A Framework for the Construction of a Sentiment-Driven Performance Index: The Case of DAX40

ArXiv ID: 2409.20397 “View on arXiv”

Authors: Unknown

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.

Keywords: sentiment analysis, index construction, DAX40, annualized return, news mining, equities

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: 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.

  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"]