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Advances in Business and Management Forecasting
Forecasting the 2008 U.S. presidential election using options data
Christopher M. Keller,
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election using options data" In Advances in Business and Management Forecasting.
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FORECASTING THE 2008 U.S.
PRESIDENTIAL ELECTION
USING OPTIONS DATA
Christopher M. Keller
ABSTRACT
The 2008 U.S. presidential election was of great interest nationally and
internationally. Interest in the 2008 election was sufficient to drive a
$2.8 million options market by a U.K.-based company INTRADE.
The options in this market are priced as European style fixed return
options (FRO). In 2008, the Security and Exchanges Commission
approved, and both the American Stock Exchange and the Chicago Board
Options Exchange began to trade FROs. Little research is available on
trading in FROS because these markets are very new. This chapter
uses the INTRADE options market data to construct exponential
smoothing forecasts, which are then compared under a hypothetical
trading strategy. The trading returns indicate that this market is
relatively efficient at least in the short term but that because of the all
or nothing payout structure of a FRO, there may exist small arbitrage
opportunities.
Advances in Business and Management Forecasting, Volume 7, 173–182
Copyright r 2010 by Emerald Group Publishing Limited
All rights of reproduction in any form reserved
ISSN: 1477-4070/doi:10.1108/S1477-4070(2010)0000007015
173
174
CHRISTOPHER M. KELLER
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INTRODUCTION AND BACKGROUND
The INTRADE market is a speculative prediction market. Prediction
markets are also known as information markets, idea futures, event
derivatives, or virtual markets and exist for the purpose of making
predictions (Spann & Skiera, 2003). There are many prediction markets
publicly available: IOWA ELECTRONIC MARKETS – generally
economic or political issues; TRADESPORTS – sporting events; SIMEXCHANGE – video games; HOLLYWOOD STOCK EXCHANGE – films
and film-related people; and INTRADE – from business to politics to
art and wine and even weather, but excluding sports, which is covered
by BETFAIR. Raban and Geifman (2009) provide a useful wiki page. The
promise and potential of prediction markets is also discussed in Arrow et al.
(2008) and Wolfers and Zitzewitz (2004, 2008).
Most evidence on prediction market efficiency compares final pre-election
forecasts with actual outcomes but does not analyze the efficacy of ongoing
forecasts throughout the entire market time. The results of final election
market forecasts is mixed: Erikson and Wlezien (2008) claim that election
prediction markets are inferior to polling estimates, whereas Jones (2008)
claims that candidate futures provided the most accurate popular-vote
forecasts. Lee and Moretti (2009) consider a Bayesian model of adaptive
investor learning in the 2008 U.S. presidential futures market but considers
only the final outcome not the state-by-state electoral results. Chen, Wang,
Yang, and Yen (2009) suggest that the minimum number of market
participants in a futures market may be quite small (75).
In the INTRADE market, the options are priced as European style FROs
(FROs). A European style FRO pays out a fixed amount at a fixed time in
the future. In the case of the INTRADE market, the pay out for each option
is $10, and the time is fixed by the U.S. presidential election. The option
payouts are tied to whether a Republican candidate or a Democratic
candidate wins the electoral votes of a particular state.
INTRADE MARKET DATA
This INTRADE market was composed of 102 different assets, one for each
of the two primary parties within each of 51 voting districts (including the
District of Columbia), and modeled accurately the electoral process of
the U.S. presidential election. The INTRADE market is a more accurate
election model than the perhaps more well-known Iowa Electronic Market
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Forecasting the 2008 U.S. Presidential Election Using Options Data
Fig. 1.
175
Daily Closing Prices for Pennsylvania.
since the Iowa market is modeled on total popular vote that is not the
method used in the actual election of the U.S. president, and the Iowa
Electronic Markets are limited to positions of only $500.
This chapter analyzes all daily trading data and is composed of more than
5,400 individual daily trades over the 721 calendar days after the first trade.
The data show variable forecast trends within the trading period. The
state average number of Democratic trading days is 57.8 and of Republican
trading days in 50.4. The state average of Democratic trading volume is
$30,284 and of Republican trading volume is $26,035.
Fig. 1 shows an example of the daily closing prices for the state of
Pennsylvania, which was the most actively traded Democratic option.
FORECASTING METHOD
This chapter uses Holt–Winters method for forecasting as specified below
(Winters, 1960). Let pi represent the option price on trading day i. With the
standard parameters a, b 2 ð0; 1Þ, for iW1, recursively calculate the level
Li ¼ api þ (1 a) (Li 1 þ Ti 1), Ti ¼ b(Li Li 1) þ (1 b)Ti 1 from the
arbitrary initial specifications that L1 ¼ p1 and T1 ¼ p2 p1. For any
forecast period k, then a forecast can be generated as Ft þ k ¼ Lt þ kTt.
