Systematic mispricing in experimental markets –
evidence from political stock markets#
Jürgen Hubera, Florian Hausera, *
a
Department of Corporate Finance, University of Innsbruck, Austria
Abstract
Political stock markets (PSMs) are an interdisciplinary instrument of opinion research
intended to forecast election results. While the U.S.-markets regularly outperform opinion
polls, these results could not be reproduced in Europe. In our paper we show, that this is
mainly due to systematic overpricing of smaller contracts, which usually do not exist in the
U.S. The mispricing is caused by the market design, namely the fact, that the contracts traded
in PSMs are effectively futures, while the market mechanism is a stock market design. Results
from models deriving portfolio structure and concentration of investment are confirmed by
empirical data from PSMs.
JEL classification: C93; D44; D8; G1
Keywords: Political stock market, systematic mispricing, trader behaviour, futures market
#
The two political stock markets covered in this paper could not have been done without the financial support by
the two Austrian newspapers „Die Presse“ and „Tiroler Tageszeitung“ and the software companies bdf-net
and ecce-terram. We are grateful for their support and thank all employees for their great cooperation.
*
Corresponding author. Tel: +43-512-507-7555; fax: +43-512-507-2846
E-mail addresses: [email protected] (J. Huber), [email protected] (F. Hauser).
1
1. Introduction
Political stock markets (PSMs) are an interdisciplinary instrument of opinion research
intended to forecast election results. PSMs were created in the late 80’s in the U.S., after
traditional opinion research delivered imprecise forecasts for several times in a row. The
resulting dissatisfaction motivated a team of economists around Bob Forsythe at the
University of Iowa to search for new ways to forecast election results. Based on an idea of
Hayek (1945), saying that a market is superior to every planning mechanism in aggregating
scattered information, they decided to use an experimental market to collect traders’ opinions
about the outcome of an election.
The first PSM was carried out during the U.S. presidential elections in 1988 at the
University of Iowa (Iowa Electronic Markets). Forsythe et al. (1992) surprised the public by
delivering a forecast for the election result which was six times more precise than the best
forecast based on traditional opinion research. At the following U.S. presidential elections in
1992 a bigger market with more attention by the public was organized by the same team of
scientists. Again the forecast from the PSM and the election result differed by only 0.2
percent, while the best poll had a mean absolute error (MAE)1 of 1.2 percent (Jacobsen et al.,
2000).
For social scientists, forecasting election results is not the only purpose of PSMs. These
virtual markets can in addition be used as field experiments in experimental economics. One
of the central questions in economics and finance is, whether, or to what extent, markets are
efficient. This is basically a question of how good markets are in aggregating and
disseminating information. According to Hayek (1945), the essence of all available
information about the worth of an asset is aggregated by the market and reflected in market
prices. However, it is difficult to test this theory in real markets, as the ‘real’ intrinsic value of
most assets is never revealed. In PSMs however, the intrinsic value of all contracts is revealed
on Election Day. We can therefore test and estimate the ability of a PSM to aggregate
information. These results need not necessarily be applicable to other, especially financial,
markets, but some results might help to understand which driving forces underlie markets.
The idea of using markets as collectors of scattered information has already been
transferred to several prediction problems. As an industrial application, project times, which
are difficult to predict by using traditional approaches, can be forecasted very precisely by
1
The MAE is the most common measure of prediction accuracy for political stock markets. All the absolute
differences of last stock price before election and election result are added and the sum is then divided by
the number of contracts in the market.
2
using experimental markets (Ortner, 1997, 1998). As Spann (2002) shows, virtual markets
have already been used to predict the financial success of Hollywood movies, music releases
and the utilization of mobile services. At the moment, the Iowa Electronic Markets (IEM2) try
to predict the capitalization of the ‘Google’ initial public offering. A future vision, described
by Eymann et al. (2003) uses Hayek’s concept of economic self-organization for the
coordination of decentralized information systems consisting of autonomous software agents
with limited information processing capacity and incomplete information. The market
mechanism is used as an interface between the software-agents, allowing them to solve
complex logistic optimization problems with conflicting goals. These examples show that
there is serious future potential for prediction markets in several fields. Although findings of
this article concern prediction markets in general, we will use only the term ‘PSM’ in the
following, as all the data presented in the paper is from two PSMs.
