Transaction costs, liquidity and
expected returns at the Berlin
Stock Exchange, 1892-1913
Carsten Burhop, Universität zu Köln
Sergey Gelman, ICEF, Higher School of
Economics, Moscow
1st ILFE Workshop,
Moscow, September 18, 2010
Motivation
Explore effective transaction cost
determinants & effects in a ‘friendly
environment’: on an early call auction
stock market over a long time span
2
Outline
1.
2.
3.
4.
5.
Literature review
Historical background
Data & Methodology
Results
Conclusion
3
1. Literature Review I: liquidity &
asset pricing
• Amihud (2002, JFM)
– Positive risk premium for expected illiquidity
– Inverse relation of returns and unexpected illiquidity shocks
• Eleswarapu/Reinganum (1993), Brennan and
Subrahmanyam (1996)
– Negative/insignificant risk premia
• Bekaert et al. (2007, RFS)
– Dynamic interdependence of liquidity and returns on the market
level (whereby liquidity only weakly dependent);
– Transaction cost adjustment + liquidity risk premium
• Goyenko et al. (2009, JFE)
– Effective transaction cost measures capture liquidity (incl. price
impact)
4
1. Literature Review I: economic
history
• Rajan & Zingales (2003): German pre-1913
stock market development higher than US
• Baltzer (2006): price differentials across stock
exchanges negligible
• Gelman & Burhop (2008)
– weak information efficiency on a rather high level
– Efficiency worsens during crises 1901, 1913
• Gehrig & Fohlin (2006)
– estimate effective transaction costs for Berlin stock
exchange in 1880, 1890, 1900, 1910. Find gradual
decline.
– find inverse relationship to size
5
1. Contribution
• Transaction costs were on average low,
but rather variable in time and crosssection
• Transaction costs are inversely influenced
by size and previous year returns; are
higher in crises
• There is a significant positive liquidity
premium, which is more pronounced than
market risk and size premia
6
2. Historical background I
• Berlin Stock Exchange (BSE) was the
major German stock exchange since
1870-s
• Steadily increasing # of traded companies,
around 1000 in 1913
• Trading 6 days per week, one price per
day
• Call-auction mechanism with a specialist
• Presence of informed insiders possible
7
Berlin Stock market performance
Balkan war
Bank run in US
Leipziger
Bank defaults
500
450
400
350
300
250
200
150
100
50
Daily Index
Eube's market index
Ronge's DAX-30
18.04.1913
30.11.1910
23.07.1908
08.03.1906
24.10.1903
15.06.1901
31.01.1899
14.09.1896
05.05.1894
31.12.1891
0
8
German aggregate stock trading
volume (in bln mark)
100
80
60
40
1912
1910
1908
1906
1904
1902
1900
1898
1896
1894
1892
20
9
2. Historical background II
• Major crises with impact on efficiency:
– Bankruptcy of Leipziger Bank 1901
– Balkan war fear 1913
• Fixed relative transaction costs:
– Transaction tax: 0.01% up to 04/1894; 0.02% to
10/1900 and 0.03% until the end of the sample
– Broker fee: official 0.05%; private 0.025%
– Provisions for intermediaries: 0.1-0.33%
– Total round-trip transaction cost: 0.252-0.82%
– Tick size 0.05 Mark (by stock prices of 40 Mark and
above)  less than 0.125%
10
3. Data
• Daily stock prices for 27 stocks (hand-collected
from Berliner Börsenzeitung) 1892-1913, 6692
observations per company
– Industries: banking, machinery, chemicals, mining,
textile, etc.
– Requirement: listed during the whole period, <30%
zero returns
• Trading volume is available only on annual basis
aggregated for all German exchanges!
• Daily stock index values (from Gelman/Burhop 2008)
• Annual values for market capitalization
– Heterogeneous: from 0.3 bln RM to 32.8 bln RM
• Dividend amounts and dates
11
Descriptive statistics (selection)
zeros
1
2
5
9
10
22
23
26
Name
AG für Anilinfabrikation
Allgemeine
Elektricitätsgesellschaft
Deutsche Bank
Deutsche Spiegelglas
Erdmannsdorfer
Spinnerei
Schering
Schlesische Zinkhütten
Siemens Glas-Industrie
Value-weighted index
Mean
(ann.)
0.0678
(1)
Average
MCap.
(mill M)
0.0008
2727
Max.
Min.
0.1257 -0.2270
Std.
Dev.
0.0082
Skewness
-3.85
Kurtosis
126.25
0.1638
0.0336
0.0294
0.0687
0.0526 -0.0611
0.0333 -0.0544
0.0921 -0.0838
0.0065
0.0042
0.0080
-0.18
-1.64
-0.30
11.94
24.72
18.32
0.0807
0.1001
0.1877
0.0820
-0.0119
0.0716
14997
32778
643
0.0001
0.0162
0.0336
0.0287
0.0687
0.1143
0.0652
0.1079
0.0438
0.0296
0.0106
0.0083
0.0066
0.0058
0.0032
0.62
0.17
-0.52
-1.12
-1.68
13.70
10.17
33.99
19.17
30.78
0.2497
0.1630
0.2360
0.1966
n/a
-0.0425
0.0610
-0.0582
-0.0273
0.165
286
1298
7947
2290
161344
-0.0774
-0.0657
-0.0853
-0.0576
-0.0562
12
3. Methodology I
• Measure of full transaction costs (fixed
costs + price impact):
– LOT (1999): information-based measure
ri,t  ri,t*  il if ri,t*  il
ri,t  0 if il < ri,t*  ih
ri,t  ri,t*  ih if ri,t*  ih
ri,t*  i rm,t  ei,t
13
3. Methodology I
• Estimate with MLE
L  ,  , i ,  i rit , rmt 
l
i
h
i
 rit   il  i rmt
 
