In his attempt to see the accuracy of prediction markets when forecasting international conflicts, Sebastian Worle analyzed the results of a prediction market (PM) predicated around the ousting of Muammar Gaddafi. The prediction market ran from 19 February 2011, until 29 August 2011, where participants were allowed to trade futures on whether Gaddafi would still be in power on 31 December 2011. The closing price of the market would establish the probability that Gaddafi would not be in power.
Worle's hypotheses for this experiment were,
- A PM's price correctly forecasts the outcome of international conflicts
- A PM's price approximates the event's true probability as the future approaches the end date
- New good news (evidence suggesting Gaddafi will be removed from power, such as a country joining in airstrikes against Gaddafi) makes market price rise, bad news makes it fall (evidence suggesting Gaddafi will remain in power such as failed UN operation), and irrelevant news has no influence on the market price
- Good news leads to relatively lower increase in volatility as opposed to bad news which will increase volatility.
- PM's anticipate publicly foreseeable events and do not show significant reactions once the event takes place
- PM's do not anticipate events that are not foreseeable and react once the event takes place
To answer these questions, Worle broke the analysis into two parts. Worle used a GARCH regression model in order to examine patterns in trading. The GARCH model allowed Worle to analyze how the market reacted to different types of events (news stories) over the course of the experiment. Worle then used an event study design to examine when and how the market reacted to certain events.
Worle was not able to definitively confirm hypothesis one or two due to the research design. The PM determined that chances of Gaddafi being removed from power by 31 December 2011 to be 68.5%. Worle recognizes that he did not have a way of comparing the results to a benchmark. While he was able to identify a probability of Gaddafi being removed from power, the accuracy of that percentage could not be confirmed.
Worle found that the market moved as expected when good and bad news was received. News articles suggesting Gaddaffi's ouster made the stock rise while those the suggested against it made the price fall (Hypothesis 3). He found that the change occurred rather quickly, as it usually took fewer than 24 hours for the price to change after a new news article was published. Worle rejected hypothesis 4, as he found that good news increased trading volume more than bad news. Worle believes this may be due to investors risk aversion levels.
Worle "cautiously accepts" hypothesis 5 and 6, adding that they are only "semi-strong efficient". The PM was able to correctly identify publicly foreseeable events, such as the Security Council resolution. Throughout the course of the PM, very few "unforeseeable" events were forecasted by the market (no significant increase or decrease in trading price before the event).
Worle's research into prediction markets was rather intriguing and well put together. I warn against using this research to prove the effectiveness of PM's though. First off, there was no way to confirm the accuracy of the forecast. He did not compare it to polls or other research on the subject. Worle could have asked analysts, independent of the PM, what they believe the chances of the Gadaffi being removed. This could have given him a benchmark to examine the accuracy a little better.
Secondly,he found that this PM did a decent job of identifying publicly foreseeable events, but was not efficient at identifying those that were not as easy to predict. As analysts, we are concerned with the harder to predict events, the ones that most have a hard time predicting. If there is limited application towards identifying these events, PM's are limited in their forecasting abilities to big picture events.
Worle, M.S. (2013). Wanna bet there will be war? A time-series analysis of prediction markets during the Libya conflict 2011. The Journal of Prediction Markets.