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  1. Macroeconomic forecasting and evaluation with supervised and neural network reinforced factor models
    Erschienen: 2021
    Verlag:  Universitäts- und Stadtbibliothek Köln, Köln

    Universitätsbibliothek Braunschweig
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    Staats- und Universitätsbibliothek Bremen
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    Fachhochschule Erfurt, Hochschulbibliothek
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    Niedersächsische Staats- und Universitätsbibliothek Göttingen
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    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
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    Technische Universität Hamburg, Universitätsbibliothek
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    Bibliothek der Hochschule Hannover
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    Bibliothek im Kurt-Schwitters-Forum
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    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Zentrale Hochschulbibliothek Lübeck
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    Leuphana Universität Lüneburg, Medien- und Informationszentrum, Universitätsbibliothek
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    Hochschule Magdeburg-Stendal, Hochschulbibliothek
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    Hochschule Osnabrück, Bibliothek Campus Westerberg
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    Hochschule Magdeburg-Stendal, Standort Stendal, Bibliothek
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    UB Weimar
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    Beteiligt: Breitung, Jörg (AkademischeR BetreuerIn); Kruse-Becher, Robinson (AkademischeR BetreuerIn)
    Sprache: Englisch
    Medientyp: Dissertation
    Format: Online
    Weitere Identifier:
    Schlagworte: Macroeconomic Forecasting; Factor Models; Neural Networks; Forecast Evaluation Tests
    Umfang: 1 Online-Ressource (circa 113 Seiten), Illustrationen
    Bemerkung(en):

    Dissertation, Universität zu Köln, 2021

  2. Bankruptcy prediction model based on business risk reports
    use of natural language processing techniques
    Erschienen: April.2021
    Verlag:  Faculty of Economics and Business, Hokkaido University, Sapporo, Japan

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    Sprache: Englisch
    Medientyp: Buch (Monographie)
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    Weitere Identifier:
    hdl: 2115/81088
    Schriftenreihe: Array ; no. 358 (2021)
    Schlagworte: Bankruptcy prediction; Business risk; Natural language processing; NLP; Sentiment analysis; Neural Networks
    Umfang: 1 Online-Ressource (circa 16 Seiten)
  3. A deep learning approach to estimate forward default intensities
    Erschienen: July 21, 2020
    Verlag:  Swiss Finance Institute, Geneva

    This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default... mehr

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    This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous literature is to allow the estimation of non-linear forward intensities by using neural networks instead of classical maximum likelihood estimation. The model specification allows an easy replication of previous literature using linear assumption and shows the improvement that can be achieved

     

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    Sprache: Englisch
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    Auflage/Ausgabe: Preliminary draft
    Schriftenreihe: Research paper series / Swiss Finance Institute ; no 20, 79
    Swiss Finance Institute Research Paper ; No. 20-79
    Schlagworte: Bankruptcy; Credit Risk; Default; Machine Learning; Neural Networks; Doubly Stochastic; Forward Poisson Intensities
    Umfang: 1 Online-Ressource (circa 39 Seiten), Illustrationen
  4. Deep learning, predictability, and optimal portfolio returns
    Erschienen: December 2020
    Verlag:  Charles University, Center for Economic Research and Graduate Education, Prague

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    Sprache: Englisch
    Medientyp: Buch (Monographie)
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    ISBN: 9788073434847; 9788073445669
    Schriftenreihe: Working paper series / CERGE-EI ; 677
    Schlagworte: Return Predictability; Portfolio Allocation; Machine Learning; Neural Networks; Em-pirical Asset Pricing
    Umfang: 46 Seiten, Illustrationen
    Bemerkung(en):

    Erscheint auch als Online-Ausgabe

  5. Neural networks and value at risk
    Erschienen: [2020]
    Verlag:  Financial Mathematics and Computation Research Cluster, [Dublin]

    Inspired by Gu, Kelly & X ... mehr

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    Inspired by Gu, Kelly & X ...