In PSMs self selected traders take part in a virtual market. To maximize their invested
money or to win prices, traders can buy and sell ‘stocks’ of the political parties taking part in
the election. Today organizers of PSMs use the internet to implement markets where
participants can trade 24 hours a day without permanent interference by researchers. These
markets are usually designed as full electronic double-auction-markets, a design that has
proved its functionality in experimental economics as well as on many of today’s capital
markets (Kagel and Roth, 1995).
16 years have passed since the first PSM started and in this time many markets were
carried out in several countries. To the regret of scientists the success of Forsythe could not be
reproduced most of the time, although the market construction of PSMs stayed almost the
same as in the first markets. Especially in Europe markets were not significantly better than
traditional polls, and in the U.S. the market for the presidential election 1996 failed miserably
with a mean absolute error of 4.6 percent – twice as far off as traditional polls (IEM). In the
past, markets in the U.S. were almost always able to outperform polls, while this was not the
case in Europe. This is mainly due to the overpricing of smaller contracts3 that can regularly
be seen in PSMs in European countries (Schmidt, 2001).
2
IEM: http://www.biz.uiowa.edu/iem/
3
A contract can be defined as ‘small’ when the real vote share of the corresponding party is smaller than the
markets average contract (Schmidt, 2001).
3
In this paper we present data from two PSMs conducted in Austria in 2002 and 2003.
The two markets are NP02, a PSM with more that one thousand traders, covering the election
for the national parliament in 2002, and TE03 a market with about 630 traders covering the
election of the Tyrolean regional assembly in 2003. Each market ran for six weeks – NP02
from October 10th to November 3rd 2002, TE03 from August 18th to September 27th 2003.
Both markets were closed at midnight before Election Day. The cooperation partners were a
large national daily newspaper and an Austrian software company for NP02 and the marketdominating regional daily paper and a German software company for TE03. With MAEs of
2.4 (NP02) and 2.7 (TE03) both markets predicted the election outcome roughly as precise as
the respective polls.
The paper is structured as follows: next we discuss three different sources for systematic
mispricing in PSMs and illustrate them with empirical findings. In the following chapter we
will introduce a simple model that allows further insights in the market-mechanism of PSMs
and shows why this market mechanism fails to reflect traders’ opinions properly. At this point
we will present further empirical facts to support the validity of our model. Finally we explain
how changing the market mechanism of PSMs to a futures market would most likely
eliminate the mispricing.
2. Theoretical preconditions to systematic mispricing in PSMs
According to Hayek (1945) a market is superior in aggregating scattered information to
every planning mechanism, as the essence of all available information is collected in market
prices. Stock prices in capital markets are therefore nothing more and nothing less than an
aggregation of all available information about the value of a company. In PSMs the contract
prices represent the opinions of all participating traders about the election result and can
therefore be used as a forecast of this result. However, to deliver a good forecast for elections,
it is necessary that the market aggregates traders’ opinions properly and that these are
unbiased. In this chapter we will discuss three different phenomena potentially causing
systematic mispricing in PSMs.
2.1. Market design
Similar to stock markets, PSMs are divided into primary and secondary market. The
secondary market, usually designed as a continuous double-auction-market, allows
participants to trade contracts at prices determined solely by demand- and supply. As in stock
markets, short-selling is not allowed – a trader can only sell a share if she already owns it.
4
This is a major constraint on traders’ ability to speculate on future downward price
movements (see Diamond and Verrecchia, 1987; Jarrow, 1980, Figlewski and Webb, 1993).
As Haruvy and Noussair (2004) argue, this limitation might constrain especially the most
rational and active traders, who could otherwise arbitrage away any differences from
fundamental values. In stock markets this can lead to bubbles, in PSMs it causes overpricing
of smaller parties’ shares. This is due to a second feature of the market mechanism: the
primary market. As we know, in capital markets new stocks are only offered when a company
makes a capital increase or an IPO. Once the stocks are issued, they are held by investors and
are then traded in the secondary market. PSMs can not follow this concept because the
number of contracts needed to assure a liquid market rises continuously as more traders join
the market. To regulate the number of contracts needed in the market endogenously, PSMs
make use of the fact, that the sum of the intrinsic values of all contracts must be 100 at all
times, as they represent per definition 100 percent of the total vote. Traders can therefore buy
and sell base portfolios (one piece of every contract) for a price of 100 at all times. This
feature assures a liquid secondary market and allows traders to profit from arbitrage
opportunities, but it also leads to problems: with this design, all contracts are in the market in
exactly the same number. A simple equilibrium model, designed by Jacobsen et al (2000) to
analyse the ‘winners curse’ phenomenon, shows that the direct consequence of this issuing
rule is the overpricing of small contracts, a serious and often described problem of PSMs
(e.g., Schmidt, 2001; Huber, 2001).
