Paper: arXiv 2405.10449

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.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

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0 of 88 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.