WWP Wolfsburg Working Papers No. 11-01

Transcription

WWP
Wolfsburg Working Papers No. 11-01
Trapped in the Here and Now – New Insights into
Financial Market Analyst Behavior
Markus Spiwoks, Zulia Gubaydullina and Oliver Hein, 2011
Trapped in the Here and Now
New Insights into Financial Market Analyst Behavior
Markus Spiwoks*, Zulia Gubaydullina** and Oliver Hein***
ABSTRACT
Observing forecast time series reveals much about the behavior of financial market analysts. This study explores the phenomenon of topically oriented trend adjustment on the basis of close to 160,000 interest rate predictions. The analysis
shows that 98.5% of the almost 1,200 forecast time series reflect current rather
than future financial market trends. In addition, the study discusses the background for this forecast behavior and points to the need for further research.
KEYWORDS:
Behavioral finance, financial market analysts, interest rate
forecasts, topically oriented trend adjustment (TOTA)
JEL CLASSIFICATION CODES: D84, E47, G12, G21
__________________
* Ostfalia University of Applied Sciences, Faculty of Business Administration, chair in Finance,
Robert-Koch-Platz 10-14, D-38440 Wolfsburg, Germany, Email: [email protected]
** Georg August University Göttingen, Faculty of Economic Sciences, Chair in Economic Policy
and SME Research, Platz der Göttinger Sieben 3, D-37073 Göttingen, Germany, Email:
[email protected]
*** Giessen Friedberg University of Applied Sciences, Department of Mathematics, Natural Sciences and Computer Science, Chair in Information Systems, Wilhelm-Leuschner-Str. 13, D-61169
Friedberg, Germany, Email: [email protected]
1
SUMMARY
This study deals with the phenomenon of topically oriented trend adjustment in
the time series of financial market forecasts. A total of 1,182 time series with altogether 158,022 interest rate predictions are examined. Forecasts refer to threemonth interest rates and ten-year government bond yields in the USA, Japan,
Germany, France, the United Kingdom, Italy, Spain, Canada, the Netherlands,
Switzerland, Sweden and Norway. The forecasts are generated for horizons of
four and thirteen months.
Topically oriented trend adjustments arise in 1,164 of the 1,182 forecast time series. Thus 98.5% of these forecast time series reflect the present rather than the
future. In other words, they correlate more strongly with the time series of naive
forecasts than with actual events. Independent of the forecast object, forecast horizon and countries concerned, the overwhelming majority of the forecast time
series is distinguished by topically oriented trend adjustment.
Five explanation patterns for topically oriented trend adjustment are meanwhile
under discussion: 1. Anchoring heuristics, 2. The tendency to underestimate the
variability of reality, 3. Avoidance of major blunders through protective estimates,
4. Normative herd behavior such as externally triggered herding, and 5. Information-based herd behavior.
2
I. INTRODUCTION
Financial market forecasts are of enormous practical significance. Interest rate
forecasts are vital to borrowed capital procurement in terms of time and maturity.
Share market forecasts are indispensable to the scheduling of share emissions.
Exchange rate forecasts can influence international trade transactions considerably. Furthermore, active portfolio management strategies will only be successful
in the long-run if reliable interest rate, share and exchange rate forecasts are available.
Hence a great deal of academic research in this field is devoted to the question of
how accurate financial market forecasters are (for a comprehensive overview of
the literature, see Spiwoks, Bedke and Hein, 2008, as well as Spiwoks and Hein,
2007).
The routine evaluation criteria for the proficiency of financial market forecasts are
the unbiasedness test, the sign accuracy test and the efficiency test, as well as the
comparison with naive forecasts or ARIMA models, e.g., within the framework of
a modified Diebold/Mariano test for forecast encompassing.
Although these procedures are a telling indication of forecast success, they reveal
little about the forecast behavior of financial analysts. Only the unbiasedness test
gives some initial pointers. Thus long-term over- and underestimates can be identified when the intercept of the regression line in a forecast/realization graph is
positive or negative, respectively. A deviation from the value of 1 in the slope of
the regression line can, for example, be interpreted as a tendency to overestimate
small events, while large events are routinely underestimated. These reference
points go a long way to explaining the behavior of financial market forecasters,
under certain circumstances even suggesting how the drawing up of forecasts can
be refined.
Indeed, financial market forecasts bring even more to light about financial behavior. In the last ten years, the phenomenon of topically oriented trend adjustment
(TOTA) in forecast time series has gradually gained more attention. When fore3
casts strongly reflect actual market trends but neglect future developments, we
speak of a topically oriented trend adjustment. An excellent example of this phenomenon is the consensus forecasting on interest rate trends in Germany produced
by the organization Consensus Economics.
Figure 1 shows forecasts (thin line) at their respective dates of validity. Financial
forecasters were clearly unable to capture interest rate trends (bold line) to an
adequate degree. They anticipated a local minimum at 5.8% for the turn of the
year 1994/1995. In reality, the local maximum at the time levelled off at 7.6%.
One year later analysts expected a local maximum at 7.6%. In effect, the local
minimum was 5.9%. At the beginning of 2000, experts predicted a local minimum
at 4.2%. Yet in reality the local maximum was 5.5%. For the summer of 2003,
forecasters expected a local maximum at 5.5%. In reality, however, a local minimum was established at 3.8%. The summer of 2005 saw a repeat of this constellation. Analysts predicted a local maximum at 4.8%, while in reality the local minimum evened out at 3.1%. Thus over long periods forecasting efforts backfired.
At the same time, these forecasts show a definite correlation with actual interest
rate trends. The forecast time series appears to be a delayed reflection of actual
changes. This is particularly obvious when the data predicted for the forecast horizon (13 months) is shifted to the left. This allows forecasts to be shown at their
date of issue rather than their date of validitiy (Figure 2).
Figure 2 emphasizes that forecasters are heavily influenced in their predictions by
the respective market situation. As soon as the interest rate drops, analysts adjust
their forecasts downwards. When it increases, on the other hand, experts make an
upward adjustment. In its basic progression the resulting forecast time series
strongly resembles that of naive forecasts. It is this constellation that is described
as topically oriented trend adjustment.
Figures 3 and 4 suggest that topically oriented trend adjustment in financial market predictions is by no means a rare phenomenon. On the contrary, it is a recurring feature in all 37 banks and research institutions that publish their predictions
for interest rate trends in Germany in the magazine Consensus Forecasts.
4
Figure 1
10-Year German Government Bond Yield in %
Ten-year German government bond yield (bold line) and respective forecasts from
Consensus Economics with a 13-month forecast horizon (thin line).
11
10
9
8
7
6
5
4
3
2
Oct 89
Oct 91
Oct 93
Oct 95
Oct 97
Oct 99
Oct 01
Oct 03
Oct 05
Oct 07
Oct 09
Figure 2
Ten-year German government bond yield (bold line) and forecasts from Consensus Economics shifted to the left by 13 months (thin line).