Jacobsen et al. (2000) designed the model describing a PSM with two contracts. They
assume that cash constrained, risk neutral traders base trades solely on their private
information. The private information is different for every trader and can be described by the
uniform distribution [v-ε ; v+ε], with v being the true vote share for a party, and ε the
maximum error. If many traders take part in this market, the equilibrium price is given by:
v+ε
p=
(1)
1 + 2ε
As we can see, the equilibrium market price will only give a correct prediction of the
true vote share if v = 0.5. The smaller v gets, the bigger will be the overpricing p - v. The
overpricing effect described by this model is the direct result of the fact that the one half of
the traders, who estimate the price for the small party higher than the median expectation, can
5
afford to buy a higher number of these contracts than the other half, simply because the
contract of the smaller party is cheaper.
Empirical analyses of NP02 (Hauser, 2003) gives strong support to this assumption. A
user poll was conducted to by-pass the market mechanism and get direct access to traders’
opinions. More than 250 traders disclosed their believes about the election result. For every
political party, the median of all user expectations for the vote share of this party was
compared with the average stock price of the PSM in the same period.
Table 1
Expectations and Stock Prices in NP02
Expectation Median
Average Stock Price
Difference
OEVP
35.00
33.40
-1.60
SPOE
36.00
34.39
-1.61
FPOE
13.85
14.13
0.28
GREEN
13.00
13.26
0.26
ROF
3.00
4.70
1.70
Sum
100.85
99.88
By regressing the difference of prediction and stock price against the vote share (also
represented by the expectation median) we get the following results:
Difference Expectation / Stock Rate
2
y = -0.0958x + 1.7375
R2 = 0.982
1
0
0
5
10
15
20
25
30
35
40
-1
-2
Expectation Median
Fig. 1. Regression of difference between expectations and stock prices in NP02.
The regression shows a highly significant relation of a parties’ expected vote share and
the difference of the market prediction and the expected vote share. The analysis was repeated
three days before Election Day, delivering a similar regression line with a slightly less
significant coefficient of determination (y= -0.062x + 1.19; R2 = 0.71), confirming that the
mispricing effect does not disappear until the end of the market.
6
This phenomenon is not limited to Europe. Usually elections in the U.S. feature just two
major candidates, each reaching between 45 and 55 percent. Here mispricings are rather
small, as reflected by the usually good forecasts of markets in the U.S. However the same
mispricing just described was evident in a recent American PSM as well.
The California recall election of October 2003 was different than other elections. In the
election two questions were asked: first the voters had to decide, whether Governor Davis
should be recalled. Here they had to mark “yes” or “no.” In case that the majority of voters
wanted Davis to step down, the candidate with most votes in the second question (“Who
should be the next Governor if Davis is recalled?”) would be the new Governor. 135
candidates wanted to replace Davis, but only two of them – Arnold Schwarzenegger and Cruz
Bustamante had serious chances of winning. The Iowa Electronic Markets (IEM) started two
markets to predict the vote shares for both questions. For the first question there were only
two choices and the outcome was predicted quite accurately by the PSM. For the second
question three shares were available: AS (Schwarzenegger), CB (Bustamante), and ROF (rest
of field, including 133 candidates). The market was relatively thin and volatile. We therefore
took the average prices of the last two days to get reliable average prices for the PSMs.
Table 2
Results of the California Recall Election of October 7th 2003 and the IEM PSMs
Question I: Should Governor Davis be recalled?
PSMa
YES
53.9
NO
45.3
Resultb
55.3
44.7
Difference
-1.4
0.6
Question II: Who should replace Davis if he is recalled?
PSMa
Resultb
AS (Schwarzenegger)
45.9
48.7
CB (Bustamante)
32.2
31.7
ROF
21.7
19.6
Difference
-2.8
0.5
2.1
a
b
Average of election day and the day before, source: IEM, http://www.biz.uiowa.edu/iem/
Source: Los Angeles Almanc, http://www.losangelesalmanac.com/topics/Election/el07.htm
For Question I we find the larger contract (YES) to be underpriced, while the smaller
contract is slightly overpriced. This is more visible in Question II: the largest contract (AS) is
valued too low, while the smallest contract (ROF) is overvalued by more than two percent.
