Optimal Text-Based Time-Series Indices

ArXiv ID: 2405.10449 “View on arXiv”

Authors: Unknown

Abstract

We propose an approach to construct text-based time-series indices in an optimal way–typically, indices that maximize the contemporaneous relation or the predictive performance with respect to a target variable, such as inflation. We illustrate our methodology with a corpus of news articles from the Wall Street Journal by optimizing text-based indices focusing on tracking the VIX index and inflation expectations. Our results highlight the superior performance of our approach compared to existing indices.

Keywords: text-based index, VIX, inflation expectations, Wall Street Journal, natural language processing, Multi-asset

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced optimization techniques (genetic algorithms with domain-specific operators) and formal mathematical notation for selection matrices, indicating high mathematical complexity. It also demonstrates rigorous empirical validation with a large corpus, out-of-sample testing, and comparative performance against established benchmarks.

  flowchart TD
    A["Research Goal<br>Create Optimal Text-Based Indices<br>to Track VIX & Inflation"] --> B["Data Input<br>Wall Street Journal Corpus"]
    B --> C["Methodology<br>Optimization Algorithm<br>Maximizes Contemporaneous/Predictive Power"]
    C --> D["Computational Process<br>Text Processing<br>Feature Extraction<br>Statistical Optimization"]
    D --> E["Comparison<br>vs. Existing Baseline Indices"]
    E --> F{"Key Findings/Outcomes"}
    F --> G["Superior Tracking Performance<br>(VIX & Inflation)"]
    F --> H["Optimal Index Construction<br>Methodology"]