Narrative Economics Alliance Ruhr

The NEAR project lays the foundation for the development of the University Alliance Ruhr (UAR) into a leading European location in the new field of Narrative Economics and strengthens the UAR’s competence in Empirical Economic Research. NEAR contributes theoretical, methodological, and empirical research:

In theoretical development, NEAR is focused on a conceptual clarification of the narrative concept and the development of new modeling approaches dealing with the diffusion of economic narratives using agent-based models.

Methodologically, NEAR researchers develop methods for economic narrative extraction from text data, for improving the calibration of topic models for narrative economic issues and for establishing inference methods tailored to narrative research.

Empirically, NEAR investigates and tests the causal connections between media-conveyed narratives and macroeconomic and financial dynamics. NEAR also examine whether narratives have predictive power for economic variables. NEAR also puts substantial focus on sustainability narratives.

NEAR pools expertise from the fields of Economic Theory, Economic Journalism, Finance, Statistics/Econometrics and Natural Language Processing. A series of workshops have already initiated interdisciplinary exchange within the UAR and will continue internationally.

People

JProf. Dr. Nina Boberg-Fazlic

economic history • economic growth and development • demographic economics • social mobility and inequality

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Prof. Dr. Martin Hibbeln

empirical banking and asset pricing • textual analysis in finance • risk management in financial institutions • regulating financial markets • financial technology

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Prof. Dr. Zohal Hessami

political selection • voting behavior • policy choices and public finances • electoral systems and accountability • economic and social/political consequences of migration

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Prof. Dr. Carsten Jentsch

resampling methods for dependent data • time series econometrics • economic narratives, fake news and disinformation • stochastic network analysis

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Prof. Dr. Laetitia Lenel

economic history • relationship between economic expectations and economic crises • narratives and policy-making

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Prof. Dr. Matthias Pelster

Social interactions, communication, and economic decision-making • Trading activities of retail investors • Financial education and financial decision-making • Financial technology • Behavioral Finance

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Prof. Dr. Henrik Müller

economic policy and the media • cross-directional influences between public discourses, policy making and markets

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Prof. Dr. Michael Roos

complexity economics & economic complexity • agent-based modeling • societal transformation • sustainability • economic discourses and economic policy

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Prof. Dr. Torsten Schmidt

macroeconomics • business cycle • forecasting • big data • text as data

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Dr. Kai-Robin Lange

natural language processing • economic narratives • political text analysis • temporal text modeling • causal inference

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Prof. Dr. Galina Zudenkova

political economy • public economics • applied theory • model uncertainty • behavioral economics

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Dr. Jonas Rieger

nlp methods • few/zero-shot narrative extraction • parameter-efficient fine-tuning

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Niklas Benner

natural language processing • machine learning • economic policy • central bank communication

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Ralf Metzler

textual analysis of investment brochures • transmission of firm communication my financial media

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Matthias Reccius

economic policy narratives • populism • belief formation • natural language processing • semantic representation learning

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Tobias Schmidt

economic narratives • coverage of inflation in large text data • natural language processing • european public sphere

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Noah Urban

financial data analytics, in particular machine learning in finance • financial media and firm communication • credit risk modeling

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Research

This is a list of recent research items relevant to NEAR.
For full lists, visit the team members‘ linked websites.

Bentzen, J., Boberg-Fazlic N., Sharp P., Skovsgaard C., Vedel C. (2025). Holy Cows and Spilt Milk: The Impact of Religious Conflict on Firm-Level Productivity. Journal of Development Economics 179, 103651. https://doi.org/10.1016/j.jdeveco.2025.103651

Baskaran, T., Hessami Z. (2025). Women in Political Bodies as Policymakers. Review of Economics and Statistics, forthcoming. https://doi.org/10.1162/rest_a_01352

Roos, M., Reccius, M. (2025). Attitude and Expectation Change Through Economic Narratives. In: Czupryna, M., Kamiński, B., Verhagen, H. (eds) Advances in Social Simulation. Springer Proceedings in Complexity. https://doi.org/10.1007/978-3-031-91782-0_2

Golosnoy, V., Okhrin, Y., Roos, M. W. (2025). Empirical Similarity for Revealing the US Interest Rate Policy: Modeling Case-Based Decisions of the FOMC. Empirical Economics, 1-30. https://doi.org/10.1007/s00181-024-02709-6

Klocke, N., Müller, D., Hasso, T., Pelster, M. (2025): The impact of peer returns in social trading, Journal of Behavioral and Experimental Finance 46, 101057. https://doi.org/10.1016/j.jbef.2025.101057

Lenel L. (2025, forthcoming) Tools of Trust. Business Forecasting in the Twentieth Century. Cambridge: Cambridge University Press. 