This mispricing is similar to what we found in many European markets, where smaller parties
are more common. This result suggests that the good performance of PSMs in the U.S. may at
least partly be due to the fact that they usually have only two parties of nearly equal size.
7
When smaller parties participate in the election, the mispricing seems to be systematic – at
least with the market design that is currently used.
2.2. Traders preference for small contracts due to risk-seeking behavior
Traders’ preferences together with the fact that prices of different contracts vary
depending on the expected vote share, give some more hints possibly explaining why smaller
contracts are often overpriced due to their higher attractiveness (Huber, 2001): first, the
market power of a single trader is higher if she trades contracts with low absolute prices; e.g.
with an amount of 1,000 she can buy only 20 shares of a 50-percent contract, but 500 shares
of a 2-percent contract. Traders who want to influence market prices would therefore prefer
contracts of small parties.4 Second, absolute changes in stock prices of smaller parties have a
higher impact on the traders’ portfolio. For example: if the price of party A rises from 2 to 3
while the price of party B rises by the same absolute amount from 20 to 21, capital gains of
traders who have invested their whole capital in contract A are higher than gains of traders
fully invested in contract B, because the relative price change is higher (50 percent vs. 5
percent). While traders in traditional capital markets tend to avoid risks, traders in PSMs
prefer contracts with higher volatility, as they have a strong motivation to outperform all other
traders and reach the top end of the ranking. The danger of loosing capital is not a big
problem for them, as usually the amount of money that can be invested is quite low (many
PSMs are even conducted without investment of real money). Finally a higher volatility leads
to a kind of premium for small contracts, as even the final election result may bring surprises.
Many traders in PSMs are risk-seekers, and when they form their final portfolios before
Election Day, they are likely to buy those contracts where surprises have greater impact on
their portfolio. For this reason they still prefer small contracts directly before Election Day.
To show that the volatility of stock prices really depends on the absolute price of the
contract, we determined the relative standard deviations (STD) of the hourly stock price
changes in both markets. To get relative numbers we divided the standard deviation by the
true vote share (the election result). The results presented in table 3 speak a clear language:
with increasing size of the party the relative standard deviation drops, making the smaller
parties more attractive for risk-seeking traders.
4
With user-polls Hauser (2003) and Huber (2001) showed, that influencing market prices is a motivation to take
part at political stock markets for almost 25% of all traders.
8
Table 3
Relative standard deviation in dependence on party size
NP02
Election Result (%)
STD (%)
OEVP
42.3
0.18
SPOE
36.51
0.19
FPOE
10.01
0.65
GREEN
9.47
0.53
ROF
1.71
1.22
TE03
Election Result (%)
STD (%)
OEVP
49.95
0.30
SPOE
25.91
0.58
FPOE
7.99
1.69
GREEN
15.45
1.53
ROF
0.70
n.a.
2.3. Judgement Bias
Already in the first PSM Forsythe analysed a phenomenon that he called ‘judgement
bias’: traders who have strong party ties systematically overestimate the election result of
‘their’ party. Forsythe et al (1992) and later Brüggelambert (1999) found clear evidence for
the existence of this judgement bias. By analysing the NP02 market, Hauser (2003) could
show that traders with party ties expect a significantly higher election result for the party they
sympathise with (Mann-Whitney U-test: p<0.01). This result was confirmed in the TE03
market (Mann-Whitney U-test: p<0.05).
However, the Iowa Electronic Markets delivered good forecasts although a measurable
judgement bias influenced traders decisions. Forsythe et al. explained this by the actions of a
group of participants which they called ‘marginal traders’. These traders are defined as “those
who submit limit orders at prices close to the market price” (Forsythe et al., 1992). These
traders are assumed not to be affected by any kind of biases when making their trading
decisions. By taking advantage of the biased traders, they are able to profit by acting as
arbitrageurs. For the market, these traders play an important role because their activities have
a huge impact on the market prices. Due to their high trading activity, marginal traders act as
market-makers instead of price-takers, and they are the first ones who react to arbitrage
opportunities, thereby lowering bid-ask-spreads and increasing liquidity in the market.
The ‘marginal traders’ hypothesis formulated by Forsythe et al. was a topic leading to
controversial discussions, as some authors rejected the idea. Brüggelambert (1999), who
analysed several PSMs carried out in Germany, could not find any evidence that the
performance of traders depended on their market activity. Beckmann and Werding (1998)
objected that Forsythe’s definition of a ‘marginal trader’ was so arbitrary that little variance of
the selection criteria leads to totally different results.
