Algorithmic Trading Strategies Enhanced by Real-Time Sentiment Analysis

EasyChair Preprint 14295

Abstract

Algorithmic trading, the use of automated systems to execute trading orders, has become a cornerstone of modern financial markets. This paper explores the enhancement of algorithmic trading strategies through the integration of real-time sentiment analysis. Sentiment analysis, a subfield of natural language processing, involves the computational identification and extraction of subjective information from textual data sources such as news articles, social media, and financial reports. By incorporating real-time sentiment analysis, trading algorithms can gain insights into market sentiment, allowing for more informed and adaptive decision-making.

The study investigates various methodologies for integrating sentiment analysis into trading algorithms, including machine learning models trained on large datasets of market-related texts. The performance of these enhanced algorithms is evaluated against traditional algorithmic trading strategies, with metrics such as profitability, risk-adjusted returns, and execution speed. Results indicate that real-time sentiment analysis provides a significant edge in anticipating market movements, managing risks, and optimizing trade execution.

Furthermore, the paper addresses the challenges of implementing real-time sentiment analysis, such as data quality, processing latency, and the dynamic nature of market sentiment. It also explores the ethical considerations and potential market impacts of widespread adoption of sentiment-enhanced trading strategies.

Keyphrases: Algorithmic Trading, Electronic Communication Networks (ECNs), high-frequency trading (HFT)

@booklet{EasyChair:14295,
  author    = {Abill Robert},
  title     = {Algorithmic Trading Strategies Enhanced by Real-Time Sentiment Analysis},
  howpublished = {EasyChair Preprint 14295},
  year      = {EasyChair, 2024}}