Detection of financial opportunities in micro-blogging data with a stacked classification system

ArXiv ID: 2404.07224 “View on arXiv”

Authors: Unknown

Abstract

Micro-blogging sources such as the Twitter social network provide valuable real-time data for market prediction models. Investors’ opinions in this network follow the fluctuations of the stock markets and often include educated speculations on market opportunities that may have impact on the actions of other investors. In view of this, we propose a novel system to detect positive predictions in tweets, a type of financial emotions which we term “opportunities” that are akin to “anticipation” in Plutchik’s theory. Specifically, we seek a high detection precision to present a financial operator a substantial amount of such tweets while differentiating them from the rest of financial emotions in our system. We achieve it with a three-layer stacked Machine Learning classification system with sophisticated features that result from applying Natural Language Processing techniques to extract valuable linguistic information. Experimental results on a dataset that has been manually annotated with financial emotion and ticker occurrence tags demonstrate that our system yields satisfactory and competitive performance in financial opportunity detection, with precision values up to 83%. This promising outcome endorses the usability of our system to support investors’ decision making.

Keywords: financial opportunity detection, stacked machine learning, feature engineering, linguistic analysis, market prediction, Equities (Stocks)

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper uses standard ML/NLP techniques without advanced mathematical derivations, but employs a structured empirical approach with a manually annotated dataset, feature engineering, and precision metrics (up to 83%), making it data-driven and practical for trading applications.

  flowchart TD
    A["Research Goal<br>Detect 'Opportunities'<br>in Financial Tweets"] --> B["Data<br>Manually Annotated Dataset<br>Tweets w/ Emotion & Ticker Tags"]
    B --> C["Feature Engineering<br>Linguistic & Semantic Feature Extraction"]
    C --> D["Stacked ML System<br>Three-Layer Classification Architecture"]
    D --> E["Experimental Evaluation<br>Performance Testing"]
    E --> F{"Key Findings & Outcomes"}
    F --> G["Precision up to 83%<br>High Detection Specificity"]
    F --> H["System Usability<br>Supports Investor Decision Making"]