Harnessing the Power of Unstructured Data: Sentiment Analysis of Financial News and Social Media for Algorithmic Trading Strategies

EasyChair Preprint 14311

Abstract

In the rapidly evolving financial markets, the ability to leverage unstructured data for informed decision-making has become increasingly critical. This study explores the integration of sentiment analysis of financial news and social media into algorithmic trading strategies. By harnessing advanced natural language processing (NLP) techniques and machine learning algorithms, we aim to extract and quantify sentiment from vast volumes of unstructured text data. The sentiment scores are then incorporated into trading algorithms to enhance prediction accuracy and trading performance. Our research demonstrates how real-time sentiment analysis can identify market trends, gauge investor sentiment, and provide a competitive edge in high-frequency trading environments. Through comprehensive backtesting and live trading experiments, we evaluate the effectiveness of sentiment-driven strategies compared to traditional quantitative methods. The findings underscore the potential of unstructured data as a valuable asset in developing robust, adaptive, and profitable trading systems, paving the way for innovative approaches in the realm of algorithmic trading.

Keyphrases: Natural Language Processing (NLP), Recurrent Neural Networks (RNN), Support Vector Machines (SVM)

@booklet{EasyChair:14311,
  author    = {Abil Robert},
  title     = {Harnessing the Power of Unstructured Data: Sentiment Analysis of Financial News and Social Media for Algorithmic Trading Strategies},
  howpublished = {EasyChair Preprint 14311},
  year      = {EasyChair, 2024}}