Social Network based Short-Term Stock Trading System

TL;DR

This paper introduces an adaptive algorithm that uses Twitter sentiment analysis and past stock data to improve short-term stock trading decisions, outperforming benchmarks and nearing optimal results.

Contribution

The paper presents a novel adaptive trading algorithm combining semantic sentiment analysis of social media with historical data for enhanced decision-making.

Findings

Outperforms benchmark DJTATO index in simulations

Achieves performance close to the theoretical optimal

Demonstrates effectiveness of social media sentiment in trading

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.