Our paper relies on stock price reactions to colour words, in order to provide new dictionaries of positive and negative words in a finance context. We extend the machine learning algorithm of Taddy (2013), adding a cross-validation layer to avoid over-fitting. In head-to-head comparisons, our dictionaries outperform the standard bag-of-words approach (Loughran and McDonald, 2011) when predicting stock price movements out-of-sample. By comparing their composition, word-by-word, our method refines and expands the sentiment dictionaries in the literature. The breadth of our dictionaries and their ability to disambiguate words using bigrams both help to colour finance discourse better.

我们的论文依赖于股票价格对颜色词的反应，以便在金融环境中提供新的正面和负面词汇词典。我们扩展了 Taddy (2013) 的机器学习算法，添加了一个交叉验证层来避免过度拟合。在面对面的比较中，我们的词典在预测样本外的股票价格变动时优于标准的词袋方法（Loughran 和 McDonald，2011）。通过逐字比较它们的组成，我们的方法改进和扩展了文献中的情感词典。我们词典的广度及其使用双字母组消除单词歧义的能力都有助于更好地为金融话语增光添彩。