For TE03 a user poll was conducted allowing us to analyse trading behaviour and
success of several groups of traders. A remarkable result is, that traders declaring themselves
9
supporter of a party had on average lower total return (-0.18%) than traders without party ties
(+4.30%)5. In addition the participants without party ties were on average three times more
active in trading than the other traders (Mann-Whitney U-test: p<0.01). This gives strong
support to the marginal-traders-hypothesis as it shows that these unbiased and active traders,
which Forsythe called marginal traders, were indeed able to outperform other traders.
3. A simple three-trader-model explaining the systematic mispricing
Under very few reasonable assumptions we will show, that equilibrium prices under the
current market design do regularly not reflect market beliefs properly. To test the model close
to the stylised facts we know from PSMs, we assume that (i) traders have heterogeneous
expectations about the election outcome and (ii) traders invest about seventy percent of their
initial endowment in base portfolios, the rest is held cash to trade in the secondary market.
The number of seventy percent is the average of the two PSMs covered in this paper. To
receive clear results we set the trading behaviour of traders as follows: (iii) whenever the price
is below (above) a traders’ expectation, he buys (sells) as many shares as possible, (iv) the
traders with the expectation deviating most from the actual price trade first, as based on their
expectations they have the biggest incentive to trade to maximize their expected profits. (v)
Whenever traders with different expectations trade, the price of the transaction is set exactly
in the middle of their expectations. In a real market the price may vary depending on the skill
and luck of the traders, but we can not model this feature here.
We consider only two contracts: a small-party-share called SMALL and the sum of all
other shares called ROF (rest of field). As the two contracts represent the whole market the
sum of their values has to add to 100 at all times.
There are three traders, A, B and C, in the market, who have different expectations
about the election outcome. A expects 12 for SMALL and 88 for ROF. B is less optimistic for
SMALL and values them at 10, ROF at 90. C expects only 8 for SMALL and 92 for ROF.
The median and average expectation of the election outcome is therefore 10 for SMALL and
90 for ROF.
Each trader has an initial endowment of $100,000 and a base portfolio consisting of one
share SMALL and one share ROF costs $100. Each of the traders first buys base portfolios for
seventy percent of his cash, leaving him with 700 shares of each kind and $30,000 cash. This
is depicted in step (I) of table 4. Then the three start trading on the secondary market.
5
A Mann-Whitney U-test does not deliver a significant connectivity (p 1-tailed=0.14).
10
Table 4
The simple market reaches equilibrium in five steps
(I)
A
B
C
SMALL
700
700
700
portfolios
ROF
700
700
700
$
30.000
30.000
30.000
(II)
A
B
C
SMALL
1.400
700
0
ROF
700
700
700
$
23.000
30.000
37.000
transactions
A buys 700 SMALL from C at a price of 10
(III)
A
B
C
SMALL
1.400
700
0
ROF
289
700
1.111
$
59.990
30.000
10
transactions
C buys 411 ROF from A at a price of 90
(IV)
A
B
C
SMALL
2.100
0
0
ROF
289
700
1.111
$
52.290
37.700
10
transactions
A buys 700 SMALL from B at a price of 11
(V)
A
B
C
SMALL
2.100
0
0
ROF
0
989
1.111
$
78.011
11.979
10
transactions
B buys 289 ROF from A at a price of 89
A
B
C
expectations
SMALL
12
10
8
ROF
88
90
92
The traders A and C have the most diverging expectations, consequently they see the
biggest opportunities in the market. A wants to buy shares of SMALL, as she thinks, that the
party will do well in the election. C wants to get rid of his SMALL and buy ROF instead.
Every price between 8.01 (where C would start selling) and 11.99 (where A would stop
buying) for SMALL would be possible; but as outlined above the transaction price is the
median of the two expectations. We arbitrarily start our analysis with A buying SMALL from
C in step (II) and continue by C buying ROF from A in (III). The analysis is robust to changes
in the sequence of steps (II) and (III).
After A and C have changed shares to the extent possible by their budgets the market
prices are 10 and 90 for SMALL and ROF respectively. This reflects the average expectations
and could be a good forecast (after step III).
However, the market is not yet in equilibrium. The expectations of A and B vary as well
and both have an incentive to trade with each other. A buys more SMALL in (IV) for 11,
while in step (V) B buys the last ROF-contracts A owns for 89. As traders A and B still have
cash, there are further incentives to trade. Both of these traders may buy base-portfolios and
11
start trading again. However, this has no further influence on market prices. As trader C is out
of cash and has no more trading-incentives, prices are only determined by trader A and B.
