231 research outputs found
An Author Writing to Remember and Celebrate Black Children
No full textWith an undergraduate degree in sociology from Morgan State University (Baltimore, MD) and a master’s degree in Library Science from the Catholic University of America (Washington, DC), Sharon Bell Mathis is a librarian and a multiple award-winning children’s and young adult book author [...
Pretrained Transformers of "B-spline Curve Approximation With Transformer Neural Networks" article
No full textPretrained Transformers of B-spline Curve Approximation With Transformer Neural Networks article
This dataset contains model checkpoints along with configuration and log files of trained transformer neural networks. Those networks have been trained following the methodology described in the link article. The following github repository can be used to read, test and process the data found in this dataset : bspline-curve-approximation-transformer.
The Readme file can help you understand the nature of the data to help you in treating it yourself. A recent version of Pytorch is required to load some of the data (i.e. model checkpoints and parameters).
The training logs and inference results come as csv and txt files and can be read and processed by any software of your choice.
See Readme.md for a more detailed description of files and parameters. Feel free to contact the author regarding questions/problems with the data.</p
Global and local estimates of environmental flow requirements to sustain river ecosystems are poorly correlated
No full textData repository for ‘Global and local estimates of environmental flow requirements to sustain river ecosystems are poorly correlated ‘
prepared by Mathis L. Messager ([email protected])
1. Overview and background ----------------------------------------------------------
This documentation describes the input and output data associated with the analysis presented in: Messager, M. L., Dickens, W. S. C., Eriyagama, N., Tharme, R. E., Stassen, R. (2024). Limited comparability of global and local estimates of
environmental flow requirements to sustain river ecosystems. https://doi.org/10.1088/1748-9326/ad1cb5.
Environmental flows (e-flows) are a central element of sustainable water resource management to mitigate the detrimental impacts of hydrological alteration on freshwater ecosystems and their benefits to people. Many nations strive to protect e-flows through policy, and thousands of local-scale e-flows assessments have been conducted globally, leveraging data and knowledge to quantify how much water must be provided to river ecosystems, and when, to keep them healthy. However, e-flows assessments and implementation are geographically uneven and cover a small fraction of rivers worldwide. This hinders globally consistent target-setting, monitoring and evaluation for international agreements to curb water scarcity and biodiversity loss. Therefore, dozens of models have been developed over the past two decades to estimate the e-flows requirements of rivers seamlessly across basins and administrative boundaries at a global scale.There has been little effort, however, to benchmark these models against locally derived e-flows estimates, which may limit confidence in the relevance of global estimates. The aim of this study was to assess whether current global methods reflect e-flows estimates used on the ground, by comparing global and local estimates for 1194 sites across 25 countries. We found that while global approaches broadly approximate the bulk volume of water that should be precautionarily provided to sustain aquatic ecosystems at the scale of large basins or countries, they explain a remarkably negligible 0%–1% of the global variability in locally derived estimates of the percentage of river flow that must be protected at a given site. Even when comparing assessments for individual countries, thus controlling for differences in local assessment methods among jurisdictions, global e-flows estimates only marginally compared (R2 ⩽ 0.31) to local estimates. Such a disconnect between global and local assessments of e-flows requirements limits the credibility of global estimates and associated targets for water use. To accelerate the global implementation of e-flows requires further concerted effort to compile and draw from the thousands of existing local e-flows assessments worldwide for developing a new generation of global models and bridging the gap from local to global scales..
The data repository includes data required to perform this analysis as well as the data outputs from this analysis. Input data from local e-flow assessments included herein were either provided by collaborators or extracted from published governmental and academic reports by the authors. Input hydrographic data not available for download elsewhere were provided by Dr. Bernhard Lehner and hydrological simulations from PCR-GLOBWB 2.0 at a spatial resolution of 5 arc-min (not provided herein) were provided by Dr. ir. Edwin H. Sutanudjaja.
All scripts necessary to reproduce this analysis are freely available for all purposes (and can be copied, modified and distributed) at: https://github.com/messamat/globalEF_testPy (for data-preformatting and global e-flow calculations) and https://github.com/messamat/globalEF_testR (for comparing global and local MAF and e-flow estimates). The structure of the analysis relies as much as possible on good enough practices in scientific computing, which users are encouraged to read.
2. Repository content ----------------------------------------------------------
The data repository has the following structure, which must be conserved to run the analysis workflow:
---------------------------------------------------------
data/
Formatted_data_Chandima_20211018: pre-formatted local e-flow assessment sites.
Formatted_data_Chandima_20211102: pre-formatted local e-flow assessment sites.