     

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    Format: Online
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    Schriftenreihe: Michael J. Brennan Irish finance working paper series research paper ; no. 20, 7
    Schlagworte: Asset Management; Downside Risk; Initialization; Loss Function; Machine Learning; Neural Networks
    Weitere Schlagworte: Array
    Umfang: 1 Online-Ressource (circa 43 Seiten), Illustrationen
  6. Machine learning for predicting stock return volatility
    Erschienen: 2021
    Verlag:  Swiss Finance Institute, Geneva

    We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised... mehr

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    We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as double the ones reported in the literature. We further show that machine learning methods can capture the stylized facts about volatility without relying on any assumption about the distribution of stock returns. Finally, we show that our long short-term memory model outperforms other models by properly carrying information from the past predictor values

     

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    Schriftenreihe: Research paper series / Swiss Finance Institute ; no 21, 95
    Schlagworte: Volatility Prediction; Volatility Clustering; LSTM; Neural Networks; Regression Trees
    Umfang: 1 Online-Ressource (circa 63 Seiten), Illustrationen
  7. DSGE models and machine learning
    an application to monetary policy in the euro area
    Erschienen: [2022]
    Verlag:  Philipps-University Marburg, School of Business and Economics, Marburg

    In the euro area, monetary policy is conducted by a single central bank for 19 member countries. However, countries are heterogeneous in their economic development, including their inflation rates. This paper combines a New Keynesian model and a... mehr

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    In the euro area, monetary policy is conducted by a single central bank for 19 member countries. However, countries are heterogeneous in their economic development, including their inflation rates. This paper combines a New Keynesian model and a neural network to assess whether the European Central Bank (ECB) conducted monetary policy between 2002 and 2022 according to the weighted average of the inflation rates within the European Monetary Union (EMU) or reacted more strongly to the inflation rate developments of certain EMU countries. The New Keynesian model first generates data which is used to train and evaluate several machine learning algorithms. We find that a neural network performs best out-of-sample. Thus, we use this algorithm to classify historical EMU data. Our findings suggest disproportional emphasis on the inflation rates experienced by southern EMU members for the vast majority of the time frame considered (80%). We argue that this result stems from a tendency of the ECB to react more strongly to countries whose inflation rates exhibit greater deviations from their long-term trend.

     

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    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/266030
    Auflage/Ausgabe: Draft: August 15, 2022
    Schriftenreihe: Joint discussion paper series in economics ; no. 2022, 32
    Schlagworte: New Keynesian Models; Monetary Policy; European Monetary Union; Neural Networks; Transfer Learning
    Umfang: 1 Online-Ressource (circa 25 Seiten), Illustrationen
  8. Emotion in euro area monetary policy communication and bond yields
    the Draghi era
    Erschienen: [2022]
    Verlag:  Australian National University, Crawford School of Public Policy, Canberra

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    Format: Online
    Schriftenreihe: CAMA working paper series ; 2022, 75 (December 2022)
    Schlagworte: Communication; ECB; Neural Networks; High-Frequency Data; Speech Emotion Recognition; Asset Prices
    Umfang: 1 Online-Ressource (circa 43 Seiten), Illustrationen
  9. A deep solver for BSDEs with jumps
    Erschienen: [2022]
    Verlag:  Università die Verona, Department of Economics, [Verona]

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    Sprache: Englisch
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    Format: Online
    Schriftenreihe: Working paper series / Department of Economics, University of Verona ; WP number 9 (November 2022)
    Schlagworte: BSDE with jumps; Deep BSDE Solver; Neural Networks
    Umfang: 1 Online-Ressource (circa 32 Seiten), Illustrationen
  10. Whose inflation rates matter most?
    a DSGE model and machine learning approach to monetary policy in the Euro area
    Erschienen: 2023
    Verlag:  Institute for Monetary and Financial Stability, Goethe University Frankfurt, Frankfurt am Main

    In the euro area, monetary policy is conducted by a single central bank for 20 member countries. However, countries are heterogeneous in their economic development, including their inflation rates. This paper combines a New Keynesian model and a... mehr

