Paper: arXiv 2409.20397

Abstract

We extract the sentiment from german and english news articles on companies in the DAX40 stock market index and use it to create a sentiment-powered pendant. Comparing it to existing products which adjust their weights at pre-defined dates once per month, we show that our index is able to react more swiftly to sentiment information mined from online news. Over the nearly 6 years we considered, the sentiment index manages to create an annualized return of 7.51% compared to the 2.13% of the DAX40, while taking transaction costs into account. In this work, we present the framework we employed to develop this sentiment index.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper applies established NLP and convex optimization techniques (low advanced math) but demonstrates strong empirical rigor with a 6-year backtest on real news data, transaction costs, and a live benchmark comparison.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Create a Sentiment-Driven<br>DAX40 Performance Index"] --> B["Data Collection<br>German & English News Articles"]
  B --> C["Methodology<br>Deep Learning Sentiment Extraction<br>Company Stock Linkage"]
  C --> D["Computation<br>Rebalancing & Weight Calculation<br>Transaction Cost Deduction"]
  D --> E["Benchmarking<br>vs. Standard DAX40 (2.13% return)"]
  E --> F["Key Outcomes<br>7.51% Annualized Return<br>Faster Market Reaction"]