11
10
9
8
7
6
5
4
3
2
Oct 89
Oct 91
Oct 93
Oct 95
Oct 97
Oct 99
Oct 01
Oct 03
Oct 05
Oct 07
Oct 09
5
Figure 3
Ten-year German government bond yield (bold black line) and respective forecast
time series of 37 banks and research institutions with a 13-month forecast horizon
(thin grey lines).
11
10
9
8
7
6
5
4
3
2
Oct 89
Oct 91
Oct 93
Oct 95
Oct 97
Oct 99
Oct 01
Oct 03
Oct 05
Oct 07
Oct 09
Figure 4
Ten-year German government bond yield (bold black line) and forecast time series of 37 banks and research institutions shifted to the left by 13 months (thin
grey lines).
11
10
9
8
7
6
5
4
3
2
Oct 89
Oct 91
Oct 93
Oct 95
Oct 97
Oct 99
Oct 01
Oct 03
Oct 05
Oct 07
Oct 09
6
Empirical findings indicating the phenomenon of topically oriented trend adjustment in several forecast time series first appeared at the end of the 1980s. Manzur
(1988), Hafer and Hein (1989), Allen and Taylor (1990), Takagi (1991), and
Hafer, Hein and MacDonald (1992) can all be regarded as pioneers in this research domain. The systematic search for topically oriented trend adjustment in
survey forecasts begins with the study by Andres and Spiwoks (1999): Bofinger
and Schmidt (2003) examine exchange rate predictions. Spiwoks (2003), Spiwoks, Bedke and Hein (2008), Spiwoks, Bedke and Hein (2009), and Spiwoks,
Bedke and Hein (2010) explore interest rate forecasts. Spiwoks (2004a) deals with
share index forecasts. Spiwoks and Hein (2007) evaluate exchange rate, interest
rate and share index forecasts.
These studies provide a first indication that topically oriented trend adjustment
may not be a question of isolated cases but possibly a common phenomenon in
financial market forecasting. Several of these studies, however, are confined to
observing only a small number of forecast time series. Others consider relatively
short forecast time series. Most of them deal solely with predictions on certain
domestic financial market segments. A comprehensive comparative study of international dimensions that investigates a vast number of long-range forecast time
series is the missing link. The present study aims to close this research gap.
In the following 158,022 interest rate predictions in 1,182 forecast time series
from twelve different countries will be examined. Only a data analysis of this proportion can arrive at a sound evaluation of the frequency of topically oriented
trend adjustments. Furthermore, we examine whether topically oriented trend adjustment occurs irrespective of the forecast horizon, capital market segment or
countries under review.
The next chapter introduces the data base and the methodology used. The chapter
after next is reserved for the presentation of results. The last chapter but one identifies the possible causes of topically oriented trend adjustment. A summary can
be found in the last chapter.
II. DATA AND METHODS
The forecast data under consideration was taken from Consensus Forecasts, a
magazine that has appeared mid-monthly since October 1989. Forecasts of gov7
ernment bond yields with a ten-year term to maturity and forecasts of interest
rates for short-term loans with a three-month duration are both part of the investigation.
Consensus Forecasts distinguishes two forecast horizons: three months and
twelve months. In practical terms, however, forecast horizons are of four and thirteen months. The following example clarifies this discrepancy: the September
2001 issue of Consensus Forecasts published forecasts for the end of December
2001 and the end of September 2002. The participating institutions compiled them
at the beginning of September. From this point in time to the end of December,
however, is four months and from the beginning of September of the year in question to the end of September of the following year is in fact thirteen months.
Consensus Forecasts published forecasts for twelve countries, specified according
to individual forecasters. The twelve countries concerned are the topic of this research. When Consensus Forecasts was launched in October 1989, forecasts were
published for USA, Japan, Germany, France, UK, Italy and Canada. Spain, the
Netherlands and Sweden were added in January 1995, while Switzerland and Norway were included as of June 1998.
The analysis took forecast time series into account from institutions that had produced at least 50 forecasts for one or more of the forecast objects under review. In
the case of name changes or company mergers, the forecast time series was continued under the new name when the forecast time series concerned did not overlap.1
With its 158,022 individual forecasts, which are an integral part of 1,182 forecast
time series, this study boasts a wide data base that allows for a detailed estimate of
1
The Bank First Union, for example, took over the Bank CoreStates in May 1998. In September
2001, the First Union and Wachovia banks merged and have operated since then under the name
Wachovia. CoreStates released its forecasts to Consensus Forecasts up until May 1998, while First
Union and Wachovia were not yet represented in the magazine. First Union subsequently began to
release its forecasts to Consensus Forecasts, whereas CoreStates withdrew and Wachovia remained unrepresented. In September 2001, Wachovia began to report its forecasts to Consensus
Forecasts. First Union withdrew its participation at the same time. These three time series, with
their detectable inner relationship, are then dovetailed to one long time series. Where the forecast
time series concerned show no evidence of overlapping and the content permits, they are amalgamated in this study to long time series and quoted under their current name.
8
how fundamental topically oriented trend adjustment is to the behavior of bond
market analysts.
Table 1
Data base overview
Observation period
USA
Japan
Germany
France
UK
Italy
Spain
Canada
Netherlands
Switzerland
Sweden
Norway
Oct. 1989
Oct. 1989
Oct. 1989
Oct. 1989
Oct. 1989
Oct. 1989
Jan. 1995
Oct. 1989
Jan. 1995
June 1998
Jan. 1995
June 1998
–
–
–
–
–
–
–
–
–
–
–
–
Dec. 2009
Dec. 2009
Dec. 2009
Dec. 2009
Dec. 2009
Dec. 2009
Dec. 2009
Dec. 2009
Dec. 2009
Dec. 2009
Dec. 2009
Dec. 2009
Number of
forecasts
Number of
forecast time
series
21,641
15,508
24,251
15,620
23,373
10,058
8,551
14,707
6,069
5,671
8,414
4,159
164
124
148
104
180
76
68
100
44
52
76
46
158,022
1,182
When forecasts at their date of issue (Fig. 2) show a stronger correlation to actual
trends than they do at their date of validity (Fig. 1), we speak of topically oriented
trend adjustment (TOTA). Such an adjustment is present when forecasts describe
the progression of naive forecast time series rather than the actual future progression of the forecast object. The TOTA coefficient serves to detect possible topically oriented trend adjustments.
Prior to calculating the TOTA coefficient (see Andres and Spiwoks, 1999, Bofinger and Schmidt, 2003), the coefficient of determination of the forecast data and
actual events is worked out (R2A ; Figure 1). This is followed by calculation of the
coefficient of determination of the forecast data and actual events from the forecast date of issue (R2B; Figure 2).
2
TOTA coefficient
R forecasts (validity date); actual
2
R forecasts (issue date); actual
2
RA
2
(1)
RB
9
If the value of the TOTA coefficient is < 1, a topically oriented trend adjustment
must be assumed. In this case the forecast time series transferred back to the issue
date shows a higher correspondence with actual values than it did at the date of
validity. Hence for a TOTA coefficient < 1, the forecast time series reflects the
present more strongly than the future.
