Restocchi Valerio, McGroarty Frank, Gerding Enrico, Brede Markus
School of Informatics, The University of Edinburgh, Edinburgh EH8 9AB, UK.
Southampton Business School, University of Southampton, Southampton SO17 1BJ, UK.
Entropy (Basel). 2023 Aug 1;25(8):1152. doi: 10.3390/e25081152.
Prediction markets are heralded as powerful forecasting tools, but models that describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong to a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable of replicating the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, which is an exchange platform whose data have never been used before to validate models of opinion diffusion.
预测市场被誉为强大的预测工具，但描述它们的模型往往无法捕捉驱动价格动态的潜在机制的全部复杂性。为了解决这个问题，我们提出了一个模型，其中主体属于一个社会网络，对特定事件发生的概率有一个看法，并据此在预测市场上进行投注。主体通过遵循意见动态的德夫安特模型，与网络中的邻居进行互动来更新他们对事件的看法。我们的结果表明，一个考虑到意见形成动态的简单市场模型能够复制历史预测市场时间序列的实证特性，包括波动聚集和收益的肥尾分布。有趣的是，当主体意见存在适当水平的差异时，能获得最佳结果。此外，本文提供了一种新方法，通过使用从PredictIt获得的历史数据，针对真实数据间接验证意见动态模型，PredictIt是一个交易平台，其数据此前从未被用于验证意见传播模型。