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    In the euro area, monetary policy is conducted by a single central bank for 20 member countries. However, countries are heterogeneous in their economic development, including their inflation rates. This paper combines a New Keynesian model and a neural network to assess whether the European Central Bank (ECB) conducted monetary policy between 2002 and 2022 according to the weighted average of the inflation rates within the European Monetary Union (EMU) or reacted more strongly to the inflation rate developments of certain EMU countries. The New Keynesian model first generates data which is used to train and evaluate several machine learning algorithms. They authors find that a neural network performs best out-of-sample. They use this algorithm to generally classify historical EMU data, and to determine the exact weight on the inflation rate of EMU members in each quarter of the past two decades. Their findings suggest disproportional emphasis of the ECB on the inflation rates of EMU members that exhibited high inflation rate volatility for the vast majority of the time frame considered (80%), with a median inflation weight of 67% on these countries. They show that these results stem from a tendency of the ECB to react more strongly to countries whose inflation rates exhibit greater deviations from their long-term trend.

     

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    Format: Online
    Weitere Identifier:
    hdl: 10419/273083
    Auflage/Ausgabe: This version: May 31, 2023
    Schriftenreihe: Working paper series / Institute for Monetary and Financial Stability ; no. 188 (2023)
    Schlagworte: New Keynesian Models; Monetary Policy; European Monetary Union; Neural Networks; Transfer Learning
    Umfang: 1 Online-Ressource (circa 45 Seiten), Illustrationen
  11. Measuring job loss during the pandemic recession in real time with Twitter data
    Erschienen: May 22, 2023
    Verlag:  Divisions of Research & Statistics and Monetary Affairs, Federal Reserve Board, Washington, D.C.

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    Format: Online
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    Schriftenreihe: Finance and economics discussion series ; 2023, 035
    Schlagworte: Job Loss; Natural Language Processing; Neural Networks
    Umfang: 1 Online-Ressource (circa 16 Seiten), Illustrationen
  12. Data driven enrichment of historical low-resource languages for foundational NLP tasks and their neural network models
    Autor*in: Ahmed, Sajawel
    Erschienen: 2023
    Verlag:  Universitätsbibliothek Johann Christian Senckenberg, Frankfurt am Main

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    Beteiligt: Roig Noguera, Gemma (Akademischer Betreuer)
    Sprache: Englisch
    Medientyp: Dissertation
    Format: Online
    Weitere Identifier:
    Schlagworte: Natürliche Sprache; Sprachverarbeitung; Computerlinguistik; Neuronales Netz; Maschinelle Übersetzung
    Weitere Schlagworte: Information Retrieval; Historical Document Analysis; Script Compression; Neural Networks; Natural Language Processing; Machine Learning
    Umfang: Online-Ressource
    Bemerkung(en):

    Dissertation, Frankfurt am Main, Johann Wolfgang Goethe-Universität, 2023

  13. Liquidity stress detection in the European banking sector
    Erschienen: [2019]
    Verlag:  De Nederlandsche Bank NV, Amsterdam, the Netherlands

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    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: Working paper / De Nederlandsche Bank NV ; no. 642 (June 2019)
    Schlagworte: Risk Monitoring; Liquidity Stress; Neural Networks; Financial Market Infrastructures; Large-Value Payment Systems
    Umfang: 1 Online-Ressource (circa 15 Seiten), Illustrationen
  14. The effectiveness of central bank purchases of long-term treasury securities
    a neural network approach
    Autor*in: Tänzer, Alina
    Erschienen: [2024]
    Verlag:  Institute for Monetary and Financial Stability, Goethe University Frankfurt, Frankfurt am Main

    Central bank intervention in the form of quantitative easing (QE) during times of low interest rates is a controversial topic. This paper introduces a novel approach to study the effectiveness of such unconventional measures. Using U.S. data on six... mehr

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    Central bank intervention in the form of quantitative easing (QE) during times of low interest rates is a controversial topic. This paper introduces a novel approach to study the effectiveness of such unconventional measures. Using U.S. data on six key financial and macroeconomic variables between 1990 and 2015, the economy is estimated by artificial neural networks. Historical counterfactual analyses show that real effects are less pronounced than yield effects. Disentangling the effects of the individual asset purchase programs, impulse response functions provide evidence for QE being less effective the more the crisis is overcome. The peak effects of all QE interventions during the Financial Crisis only amounts to 1.3 pp for GDP growth and 0.6 pp for inflation respectively. Hence, the time as well as the volume of the interventions should be deliberated.