III. RESULTS
All of the 164 forecast time series examined for the USA indicate topically oriented trend adjustments (Table 2).
The picture in Japan is less unified (Table 3). Although here, too, all of the 62
forecast time series for trends in ten-year government bond yield are characterized
by topically oriented trend adjustment. With regard to forecast time series for the
three-month interest rate, however, only 53 of the 62 forecast time series observed
(85.5%) showed topically oriented trend adjustments. In the case of forecasts with
a four-month horizon, four of the 31 time series are not characterized by topically
oriented trend adjustment. This also applies to five of the 31 time series for forecasts with a 13-month horizon. None of the other national segments under review
produced a comparable number of forecast time series with no evidence of topically oriented trend adjustment. This is most likely due to the unusually consistent
trend in three-month interest rates in Japan. It began with a steady downward
trend over a long period (from September 1990 until December 1993). At no time
between July 1995 and September 2008 did the interest rate increase beyond 1%,
i.e., over a period of thirteen years. From April 2001 until April 2006 the interest
rate did not exceed 0.1%. Thus in the course of five years, it remained almost unchanged.
The TOTA coefficient is merely devised for time series with a satisfactory number of (local) maxima and (local) minima (cf. Andres and Spiwoks, 1999, pp. 533534). Steady, long-lasting time series progressions can lead to TOTA coefficient
results > 1 without the positive exclusion of the topically oriented trend adjustment phenomenon.
10
The nine forecast time series displaying a TOTA coefficient > 1 have an average
of only 93.0 values. The forecast time series in this study comprise an average of
133.7 values. We are thus dealing with fairly short time series. Combined with the
unusually steady progression of the interest rate trend this leads to a conservative
evaluation of the nine time series.
In Germany, 146 of the 148 forecast time series (98.7%) indicate topically oriented trend adjustments (Table 4). Only the Hypo Bank predicted the three-month
interest rate trend without reflecting the naive forecast time series more strongly
than the actual interest rate.
In France, all of the 104 forecast time series reflect naive forecast time series rather than the actual interest rate trend (Table 5). This implies topically oriented
trend adjustments in all 104 forecast time series.
In the United Kingdom, only one of the 180 forecast time series (0.6%) examined
is unaffected by the phenomenon of topically oriented trend adjustment (Table 6).
As a sole exception, the independent consultancy Economic Perspectives Ltd.
made predictions on the British government bond yield with a 13-month horizon
that showed no evidence of topically oriented trend adjustment.
In Italy, all of the 76 forecast time series were distinguished by topically oriented
trend adjustments (Table 7).
The constellation in Spain resembles that of the United Kingdom (Table 8). Here,
with one exception, all of the 68 forecast time series (98.5%) are subject to topically oriented trend adjustment. Only forecasting by the mutual savings bank Caja
de Madrid for the Spanish government bond yield over a 13-month term showed
no evidence of topically oriented trend adjustment.
In Canada, 98 of the 100 forecast time series are subject to topically oriented trend
adjustment (Table 9). Only the Bank Desjardin and the export credit agency EDC
Economics were in a position to produce short-term forecasts of three-month interest rates that reflected actual interest trends rather than naive forecast time series.
11
Table 2
Number of forecasts (N) and TOTA coefficients (TOTA) in forecast time series
for interest rate trends in the USA
3-month interest rate
4 M forecasts
13 M forecasts
10-year gov. bond yield
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
Bank of America Corp.
Barclays Capital
Bear Stearns
BP Amoco Corp.
Chase Manhattan
Chemical Bank
Chrysler
Continental Bank
Credit Suisse First Boston
Dun & Bradstreet
DuPont
Eaton Corporation
Fannie Mae
Ford Motors
General Motors
Georgia State University
Goldman Sachs
Griggs & Santow
IHS Global Insight
Inforum – Univ. Maryland
JP Morgan
Merrill Lynch
Macroeconomic Advisers
Metropolitan Life
Moody’s Economy.com
Morgan Stanley
Mortgage Bankers
Nat. Assn. of Manufact.
Nat. Assn. of Homeb.
Northern Trust
Oxford Economics
Prudential Financial
Smith Barney
Standard & Poor’s
Swiss Re
Conference Board
University of Michigan
US Trust
Wachovia
Wells Fargo
243
104
136
118
90
72
71
156
57
41
66
200
197
148
233
201
121
99
116
154
135
140
122
96
84
182
109
74
71
176
172
133
105
83
162
54
173
168
158
219
188
0.954
0.972
0.987
0.960
0.890
0.915
0.889
0.933
0.911
0.970
0.778
0.944
0.927
0.951
0.945
0.953
0.993
0.981
0.854
0.953
0.945
0.978
0.954
0.953
0.893
0.941
0.992
0.957
0.891
0.977
0.972
0.950
0.825
0.943
0.927
0.988
0.936
0.947
0.936
0.962
0.961
243
104
135
102
90
67
67
156
57
36
66
200
197
148
220
201
121
99
115
154
135
133
113
96
84
182
109
66
71
176
163
133
104
83
160
54
172
168
156
219
189
0.458
0.571
0.657
0.653
0.089
0.141
0.062
0.424
0.216
0.515
0.004
0.437
0.455
0.389
0.495
0.426
0.738
0.600
0.129
0.419
0.424
0.610
0.393
0.359
0.107
0.464
0.686
0.531
0.197
0.490
0.542
0.475
0.102
0.145
0.247
0.645
0.481
0.457
0.514
0.489
0.472
243
108
139
126
86
70
69
155
56
133
65
200
199
149
231
195
121
110
115
152
135
140
119
106
83
182
111
74
69
166
173
133
105
82
158
53
174
144
154
217
187
0.879
0.637
0.914
0.955
0.697
0.690
0.479
0.854
0.745
0.917
0.405
0.820
0.835
0.767
0.895
0.878
0.774
0.781
0.796
0.828
0.725
0.810
0.861
0.609
0.630
0.815
0.947
0.755
0.684
0.810
0.896
0.707
0.499
0.630
0.819
0.679
0.811
0.812
0.755
0.878
0.779
243
108
139
109
86
65
66
155
56
110
65
200
198
149
220
195
121
110
115
152
135
133
110
106
83
182
111
66
69
165
163
133
104
82
156
53
173
144
151
215
187
0.627
0.136
0.822
0.991
0.163
0.091
0.001
0.560
0.057
0.414
0.157
0.427
0.443
0.479
0.706
0.704
0.425
0.487
0.345
0.473
0.300
0.415
0.570
0.156
0.000
0.534
0.936
0.222
0.035
0.355
0.749
0.483
0.075
0.261
0.347
0.631
0.548
0.420
0.363
0.599
0.410
12
Table 3
for interest rate trends in Japan
4 M forecasts
13 M forecasts
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
Bank of Tokyo Mitsubishi
Barclays Capital
Baring Securities – Japan
Credit Suisse
Dai-Ichi Kangyo Bank
Daiwa Research Institute
Deutsche Securities
Dresdner Kleinwort Bens.