Lenel, L., Nützenadel, A., Streb, J., & Köhler, I. (eds.) (2025, forthcoming). The Routledge Handbook of Economic Expectations in Historical Perspective. London: Routledge.

Müller, H., Blagov, B., Schmidt, T., Rieger, & J., Jentsch, C. (2025) The Macroeconomic Impact of Asymmetric Uncertainty Shocks. The Journal of Economic Asymmetries, 31, e00410. https://doi.org/10.1016/j.jeca.2025.e00410

Baskaran, T., Hessami, Z., and Schirner S. (2024). Young Versus Old Politicians and Public Spending Priorities. Journal of Economic Behavior and Organization, 225, 88-106. https://doi.org/10.1016/j.jebo.2024.07.002

Boberg-Fazlic, N. and Sharp P. (2024). Immigrant Communities and Knowledge Spillovers: Danish-Americans and the Development of the Dairy Industry in the United States. American Economic Journal: Macroeconomics, Vol. 16, No. 1, 102-46. 10.1257/mac.20210074

Deng, J., Yang, M., Pelster, M., Tan, Y. (2024): ‚Social trading, communication, and networks‘, Information Systems Research 35, 1546-1564. https://doi.org/10.1287/isre.2021.0143

Hibbeln, M., Kopp, R. M., & Urban, N. (2024) Heterogeneous Machine Learning Ensembles for Recovery Rate Forecasting. http://dx.doi.org/10.2139/ssrn.3913710

Hibbeln M., Metzler R., Osterkamp, W. (2024) Not on the Same Page – Comprehensibility of Investment Prospectuses. Working Paper. https://dx.doi.org/10.2139/ssrn.4182623

Hufnagel, L. M., & Metzler, R. (2024). Identifying the drivers of economic uncertainty perception in China: a news-based approach. Asia-Pacific Journal of Accounting & Economics, 1–18. https://doi.org/10.1080/16081625.2024.2405494

Lange, K.-R., Rieger, J. and Jentsch, C. (2024). Lex2Sent: A bagging approach to unsupervised sentiment analysis. Proceedings of the 20th KONVENS Conference, 281–291. Link.

Müller, H., Blagov, B., Schmidt, T., Rieger, J., Jentsch, C. (2024) The Macroeconomic Impact of Asymmetric Uncertainty Shocks. Ruhr Economic Paper #1124.

Rieger, J., Jentsch, C. and Rahnenführer, J. (2024). LDAPrototype: A model selection algorithm to improve reliability of latent Dirichlet allocation. PeerJ Computer Science 10.2279. https://doi.org/10.7717/peerj-cs.2279

Rieger, J., Yanchenko, K., Ruckdeschel, M., von Nordheim, G., Kleinen-von Königslöw, K., & Wiedemann, G. (2024). Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine. Studies in Communication and Media, 13. Jg., 1/2024, 72–100. https://doi.org/10.5771/2192-4007-2024-1-72

Banning, F. & Reale, J. & Roos, M. (2023). The Complexity of Corporate Culture as a Potential Source of Firm Profit Differentials. https://dx.doi.org/10.2139/ssrn.4508695

Boumans, D., Müller, H., & Sauer, S. (2023). How media content influences economic expectations: Evidence from a global expert survey. Journal of Forecasting. https://doi.org/10.1002/for.2961

Hibbeln M., Osterkamp, W. (2023) Simple is Simply not Enough – Features versus Labels of Complex Financial Securities. Review of Derivatives Research, Vol. 27, 2024, pp. 113–150. https://doi.org/10.1007/s11147-024-09201-4

Jentsch, C., & Lange, K.-R. (2023). SpeakGer: A meta-data enriched speech corpus of German state and federal parliaments. Proceedings of the 3rd Workshop on Computational Linguistics for Political Text Analysis@KONVENS 2023.

