Correcting the January optimism effect

• Date: 01 September 2020
• DOI: http://doi.org/10.1002/for.2670
• Published date: 01 September 2020

RESEARCH ARTICLE

Correcting the January optimism effect

Philip Hans Franses

Econometric Institute, Erasmus School of

Economics, Rotterdam, The Netherlands

Correspondence

Philip Hans Franses, Econometric

Institute, Erasmus School of Economics,

POB 1738, NL-3000 DR. Rotterdam, The

Netherlands.

Email: franses@ese.eur.nl

Abstract

Each month, various professional forecasters give forecasts for next year's real

gross domestic product (GDP) growth and unemployment. January is a special

month, when the forecast horizon moves to the following calendar year.

Instead of deleting the January data when analyzing forecast updates, I pro-

pose a periodic version of a test regression for weak-form efficiency. An appli-

cation of this periodic model for many forecasts across a range of countries

shows that in January GDP forecast updates are positive, whereas the forecast

updates for unemployment are negative. I document that this January opti-

mism about the new calendar year is detrimental to forecast accuracy. To

empirically analyze Okun's law, I also propose a periodic test regression, and

its application provides more support for this law.

KEYWORDS

forecast updates, January effect, Okun's law, periodic regression model, weak-form efficiency

1|INTRODUCTION

Professional forecasters, like those in the Survey of Pro-

fessional Forecasters

1

and the Consensus Forecasters,

2

can quote forecasts in each month of the year. Important

variables, for which these forecasts are given, are real

gross domestic product (GDP) growth and unemploy-

ment. The forecast targets are usually yearly real GDP

growth and unemployment, where the years are the cur-

rent year and the following year. For example, in January

2019, forecasts are given for the years 2019 and 2020.

Often, the focus is on the average forecast (“consensus”;

see, among many others, Ager, Kappler, & Osterloh,

2009; Ashiya, 2003, 2006; Cho, 2002; Dovern & Weisser,

2011; Isiklar, Lahiri, & Loungani, 2006). There are also

many studies that include measures of dispersion (see,

among others, Capistran & Timmermann, 2009; Lahiri &

Sheng, 2008; Legerstee & Franses, 2015; Manzan, 2011).

The month January each year can be viewed as a spe-

cial month.

3

It is the first month for which the forecast

horizon switches to a new year. Whereas the other

months concern the forecasts for years Tand T+1,in

January for the first time, this changes from T+1to

T+ 2. Strictly speaking, the quote in January does not

1

https://www.philadelphiafed.org/research-and-data/real-time-center/

survey-of-professional-forecasters/

2

https://www.consensuseconomics.com/

3

This also holds for variables like consumer confidence and stock

returns. Ciccone (2011, table 1) reports that consumer confidence

generally peaks in January, even though the survey questions ask

respondents to think about comparing the next year with this year.

Furthermore, there is evidence that stock returns can show a so-called

January effect, which is called investor optimism, and which entails

that stock returns can be higher on average in January than in other

months (see, e.g., Chen & Daves, 2018; Ciccone, 2011). There is a large

body of research on optimism or pessimism bias in economic forecasts

(see, among many more, Batchelor, 2007). In the present study I do not

focus on explaining or analyzing any bias, but I merely focus on

correcting for it when studying efficiency and Okun's law.

Received: 17 July 2019 Revised: 4 December 2019 Accepted: 17 January 2020

DOI: 10.1002/for.2670

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided

the original work is properly cited.

© 2020 The Author. Journal of Forecasting published by John Wiley & Sons Ltd

Journal of Forecasting. 2020;39:927–933. wileyonlinelibrary.com/journal/for 927