We propose an approach to construct text-based time-series indices in an optimal way--typically, indices that maximize the contemporaneous relation or the predictive performance with respect to a target variable, such as inflation. We illustrate our methodology with a corpus of news articles from the Wall Street Journal by optimizing text-based indices focusing on tracking the VIX index and inflation expectations. Our results highlight the superior performance of our approach compared to existing indices.

翻译：我们提出一种以最优方式构建文本时间序列指数的方法——通常指最大化与目标变量（如通货膨胀）的同期关联性或预测性能的指数。我们以《华尔街日报》新闻文章语料为例，通过优化追踪VIX指数和通胀预期的文本指数来演示该方法。实验结果表明，我们的方法相较于现有指数具有更优表现。