Hence, the closing prices are 11 and 89 for SMALL and ROF respectively. We see that these
prices do not reflect market beliefs properly: the smaller contract is overpriced, while the
larger one is underpriced with respect to the average market expectation. The same has been
found in most PSMs. Schmidt (2001) points out, that in 25 markets he surveyed 67.4 percent
of all smaller parties were overpriced and in the five markets the authors conducted in the past
years, smaller contracts were overpriced at market closing all but once.6 A similar
overestimation of smaller parties can not be found in polls (Schmidt, 2001).
The reason for this mispricing is that the fixed amount of starting money can buy more
shares of small contracts, than of larger ones. Therefore a few participants with the highest
expectations can buy all shares of the small party. These traders with higher-than-average
expectations thereby set the final price, which is regularly too high. This result is in line with
theorists, who have argued, that in the absence of short-selling and liquidity constraints, the
equilibrium price of an asset has a lower bound at the most optimistic traders’ estimate of the
value (Harrision and Kreps, 1978; Morris, 1995).
In PSMs there are of course liquidity constraints, as no trader can buy all shares of a
party. Therefore it is not the opinion of one trader which sets the price, but rather the lowest
opinion of a number of traders (who need not know each other) with the highest estimates for
a party. The traders just need to have enough money to buy all the shares of a party. To
illustrate this mechanism, Fig. 2 shows the deviation of traders’ expectations for the FPOE
election result, collected by a user-poll at the NP02 market7. We see that the market price of
the FPOE contract is more than 1 percent-point higher than the median of traders’
expectations. Only 20 percent of the traders expect an election result higher than the market
price but this group has enough market power to keep the price up by buying nearly all
outstanding FPOE-shares. This is due to the fact that the 80 percent of the traders, expecting
an election result below market price, have to buy a base-portfolio for 100 for every FPOE
contract they want to sell, while the FPOE-supporters have to invest just around 12 to buy the
contract. Therefore one FPOE-supporter who invests all his money in FPOE-shares can only
be countered by eight (100 divided by 12) traders buying base portfolios for all their money
and selling all their FPOE-shares. This constraint to sell shares beyond the own ownership,
6
7
This one exception was the Green Party at TE03.
At NP02 two user polls were conducted at different times. For this reason, data does not exactly correspond
with Table 1.
12
i.e. the limitation on short-selling, has already been named as a source for an overpricing of a
share by Miller (1977) and Jarrow (1980).
50
Number of traders
45
40
35
30
25
20
15
10
5
0
6
7
8
9
10
11
12
13
14
15
16
17
Expected Election Result
Fig. 2. Deviation of traders’ expectations for FPOE contracts (NP02 Market).
A further implication of the three-trader-model presented above is that the concentration
of contracts in traders’ portfolios increases over time. Empirical analysis of the TE03 market
supports this argumentation.
10000
05.11.03
9000
12.11.03
8000
19.11.03
7000
25.11.03
6000
26.11.03
5000
27.11.03
4000
3000
2000
1000
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Fig. 3. Number of GREEN contracts of the Top-20 holders in TE03 Market.
13
Fig. 3 shows the number of GREEN contracts in the portfolios of the top-20 holders at
six different times. As we can see, the number of contracts held at the last observation time is
higher than on first for all 20 traders. Indeed, a decrease of GREEN shares in one period can
only be observed five times in the whole sample of 100 observations.
Critical readers may argue that the increase of shares held by the top-holders is a
consequence of the fact that the total number of contracts increases over time, like predicted
by the three-trader-model. Indeed, the total number of GREEN contracts rose from 212,703
(first observation) to 276,939 (last observation) due to the issue of base portfolios. However,
the share of all GREEN contracts held by the top-20 holders increased from 40.20% to
44.47%8. A Wilcoxon signed ranks test, comparing the relative share of all GREEN contracts
for every single top-20 holder at the first and last observation, shows that this increase is
significant at the 1% level (Asymptotic significance 1-tailed: 0.006).
4. Introducing a futures trading mechanism to allow short-selling
In the past 16 years PSMs have been conducted in numerous countries with almost the
same design as the first markets in Iowa. However, most markets were not able to replicate
the good results of the first two PSMs. We have shown that this is mainly due to a systematic
mispricing which is caused by the market design. Therefore we think it is time for some
changes in the design, as the present one has several weaknesses that worsen the market’s
performance.