Goldman Sachs
HSBC
IHS Global Insight
ITOCHU Institute
Japan Ctr. for Econ. Res.
Jardine Fleming
JP Morgan - Japan
Merrill Lynch - Japan
Mitsubishi Research Inst.
Mitsubishi UFJ Research
Mizuho Research Institute
NCB Research Institute
Nikko Citigroup Sal. S. B.
Nikko Research Center
NLI Research Institute
Nomura Securities
Shinsei Bank
Sumitomo Life Res. Inst.
Tokai Bank
Toyota Motor Corporation
UBS
Yamaichi Research Inst.
243
109
56
65
99
139
126
161
46
101
93
73
76
160
54
162
181
123
145
180
112
153
99
136
184
137
144
85
155
189
86
0.983
0.977
0.836
0.933
0.779
0.983
0.982
0.972
0.672
1.037
1.044
1.044
0.960
0.981
0.984
0.950
0.989
0.978
0.988
0.985
0.977
0.990
0.976
1.020
0.984
0.975
0.984
0.966
0.834
0.983
0.972
243
102
56
65
91
116
93
140
42
101
93
73
76
154
54
156
176
122
145
158
108
151
99
129
140
133
137
85
155
189
86
0.863
0.819
0.378
0.625
1.129
0.873
0.869
0.798
0.539
1.295
1.347
1.347
1.080
0.858
0.843
0.785
0.866
0.857
0.886
0.857
0.807
0.891
0.843
0.973
0.879
0.847
0.851
0.809
0.351
0.854
0.799
243
163
56
61
112
124
187
161
51
104
103
73
82
167
53
174
206
147
128
183
111
216
97
143
187
148
145
85
154
186
85
0.953
0.957
0.722
0.796
0.829
0.929
0.949
0.956
0.706
0.390
0.394
0.194
0.339
0.957
0.951
0.896
0.951
0.965
0.963
0.951
0.912
0.957
0.922
0.716
0.958
0.937
0.949
0.903
0.556
0.956
0.875
243
152
56
60
83
109
122
140
47
104
103
73
82
160
52
165
187
142
127
161
106
195
97
136
143
143
139
84
154
184
84
0.852
0.842
0.570
0.507
0.123
0.828
0.782
0.878
0.404
0.009
0.011
0.772
0.171
0.874
0.905
0.722
0.811
0.893
0.909
0.867
0.795
0.862
0.777
0.106
0.880
0.828
0.839
0.780
0.045
0.820
0.641
13
Table 4
for interest rate trends in Germany
4 M forecasts
13 M forecasts
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
Bank Julius Baer
Bank of America
BHF Bank
BayernLB
Citigroup
Commerzbank
Deka Bank
Delbrück & Co.
Deutsche Bank
DIW – Berlin
Dresdner Bank
DZ Bank
F.A.Z.-Institute
Helaba Frankfurt
HSBC Trinkaus
HWWI
Hypo Bank
Ifo – Munich Institute
IfW – Kiel Institute
IHS Global Insight
Invesco
IW - Cologne Institute
JP Morgan Chase
Landesbank Berlin
Lehman Brothers
MM Warburg
Morgan Stanley
RWI Essen
Sal Oppenheim
SEB
SMH Bank
UBS
UBS Warburg
UniCredit MIB
WestLB
WGZ Bank
243
175
95
103
237
89
241
235
150
238
51
224
241
160
239
233
82
106
81
218
57
161
93
102
239
71
189
128
151
225
237
102
170
50
219
241
224
0.975
0.942
0.805
0.901
0.978
0.986
0.974
0.964
0.983
0.981
0.698
0.964
0.971
0.966
0.978
0.971
0.818
1.002
0.994
0.967
0.852
0.984
0.876
0.937
0.974
0.883
0.889
0.882
0.876
0.977
0.978
0.990
0.860
0.879
0.966
0.979
0.979
243
174
96
103
237
88
241
235
150
238
51
221
241
160
239
233
77
105
77
211
56
161
59
92
239
70
189
127
150
223
237
102
169
50
217
241
224
0.811
0.191
0.018
0.359
0.846
0.926
0.809
0.725
0.842
0.840
0.052
0.802
0.796
0.703
0.810
0.779
0.018
1.010
0.895
0.788
0.164
0.828
0.346
0.409
0.802
0.143
0.208
0.062
0.105
0.768
0.842
0.964
0.357
0.076
0.784
0.867
0.864
243
175
95
103
237
89
240
235
151
237
49
225
241
160
239
233
82
107
72
220
57
172
92
101
239
72
189
128
151
225
237
103
168
50
216
240
225
0.928
0.872
0.641
0.775
0.945
0.953
0.929
0.927
0.893
0.927
0.394
0.927
0.927
0.896
0.933
0.934
0.542
0.855
0.963
0.935
0.413
0.905
0.859
0.877
0.929
0.379
0.837
0.749
0.879
0.927
0.932
0.811
0.857
0.394
0.930
0.928
0.937
243
174
94
103
237
88
240
235
151
235
49
225
241
160
239
233
77
106
68
213
57
172
58
91
239
71
188
127
150
225
237
103
168
50
215
240
225
0.731
0.685
0.050
0.445
0.735
0.895
0.744
0.692
0.529
0.776
0.034
0.741
0.704
0.557
0.775
0.774
0.073
0.592
0.920
0.766
0.006
0.635
0.400
0.628
0.738
0.004
0.531
0.155
0.595
0.691
0.755
0.465
0.577
0.014
0.769
0.741
0.730
14
Table 5
for interest rate trends in France
4 M forecasts
13 M forecasts
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
Bank of America
Banque d'Orsay
Banque Indosuez
Banque Paribas
Banque Populaire
BIPE
BNP - Paribas
Centre Prev l'Expansion
Crédit Agricole
Crédit Commercial de Fr.
Crédit Lyonnais
Crédit National
COE - CCIP
Coe - Rexecode
Deutsche Bank
EXANE
GAMA
IXIS CIB
JP Morgan
Morgan Stanley
Natixis
OFCE
Societe Generale
Total
UBS
243
83
109
78
77
105
149
228
137
156
186
170
75
183
199
85
100
116
177
166
135
210
211
229
216
120
0.968
0.831
0.921
0.985
0.895
0.970
0.830
0.971
0.847
0.957
0.981
0.969
0.899
0.960
0.953
0.923
0.843
0.760
0.966
0.897
0.997
0.972
0.942
0.962
0.964
0.978
243
82
109
76
75
106
147
224
136
156
186
169
75
180
199
85
95
116
174
165
132
210
208
225
216
119
0.804
0.091
0.571
0.943
0.354
0.672
0.058
0.810
0.181
0.820
0.828
0.849
0.789
0.777
0.472
0.356
0.341
0.098
0.715
0.192
0.800
0.838
0.562
0.825
0.846
0.933
243
82
109
72
76
105
149
221
136
150
182
166
66
178
199
85
99
116
177
187
133
204
210
218
213
117
0.941
0.649
0.861
0.764
0.796
0.845
0.839
0.935
0.932
0.979
0.922
0.924
0.757
0.922
0.891
0.866
0.708
0.740
0.911
0.918
0.940
0.942
0.921
0.936
0.948
0.943
243
81
109
75
74
106
146
221
135
152
182
165
65
176
199
85
96
116
174
184
130
207
207
216
216
116
0.724
0.144
0.314
0.244
0.096
0.358
0.337
0.732
0.560
0.824
0.678
0.579
0.183
0.597
0.408
0.362
0.295
0.289
0.617
0.531
0.636
0.703
0.621
0.681
0.762
0.741
15
Table 6
for interest rate trends in the United Kingdom
4 M forecasts
13 M forecasts
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
ABN Amro
Barclays Bank
Barclays Capital
Capital Economics
Chase Manhattan Bank
Citibank
Citigroup
Credit Lyonnais Securities
CSFB
Deutsche Bank
Dresdner Kleinwort Bens.