GEFIS_test_data/:
Master Data Table_20230424.xlsx: final database of local e-flow assessments.
HydroATLAS/: hydrographic data required for downscaling and mapping global MAF and e-flow estimates
HydroATLAS_metadata_MLMv11.xlsx: metadata of RiverATLAS attributes used in producing distribution histogram in Supplementary Material.
----------------------------------------------------------
results/
france_preprocessing.gdb: outputs from spatial formatting of local e-flow assessment data for the Rhone River basin in France. The main output file is /Rhone_EFpoints_cleanjoin.
mexico_preprocessing.gdb: outputs from spatial formatting of local e-flow assessment data for Mexico. The main output file is /Mexico_EFpoints_cleanjoin.
processing_outputs.gdb: outputs from overall spatial formatting of local e-flow assessment data. The fully formatted point data of the sites is: EFpoints_20230424_clean_riverjoin. Associated with global e-flow estimates: EFpoints_20230424_clean_globalEF.
victoria_preprocessing.gdb: outputs from spatial formatting of local e-flow assessment data for the state of Victoria, Australia. The main output file is /Victoria_EFpoints_cleanjoin.
EFpoints_20230424_clean_globalEF.csv: all global e-flow estimates extracted for local e-flow assessment sites.
----------------------------------------------------------
isimp2_qtot_accumulated15s.gdb.zip: all global MAF and e-flow estimates in raster format. In the analytical workflow, these data are in the results/ folder but here they have been placed outside to conform with the maximum file size limit of this dataverse.
----------------------------------------------------------
README_Technical_documentation_globalEFcomparison_Messageretal2023.pdf : documentation for this repository
3. Data format and projection ----------------------------------------------------------
The spatial datasets are distributed in ESRI® file geodatabase format. Please contact the author should you want the data in another format. These datasets are available in compressed zip file format. To use the data files, the zip files must first be decompressed.
All data layers are provided in geographic (latitude/longitude) projection, referenced to datum WGS84. In ESRI® software this projection is defined by the geographic coordinate system GCS_WGS_1984 and datum D_WGS_1984 (EPSG: 4326).
4. License and citations ----------------------------------------------------------
4.1 License agreement
This documentation and datasets are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-4.0 License). For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement (https://creativecommons.org/licenses/by/4.0/legalcode). For a human-readable summary of the license, please see https://creativecommons.org/licenses/by/4.0/.
4.2 Citations and acknowledgements.
Citations and acknowledgements of this dataset should be made as follows:
Messager, M. L., Dickens, W. S. C., Eriyagama, N., Tharme, R. E., Stassen, R. (2024). Limited comparability of global and local estimates of
environmental flow requirements to sustain river ecosystems. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ad1cb5.
We kindly ask users to cite this study in any published material produced using it. If possible, online links to this repository (DOI) should also be provided
Amélioration des stratégies de portefeuille crédit : perspectives sur les opérations sur titres, les algorithmes d'apprentissage du classement et la couverture des taux d’intérêt
No full textThe evolution of credit markets, driven by regulatory reforms, trading electronification, and the increasing availability of data, has reshaped the landscape for asset managers. This thesis contributes to the advancement of systematic credit strategies by investigating credit risk dynamics, machine learning model design, and interest rate hedging techniques.The first contribution examines the impact of corporate action announcements—such as stock buybacks, dividends, and mergers & acquisitions—on credit default swap spreads. Quantifying abnormal spread movements around these events allows for the disentangling of wealth transfer from signaling effects, providing insights into the mechanisms driving credit market reactions.The second contribution focuses on improving asset ranking models for long-short portfolio construction. Traditional learning-to-rank algorithms favor top-ranked assets while neglecting underperformers, creating challenges for market-neutral strategies. This thesis proposes refined loss functions that enhance ranking symmetry across the full asset distribution, thereby improving portfolio balance and performance.The third contribution introduces a multi-task neural network framework that unifies asset ranking and portfolio weighting within a single optimization model. In contrast to conventional two-step approaches, this framework simultaneously optimizes both ranking and allocation, resulting in more efficient investment decisions.Finally, this thesis explores the relationship between equity and corporate bond markets by decomposing their correlation into credit risk and interest rate sensitivity components. Since these drivers vary across credit qualities and market conditions, this thesis proposes two adaptive interest rate hedging strategies. These strategies outperform traditional duration-convexity hedging by dynamically adjusting hedge ratios in response to evolving market regimes.These contributions collectively advance systematic credit investing by