The ‘stocks’ of the political parties have characteristics of both, stocks and futures. This
construction causes two major problems: first, it affects the forecasting ability of PSMs;
second, scientific findings concerning the market mechanism can not be transferred to capital
markets without problems. Experimental and theoretical evidence suggest that constructing
PSMs as pure futures markets can solve the described problems by eliminating the constraint
of traders to go short in contracts which they consider overpriced. In addition futures will
remove the connection between the expected vote share and the attractiveness of the contract
for trading as only absolute price changes are relevant for profits and losses.
An extensive experimental literature has tackled the question whether short selling does
change market forces. Experimental markets without short-selling often show bubble
phenomena and consistent overpricing of assets (see for example Smith et al., 1988 and King
et al., 1993). When short-selling is allowed, prices move closer to fundamental values and
bubbles often disappear (Haruvy and Noussair 2004). Ackert et al. (2001) concluded from
8
The absolute increase was from 85,511 to 123,156.
14
their experiments, that “allowing short-selling enhances the pricing mechanism and allows
traders to move prices to levels justified by fundamentals ... Short-selling provides an
equilibrium force to the market.”
By introducing futures the direct link between market prices and forecast of the election
result is not affected: the futures price of an asset that provides no income until maturity, like
the ‘stocks’ of a PSM, is SerT (S=spot price; r=risk free interest rate; T=maturity, see Hull,
1993). In PSMs, no interest is paid, therefore r=0 and the futures price is identical to the spot
price, and can therefore be used to predict the parties’ vote share. Without the biases and
constraints described for the usual design the new design should be able to deliver very exact
predictions.
To implement a futures market the changes necessary in the traditional design of PSMs
are remarkably small. The double-auction-market, the trading screen, order queues and most
other functions stay the same. Still a buyer and a seller have to agree on a price to settle a
transaction. The only major difference is that a trader can sell a contract even if he does not
own it. With the new design no money has to be paid immediately. A futures contract is
signed by one trader taking the long position, the other the short position at an agreed price. A
trader going short expresses his opinion that prices will fall, while traders going long expect
rising prices. This system also assures that there is no longer a premium for small contracts, as
now only absolute changes in the stock price are relevant and the dimension of the absolute
change is per definition equal for all parties. To ensure traders ability to fulfil their contracts,
we propose to use a margin which is big enough to cover most possible election results, for
example 10, representing a change in the vote share of 10 percent. This high margin is
proposed to be able to abstain from daily settlement of all contracts, which could be confusing
for traders.
As Huber (2001) points out, another major advantage of this trading mechanism is, that
it eliminates the constraint of the emission rule of traditional PSMs that the number of shares
is the same for every party in the market. When two traders agree on a price for a party a new
contract is formed for that party only. The futures trading mechanism should therefore
eliminate both major reasons for mispricing: the equal number of shares which leads to higher
attractiveness of smaller contracts and the limitations to sell in papers which a trader
considers overpriced.
15
5. Conclusion
In this paper we showed that PSMs have a tendency to overprice smaller parties’
contracts while those of larger parties are underpriced. This is due to the construction of the
markets, namely the fact that all shares are in the market in equal numbers, while the fixed
starting amount buys different numbers of shares. As short-selling is not allowed, this leads to
asymmetric market power of traders expecting good results for small vs. large parties.
Especially in Europe, where the participation of several smaller parties in elections is
common, this leads to systematically skewed forecasting results. As a result PSMs which
regularly outperform polls in the U.S. are on average not much better than polls in Europe.
We argue that changing the market construction of PSMs to a futures market can solve
this mispricing problem: the possibility of trades to go short in contracts rebalances the
market forces and allows the market to work as an efficient aggregator of information.
Appendix A: Austrian Parties and Politics
At present the four parties represented in the Austrian parliament are: the christiandemocratic Peoples Party OEVP, the social-democrats SPOE, the right-wing populist FPOE,
and the green party GREEN. The OEVP and SPOE are the two “state-bearing” parties that
dominated Austrian Politics after the Second World War. For long periods the two formed a
grand coalition (1945-1966 and 1986 to 2000) and they have occupied all important positions
in politics, media and state-near industries in the renowned “Proporzsystem”. From 1945 to
1969 and again since 2000 the OEVP held the chancellorship; from 1969 to 2000 the
chancellor was a social-democrat.
