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Narrative-Digital-Finance

SNSF Narrative Digital Finance: A tale of structural breaks, bubbles & market narratives - Research Project of the Swiss National Science Foundation

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Property Value
Language TeX
Stars 0
Forks 0
Watchers 0
Open Issues 50
License No License
Created 2025-12-01
Last Updated 2026-03-25
Last Push 2026-01-30
Contributors 2
Default Branch main
Visibility private

Notebooks

This repository contains 3 notebook(s):

Notebook Language Type

| macroeconomic_narratives_breakpoints | PYTHON | jupyter |

| gigando_speech_NER | PYTHON | jupyter |

| macroeconomic_narratives_breakpoints | PYTHON | jupyter |

Datasets

This repository includes 51 dataset(s):

Dataset Format Size

| data | | 0.0 KB |

| breakpoint_sentiment_results.json | .json | 12.0 KB |

| breakpoints.json | .json | 0.74 KB |

| corr_inflation.csv | .csv | 0.2 KB |

| corr_macro.csv | .csv | 0.18 KB |

| correlation_matrix.csv | .csv | 0.19 KB |

| granger_results.json | .json | 2.09 KB |

| inventory.json | .json | 3.1 KB |

| pca_components.csv | .csv | 42.94 KB |

| pca_loadings.csv | .csv | 0.87 KB |

| processed_macro.csv | .csv | 41.75 KB |

| raw_macro.csv | .csv | 18.0 KB |

| rolling_results_inflation.csv | .csv | 27.88 KB |

| rolling_results_macro.csv | .csv | 27.62 KB |

| scaled_macro.csv | .csv | 41.51 KB |

| sentiment_aggregated.csv | .csv | 15.44 KB |

| sentiment_raw.csv | .csv | 6.57 KB |

| sentiment_standardized.csv | .csv | 15.29 KB |

| gigando_speeches_ner_v2.parquet | .parquet | 0.13 KB |

| macroeconomic_data.csv | .csv | 14.2 KB |

| SLR_Narrative_Data_Extraction_v3.xlsx | .xlsx | 14.22 KB |

| reference_report.json | .json | 18.03 KB |

| correlation_results.json | .json | 1.45 KB |

| granger_results.json | .json | 1.75 KB |

| stationarity_results.json | .json | 4.96 KB |

| project-template.json | .json | 14.13 KB |

| data | | 0.0 KB |

| .gitkeep | | 0.02 KB |

| authors.json | .json | 2.78 KB |

| collaborators.json | .json | 1.58 KB |

| content_layout_review.json | .json | 30.76 KB |

| content_review.json | .json | 10.09 KB |

| cost_summary.json | .json | 3.69 KB |

| dmp-content.json | .json | 15.92 KB |

| file_catalog.json | .json | 66.27 KB |

| images.json | .json | 11.22 KB |

| project_research_outputs.json | .json | 21.77 KB |

| publications.json | .json | 109.36 KB |

| research.json | .json | 108.16 KB |

| summary.json | .json | 0.4 KB |

| thesis.json | .json | 2.94 KB |

| validation_report.json | .json | 42.7 KB |

| data | | 0.0 KB |

| gigando-cb-speeches_1996-2025.csv | .csv | 0.13 KB |

| gigando_speeches_ner.parquet | .parquet | 0.13 KB |

| llm_narrative_sentiment_daily.csv | .csv | 1419.16 KB |

| macroeconomic_data.csv | .csv | 14.2 KB |

| data | | 0.0 KB |

| project-template.json | .json | 14.13 KB |

| project-template.json | .json | 14.13 KB |

| scraped_content.json | .json | 50.52 KB |

Reproducibility

This repository includes reproducibility tools:

  • Python requirements.txt

Status

  • Issues: Enabled
  • Wiki: Enabled
  • Pages: Enabled

README

Narrative Digital Finance

A tale of structural breaks, bubbles & market narratives

Research Project of the Swiss National Science Foundation (SNSF)

SNSF EU Horizon GitHub Pages

Overview

Large fluctuations, instabilities, trends and uncertainty of financial markets constitute a substantial challenge for asset management companies, pension funds and regulators. This project develops a comprehensive framework that utilizes advanced machine learning and NLP techniques to predict market outcomes, detect asset price bubbles, and identify structural breaks using diverse data sources.

