We use an extended sample of tweets relating to energy markets in order to examine and quantify the existence of market efficiency. The tweets are used as a proxy for publicly available information and we examine the degree to which this information determines market movements on the next trading day for nine energy market indices. We mine the topics of increasing and decreasing days using latent Dirichlet allocation and find that the topics of tweets in increasing and decreasing days differ. We validate our approach by feeding the extracted topics into three classifier machines and find that the classifiers provide forecasts on market movements with accuracy 57.83% (39.02%) in bull (bear) markets. Our findings support the presence of semi-strong efficiency, since we find evidence of price movements not reflecting public information, while the asymmetry of forecast accuracy over increasing and decreasing markets suggests a different rate of information propagation across market regimes. Our findings can provide useful input to valuation models linked to market efficiency.

我们使用与能源市场相关的推文扩展样本来检查和量化市场效率的存在。这些推文被用作公开可用信息的代理，我们检查了这些信息在多大程度上决定了九个能源市场指数在下一个交易日的市场走势。我们使用潜在狄利克雷分配挖掘增加和减少天数的主题，发现增加和减少天数的推文主题不同。我们通过将提取的主题输入三个分类器机器来验证我们的方法，并发现分类器在牛市（熊市）中以 57.83%（39.02%）的准确率提供市场走势预测。我们的研究结果支持半强效率的存在，因为我们发现价格变动的证据不能反映公共信息，而随着市场的增加和减少，预测准确性的不对称性表明不同市场机制的信息传播速度不同。我们的研究结果可以为与市场效率相关的估值模型提供有用的输入。