, Pantzalis C (2003) Global/industrial diversification and analyst herding. Financ Anal J 59(2):69–79
Lahiri K, Monokroussos G (2013) Nowcasting US GDP: the role of ISM business surveys. Int J Forecast 29:644–658
Lamont O (1995) Macroeconomic forecasts and microeconomic forecasters. NBER working paper 5284. National Bureau of Economic Research, Cambridge, Massachusetts
Lansing KJ, Pyle B (2015) Persistent overoptimism about economic growth. FRBSF Econ Lett 3:1–5
Laster D, Bennett P, Geoum IS (1999) Rational bias in macroeconomic forecasts. Q J Econ 114(1):293–318
Loungani P (2002) How accurate are private sector forecasts? Cross-country evidence from consensus forecasts of output growth. International Monetary Fund Working Paper No. 00/77
Nakazano Y (2013) Strategic behavior of Federal Open Market Committee board members: evidence from members’ forecasts. J Econ Behav Organ 93:62–70
Olsen R (1996) Implications of herding behavior for earnings estimation, risk assessment, and stock returns. Financ Anal J 52(4):37–41
Pierdzioch C, Reid MB, Gupta R (2016) Inflation forecasts and forecaster herding: evidence from South African survey data. J Behav Exp Econ 62:42–50
Pierdzioch C, Rülke JC (2013a) A note on the anti-herding instinct of interest-rate forecasters. Empir Econ 45(2):665–673
Pierdzioch C, Rülke JC (2013b) Do inflation targets anchor inflation expectations? Econ Model 35:214–223
Pierdzioch C, Rülke JC, Stadtmann G (2013a) Forecasting metal prices: do forecasters herd? J Bank Financ 37:150–158
Pierdzioch C, Rülke JC, Stadtmann G (2013b) House price forecasts, forecaster herding, and the recent crisis. Int J Financ Stud 1:16–29
Pons-Novell J (2003) Strategic bias, herding behavior and economic forecasts. J Forecast 22(1):67–77
Schuh S (2001) An evaluation of recent macroeconomic forecast errors. New Engl Econ Rev Jan/Feb(1):35–56
Truman B (1994) Analyst forecasts and herding behavior. Rev Financ Stud 7:97–124
Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185:1124–1131
Acknowledgements
The authors gratefully acknowledge the financial support of the Argyros School of Business at Chapman University and a summer research grant from the College of Business at the University of Tennessee at Chattanooga.
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Broughton, J.B., Lobo, B.J. Herding and anchoring in macroeconomic forecasts: the case of the PMI. Empir Econ 55, 1337–1355 (2018). https://doi.org/10.1007/s00181-017-1306-6
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DOI: https://doi.org/10.1007/s00181-017-1306-6