DTZ Research
Economic Perspectives
Experian Business Strateg.
Goldman Sachs
Hambros Bank
HBOS
Henley Centre
HSBC
IHS Global Insight
Imperial Chemical Indust.
Industrial Bank of Japan
ING Financial Markets
ITEM Club
JP Morgan
Liverpool Macro Research
Lehman Brothers
Lloyds TSB Financial M.
Lombard Street Research
Merrill Lynch
Morgan Stanley
National Westminster
NatWest Markets Greenw.
NIESR
Nomura Research Inst.
Oxford Economics
RBS Financial Markets
Robert Fleming Securities
SBC Warburg
Schroders
Smith New Court
Société Générale
UBS
West/LB Panmure
Williams de Broe
243
111
138
233
102
108
55
147
77
73
61
71
53
86
129
172
100
154
104
222
98
118
80
222
129
151
182
199
172
156
157
147
127
118
112
62
228
134
84
77
227
72
92
197
113
197
0.963
0.989
0.959
0.968
0.800
0.972
0.718
0.968
0.980
0.838
0.908
0.964
0.878
0.842
0.864
0.935
0.964
0.832
0.964
0.969
0.829
0.962
0.723
0.966
0.960
0.934
0.810
0.969
0.936
0.874
0.972
0.931
0.961
0.962
0.976
0.981
0.959
0.858
0.965
0.970
0.968
0.968
0.919
0.971
0.959
0.966
243
111
138
233
102
108
55
147
77
70
61
71
53
86
129
172
100
154
103
220
98
118
80
222
132
142
183
196
171
156
154
145
125
118
112
62
227
134
84
77
227
72
92
196
114
197
0.728
0.740
0.760
0.722
0.207
0.704
0.275
0.719
0.699
0.147
0.630
0.679
0.253
0.000
0.133
0.675
0.777
0.526
0.737
0.689
0.296
0.689
0.002
0.753
0.771
0.465
0.141
0.730
0.407
0.278
0.810
0.596
0.765
0.684
0.739
0.684
0.687
0.136
0.723
0.788
0.715
0.773
0.213
0.830
0.725
0.809
243
92
137
207
102
106
54
148
75
57
58
71
53
86
127
174
99
151
17
211
96
117
78
221
126
137
6
197
172
152
153
147
116
114
106
61
218
133
84
75
219
71
91
195
114
197
0.949
0.958
0.922
0.947
0.441
0.924
0.523
0.946
0.839
0.564
0.930
0.792
0.540
0.582
0.541
0.935
0.838
0.884
–
0.938
0.469
0.898
0.804
0.957
0.951
0.925
–
0.968
0.938
0.869
0.972
0.908
0.895
0.910
0.948
0.752
0.967
0.540
0.766
0.855
0.941
0.832
0.892
0.950
0.932
0.956
243
91
136
205
102
104
54
147
74
35
58
69
53
86
127
172
98
151
17
209
97
116
77
221
128
127
7
195
172
151
148
145
119
112
106
60
216
133
83
74
218
70
90
194
113
196
0.844
0.887
0.702
0.822
0.134
0.763
0.146
0.813
0.458
0.283
0.525
0.470
0.062
2.905
0.039
0.833
0.740
0.706
–
0.822
0.002
0.659
0.503
0.841
0.833
0.676
–
0.855
0.718
0.441
0.909
0.754
0.732
0.825
0.919
0.268
0.851
0.302
0.438
0.603
0.801
0.433
0.657
0.900
0.732
0.886
16
Table 7
for interest rate trends in Italy
4 M forecasts
13 M forecasts
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
Banca Commerciale
Banca IMI
Banca Nazion. del Lavoro
Bank of America
Capitalia
Centro Europe Ricerche
Citigroup
ENI
Euromobiliare
Fiat
Goldman Sachs
Intesa Sanpaolo
JP Morgan
Morgan Stanley
Prometeia
Ricerche economia finanz.