Krause, C., Rieger, J., Flossdorf, J., Jentsch, C. and Beck, F. (2023). Visually Analyzing Topic Change Points in Temporal Text Collections. Vision, Modeling, and Visualization. The Eurographics Association. https://doi.org/10.2312/vmv.20231231

Müller. H. (2023). Challenging Economic Journalism. Covering Business and Politics in an Age of Uncertainty. Palgrave Macmillan. https://doi.org/10.1007/978-3-031-31030-0

Müller, H., Rieger. J., & Hornig, N. (2023). “We’re rolling”. Our Uncertainty Perception Indicator (UPI) in Q4 2020: introducing RollingLDA, a new Method for the Measurement of Evolving Economic Narratives. DoCMA Working Paper #6. http://dx.doi.org/10.17877/DE290R-21974

Müller, H., Schmidt, T., Rieger, J., & Hufnagel, L. M. (2023). “The Inflation Attention Cycle. Updating the Inflation Perception Indicator (IPI) up to February 2023 – a Research Note”. DoCMA Working Paper #13. http://dx.doi.org/10.17877/DE290R-23141

Rieger, J., Hornig, N., Flossdorf, J., Müller, H., Mündges, S., Jentsch, C., Rahnenführer, J. and Elmer, C. (2023). Debunking Disinformation with GADMO: A Topic Modeling Analysis of a Comprehensive Corpus of German-language Fact-Checks. Proceedings of the 4th Conference on Language, Data and Knowledge. Link. GitHub.

Rieger, J., Hornig, N., Schmidt, T., & Müller, H. (2023). Early Warning Systems? Building Time Consistent Perception Indicators for Economic Uncertainty and Inflation Using Efficient Dynamic Modeling. Proceedings of the 3rd Workshop on Modelling Uncertainty in the Financial World. https://github.com/JonasRieger/mufin23/blob/master/paper.pdf

Roos, M. & Reccius, M. (2023). Narratives in economics. Journal of Economic Surveys, 0, 1–39. https://doi.org/10.1111/joes.12576

Schmidt, T., Müller, H., Rieger, J., Schmidt, T., & Jentsch, C. (2023). Inflation Perception and the Formation of Inflation Expectations. Ruhr Economic Papers #1025. http://dx.doi.org/10.4419/96973191

Bittermann, A. and Rieger, J. (2022). Finding scientific topics in continuously growing text corpora. Proceedings of the 3rd Workshop on Scholarly Document Processing. Link. GitHub. PsychTopics App.

Gsottbauer, E., D. Müller, S. Müller, S. Trautmann, and Zudenkova G. (2022). Social class and (un)ethical
behaviour: Causal and correlational evidence. The Economic Journal 132, 2392–2411. DOI: 10.1093/ej/ueac022.

Lange, K.-R., Rieger, J., Benner, N., & Jentsch, C. (2022). Zeitenwenden: Detecting changes in the German political discourse. Proceedings of the 2nd Workshop on Computational Linguistics for Political Text Analysis, 47-53. https://old.gscl.org/media/pages/arbeitskreise/cpss/cpss-2022/workshop-proceedings-2022/254133848-1662996909/cpss-2022-proceedings.pdf

Lange, K.-R., Reccius, M., Schmidt, T., Müller, H., Roos, M., & Jentsch, C. (2022). Towards extracting collective economic narratives from texts. Ruhr Economic Papers #963. https://doi.org/10.4419/96973127

Rieger, J., Lange, K.-R., Flossdorf, J., & Jentsch, C. (2022). Dynamic change detection in topics based on rolling LDAs. Proceedings of the Text2Story’22 Workshop. CEUR-WS 3117, 5-13. https://ceur-ws.org/Vol-3117/paper1.pdf

Rieger, J., Yanchenko, K., Ruckdeschel, M., von Nordheim, G., Königslöw, K. K. V., & Wiedemann, G. (2023). Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine. https://doi.org/10.48550/arXiv.2312.16975

Shrub, Y., Rieger, J., Müller, H. and Jentsch, C. (2022). Text data rule – don’t they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators. Ruhr Economic Papers #964. https://doi.org/10.4419/96973128

Roos, M., Reale, J., Banning, F. (2022). A value-based model of job performance. PLOS ONE, 17. https://doi.org/10.1371/journal.pone.0262430

