Predicting the Next Terror Strike ?

Understanding the Evolution of Prediction Markets.

Interest in prediction markets has increased in the last decade, driven in part by the hope that these markets will prove to be valuable tools in forecasting, decision-making and risk management - in both the public and private sectors. Prediction Markets, sometimes referred to as "information markets," "idea futures" or "event futures", are markets where participants trade contracts whose payoffs are tied to a future event, thereby yielding prices that can be interpreted as market-aggregated forecasts.

Justin Wolfers from Wharton, University, Eric Zitzewitz and Bo Cowgill(Google) in their research, Using Prediction Markets to Track Information Flows: Evidence from Google propound:

“ Last 2.5 years, Google has conducted the largest corporate experiment with prediction markets we are aware of. In this paper, writers illustrate how markets can be used to study how an organization processes information. We document a number of biases in Google’s markets, most notably an optimistic bias. Newly hired employees are on the optimistic side of these markets, and optimistic biases are significantly more pronounced on days when Google stock is appreciating. Writers find strong correlations in trading for those who sit within a few feet of one another; social networks and work relationships also play a secondary explanatory role.

There are two main conclusions. The first is that Google’s markets, while reasonably efficient, reveal some biases. During our study period, the internal markets overpriced securities tied to optimistic outcomes by 10 percentage points.3 The optimistic bias in Google’s markets was significantly greater on and following days when Google stock appreciated. Securities tied to extreme outcomes were underpriced by a smaller magnitude, and favorites were also overpriced slightly. These biases in prices were partly driven by the trading of newly hired employees; Google employees with longer tenure and more experience trading in the markets were better calibrated. Perhaps as a result, the pricing biases in Google’s markets declined over our sample period, suggesting that corporate prediction markets may perform better as collective experience increases.

The second conclusion is that opinions on specific topics are correlated among employees who are proximate in some sense. Physical proximity was the most important of the forms of proximity we studied. Physical proximity needed to be extremely close for it to matter.

An important caveat to our results is that they tell us about information flows about prediction market subjects, many of which are ancillary to employees’ main jobs. This may explain why physical proximity matters so much more than work relationships – if prediction market topics are lower‐priority subjects on which to exchange information, then information exchange may require the opportunities for low‐opportunity‐cost communication created by physical proximity. Of course, introspection suggests that genuinely creative ideas often arise from such low‐opportunity‐cost communication. Google’s frequent office moves and emphasis on product innovation may provide an ideal testing ground in which to better understand the creative process.”

While Eric Zitzewitz in their research Prediction Markets analyze the extent to which simple markets can be used to aggregate disperse information into efficient forecasts of unknown future events. Drawing together data from a range of prediction contexts, they show that market-generated forecasts are typically fairly accurate, and they outperform most moderately sophisticated benchmarks. In their further quest Five Open Questions About Prediction Markets Eric Zitzewitz outlines five open questions in the literature, and we argue that resolving these questions is crucial to determining whether current optimism about prediction markets will be realized.

The evolving nature of these markets would make it possible to tentatively predict events like the next terror strikes, stock corrections, new social trends or socio-political situations. The future is not perfect and it will never be. So are the prediction markets.

Prediction Markets could never be Precision Markets but they will play an important role in how we see through the information maze.

The complex nature of these markets make it inaccessible to a layman but as the technology and thinking converges these markets would have a broader reach and easy to understand patterns. The prognosis of this imperfect market “prediction market” is a prediction in itself. A prediction very few would want to bet on, at least for now.