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Classifying payment patterns with artificial neural networks : an autoencoder approach
Rubio, Jeniffer; Barucca, Paolo; Gage, Gerardo; Arroyo, John - In: Latin American journal of central banking : LAJCB 1 (2020) 1/4, pp. 1-16
Payments and market infrastructures are the backbone of modern financial systems and play a key role in the economy … time-intervals for the SPI settlement activity to reconstruct its related payments network. We introduce bank run … unusual behavior of individual banks to systemic changes in the overall structure of the payments network. We also found that …
Persistent link: https://www.econbiz.de/10012545615
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Pattern recognition of financial institutions' payment behavior
León, Carlos; Barucca, Paolo; Acero, Oscar; Gage, Gerardo - 2020
Persistent link: https://www.econbiz.de/10012320547
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Optimal monetary policy using reinforcement learning
Hinterlang, Natascha; Tänzer, Alina - 2021
This paper introduces a reinforcement learning based approach to compute optimal interest rate reaction functions in terms of fulfilling inflation and output gap targets. The method is generally flexible enough to incorporate restrictions like the zero lower bound, nonlinear economy structures...
Persistent link: https://www.econbiz.de/10012792732
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Vorhersage kurzfristiger Aktienkursrenditen : Entwicklung eines maschinellen Lernverfahrens
Thom, Patrick René - 2012 - 1. Aufl.
Persistent link: https://www.econbiz.de/10009577201
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High-dimensional learning under approximate sparsity with applications to nonsmooth estimation and regularized neural networks
Liu, Hongcheng; Ye, Yinyu; Lee, Hung Yi - In: Operations research 70 (2022) 6, pp. 3176-3197
Persistent link: https://www.econbiz.de/10014307657
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Swag: a wrapper method for sparse learning
Molinari, Roberto; Bakalli, Gaetan; Guerrier, Stéphane; … - 2020
Predictive power has always been the main research focus of learning algorithms with the goal of minimizing the test error for supervised classification and regression problems. While the general approach for these algorithms is to consider all possible attributes in a dataset to best predict...
Persistent link: https://www.econbiz.de/10012270791
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CapitalVX : A Machine Learning Model for Startup Selection and Exit Prediction
Ross, Greg - 2020
Using a big data set of venture capital financing and related startup firms from Crunchbase, this paper develops a machine-learning model called CapitalVX (for “Capital Venture eXchange”) to predict the outcomes for startups, i.e., whether they will exit successfully through an IPO or...
Persistent link: https://www.econbiz.de/10012824187
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Whose Balance Sheet is This? Neural Networks for Banks’ Pattern Recognition
León, Carlos - 2017
The balance sheet is a snapshot that portraits the financial position of a firm at a specific point of time. Under the reasonable assumption that the financial position of a firm is unique and representative, we use a basic artificial neural network pattern recognition method on Colombian banks'...
Persistent link: https://www.econbiz.de/10012962410
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A patent search strategy based on machine learning for the emerging field of service robotics
Kreuchauff, Florian Heiko; Korzinov, Vladimir V. - 2015
Emerging technologies are in the core focus of supra-national innovation policies. These strongly rely on credible data bases for being effective and efficient. However, since emerging technologies are not yet part of any official industry, patent or trademark classification systems, delineating...
Persistent link: https://www.econbiz.de/10011342183
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The effectiveness of central bank purchases of long-term treasury securities : a neural network approach
Tänzer, Alina - 2024
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...
Persistent link: https://www.econbiz.de/10014532350