integrating financial expertise with advanced quantitative techniques. The findings offer asset managers practical methodologies for improving credit trading strategies in an increasingly data-driven and complex financial environment.L’évolution du marché du crédit, portée par les réformes réglementaires, l’électronification des passages d'ordre et l’augmentation des données disponibles, a profondément modifié les pratiques des gestionnaires d'actifs. Cette thèse contribue à l’amélioration des stratégies de crédit systématiques en explorant la dynamique du risque de crédit, la conception de modèles d’apprentissage automatique et les techniques de couverture des taux d’intérêt.La première contribution analyse l’impact des annonces d’opérations sur titres—telles que les rachats d’actions, les dividendes et les fusions-acquisitions—sur les spreads des credit default swaps. En quantifiant les mouvements anormaux des spreads autour de ces événements, il a été possible de dissocier l'effet "wealth transfer" de l'effet "signaling", offrant ainsi une meilleure compréhension des mécanismes sous-jacents aux réactions du marché du crédit.La deuxième contribution améliore les modèles de classement d'actifs pour la construction de portefeuilles long-short. Les algorithmes traditionnels d’apprentissage du classement favorisent les actifs les mieux classés au détriment des moins performants, rendant les stratégies neutres au marché moins efficaces. Cette thèse propose des fonctions de perte ajustées afin de renforcer la symétrie du classement sur l’ensemble de la distribution des actifs, améliorant ainsi l’équilibre et la performance des portefeuilles.La troisième contribution propose un modèle de réseau neuronal multitâche unifiant le classement des actifs et la pondération des portefeuilles dans un modèle d’optimisation unique. Contrairement aux méthodes conventionnelles en deux étapes, cette approche produit une construction de portefeuille plus efficiente.Enfin, cette thèse examine la relation entre les marchés actions et des obligations d’entreprise en décomposant leur corrélation en deux composantes : le risque de crédit et la sensibilité aux taux d’intérêt. Puisque ces facteurs varient selon les qualités de crédit et les conditions de marché, deux stratégies adaptatives de couverture des taux d’intérêt sont proposées. Ces stratégies surpassent la couverture traditionnelle "duration-convexity" en ajustant dynamiquement les ratios de couverture selon les régimes de marché.Les contributions de cette thèse offrent aux gestionnaires d’actifs de nouvelles méthodologies afin d'optimiser les stratégies de gestion sur le marché du crédit, dans un environnement financier toujours plus complexe et axé sur les données
Amélioration des stratégies de portefeuille crédit : perspectives sur les opérations sur titres, les algorithmes d'apprentissage du classement et la couverture des taux d’intérêt
No full textThe evolution of credit markets, driven by regulatory reforms, trading electronification, and the increasing availability of data, has reshaped the landscape for asset managers. This thesis contributes to the advancement of systematic credit strategies by investigating credit risk dynamics, machine learning model design, and interest rate hedging techniques.The first contribution examines the impact of corporate action announcements—such as stock buybacks, dividends, and mergers & acquisitions—on credit default swap spreads. Quantifying abnormal spread movements around these events allows for the disentangling of wealth transfer from signaling effects, providing insights into the mechanisms driving credit market reactions.The second contribution focuses on improving asset ranking models for long-short portfolio construction. Traditional learning-to-rank algorithms favor top-ranked assets while neglecting underperformers, creating challenges for market-neutral strategies. This thesis proposes refined loss functions that enhance ranking symmetry across the full asset distribution, thereby improving portfolio balance and performance.The third contribution introduces a multi-task neural network framework that unifies asset ranking and portfolio weighting within a single optimization model. In contrast to conventional two-step approaches, this framework simultaneously optimizes both ranking and allocation, resulting in more efficient investment decisions.Finally, this thesis explores the relationship between equity and corporate bond markets by decomposing their correlation into credit risk and interest rate sensitivity components. Since these drivers vary across credit qualities and market conditions, this thesis proposes two adaptive interest rate hedging strategies. These strategies outperform traditional duration-convexity hedging by dynamically adjusting hedge ratios in response to evolving market regimes.These contributions collectively advance systematic credit investing by integrating financial expertise with advanced quantitative techniques. The findings offer asset managers practical methodologies for improving credit trading strategies in an increasingly data-driven and complex financial environment.L’évolution du marché du crédit, portée par les réformes réglementaires, l’électronification des passages d'ordre et l’augmentation des données disponibles, a profondément modifié les pratiques des gestionnaires d'actifs. Cette thèse contribue à l’amélioration des stratégies de crédit systématiques en explorant la dynamique