Benner, N., Lange, K.-R., & Jentsch, C. (2022). Named Entity Narratives. Ruhr Economic Papers #962. https://doi.org/10.4419/96973126

Müller, H., Schmidt, T., Rieger, J., Hufnagel, L. M., & Hornig, N. (2022). “A German Inflation Narrative – How the Media frame Price Dynamics: Results from a RollingLDA Analysis”. DoCMA Working Paper #9. https://doi.org/10.17877/de290r-22632

Blagov, B,, Müller, H., Jentsch, C., Schmidt, T. (2021). The investment narrative: Improving private investment forecasts with media data. Ruhr Economic Papers, No. 921. https://doi.org/10.4419/96973067

Jentsch, C., Lee, E. R., & Mammen, E. (2021). Poisson reduced rank models with an application to political text data. Biometrika, 108(2), 455-468. https://doi.org/10.1016/j.csda.2019.106813

Jentsch, C., Mammen, E., Müller, H., Rieger, J., & Schötz, C. (2021). Text mining methods for measuring the coherence of party manifestos for the German federal elections from 1990 to 2021. DoCMA Working Paper #8. https://doi.org/10.17877/de290r-22363

Müller, H. (2017). Populism, de-globalisation, and media competition: The spiral of noise. Central European Journal of Communication, 9, 1 (18), 64–78. https://doi.org/10.19195/1899-5101.10.1(18).5

  • NEAR Conference Details

    We are thrilled for our first NEAR Conference coming up (May 23rd and 24th). Participation is free of charge for all interested participants. Participation is possible in-person or digitally. In both cases, please register at the following link: https://forms.gle/YL1JGLGGyyFMkag96

    We will send the link for digital participation to all registered e-mail addresses the evening before the conference (May 22nd).

    Please find the timetable of the conference here: https://docs.google.com/spreadsheets/d/1L3FiI7xpGlyMd_ND5xlJkUwsY3Zb8XFFZ7s44a3Kezg

    For on-site participation, we would like to give you some assistance in getting to the conference location (Beckmanns Hof) near to Ruhr University Bochum.

    How to get to the location by public transport:

    From the metro stop „Ruhr-Universität“ (circled) it´s roughly a 10 minute walk across campus, passing the university library („UB“), the main auditorium („Audimax“) and the canteen („Mensa“), down a few flights of stairs and following a short winding path to Beckmanns Hof.

    How to get to the location by car:

    There are different parking spaces available: You can access the parking spaces at the southern end of campus by exiting Universitätsstraße at „Ruhr-Universität-Mitte“ and passing through the main parking garage. From there, you can quickly access the short path to Beckmanns Hof on foot.

    Alternatively, you can also use the main parking garage itself. Upon exiting the garage at the university „Forum“ (cicled), you can pass the main auditorium („Audimax“) and the canteen („Mensa“), walk down a few flights of stairs and follow the path to Beckmanns Hof.

  • Call for Papers

    1st NEAR Conference on Narrative Economics

    Dates: May 23rd and 24th, 2024
    Location: Beckmanns Hof in Bochum, Germany

    The aim of the conference is to bring together scholars working on topics related to the emerging field of narrative economics. The conference is organized within the framework of the Narrative Economics Alliance Ruhr (NEAR) project, funded by MERCUR. At NEAR, researchers work on the intersection between economics, finance and language, contributing to economic theory and empirical methodology. NEAR is particularly committed to advancing the methodological foundations and applied usage of text-as-data methods in the context of economics and finance. NEAR researchers also work on the forefront of incorporating narrative sense-making into economic models of information diffusion and decision-making.

    We welcome submissions on topics related to this agenda, including methodological improvements and applications broadly based in economics, political economy and finance as well as their intersections with NLP and mass media studies.

    Keynote Speakers:
    Elliott Ash (ETH Zurich)
    Julia Bingler (University of Oxford)

    Submissions can be made in the form of (extended) abstracts or full papers. We request accepted presentations to be presented on site. There will be no conference fee. The conference will be organized by the Narrative Economics Alliance Ruhr project.

    Submission Deadline: April 16th, 2024
    Acceptance Notification until April 22nd, 2024

    Submission link: expired
    Registration for participation only: https://forms.gle/YL1JGLGGyyFMkag96