Title: Social Network based Short-Term Stock Trading System,
Abstract: This paper proposes a novel adaptive algorithm for the automated short-term
trading of financial instrument. The algorithm adopts a semantic sentiment
analysis technique to inspect the Twitter posts and to use them to predict the
behaviour of the stock market. Indeed, the algorithm is specifically developed
to take advantage of both the sentiment and the past values of a certain
financial instrument in order to choose the best investment decision. This
allows the algorithm to ensure the maximization of the obtainable profits by
trading on the stock market. We have conducted an investment simulation and
compared the performance of our proposed with a well-known benchmark (DJTATO
index) and the optimal results, in which an investor knows in advance the
future price of a product. The result shows that our approach outperforms the
benchmark and achieves the performance score close to the optimal result.