du risque de crédit, la conception de modèles d’apprentissage automatique et les techniques de couverture des taux d’intérêt.La première contribution analyse l’impact des annonces d’opérations sur titres—telles que les rachats d’actions, les dividendes et les fusions-acquisitions—sur les spreads des credit default swaps. En quantifiant les mouvements anormaux des spreads autour de ces événements, il a été possible de dissocier l'effet "wealth transfer" de l'effet "signaling", offrant ainsi une meilleure compréhension des mécanismes sous-jacents aux réactions du marché du crédit.La deuxième contribution améliore les modèles de classement d'actifs pour la construction de portefeuilles long-short. Les algorithmes traditionnels d’apprentissage du classement favorisent les actifs les mieux classés au détriment des moins performants, rendant les stratégies neutres au marché moins efficaces. Cette thèse propose des fonctions de perte ajustées afin de renforcer la symétrie du classement sur l’ensemble de la distribution des actifs, améliorant ainsi l’équilibre et la performance des portefeuilles.La troisième contribution propose un modèle de réseau neuronal multitâche unifiant le classement des actifs et la pondération des portefeuilles dans un modèle d’optimisation unique. Contrairement aux méthodes conventionnelles en deux étapes, cette approche produit une construction de portefeuille plus efficiente.Enfin, cette thèse examine la relation entre les marchés actions et des obligations d’entreprise en décomposant leur corrélation en deux composantes : le risque de crédit et la sensibilité aux taux d’intérêt. Puisque ces facteurs varient selon les qualités de crédit et les conditions de marché, deux stratégies adaptatives de couverture des taux d’intérêt sont proposées. Ces stratégies surpassent la couverture traditionnelle "duration-convexity" en ajustant dynamiquement les ratios de couverture selon les régimes de marché.Les contributions de cette thèse offrent aux gestionnaires d’actifs de nouvelles méthodologies afin d'optimiser les stratégies de gestion sur le marché du crédit, dans un environnement financier toujours plus complexe et axé sur les données
Amélioration des stratégies de portefeuille crédit : perspectives sur les opérations sur titres, les algorithmes d'apprentissage du classement et la couverture des taux d’intérêt
No full textThe evolution of credit markets, driven by regulatory reforms, trading electronification, and the increasing availability of data, has reshaped the landscape for asset managers. This thesis contributes to the advancement of systematic credit strategies by investigating credit risk dynamics, machine learning model design, and interest rate hedging techniques.The first contribution examines the impact of corporate action announcements—such as stock buybacks, dividends, and mergers & acquisitions—on credit default swap spreads. Quantifying abnormal spread movements around these events allows for the disentangling of wealth transfer from signaling effects, providing insights into the mechanisms driving credit market reactions.The second contribution focuses on improving asset ranking models for long-short portfolio construction. Traditional learning-to-rank algorithms favor top-ranked assets while neglecting underperformers, creating challenges for market-neutral strategies. This thesis proposes refined loss functions that enhance ranking symmetry across the full asset distribution, thereby improving portfolio balance and performance.The third contribution introduces a multi-task neural network framework that unifies asset ranking and portfolio weighting within a single optimization model. In contrast to conventional two-step approaches, this framework simultaneously optimizes both ranking and allocation, resulting in more efficient investment decisions.Finally, this thesis explores the relationship between equity and corporate bond markets by decomposing their correlation into credit risk and interest rate sensitivity components. Since these drivers vary across credit qualities and market conditions, this thesis proposes two adaptive interest rate hedging strategies. These strategies outperform traditional duration-convexity hedging by dynamically adjusting hedge ratios in response to evolving market regimes.These contributions collectively advance systematic credit investing by integrating financial expertise with advanced quantitative techniques. The findings offer asset managers practical methodologies for improving credit trading strategies in an increasingly data-driven and complex financial environment.L’évolution du marché du crédit, portée par les réformes réglementaires, l’électronification des passages d'ordre et l’augmentation des données disponibles, a profondément modifié les pratiques des gestionnaires d'actifs. Cette thèse contribue à l’amélioration des stratégies de crédit systématiques en explorant la dynamique du risque de crédit, la conception de modèles d’apprentissage automatique et les techniques de couverture des taux d’intérêt.La première contribution analyse l’impact des annonces d’opérations sur titres—telles que les rachats d’actions, les dividendes et les fusions-acquisitions—sur les spreads des credit default swaps. En quantifiant les mouvements anormaux des spreads autour de ces événements, il a été possible de dissocier l'effet "wealth transfer" de l'effet "signaling", offrant ainsi une meilleure compréhension des mécanismes sous-jacents aux réactions