i
1 i

1
   ih  i rmt 

  il  i rmt
  
    
i
i
 0  


 rit   ih  i rmt 
  


2 i

i

S.T .  il  0,  ih  0, i  0,  i  0,
1
14



3. Methodology I
• Criticism of LOT measure
– Zero returns may be due to noise trading
– The measure is driven by the market return
volatility
– Does not incorporate other factors than market
• Justification
– Is the only available measure of the full
transaction costs and not only spreads
– Widely used in recent financial literature, e.g.
Griffin et al. (2010, RFS); Lesmond (2005, JFE)
15
3. Methodology II: Determinants
• Cross-section and Panel estimation
• Dependent variable: annual effective TC
(LOT measure) of a company
• Regressors:
– Market cap (for size)
– Previous year returns
– Aggregate trading volume or Time dummies
16
3. Methodology III: impact on asset
pricing
• Fama-MacBeth(1973) regression
– monthly returns
– factor loadings & firm characteristics
• Factor: market risk (our index as proxy)
• Characteristics:
– Size
– Daily return autocorrelation (momentum)
– LOT transaction cost measure (for
illiquidity)
17
4. Results: annual transaction costs
TABLE 2: ANNUAL AVERAGE OF TRANSACTION COSTS
Year
LOT
1.454
1892
1.584
1893
1.072
1894
0.925
1895
0.805
1896
0.814
1897
0.908
1898
0.878
1899
1.029
1900
1.678
1901
0.977
1902
0.848
1903
0.825
1904
0.696
1905
0.658
1906
0.775
1907
0.846
1908
0.731
1909
1.039
1910
0.713
1911
0.883
1912
1.124
1913
Average
0.966
18
4. Results: annual transaction costs
LOT
2
1.5
1
0.5
0
1892 1895 1898 1901 1904 1907 1910 191319
29
.1
15
12
13
10
05
06
03
02
23
.0
.0
.0
.0
.0
.0
.0
.0
.1
.1
-0.5
21
1.
1
1.
1
1.
1
1.
1
1.
1
1.
1
1.
1
1.
1
2.
1
2.
1
2.
1
91
3
91
1
90
9
90
7
90
5
90
3
90
1
89
9
89
6
89
4
89
2
4. Transaction costs BSE 18921913: rolling window
2
1.5
roll
gkn
lot
1
0.5
0
20
4. Results: time series of
transaction costs
• Transaction costs are low: average LOTmeasure of 0.97%,
– lower than for the upper decile of NYSE
(1.23%) in 1963-1990 (Lesmond et al. 1999)
– better than any of the emerging stock markets
in 1990-s (Lesmond 2005)
– But a bit above than DJIA costs of 0.6% 19701980 (Goyenko et al. 2009)
• High variation: from 0.66% (1906) to
1.68% (1901)
21
Appendix 1: Average transaction costs of corporations, included in the investigation
Number
Name
Average
LOT
measure
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
AG für Anilinfabrikation
Allgemeine Elektricitätsgesellschaft
Berlin-Anhaltinische Maschinenbau
Bochumer Bergwerk (Lit C)
Bank für Handel und Industrie
Deutsche Bank
Dresdner Bank
Deutsche Jute Spinnerei und Weberei
Deutsche Spiegelglas
Erdmannsdorfer Spinnerei
Gelsenkirchener Bergwerksgesellschaft
Gerresheimer Glashütten
Hallesche Maschinenfabriken
Harpener Bergbau AG
Kattowitzer AG für Bergbau und Eisen
Maschinenfabrik Kappel
Norddeutsche Wollkämmerei
Oberschlesische Portland-Cement AG
Rheinische Stahlwerke
Rositzer Zuckerfabrik
Schaaffhausen'scher Bankverein
Chemische Fabrik vormals Schering
Schlesische Zinkhütten
Schlesische Leinen-Industrie
Schultheiss Brauerei
Siemens Glas-Industrie
Stettiner Chamottewaren
0.943
0.520
0.902
3.164
0.543
0.384
0.446
1.109
1.097
1.689
0.427
1.284
1.112
0.425
0.667
1.239
1.135
1.094
0.781
1.053
0.572
1.001
0.959
1.183
0.684
0.776
0.905
22
4. Determinants of transaction
costs: Cross-sectional results
Average LOT measure