Three notes are specified regarding this chapter’s implementation of the
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176
CHRISTOPHER M. KELLER
general method. One, since the initial conditions may be variously specified,
this chapter chose to use the least extensive data for initiating the process.
The initial level estimate is simply the first data point observed. The initial
estimate of the slope is simply the first possible slope estimate from the data.
Two, because the initial estimates are based on such limited data, it is
necessary to allow the process to ‘‘burn-in’’ over an initial period so that the
arbitrary initial estimates do not bias the overall forecasting effort. This
chapter uses a burn-in period of 20 trading days. Three, the trading days in
this model are not consecutive. That is, the most common applications of this
forecasting method are for complete data with equal and identically spaced
intervals. However, since this chapter is interested in the trading effects, the
intervals here are not equal and identical calendar-wise, but are equal and
identical in whether or not a trade occurs, that is trading days. In this regard,
this model is reasonable since there is no necessary assumption of precluded
or pent-up demand that is transacted only intermittently as in a service stock
environment example like Johnston and Boylan (1996), which may use, for
example, exponentially weighted moving averages (Johnston, 1993).
For any forecast period k, the optimal values of a and b are determined
for Republican options and for Democratic options by minimizing the total
sum of squared errors across the 50 states. Table 1 shows the optimal values
for a and b for four different forecast periods.
The optimal value of b for all forecast periods is relatively constant
at a value of about 10%. The optimal value of a however changes
dramatically for the shortest forecast period of k ¼ 1 trading day from a
value of about 80% to a value of about 20% for long forecast periods of k
beyond 35 trading days. This relationship is more extensively illustrated
in Fig. 2.
Table 1.
Optimal Values of Forecasting Parameters for Select Forecast
Periods.
Optimal values
k
Democratic options
1
15
35
50
Republican options
a
b
a
b
0.81
0.71
0.22
0.18
0.13
0.10
0.07
0.07
0.80
0.29
0.22
0.20
0.14
0.10
0.10
0.10
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Forecasting the 2008 U.S. Presidential Election Using Options Data
Fig. 2.
177
Convergence of Forecasting Parameters, a and b, as Forecast Period k
Increases.
For the shortest forecast period, k ¼ 1 trading day, the optimal a value
for both Democratic and for Republican options is very high at about 80%.
This value can be understood to indicate that over such a short forecast
horizon, the current market price is substantially the best estimate of
tomorrow’s market price. In other words, the prediction market is at least
relatively efficient over a very short forecast horizon.
The forecast period of k ¼ 15 trading days is the point at which there is
the most divergence between the optimal values for the Republican options
and for the Democratic options. For forecasts of this length, the optimal
value of a for the Democratic candidate remains relatively high at about
70%, again indicated relative market efficiency for this forecast period.
On the contrary, the optimal value of a for the Republican candidate has
dropped dramatically to a value of about 29%. This much smaller value of a
can be understood to indicate that for this longer period for this candidate
the error-minimizing forecast is much more stable from past values.
Colloquially, the support for this candidate over this period of time has
stabilized or converged to a relatively constant underlying set. Unfortunately
for the Republican candidate this rapid stabilization or supportive cohesion
was stabilized on an ultimately losing subset.
For forecasts beyond k ¼ 35 trading days, the optimal forecasting
parameters for both candidates stabilized at about the same level of about
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178
Fig. 3.
CHRISTOPHER M. KELLER
Contour Plot of a and b Parameter Optimization Sensitivity for Democratic.
a ¼ 20%. As would be expected the overall error for each of the forecasting
methods for each of the respective candidates increases dramatically as the
forecast horizon k increases. The Sum-of-Squared-Error (SSE) minimization is most greatly affected by the value of the b parameter. Sample contour
plots of the optimal solution and surrounding values are shown below for
a forecast period of k ¼ 15 in Figs. 3 and 4.
TRADING EVALUATION
An overall assessment of the forecasting process is this prediction market
is applied by retroactively assessing a simple trading strategy and petting
in prediction markets in general is discussed in Fang, Stinchcombe, and
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Forecasting the 2008 U.S. Presidential Election Using Options Data
Fig. 4.
179
Contour Plot of a and b Parameter Optimization Sensitivity for Republican.
Whiston (2007). Since the options in question are all-or-nothing options,
one of the simplest investment strategies is a buy-winner strategy that is
a variant of buy-and-hold that uses a single trigger price as the ‘‘winner’’
of the buy-winner strategy. That is, if the forecast at any time exceeds a
specified trigger price, then the entire volume of the market is purchased at
the respective closing price. The results of this analysis suggest that there
is pricing behavior in the market that warrants further research.
Since the Democratic candidate in fact won the election, virtually any
evaluation of a buy-winner strategy for Democratic options would be
profitable. This chapter instead analyzes the buy-winner trading strategy as
applied only to the Republican candidate and thus is an a fortiori analysis
in attempting to mitigate any post hoc in-sample error problem. Trades in
these stocks also incur a $0.05 cost per option to trade and a $0.10 expiry
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180
CHRISTOPHER M. KELLER
cost if the option is in the money. These costs are incorporated in the trading
strategy returns discussed later in text.
