IDEAS home Printed from https://ideas.repec.org/p/zbw/ifmmat/280979.html
   My bibliography  Save this paper

Bedrohungen und Chancen frühzeitig erkennen: Entwicklung eines Früherkennungskonzepts

Author

Listed:
  • Akalan, Rodi
  • Brink, Siegrun
  • Icks, Annette
  • Wolter, Hans-Jürgen

Abstract

Der Mittelstand ist derzeit mit vielfältigen Krisen konfrontiert. Eine frühe Erkennung relevanter Herausforderungen und Chancen ermöglicht es den mittelständischen Unternehmen und der Wirtschaftspolitik, sich darauf vorzubereiten und die geeigneten Rahmenbedingungen zu setzen. Gegenwärtig erfolgt die Früherkennung zumeist anhand von Konjunkturindikatoren, die i.d.R. anhand konkreter Zahlenwerte Rückschlüsse auf die zukünftige Wirtschaftsentwicklung ziehen. Eine systematische Auswertung wirtschaftsrelevanter Textdaten erfolgt nicht. Hier setzt das in der vorliegenden Studie entwickelte innovative Früherkennungskonzept an, das KI-gestützt Textdaten aus Medien und Wirtschaft effizient analysiert und Themen extrahiert. Mithilfe von Praxistests zeigen wir, dass das Konzept zuverlässig funktioniert und relevante Themen frühzeitig erkennen kann.

Suggested Citation

  • Akalan, Rodi & Brink, Siegrun & Icks, Annette & Wolter, Hans-Jürgen, 2023. "Bedrohungen und Chancen frühzeitig erkennen: Entwicklung eines Früherkennungskonzepts," IfM-Materialien 303, Institut für Mittelstandsforschung (IfM) Bonn.
    • Handle: RePEc:zbw:ifmmat:280979
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/280979/1/1874115443.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hinze, Jörg, 2003. "Prognoseleistung von Frühindikatoren: Die Bedeutung von Frühindikatoren für Konjunkturprognosen - Eine Analyse für Deutschland," HWWA Discussion Papers 236, Hamburg Institute of International Economics (HWWA).
    2. Anja Rossen, 2012. "Konjunkturschlaglicht: Frühindikatoren: gute Vorlaufeigenschaften," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 92(8), pages 575-576, August.
    3. Le Mezo, Helena & Ferrari Minesso, Massimo, 2020. "Using information in newspaper articles as an indicator of real economic activity," Economic Bulletin Boxes, European Central Bank, vol. 2.
    4. Hinze, Jorg, 2003. "Prognoseleistung von Fruhindikatoren: Die Bedeutung von Fruhindikatoren fur Konjunk-turprognosen - Eine Analyse fur Deutschland," Discussion Paper Series 26253, Hamburg Institute of International Economics.
    5. Chengyu Huang & Sean Simpson & Daria Ulybina & Agustin Roitman, 2019. "News-based Sentiment Indicators," IMF Working Papers 2019/273, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
    2. Katja Rietzler & Sabine Stephan, 2012. "Monthly recession predictions in real time: A density forecast approach for German industrial production," IMK Working Paper 94-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    3. Massimo Ferrari Minesso & Laura Lebastard & Helena Mezo, 2023. "Text-Based Recession Probabilities," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 415-438, June.
    4. Juan M. Londono & Stijn Claessens & Ricardo Correa, 2024. "Financial Stability Governance and Central Bank Communications," International Journal of Central Banking, International Journal of Central Banking, vol. 20(4), pages 175-220, October.
    5. Stolbov, Mikhail & Shchepeleva, Maria & Karminsky, Alexander, 2022. "When central bank research meets Google search: A sentiment index of global financial stress," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    6. repec:diw:diwwpp:dp522 is not listed on IDEAS
    7. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.
    8. Gou, Qin & Li, Xingshen & Zhao, Guojun, 2024. "Surges of cross border capital flow: The impact of digital finance," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    9. Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021. "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series 2616, European Central Bank.
    10. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    11. Daouia, Abdelaati & Abbas, Yasser, 2023. "Understanding World Economy Dynamics Based on Indicators and Events," TSE Working Papers 23-1461, Toulouse School of Economics (TSE).

    More about this item

    Keywords

    Früherkennung; Themen; Topic Modeling; Maschinelles Lernen; early detection; topics; topic modeling; machine learning;
    All these keywords.

    JEL classification:

    • M20 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - General
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:ifmmat:280979. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/ifmbode.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.