UniCredit MIB
243
132
68
84
149
158
146
82
180
75
181
73
60
175
71
56
187
228
175
0.965
0.939
0.964
0.913
0.988
0.969
0.978
0.949
0.946
0.746
0.958
0.829
0.750
0.946
0.993
0.935
0.953
0.960
0.971
243
132
68
84
149
159
147
82
180
75
181
72
60
175
66
56
187
227
170
0.848
0.780
0.501
0.276
0.719
0.859
0.878
0.397
0.709
0.352
0.860
0.123
0.175
0.784
0.913
0.175
0.767
0.862
0.869
243
132
67
83
148
159
139
89
181
75
178
69
61
178
66
62
187
226
172
0.968
0.946
0.813
0.743
0.968
0.969
0.985
0.640
0.938
0.778
0.963
0.871
0.225
0.941
0.929
0.821
0.959
0.967
0.962
243
132
67
83
148
159
143
89
181
75
179
68
61
178
63
56
187
226
169
0.829
0.736
0.263
0.379
0.756
0.816
0.967
0.037
0.673
0.234
0.837
0.243
0.159
0.690
0.768
0.385
0.748
0.814
0.801
Table 8
for interest rate trends in Spain
4 M forecasts
13 M forecasts
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
AFI
Banesto
BBVA
Caja de Madrid
Ceprede
FG Merrill Lynch
Funcas
Goldman Sachs
Grupo Santander
IFL-Univers Carlos III
Inst Estud Economicos
Institut LR Klein
Instit. de Credito Oficial
JP Morgan Madrid
La Caixa
UBS
180
166
155
156
66
171
61
167
144
165
94
152
80
123
54
115
103
0.960
0.959
0.975
0.977
0.820
0.942
0.972
0.983
0.966
0.957
0.795
0.949
0.775
0.785
0.999
0.880
0.945
180
166
155
149
66
171
61
167
143
165
94
146
80
123
51
115
102
0.649
0.653
0.652
0.719
0.229
0.617
0.705
0.729
0.517
0.680
0.235
0.592
0.023
0.112
0.879
0.230
0.746
180
166
156
156
66
170
61
167
139
164
94
154
81
120
51
115
102
0.959
0.962
0.958
0.970
0.036
0.960
0.939
0.964
0.946
0.963
0.784
0.957
0.413
0.685
0.975
0.672
0.930
180
166
156
148
66
170
61
167
138
164
94
147
81
120
48
115
102
0.789
0.828
0.765
0.713
2.335
0.788
0.654
0.808
0.696
0.786
0.494
0.794
0.131
0.235
0.824
0.167
0.846
17
Table 9
for interest rate trends in Canada
4 M forecasts
13 M forecasts
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
Bank of Montreal
Bank of Nova Scotia
BMO Capital Markets
Bunting Warburg
Caisse de Depot
CIBC
CIBC World Markets
Confer. Board of Canada
Desjardins
Economap
EDC Economics
IHS Global Insight
Informetrica
JP Morgan
Merrill Lynch
National Bank Financial
National Bank of Canada
RBC Dominion Security
Richardson Greenshields
Royal Bank of Canada
Scotia Economics
Sun Life
Toronto Dominion Bank
University of Toronto
243
194
101
239
73
229
138
231
224
64
121
61
133
199
103
79
165
121
89
70
221
209
100
141
164
0.927
0.917
0.884
0.948
0.818
0.933
0.891
0.927
0.912
1.039
0.937
1.029
0.952
0.846
0.847
0.986
0.841
0.900
0.915
0.828
0.924
0.940
0.899
0.961
0.902
243
194
101
239
73
229
137
228
224
63
121
61
133
199
99
73
163
121
89
70
219
209
100
141
164
0.548
0.543
0.386
0.560
0.265
0.505
0.444
0.567
0.536
0.191
0.255
0.721
0.675
0.185
0.243
0.858
0.230
0.560
0.464
0.265
0.527
0.633
0.447
0.743
0.190
243
191
101
237
73
229
142
226
206
49
121
61
132
197
103
78
164
119
88
71
222
207
100
138
164
0.935
0.915
0.794
0.940
0.677
0.926
0.872
0.934
0.962
0.527
0.853
0.570
0.950
0.888
0.882
0.953
0.912
0.937
0.905
0.599
0.926
0.940
0.862
0.945
0.891
243
191
101
237
73
228
142
222
206
48
121
61
132
197
98
72
162
119
88
70
220
207
100
138
164
0.800
0.732
0.437
0.814
0.052
0.747
0.741
0.880
0.816
0.002
0.611
0.587
0.854
0.583
0.545
0.990
0.705
0.789
0.637
0.102
0.797
0.842
0.543
0.913
0.647
Table 10
for interest rate trends in the Netherlands
4 M forecasts
13 M forecasts
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
ABN AMRO
Citigroup
Deutsche Bank
Dexia Securities
Fortis Bank
ING
Kempen & Co.
NIBC
Rabobank Nederland
Theodoor Gilissen
180
173
52
64
89
165
174
149
170
174
121
0.821
0.855
0.916
1.007
0.816
0.778
0.815
0.859
0.816
0.863
0.815
180
173
51
64
89
165
174
149
169
174
121
0.089
0.141
0.460
0.468
0.011
0.018
0.089
0.118
0.154
0.141
0.093
180
173
68
64
89
165
172
149
171
173
121
0.848
0.844
0.667
0.177
0.787
0.851
0.826
0.887
0.846
0.845
0.722
180
173
67
64
89
165
172
149
171
173
121
0.494
0.444
0.066
18.078
0.347
0.535
0.390
0.644
0.542
0.478
0.031
18
Table 11
for interest rate trends in Switzerland
4 M forecasts
13 M forecasts
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
BAK Basel
Bank Julius Bär
Bank Vontobel
Credit Suisse
Goldman Sachs
Institut Crea
KOF Swiss Econ. Institute
Pictet & Cie.
St.Galler Zentr. Zukunftsf.
UBS
Zürcher Kantonalbank
139
135
113
123
126
56
57
64
132
133
60
135
128
0.866
0.831
0.924
0.848
0.872
0.836
0.950
0.926
0.786
0.896
0.772
0.900
0.891
139
135
113
123
123
55
57
64
132
133
60
135
128
0.149
0.053
0.235
0.356
0.872
0.061
0.565
0.223
0.050
0.290
0.053
0.213
0.132
139
135
113
123
125
54
56
106
132
132
60
136
128
0.620
0.540
0.699
0.629
0.603
0.359
0.229
0.679
0.581
0.702
0.532
0.630
0.658
139
135
113
123
121
53
56
106
132
132
60
136
128
0.074
0.027
0.304
0.182
0.047
0.029
1.626
0.068
0.065
0.015
0.001
0.039
0.038
Table 12
for interest rate trends in Sweden
4 M forecasts
13 M forecasts
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
Confed. of Swed. Enterpr.
Finanskonsult
HQ-Banken
JP Morgan
Matteus Bank
Merrill Lynch
Mizuho Financial Group
Morgan Stanley
National Institute – NIER
Nordea
Öhman
SBAB
SEB
Skandiabanken
Svenska Handelsbanken
Swedbank
UBS
180
158
57
94
60
119
56
133
83
124
59
134
145
75
129
52
160
150
132
0.933
0.892
0.964
0.812
0.988
0.953
0.790
0.806
0.921
0.872
0.842
0.922
0.824
0.755
0.922
0.903
0.941
0.927
0.911
180
158
57
94
60
107
56
132
83
124
59
134
145
75
129
52
160
150
133
0.722
0.325
0.683
0.060
0.065
0.600
0.069
0.235
0.477
0.141
0.010
0.489
0.107
0.046
0.542
0.114
0.470
0.448
0.484
180
158
57
94
60
119
56
132
83
124
70
136
145
75
131
50
162
150
137
0.933
0.937
0.960
0.626
0.417
0.925
0.759
0.813
0.924
0.898
0.557
0.933
0.840
0.498
0.942
0.682
0.942
0.935
0.929
180
158
57
94
60
107
56
132
83
124
70
136
145
75
131
50
162
149
138
0.722
0.744
0.951
0.063
0.379
0.702
0.089
0.362
0.660
0.608
0.169
0.712
0.437
0.025
0.856
0.058
0.692
0.746
0.798
19
Table 13
for interest rate trends in Norway
4 M forecasts
13 M forecasts
4 M forecasts
13 M forecasts
Institute
N
TOTA
N
TOTA
N
TOTA
N
TOTA
Consensus (Mean)
Danske Bank
Deutsche Bank
DnB Nor
First Securities
Handelsbanken Oslo
JP Morgan
Nordea Markets
Norw. Financ. Serv. Assn.