The buy-winner strategy can be applied even with no forecast. That is,
this strategy could be affected by setting a trigger price, so that anytime the
market price is above this trigger price, all options in the market at that time
are purchased. Basically, this strategy assumes that there is a ‘‘tipping
point’’ (Gladwell, 2000), at which the current price indicates a future winner.
This strategy yields a positive return on investment for every trigger price
above $6.60. When the k ¼ 1 forecast is applied, the buy-winner strategy
yields a positive return on investment for every trigger price above
$6.80. The evaluation of the buy-winner strategy gets more interesting as
the forecast period increases. At the point of maximal candidate optimal
parameter difference, k ¼ 15, this strategy shown a dramatic positive
return on investment for every trigger price above $8.80. For forecasts
beyond k ¼ 15 periods, the buy-winner strategy shows negative returns
on investment. The three positive trading return strategy models current
market price, k ¼ 1 forecast, and k ¼ 15 forecast are illustrated in Fig. 5.
Fig. 5 establishes that both the market price basis and the k ¼ 1 forecast
basis yield positive returns in the range of 5%–10% when implemented
with any trigger price above approximately $6.50. Furthermore, this rate of
Fig. 5.
Percentage Return on Investment for Buy-Winner Strategy Based on
‘‘Winner’’ Determination Using Three Different Data Sets.
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Forecasting the 2008 U.S. Presidential Election Using Options Data
181
return increases as the strategy trigger price increases to approximately
$8.25, and then the rate of return begins to decrease. A simple least-squares
approximation is added to the graph that suggests that overall the k ¼ 1
forecast very slightly outperforms the simple market price forecast. Fig. 5
also establishes that the k ¼ 15 forecast is not profitable until the trigger
price is at the relatively high value of $8.60, but thereafter the rate of return
increases dramatically as the trigger price exceeds this value. It should be
noted that as the trigger price increases, the volume, and hence the total
profit realized, must simultaneously decrease. This again is just a cautionary
note of care that the analysis is not in any way suggesting some fool’s gold
of a perfect arbitrage opportunity. Rather, this analysis suggests conservatively that further research into the behavior of pricing past a ‘‘tipping
point’’ for FROs is imperative. At the least, one would expect that once
prices or forecast of prices exceed some fixed level, that trading at any level
short of the all-or-nothing payout should virtually cease otherwise there
appears to be a valuable arbitrage opportunity.
DISCUSSION AND CONCLUSION
These INTRADE futures are priced as European style FRO. In 2008, the
Security and Exchanges Commission approved and both the American
Stock Exchange and the Chicago Board Options Exchange began to trade
FROs. Little research is available on trading in FROs because the markets
are very new. This chapter provides illustrative information on this new
market by examining the INTRADE trading data. In particular, the results
from simulated simple trading strategies on the forecasted data indicate
pricing anomalies that may be both potentially profitable and inferentially
generate better forecasts. Although many valid and justified qualifiers may
be well applied to any simulated trading results, in any case, this chapter
provides useful and informative forecasting analysis of a rich set of data
with implications to a newly opened type of market.
REFERENCES
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Shiller, R. J., Smith, V. L., Snowberg, E., Sunstein, C. R., Tetlock, P. C., Tetlcok, P. E.,
Varian, H. R., Wolfers, J., & Zitzewitz, E. (2008). The promise of prediction markets.
Science, 320, 977, May 16 2008.
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Chen, A., Wang, J., Yang, S., & Yen, D. C. (2009). The forecasting ability of Internet-based
virtual futures market. Expert Systems with Applications, 36(10), 12578–12584.
Erikson, R. S., & Wlezien, C. (2008). Are political markets really superior to polls as election
predictors? Public Opinion Quarterly, 72(2), 190–215.
Fang, F., Stinchcombe, M., & Whiston, A. (2007). Put your money where your mouth is –
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Gladwell, M. (2000). The tipping point. New York: Little Brown and Company.
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updating periods. Journal of the Operational Research Society, 44(7), 711–716.
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Jones, R. J., Jr. (2008). The state of presidential election forecasting: The 2004 experience.
International Journal of Forecasting, 24(2), 310–321.
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99(2), 330–336.
Raban and Geifman (2009). Prediction markets wiki. Available at http://pm.haifa.ac.il
Spann, M., & Skiera, B. (2003). Internet-based virtual stock markets for business forecasting.
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Wolfers, J., & Zitzewitz, E. (2004). Prediction markets. Journal of Economic Perspectives, 18(2),
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Wolfers, J., & Zitzewitz, E. (2008). Prediction markets in theory and practice. In: L. Blume &
S. Durlauf (Eds), The new Palgrave dictionary of economics (2nd ed.). London: Palgrave
Macmillan.
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