Análisis externo

tify shocks. Thus, the results of this paper hold independent of an economic model and require only a minimum of auxiliary assumptions. Therefore, my tests of the Sticky and Noisy Information models will build on Coibion and Gorodnichenko (2015).

3.4

Data

3.4.1

Survey Data on Investment Professionals’ Expec-

tations

I use three distinct data sets on stock market expectations, namely the ZEW Financial Market Survey, Livingston Survey, and Robert Shiller’s Stock Mar- ket Confidence Indices Survey data.12 _{The most important characteristics of} these data sets are summarized in table 3.1.13 _{To my knowledge, this is the} most comprehensive collection of data on investment professionals stock mar- ket expectations analyzed so far.

I have chosen to use these three data sets, in order to cover several features which are desirable for the purpose of this paper. These are:

1. large cross-section dimension (reliable aggregate statistics)

2. long time series dimension (limit impact of sample period)

3. micro data available withpanel structure (analysis at individual level)

4. expectations of both investment professionals and households available (differences in expectations of the two groups)

None of the three data sets in isolation satisfies all of these points, but taken together, the three data sets cover all of the desired features. While differences between the three surveys naturally imply some degree of heterogeneity of results, all main findings of my paper are robust across data sources.

12_{ZEW Financial Market Survey data provided by the Centre of European Economic}

Research (ZEW) at Mannheim. Livingston survey data provided by the Federal Reserve Bank of Philadelphia. Stock Market Confidence Indices data provided by International Center for Finance at Yale School of Management. I thank the respective institutions and their staff for making these data sets available to me.

13

Corresponding realizations data has been obtained from Datastream (see Appendix for details).

ZEW Livingston Shiller Prof. Shiller HH

Respondent

Type Professionals Professionals Professionals Households Time Period 2003:02-2015:10 1952:6-2015:12 1989:7-2016:5 1999:2-2016:5 Frequency Monthly Semi-annually Monthly Monthly Forecast of DAX S&P 500 Dow Jones Dow Jones

Cross-section observations per wave (mean)

233 32 26 28

Panel Yes Yes No No

Horizon (months) 6 6, 12, (18, 24) 1, 3, 6, 12, 120 1, 3, 6, 12, 120

T (# of waves) 153 128 249 190

Total # of obs. 34993 4134 6397 5245

Table 3.1: Overview of Survey Data Sources

The longest available time series of stock market expectations is found in the Livingston survey, which is at the same time an (unbalanced) panel.14 However, its cross-section is quite small, which may lead to considerable noise in cross-section aggregates. The latter is a crucial drawback given that the Coibion and Gorodnichenko (2015) tests are based purely on cross-section aggregates.

In contrast the ZEW Financial Market Survey has the largest cross-section. It is a monthly survey of investment professionals (mainly from banks) who are asked about their expected level of the German Stock Market Index DAX in 6 months and the uncertainty surrounding their expectation. As a (rotating) panel with around 250 valid quantitative responses per month, spanning the period from 2003:02 until 2015:10, it is clearly one of the most notable data sets on stock market expectations. At the same time it appears to be relatively little known in the international literature on stock market expectations (see the above literature review).

Finally, the only data source which allows to directly compare the expecta- tions of investment professionals and households are the Shiller Surveys, which are conducted separately for these two groups of respondents. Similar to the Livingston survey the cross-section dimension of the Shiller surveys can be rather small.

Overall the ZEW Survey appears to offer the best quality of data, but it has to be augmented by the Livingston survey to be sure that results also hold

14

Unfortunately, the Livingston survey data on stock market expectations is known to suffer from several problems before the Federal Reserve Bank (FRB) of Philadelphia took over its administration in 1990, such as unclear base-values and several changes of the index to be forecasted (see Aretz, Bartram, and Pope (2011)). Therefore, in line with Aretz, Bartram, and Pope (2011), I restrict myself to Livingston data from 1990 onwards.

3.4. DATA

over long time periods and also hold for U.S. data. The Shiller data is needed if one wants to have directly comparable data on investment professionals and households. An additional benefit of the Shiller and Livingston data is that they do not require the use of instruments in the estimation for tests of models of information rigidities. Therefore, including these data sources also provides a natural check of the results from the ZEW Survey, where instrumental vari- able estimation is necessary to implement the tests proposed by Coibion and Gorodnichenko (2015).