SEB Oslo
Union Bank of Norway
Statistics Norway
139
78
59
125
129
74
55
113
79
50
64
130
0.885
0.896
0.863
0.878
0.893
0.905
0.940
0.869
0.906
0.850
0.808
0.841
139
78
59
125
129
74
54
113
79
50
64
130
0.354
0.298
0.309
0.313
0.462
0.383
0.888
0.303
0.447
0.428
0.413
0.222
139
75
59
126
130
95
54
115
78
50
64
0
0.825
0.779
0.576
0.806
0.830
0.792
0.896
0.769
0.811
0.584
0.612
–
139
75
59
126
130
95
54
115
78
50
64
0
0.351
0.356
0.734
0.335
0.390
0.235
0.264
0.374
0.237
0.062
0.002
–
In the Netherlands, 42 of the 44 forecast time series (95.5%) show evidence of
topically oriented trend adjustment (Table 10). The Deutsche Bank supplied shortrange forecasts for three-month interest rates and long-term forecasts for a tenyear Dutch government bond yield that surpassed the threshold value of the TOTA coefficient, slightly in one case and quite clearly in another.
The constellation in Switzerland is similar to that in the United Kingdom and
Spain. Fifty-one of the 52 forecast time series (98.1%) clearly indicate topically
oriented trend adjustment (Table 11). Only ING Financial Markets supplied a forecast for the Swiss government bond yield with a 13-month horizon that gave no
indication of topically oriented trend adjustment.
In Sweden all 76 forecast time series were, without exception, characterized by
topically oriented trend adjustment (Table 12).
A similarly uniform picture emerges in Norway (Table 13). All 46 forecast time
series are characterized by topically oriented trend adjustment.
20
Table 14
Share of forecast time series with topically oriented trend adjustment (numerator)
from the total forecast time series (denominator)
USA
Japan
Germany
France
UK
Italy
Spain
Canada
Netherlands
Switzerland
Sweden
Norway
Total
4M
forecasts
13 M
forecasts
4M
forecasts
13 M
forecasts
Total
41/41
27/31
36/37
26/26
46/46
19/19
17/17
23/25
10/11
13/13
19/19
12/12
41/41
26/31
36/37
26/26
46/46
19/19
17/17
25/25
11/11
13/13
19/19
12/12
41/41
31/31
37/37
26/26
44/44
19/19
17/17
25/25
11/11
13/13
19/19
11/11
41/41
31/31
37/37
26/26
43/44
19/19
16/17
25/25
10/11
12/13
19/19
11/11
164/164
115/124
146/148
104/104
179/180
76/76
67/68
98/100
42/44
51/52
76/76
46/46
289/297
291/297
294/294
290/294
1164/1182
Table 15
Share of forecast time series with topically oriented trend adjustment from the
total forecast time series in percentage
USA
Japan
Germany
France
UK
Italy
Spain
Canada
Netherlands
Switzerland
Sweden
Norway
Total
4M
forecasts
13 M
forecasts
4M
forecasts
13 M
forecasts
Total
100.0 %
87.1 %
97.3 %
100.0 %
100.0 %
100.0 %
100.0 %
92.0 %
90.9 %
100.0 %
100.0 %
100.0 %
100.0 %
83.9 %
97.3 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
100.0 %
97.7 %
100.0 %
94.1 %
100.0 %
90.9 %
92.3 %
100.0 %
100.0 %
100.0 %
92.7 %
98.7 %
100.0 %
99.4 %
100.0 %
98.5 %
98.0 %
95.5 %
98.1 %
100.0 %
100.0 %
97.3 %
98.0 %
100.0 %
98.6 %
98.5 %
21
A total of 1,164 out of 1,182 forecast time series (98.5%) show evidence of topically oriented trend adjustment (Table 14). Only 18 forecast time series indicate
TOTA coefficients of > 1, half of which apply to forecasts for Japanese threemonth interest rates. These are distinguished by a highly unusual progression involving an almost unchanged interest rate over a period of several years.
The forecast time series show an average of 133.7 values. The 18 forecast time
series with no evidence of topically oriented trend adjustment display an average
of merely 83.8 values. The shorter the forecast time series, the less likely a sufficient number of (local) maxima and (local) minima will appear within the time
frame in order to achieve reliable results with the TOTA coefficient.
There are no major differences between the various countries under review (Tables 14 and 15). The number of forecast time series with topically oriented trend
adjustment varies between 92.7% (Japan) and 100% (USA, France, Italy, Sweden,
Norway). Topically oriented trend adjustments clearly occur independent of the
forecast horizon. 98.7% of forecast time series with a four-month horizon were
characterized by topically oriented trend adjustment. In the case of forecast time
series with a 13-month horizon, the total was 98.3%. Nor is there an essential difference between the two capital market segments. 97.6% of forecast time series
for three-month interest rates are distinguished by topically oriented trend adjustments. Forecast time series for the government bond yield with a ten-year term
stand at 99.3%.
Finally, it can be said that not all but most interest rate forecast time series are
characterized by topically oriented trend adjustment.
IV. POSSIBLE CAUSES OF TOPICALLY ORIENTED TREND
ADJUSTMENT
Proponents of the efficient market hypothesis might well reach the conclusion that
analysts have no choice but to rely on naive forecasts since financial market
trends are impossible to predict.
Two significant aspects are overlooked here. Firstly, the above interpretation only
explains the release of naive forecasts. In reality, however, although they clearly
resemble naive forecast time series, the forecast time series under review bear no
22
exact congruence to them whatsoever. Secondly, research efforts over the last
twenty years in the area of behavioral finance have furthered massive doubts
about the assumed reliability of rational expectation formation. This is tantamount
to cancelling out the basic element of the efficient market hypothesis.
The discussion on the background and causes of topically oriented trend adjustment in financial forecasts is indeed both difficult and stimulating. Initial estimates came from Bofinger and Schmidt (2003) and from Spiwoks (2004b).
Bofinger and Schmidet (2003) see topically oriented trend adjustment as a result
of anchoring heuristics. Financial analysts are incapable of distancing themselves
mentally from actual events. This produces an anchor effect. Hence analysts generate forecasts that are unconsciously influenced by actual financial market
events.
Spiwoks (2004b) interprets topically oriented trend adjustment as the consequence
of rational herd behavior by financial market analysts. For the most part he follows the reputational herding model dating back to Keynes (1936). He furthermore assumes that analysts require a behavior coordination mechanism with only
minimal transaction costs. When professional forecasters adapt to an easily perceived external signal, behavior coordination proceeds smoothly. Spiwoks describes this variation of the reputational herding model as Externally Triggered
Herding. The financial market situation at any given moment acts as the external
signal. If financial analysts were to constantly generate forecasts marked by a
slight variation of actual events, it would allow them to circulate endlessly in the
protective environment of the herd.
Bizer, Schulz-Hardt, Spiwoks and Gubaydullina (2009) have compiled the possible causes of topically oriented trend adjustment in financial predictions more
systematically. They differentiate between explanations at the individual and the
group level.
Apart from the topic of anchoring heuristics discussed by Bofinger and Schmidt
(2003), Bizer et al. (2009) consider two further possible explanations at the individual level. Numerous economic subjects display an inclination to underestimate
the variability of reality. As a rule, imagining major changes is a more difficult
task than mentally perpetuating the status quo. This psychological phenomenon
can lead to a situation where forecasters are merely in a position to predict minor
23
deviations from actual events. The logical outcome of this behavior is topically
oriented trend adjustment.