The FPOE was in a coalition with the SPOE from 1982 to 1986 and with the OEVP
since 2000. Since Jörg Haider took power in the party in 1986 it has become increasingly
populist and right-wing, while the traditional liberal roots have receded.
The green party was founded in the early 1980ies and has grown from election to
election. They have not yet been part of a national government.
In the category ROF we find the communist party KPOE, which took part in every
election since 1945, but has not been represented in parliament since 1959, and a few very
small regional parties and candidates, none of which have more than 0.1 percent of the
popular vote.
16
References
Ackert, L.; N. Charupat; B.K. Church; R. Deaves, 2001, Bubbles in Experimental Asset
Markets: Irrational Exuberance No More, working paper.
Beckmann, K. and M. Werding, 1998, Eine Anmerkung zur ‚Hayek-Hypothese’ in der
experimentellen Ökonomie, Jahrbücher für Nationalökonomie und Statistik 217/6 (Lucius
& Lucius, Stuttgart).
Brüggelambert, G., 1999, Institutionen als Informationsträger: Erfahrungen mit Wahlbörsen
(Metropolis Verlag, Marburg).
Diamond, D.W. and R.E. Verrecchia, 1987, Constraints on short selling and asset price
adjustment to private information, Journal of Financial Economics 18, 277-311.
Eymann, T.; S. Sackmann; G. Müller, 2003, Hayeks Katallaxie - Ein zukunftsweisendes
Konzept für die Wirtschaftsinformatik?, Wirtschaftsinformatik 45, 491-496.
Figlewski S. and M. Webb, 1993, Options, short sales, and market completeness, Journal of
Finance 48, 761-777.
Forsythe, R.; F. Nelson; G. Neumann; J. Wright, 1992, Anatomy of an Experimental Political
Stock Market, American Economic Review 82, 1142-1161.
Harrisson, J.M. and D.M. Kreps, 1978, Speculative Investor Behavior in a Stock Market with
Heterogeneous Expectations, Quaterly Journal of Economics 92, 323-336.
Haruvy, E. and C. Noussair, 2004, The Effect of Short-Selling on Bubbles and Crashes in
Experimental Spot Asset Markets, Working Paper.
Hauser, F., 2003, Die Presse Online-Wahlbörse 2002 – Eine finanzwirtschaftliche
Betrachtung, Thesis, University of Innsbruck.
Hayek F. A., 1945, The Use of Knowledge in Society, American Economic Review 4, 519530.
Huber, J., 2001, Wahlbörsen: Preisbildung auf Politischen Märkten zur Vorhersage von
Wahlergebnissen, Dissertation, Universität Innsbruck.
Hull, J. C., 1993, Options, Futures and Other Derivative Securities (Prentice-Hall, New
Jersey).
Jacobsen, B.; J. Potters; A. Schram; F. van Winden; J. Wit, 2000, (In)accuracy of a European
political stock market: The influence of common value structures, European Economic
Review 44, 205-230.
Jarrow, R.A., 1980, Heterogeneous expectations, restrictions on short sales, and equilibrium
asset prices, Journal of Finance 35, 1105-1113.
17
Kagel, J. H. and A. E. Roth, 1995, Handbook of Experimental Economics (Princeton
University Press, Princeton).
King, R.; V. Smith; A. Williams; M. Van Boening, 1993, The Robustness of Bubbles and
Crashes in Experimental Stock Markets, in Day R. and P. Chen (Eds.), Nonlinear
Dynamics and Evolutionary Economics I (Oxford University Press, Oxford).
Miller, E., 1977, Risk, uncertainty and divergence of opinion, Journal of Finance 32, 11511168.
Morris, S., 1995, The Common Prior Assumption in Economic Theory, Economics and
Philosophy 11, 227-253.
Ortner, G., 1997, Forecasting Markets – An Industrial Application, Part I, TU Wien.
Ortner, G., 1998, Forecasting Markets – An Industrial Application, Part II, TU Wien.
Schmidt, C., 2001, Predictive Accuracy of Experimental Asset Markets, Dissertation,
Humboldt-University of Berlin.
Smith, V.; G. Suchanek; A. Williams, 1988, Bubbles, Crashes, and Endogenous Expectations
in Experimental Spot Asset Markets, Econometrica 5, 1119-1151.
Spann, M., 2002, Virtuelle Börsen als Instrument zur Marktforschung, Dissertation,
University of Frankfurt am Main.
18