     

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    Sprache: Englisch
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    Weitere Identifier:
    hdl: 10419/295732
    Schriftenreihe: Working paper series / Institute for Monetary and Financial Stability ; no. 204 (2024)
    Schlagworte: Artificial Intelligence; Machine Learning; Neural Networks; Forecasting and Simulation: Models and Applications; Financial Markets and the Macroeconomy; Monetary Policy; Central Banks and Their Policies
    Umfang: 1 Online-Ressource (circa 24 Seiten), Illustrationen
  15. Multivariate macroeconomic forecasting
    from DSGE and BVAR to artificial neural networks
    Autor*in: Tänzer, Alina
    Erschienen: [2024]
    Verlag:  Institute for Monetary and Financial Stability, Goethe University Frankfurt, Frankfurt am Main

    This paper contributes a multivariate forecasting comparison between structural models and Machine-Learning-based tools. Specifically, a fully connected feed forward nonlinear autoregressive neural network (ANN) is contrasted to a well established... mehr

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    This paper contributes a multivariate forecasting comparison between structural models and Machine-Learning-based tools. Specifically, a fully connected feed forward nonlinear autoregressive neural network (ANN) is contrasted to a well established dynamic stochastic general equilibrium (DSGE) model, a Bayesian vector autoregression (BVAR) using optimized priors as well as Greenbook and SPF forecasts. Model estimation and forecasting is based on an expanding window scheme using quarterly U.S. real-time data (1964Q2:2020Q3) for 8 macroeconomic time series (GDP, inflation, federal funds rate, spread, consumption, investment, wage, hours worked), allowing for up to 8 quarter ahead forecasts. The results show that the BVAR improves forecasts compared to the DSGE model, however there is evidence for an overall improvement of predictions when relying on ANN, or including them in a weighted average. Especially, ANNbased inflation forecasts improve other predictions by up to 50%. These results indicate that nonlinear data-driven ANNs are a useful method when it comes to macroeconomic forecasting.

     

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    Weitere Identifier:
    hdl: 10419/295733
    Schriftenreihe: Working paper series / Institute for Monetary and Financial Stability ; no. 205 (2024)
    Schlagworte: Artificial Intelligence; Machine Learning; Neural Networks; Forecast Comparison/ Competition; Macroeconomic Forecasting; Crises Forecasting; Inflation Forecasting; Interest Rate Forecasting; Production, Saving, Consumption and Investment Forecasting
    Umfang: 1 Online-Ressource (circa 71 Seiten), Illustrationen
  16. Predicting bankruptcy using neural networks in the current financial crisis: a study for US commercial banks
    Erschienen: 2010
    Verlag:  Fundación de las Cajas de Ahorros, Madrid

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    Sprache: Englisch
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    Format: Online
    Schriftenreihe: Documento de trabajo / Fundación de las Cajas de Ahorros ; 568
    Schlagworte: Banks; Bankruptcy; Financial Crisis; Neural Networks
    Umfang: Online-Ressource (50 S.), graph. Darst.
  17. NNPF
    Neural Network Particle Filter for time series data
    Erschienen: April 23, 2024
    Verlag:  Statistics Netherlands, The Hague

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    Schriftenreihe: Discussion paper / Statistics Netherlands
    Schlagworte: Neural Networks; non-Gaussian and non-linear State Space Modelling; Particle Filtering; Time series; Misspecification; Signal Extraction
    Umfang: 1 Online-Ressource (circa 129 Seiten), Illustrationen