A third approach to be considered at the individual level is defensive forecast behavior. Aware of their lack of ability to predict, financial forecasters are keen to
avoid dramatic mistakes. An example of the latter is when a predicted sharp
downward (upward) trend is undercut in reality by a sharp upward (downward)
trend. Forecasts primarily geared to actual events are less likely to contain such
blunders.
At group level, Bizer et al. (2009) identify two possible constellations: normative
and information-based herd behavior.
Normative herd behavior supplies analysts with incentives to behave (more or
less) similarly. As in the case of Externally Triggered Herding (Spiwoks, 2004b),
this can lead to topically oriented trend adjustment.
The principle behind information-based herd behavior is to exchange information
relevant to decision processes. Herd behavior stems from the desire of individual
analysts to maximize their prospects of success. Hence they not only take their
own information into account but also the estimates of others. Estimates of future
trends can, however, vary significantly. When some analysts see signs of a downward trend and other forecasters proclaim a trend in the opposite direction, the
arguments neutralize each other. The final outcome is then a collective estimate of
future market trends that lies fairly close to actual market events.
The debate on the possible causes of topically oriented trend adjustment is still at
an early stage. Identifying whether one or more of the above patterns plausibly
explain the emergence of this forecast behavior calls for closer scrutiny. Now that
prolific findings on the frequency of topically oriented trend adjustment in financial market forecasts are available, more intense research into the causes of this
phenomenon can be expected.
V. CONCLUSION
This study deals with the phenomenon of topically oriented trend adjustment in
the time series of financial market forecasts. A total of 1,182 time series with altogether 158,022 interest rate predictions are examined. Forecasts refer to three24
month interest rates and ten-year government bond yields in the USA, Japan,
Germany, France, the United Kingdom, Italy, Spain, Canada, the Netherlands,
Switzerland, Sweden and Norway. The forecasts are generated for horizons of
four and thirteen months.
Topically oriented trend adjustments arise in 1,164 of the 1,182 forecast time series. Thus 98.5% of these forecast time series reflect the present rather than the
future. In other words, they correlate more strongly with the time series of naive
forecasts than with actual events.
Independent of the forecast object, forecast horizon and countries concerned, the
overwhelming majority of the forecast time series is distinguished by topically
oriented trend adjustment.
97.6% of all forecast time series for three-month interest rates and 99.3% of those
for a ten-year government bond yield show evidence of this feature. 98.7% of the
time series with a four-month forecast horizon and 98.3% of the time series with a
thirteen-month forecast horizon indicate the presence of topically oriented trend
adjustment. At 92.7%, the forecast time series for Japanese interest trends are least affected by this phenomenon.
Five explanation patterns for topically oriented trend adjustment are meanwhile
under discussion: 1. Anchoring heuristics, 2. The tendency to underestimate the
variability of reality, 3. Avoidance of major blunders through protective estimates,
4. Normative herd behavior such as Externally Triggered Herding, and 5. Information-based herd behavior.
If improving the accuracy of current forecast techniques is to be worked on systematically, future research efforts must be turned towards identifying the causes
of topically oriented trend adjustment.
25
REFERENCES
Allen, H. and Taylor, M. P. (1990), Charts, Noise and Fundamentals in the London Foreign Exchange Market, in: The Economic Journal, Vol. 100, pp. 4959.
Andres, P. and Spiwoks, M. (1999), Forecast Quality Matrix, A methodological
Survey of Judging Forecast Quality of Capital Market Forecasts, in: Journal
of Economics and Statistics, Vol. 219, 1999, pp. 513-542.
Bizer, K., Schulz-Hardt, S., Spiwoks M. and Gubaydullina, Z. (2009), Einflussfaktoren auf das Phänomen der gegenwartsorientierten Verlaufsanpassung
(GOVA) bei Finanzmarktprognosen, Working Paper, Göttingen 2009.
Bofinger, P. and Schmidt, R. (2003), On the reliability of professional exchange
rate forecasts: An empirical analysis for the €/US-$ rate, in: Financial Markets and Portfolio Management, Vol. 17, 2003, pp. 437-449.
Hafer, R. W. and Hein, S. E. (1989), Comparing Futures and Survey Forecasts of
Near-Term Treasury-Bill Rates, in: Federal Reserve Bank of St. Louis Review, Vol. May/June, pp. 33-42.
Hafer R. W., Hein, S. E. and MacDonald, S. S. (1992), Market and Survey Forecasts of the Three-Month Treasury-Bill Rate, in: Journal of Business, Vol.
65, pp. 123-138.
Keynes, J. M. (1936), The General Theory of Employment, Interest, and Money,
London 1936.
Manzur, M. (1988), How Much Are Exchange Rate Forecasts Worth?, in: Australian Journal of Management, Vol. 13, pp. 93-113.
Spiwoks, M. (2003), Qualität der Zinsprognosen deutscher Banken, Eine empirische Analyse, in: Kredit und Kapital, Vol. 36, 2003, pp. 289-308.
Spiwoks, M. (2004a), The Usefulness of ZEW Stock Market Forecasts for Active
Portfolio Management Strategies, in: Journal of Economics and Statistics,
Vol. 224, 2004, pp. 557-578.
Spiwoks, M. (2004b), External Triggered Herding bei Rentenmarkt-Analysten, in:
Financial Markets and Portfolio Management, Vol. 18, 2004, pp. 58-83.
26
Spiwoks, M., Bedke, N. and Hein, O. (2008), Forecasting the past: the case of US
interest rate forecasts, in: Financial Markets and Portfolio Management, Vol.
22, 2008, pp. 357-379.
Spiwoks, M., Bedke, N. and Hein, O. (2009), The Pessimism of Swiss Bond Market Analysts and the Limits of the Sign Accuracy Test – An Empirical Investigation of Their Forecasting Success Between 1998 and 2007, in: International Bulletin of Business Administration, Vol. 4, 2009, pp. 6-19.
Spiwoks, M., Bedke, N. and Hein, O. (2010), Topically Orientated Trend Adjustment and Autocorrelation of the Residuals - An Empirical Investigation of
the Forecasting Behavior of Bond Market Analysts in Germany, in: Journal
of Money, Investment and Banking, Vol. 14, 2010, pp. 16-35.
Spiwoks, M. and Hein, O. (2007), Die Währungs-, Anleihen- und Aktienmarktprognosen des Zentrums für Europäische Wirtschaftsforschung – Eine
empirische Untersuchung des Prognoseerfolges von 1995 bis 2004, in: AStA
– Wirtschafts- und Sozialstatistisches Archiv, Vol. 1, 2007, pp. 43-52.
Takagi, S. (1991), Exchange Rate Expectations, A Survey of Survey Studies, in:
IMF Staff Papers, Vol. 38, pp. 156-183.
27

WWP Wolfsburg Working Papers No. 11-01

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