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  1. Predicting innovative firms using web mining and deep learning
    Autor*in: Kinne, Jan; Lenz, David
    Erschienen: [2019]
    Verlag:  ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung GmbH Mannheim, Mannheim

    Innovation is considered as a main driver of economic growth. Promoting the development of innovation through STI (science, technology and innovation) policies requires accurate indicators of innovation. Traditional indicators often lack coverage,... mehr

    Standort:
    Staats- und Universitätsbibliothek Bremen
    Fernleihe:
    keine Fernleihe

    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
    Fernleihe:
    keine Fernleihe

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 15
    Fernleihe:
    keine Fernleihe

    Standort:
    Universitätsbibliothek Mannheim
    Fernleihe:
    keine Fernleihe

     

    Innovation is considered as a main driver of economic growth. Promoting the development of innovation through STI (science, technology and innovation) policies requires accurate indicators of innovation. Traditional indicators often lack coverage, granularity as well as timeliness and involve high data collection costs, especially when conducted at a large scale. In this paper, we propose a novel approach on how to create firm-level innovation indicators at the scale of millions of firms. We use traditional firm-level innovation indicators from the questionnaire-based Community Innovation Survey (CIS) survey to train an artificial neural network classification model on labelled (innovative/non-innovative) web texts of surveyed firms. Subsequently, we apply this classification model to the web texts of hundreds of thousands of firms in Germany to predict their innovation status. Our results show that this approach produces credible predictions and has the potential to be a valuable and highly cost-efficient addition to the existing set of innovation indicators, especially due to its coverage and regional granularity. The predicted firm-level probabilities can also directly be interpreted as a continuous measure of innovativeness, opening up additional advantages over traditional binary innovation indicators.

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/191615
    Schriftenreihe: Discussion paper / ZEW ; no. 19-001 (01/2019)
    Schlagworte: Web Mining; Web Scraping; R&D; R&I; STI; Innovation; Indicators; Text Mining; Natural Language Processing; NLP; Deep Learning
    Umfang: 1 Online-Ressource (9 Seiten), Illustrationen
  2. Deep hedging: hedging derivatives under generic market frictions using reinforcement learning
    Autor*in: Buehler, Hans; Gonon, Lukas; Teichmann, Josef; Wood, Ben; Mohan, Baranidharan; Kochems, Jonathan
    Erschienen: 2019
    Verlag:  Swiss Finance Institute, Geneva

    This article discusses a new application of reinforcement learning: to the problem of hedging a portfolio of “over-the-counter” derivatives under under market frictions such as trading costs and liquidity constraints. It is an extended version of our... mehr

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    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
    Fernleihe:
    keine Fernleihe

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 544
    Fernleihe:
    keine Fernleihe

     

    This article discusses a new application of reinforcement learning: to the problem of hedging a portfolio of “over-the-counter” derivatives under under market frictions such as trading costs and liquidity constraints. It is an extended version of our recent work "https://www.ssrn.com/abstract=3120710" www.ssrn.com/abstract=3120710, here using notation more common in the machine learning literature.The objective is to maximize a non-linear risk-adjusted return function by trading in liquid hedging instruments such as equities or listed options. The approach presented here is the first efficient and model-independent algorithm which can be used for such problems at scale

     
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    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: Research paper series / Swiss Finance Institute ; no 19, 80
    Swiss Finance Institute Research Paper ; No. 19-80
    Schlagworte: Reinforcement Learning; Imperfect Hedging; Derivatives Pricing; Derivatives Hedging; Deep Learning
    Umfang: 1 Online-Ressource (circa 14 Seiten), Illustrationen
  3. Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning
    Autor*in: Bolte, Jérôme; Pauwels, Edouard
    Erschienen: [2019]
    Verlag:  Toulouse School of Economics, [Toulouse]

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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 330
    Fernleihe:
    keine Fernleihe
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: Working papers / Toulouse School of Economics ; no 1044 (October 2019)
    Schlagworte: Deep Learning; Automatic differentiation; Backpropagation algorithm; Nonsmooth stochastic optimization; Definable sets; o-minimal structures; Stochastic gradient; Clarke subdifferential; First order methods
    Umfang: 1 Online-Ressource (circa 35 Seiten), Illustrationen