Social media data presents challenges for forecasters since one must convert text into data and deal with issues related to these measures being collected at different frequencies and volumes than traditional financial data. In this paper, we use a deep learning algorithm to measure sentiment within Twitter messages on an hourly basis and introduce a new method to undertake MIDAS that allows for a weaker discounting of historical data that is well-suited for this new data source. To evaluate the performance of approach relative to alternative MIDAS strategies, we conduct an out of sample forecasting exercise for the consumer confidence index with both traditional econometric strategies and machine learning algorithms. Irrespective of the estimator used to conduct forecasts, our results show that (i) including consumer sentiment measures from Twitter greatly improves forecast accuracy, and (ii) there are substantial gains from our proposed MIDAS procedure relative to common alternatives.

中文翻译：

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高频社交媒体数据是否可以改善对低频消费者信心测度的预测？*

社交媒体数据给预报员带来了挑战，因为必须将文本转换为数据并处理与这些措施相关的问题，这些问题的收集频率和数量与传统财务数据相比有所不同。在本文中，我们使用深度学习算法来每小时测量一次Twitter消息中的情绪，并介绍一种进行MIDAS的新方法，该方法可以对历史数据进行较弱的折现，这非常适合此新数据源。为了评估相对于替代MIDAS策略的方法的性能，我们使用传统的计量经济学策略和机器学习算法对消费者信心指数进行了样本外预测。不管用来进行预测的估算器是什么，