4

S iLOT  4.92 0.19 ln MC i1892  e€i ,
 0.62  0.03
R 2  0.64,
e€i  0, 0.202
2


0
5
7.5
10
ln(MCap)
23
4. Determinants of transaction costs: panel
Constant
(1) FE
(2) FE
(3) FE
(4) FE
(5) RE
1.00***
0.96***
0.94***
1.79***
1.85***
(0.06)
(0.05)
(0.07)
(0.19)
(0.20)
Sit-1
(6) GMM
0.44***
(0.01)
MCit/ MCit -3.12**
(1.57)
-2.59**
-2.59
-2.58
-4.29*** -0.28
(1.19)
(1.91)
(1.59)
(0.73)
lnPit-1
-0.25
-0.34**
-0.45***
-0.44*** -0.11***
(0.16)
(0.16)
(0.16)
(0.16)
-0.20***
-0.20*** -0.22***
(0.05)
(0.05)
(0.01)
N
lnTVt
t1901
(1.50)
(0.04)
0.26***
(0.08)
t1913
0.25***
(0.07)
Time
Y
Y
N
N
N
0.56
0.60
0.48
0.46
0.27
effects
R2
24
4. Determinants of transaction costs: panel
Constant
(1) FE
(2) FE
(3) FE
(4) FE
(5) RE
1.00***
0.96***
0.94***
1.79***
1.85***
(0.06)
(0.05)
(0.07)
(0.19)
(0.20)
Sit-1
(6) GMM
0.44***
(0.01)
MCit/ MCit -3.12**
(1.57)
-2.59**
-2.59
-2.58
-4.29*** -0.28
(1.19)
(1.91)
(1.59)
(0.73)
lnPit-1
-0.25
-0.34**
-0.45***
-0.44*** -0.11***
(0.16)
(0.16)
(0.16)
(0.16)
-0.20***
-0.20*** -0.22***
(0.05)
(0.05)
(0.01)
N
lnTVt
t1901
(1.50)
(0.04)
0.26***
(0.08)
t1913
0.25***
(0.07)
Time
Y
Y
N
N
N
0.56
0.60
0.48
0.46
0.27
effects
R2
25
4. Determinants of transaction
costs: results
• Inverse relation with size
– explains about 2/3 of transaction cost variation in
cross-section and 23% in a panel set-up
– One std increase in share of m. cap. (0.05) leads to
0.125-0.2 decrease in transaction costs
– significance vanishes in FE set-up if we include past
returns
• Inverse relationship with previous year returns
explains about 10%
– One std decrease in past returns (0.126) leads to
apprx 0.05 increase in LOT
26
4. Determinants of transaction
costs: results
• Transaction costs are about 0.25
percentage points higher in crises years
• Transaction costs are inversely related to
trade volume
– One std increase in log trading volume (0.25)
induces 0.05 decrease in transaction costs
27
4. Effects of transaction costs on
asset pricing
Constant
Market beta 
Transaction
(1)
(2)
(3)
(4)
.0018
-.0024
.0024
.0034
(.0014)
(.0021)
(.0107)
(.0108)
-.0003
.0016
.0013
-.0001
(.0019)
(.0020)
(.0021)
(.0022)
.3266**
.3068*
.3055*
(.1324)
(.1773)
(.1771)
-.0002
-.0002
(.0004)
(.0004)
cost
lagged TC
Size S
Momentum M
.0105*
(.0058)
Average R2
0.07
0.12
0.16
0.20
# of cross-sections T
264
252
252
252
28
4. Asset pricing results
• We find support of Amihud (2002):
– Lagged transaction costs increase expected
return
– Contemporaneous TC – decrease returns
• CAPM doesn’t work
• Size effect is absorbed by ex-ante
transaction cost measure
• Momentum is positive with tendency to
significance
29
4. Asset pricing results
• Different specifications of liquidity risk do
not yield significant results
30
5. Conclusion
• Transaction costs of the Berlin Stock
Exchange were on average rather low as
early as 1892-1913
• Size and past returns were negatively and
crises were positively related to
transaction costs
• Illiquidity was the primary concern of
investors by asset pricing, levied a positive
premium
31
Thank you for your attention
32