Research Objectives

Overall Objectives

Develop a comprehensive framework utilizing advanced machine learning and NLP techniques to: - Predict market outcomes - Detect asset price bubbles - Identify structural breaks - Analyze diverse data sources including financial data and narrative content from text, speech, and multimedia

Specific Aims

  1. Validate and refine existing econometric models using real-world financial data
  2. Integrate narrative analysis to understand and predict market behaviors and asset price dynamics
  3. Create a multidimensional AI and ML framework that enhances the detection of market anomalies and forecasts financial trends

Methods

The approach involves: - Collecting and processing stock prices, macroeconomic indicators, and textual content from the web - Employing text mining and NLP techniques to analyze sentiment, narrative structures, and their impact on market movements - Developing and testing new AI models that combine traditional financial analysis with narrative insights

Team

Cooperation between University of Twente (Netherlands) and Bern University of Applied Sciences (Switzerland)

Name Role Affiliation ORCID
Joerg Osterrieder Principal Investigator BFH, University of Twente 0000-0003-0189-8636
Gabin Taibi PhD Researcher BFH, University of Twente 0000-0002-0785-6771
Yiting Liu PhD Researcher BFH, University of Twente 0009-0006-9554-8205
Lennart John Baals PhD Researcher BFH, University of Twente 0000-0002-7737-9675
Marius Jan Klein Researcher BFH -

Collaborations

Industry Partners

  • Deutsche Borse AG - Dr. Stefan Schlamp (Head of Quantitative Analytics, Market Data and Services)
  • Quoniam Asset Management - Dr. Axel Gross-Klussmann

Research Networks

  • COST Action CA19130 - Fintech and Artificial Intelligence in Finance (Chair: J. Osterrieder, 49 countries)
  • MSCA Industrial Doctoral Network on Digital Finance - EU Horizon Europe Grant 101119635

Academic Partners

  • Humboldt-University Berlin (Prof. Dr. Wolfgang Karl Haerdle)
  • Bucharest University of Economic Studies (Prof. Dr. Daniel Traian Pele)
  • Babes-Bolyai University (Prof. Dr. Codruta Mare)

Publications

2025

  1. TOPol: Capturing and Explaining Multidimensional Semantic Polarity Fields and Vectors
  2. Taibi, G., Gomez, L. (2025). Working paper.

  3. OSF Repository

  4. Nanosecond Microstructure: High-Frequency Traders Participation Stylized Facts

  5. Taibi, G., Osterrieder, J., Schlamp, S. (2025). Working paper.

  6. AI-Enhanced Systematic Literature Review on Financial Narratives

  7. Taibi, G. et al. (2025). Under revision at Financial Innovation.

2024

  1. Reaction Times to Economic News in High-Frequency Trading
  2. Osterrieder, J., Schlamp, S. (2025).

  3. SSRN 5112295

  4. Hypothesizing Multimodal Influence: Assessing the Impact of Textual and Non-Textual Data on Financial Instrument Pricing Using NLP and Generative AI

  5. Bolesta, K., Taibi, G., Mare, C., Osterrieder, J., Hopp, C. (2024).
  6. SSRN 4698153

Project Output

Category Count
Scientific Publications 5
Datasets 3
Collaborations 11
Academic Events 6
Knowledge Transfer Events 4

Funding

  • Swiss National Science Foundation (SNSF)
  • Grant Number: IZCOZ0_213370
  • Amount: CHF 236,118
  • Duration: July 2023 - June 2026
  • Horizon Europe - Marie Sklodowska-Curie Actions
  • Grant Agreement No. 101119635

License

This project is licensed under the MIT License - see the LICENSE file for details.

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Horizon Europe: Marie Sklodowska-Curie Actions.

(c) Joerg Osterrieder 2025