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The colour of finance words - ScienceDirect
Source: sciencedirect.com
Article summary:
1. This paper provides new dictionaries of positive and negative words in a finance context, based on stock price reactions to colour words.
2. The machine learning algorithm used outperforms the standard bag-of-words approach when predicting stock price movements out-of-sample.
3. The ML algorithm helps to refine and expand the sentiment dictionaries in the literature, and can disambiguate words using bigrams to better colour finance discourse.
Article analysis:
The article is generally reliable and trustworthy, as it provides evidence for its claims by citing relevant research papers from the literature in Finance and Accounting. It also uses a machine learning algorithm to construct new dictionaries of positive and negative words in a finance context, which are then tested against the standard bag-of-words approach when predicting stock price movements out-of-sample. The results show that the ML algorithm performs significantly better than the bag-of-words approach, suggesting that it is more effective at measuring sentiment in financial discourse.
The article does not appear to be biased or one-sided, as it presents both sides of the argument fairly and objectively. It also does not contain any promotional content or partiality towards either side of the argument. Furthermore, all possible risks associated with using machine learning algorithms are noted throughout the article, such as overfitting due to lack of cross validation layers.
The only potential issue with this article is that it does not explore any counterarguments or present any evidence for its claims from other sources outside of research papers from Finance and Accounting literature. This could potentially weaken its credibility if there were other sources available that contradicted its findings or provided different perspectives on the topic at hand.