FSAL: A Tailor-Made Financial Sentiment Lexicon in Spanish for the Argentinian Markets (BYMA)

<p class="p1"<During the last decade studies have shown that lexicon-based Sentiment Analysis of tweets combined with Machine Learning techniques can be used to enhance Algorithmic Trading strategies. The aim of the present work is to show how a specific domain lexicon in finance for...
Ausführliche Beschreibung

<p class="p1"<During the last decade studies have shown that lexicon-based Sentiment Analysis of tweets combined with Machine Learning techniques can be used to enhance Algorithmic Trading strategies. The aim of the present work is to show how a specific domain lexicon in finance for the Argentinian Markets (FSAL) provides a better outcome than a generic lexicon (SDAL). First, we introduce a finance tailor-made lexicon. Secondly, we experimentally show that our lexicon outperforms a general purpose one on a corpus of tweets previously classified collaboratively by<span class="Apple-converted-space"< </span<specialists in finance. Then, we compare the lexicons applying three different Machine Learning algorithms. Finally, we introduce some preliminary results and conclusions.</p< Ausführliche Beschreibung