In choosing data sets, I have restricted myself to surveys including quan- titative questions on stock market expectations (i.e. those where respondents had to give a number for expected index level or expected return) and surveys which are ongoing.15 _{Table 3.2 shows the exact questions used in each survey.}

3.4.2

Stock Market Realizations Data

To compute forecast errors and price-dividend ratios, I use the following stock market indices (datastream codes in parentheses): DAX-30 (DAXINDX; PI, RI), S&P 500 (S&PCOMP; PI, RI), and Dow Jones Industrial Average (DJIN- DUS; PI, RI). Details on how the price-dividend ratio is computed are given in the appendix. I also obtain from datastream the 3-month treasury bill rate (henceforth called the short term interest rate) for Germany (TRBD3MT) and the U.S. (DTB3).

3.4.3

Notation Used Throughout the Paper

Let Ft(i)xt+h denote agent i’s h-period ahead forecast of variable x made at

time t, and let Ftxt+h ≡ _N1 N

P

i=1

Ft(i)xt+h denote the average forecast across all N agents in the cross-section.16 _{In this paper, x}

t+h will be the growth rate

fromt to t+h of a stock market index.

15_{The first knock-out criterion excludes many alternative data sources (including another}

sub-question of the ZEW survey) on the grounds that it provides much coarser information than quantitative data, and on the grounds that quantification would require auxiliary assumptions which would reduce the decisiveness of the subsequent tests. The second knock- out criterion excludes the UBS-Gallup survey which was terminated in 2007 (somewhat unfortunately) rendering it the shortest time series of those mentioned here (it was also not conducted as a panel and included only households). Another well-known data set is the CFO survey, which started in 2001. The respondents were CFOs of U.S. companies (mainly from the manufacturing sector). One might a priori tend to think of these CFOs as investment professionals, however, it turns out that their aggregate expectations behave very much like those of households (see Adam, Marcet, and Beutel (2017)). Thus, the CFO survey expectations are difficult to classify neither as investment professionals nor as households and thus shed little light on the questions considered in this paper.

16

Using the median instead of the mean to aggregate the cross-section of forecasts does not induce any major changes to my results.

For the tests of information rigidities in section 5.2 I use the more precise notation Ftxt,t+h instead of the simple notation Ftxt+h to make explicit that

the growth rate is computed over the time period fromt tot+h. While for all other sections, this would only complicate the notation given that we always haves=qinFsxq,t+h, the more precise notation is necessary when computing

forecast revisions in section 5.2, where we can haves₆=q.

ZEW Livingston Shiller Prof. Shiller HH

Level of DAX in 6 months: Min (90%): Expectation: Max (90%): June 2016: Please provide your forecasts [...]: STOCK PRICES (S&P500) Monthly Data, End-of-Period 2016 29. Apr: 2065,30 30. Jun: 30. Dec: 2017 30. Jun: Annual, End-of-Period 2015: 2043,94 2016: Same as Dec 30 2016 2017:

How much of a change in percentage terms do you expect in the following (use + before your number to indicate an expected increase, a ₋ to indicate an expected decrease, leave blanks where

you do not know):

Dow Jones Industrial Average In 1 month: In 3 months: In 6 months: In 1 year: In 10 years:

Table 3.2: Exact Questions Asked in Each Survey

Notes: Exact questions are represented in very much the same format that has been used in the respective survey questionnaires. For example, in the June 2016 Livingston survey, realized values for April 2016 and end of 2015 are given to the forecaster, who is asked to provide her forecasts for the dates indicated. The ZEW Financial Market Survey and the Shiller Survey are confidential, whereas the Livingston survey data is publicly available. The data from the ZEW Financial Market Survey is not to be confused with the ZEW Indicator of Economic Sentiment, which is derived from a different question within the same survey (referring to the general macroeconomic outlook).17

17

More information about each survey can be found on the following websites: http://www.zew.de/WS99-1

https://www.philadelphiafed.org/research-and-data/real-time-center/livingston-survey http://som.yale.edu/faculty-research/centers-initiatives/international-center-for- finance/data/stock-market-confidence-indices/stock-market-confidence-indices

3.5. DIFFERENCES BETWEEN HOUSEHOLD AND INVESTMENT