Capital markets and sentiment analysis in different text structures

该项目将涉及分析信息如何在高频率上影响资产价格。创新之处在于，原始信息将以文本的形式包含在大量的Twitter提要中。本文将使用计算方法进行分析，并根据其是否对市场敏感（即好消息或坏消息）进行分类。然后，将在样本外预测工作中评估这一干净信息与资产回报之间的关系。

The project will involve the analysis of how information affects asset prices over high frequencies. The innovation is that the raw information will be in the form of text contained in a high number of Twitter feeds. This text will be analysed using computational methods and categorised according to whether it is market sensitive (i.e. good or bad news) or not. The relationship between this clean information and asset returns will then be assessed in an out-of-sample forecasting exercise.