du marché du crédit.La deuxième contribution améliore les modèles de classement d'actifs pour la construction de portefeuilles long-short. Les algorithmes traditionnels d’apprentissage du classement favorisent les actifs les mieux classés au détriment des moins performants, rendant les stratégies neutres au marché moins efficaces. Cette thèse propose des fonctions de perte ajustées afin de renforcer la symétrie du classement sur l’ensemble de la distribution des actifs, améliorant ainsi l’équilibre et la performance des portefeuilles.La troisième contribution propose un modèle de réseau neuronal multitâche unifiant le classement des actifs et la pondération des portefeuilles dans un modèle d’optimisation unique. Contrairement aux méthodes conventionnelles en deux étapes, cette approche produit une construction de portefeuille plus efficiente.Enfin, cette thèse examine la relation entre les marchés actions et des obligations d’entreprise en décomposant leur corrélation en deux composantes : le risque de crédit et la sensibilité aux taux d’intérêt. Puisque ces facteurs varient selon les qualités de crédit et les conditions de marché, deux stratégies adaptatives de couverture des taux d’intérêt sont proposées. Ces stratégies surpassent la couverture traditionnelle "duration-convexity" en ajustant dynamiquement les ratios de couverture selon les régimes de marché.Les contributions de cette thèse offrent aux gestionnaires d’actifs de nouvelles méthodologies afin d'optimiser les stratégies de gestion sur le marché du crédit, dans un environnement financier toujours plus complexe et axé sur les données
Data for Contrasting action and posture coding with hierarchical deep neural network models of proprioception
No full text#############
Contrasting action and posture coding with hierarchical deep neural network models of proprioception, eLife 2023
#############
Authors: Kai J Sandbrink, Pranav Mamidanna, Claudio Michaelis, Matthias Bethge, Mackenzie W Mathis and Alexander Mathis
Affiliation: Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Switzerland, The Rowland Institute at Harvard, Harvard University, United States; Tübingen AI Center, Eberhard Karls Universität Tübingen & Institute for Theoretical Physics, Germany
Date of upload: December, 2024
Earlier the data was available via dropbox (see github).
Link to the eLife article:
https://elifesciences.org/articles/81499
--------------------------------
Here we provide the data and code for this project:
We share the proprioceptive character recognition dataset (contained in 'pcr_data.zip') it has approximately ~29GB when uncompressed.
We share the weights of all the trained networks (contained in 'network-weights.zip'): about ~3.5GB
The compressed code is also available here ('DeepDrawCode.zip').
The activations are shared in a separate Zenodo project (due to the size). Check out the repository below to find the link.
The up to date code is at: https://github.com/amathislab/DeepDraw
--------------------------------
The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.
If you find this useful, please cite:
@article{sandbrink2023contrasting, title={Contrasting action and posture coding with hierarchical deep neural network models of proprioception}, author={Sandbrink, Kai J and Mamidanna, Pranav and Michaelis, Claudio and Bethge, Matthias and Mathis, Mackenzie Weygandt and Mathis, Alexander}, journal={Elife}, volume={12}, pages={e81499}, year={2023}, publisher={eLife Sciences Publications Limited}}UPAMATHISUPMWMATHI
Amélioration des stratégies de portefeuille crédit : perspectives sur les opérations sur titres, les algorithmes d'apprentissage du classement et la couverture des taux d’intérêt
No full textThe evolution of credit markets, driven by regulatory reforms, trading electronification, and the increasing availability of data, has reshaped the landscape for asset managers. This thesis contributes to the advancement of systematic credit strategies by investigating credit risk dynamics, machine learning model design, and interest rate hedging techniques.The first contribution examines the impact of corporate action announcements—such as stock buybacks, dividends, and mergers & acquisitions—on credit default swap spreads. Quantifying abnormal spread movements around these events allows for the disentangling of wealth transfer from signaling effects, providing insights into the mechanisms driving credit market reactions.The second contribution focuses on improving asset ranking models for long-short portfolio construction. Traditional learning-to-rank algorithms favor top-ranked assets while neglecting underperformers, creating challenges for market-neutral strategies. This thesis proposes refined loss functions that enhance ranking symmetry across the full asset distribution, thereby improving portfolio balance and performance.The third contribution introduces a multi-task neural network framework that unifies asset ranking and portfolio weighting within a single optimization model. In contrast to conventional two-step approaches, this framework simultaneously optimizes both ranking and allocation, resulting in more efficient investment decisions.Finally, this thesis explores the relationship between equity and corporate bond markets by decomposing their correlation into credit risk and interest rate sensitivity components. Since these drivers vary across credit qualities and market conditions, this thesis proposes two adaptive interest rate hedging strategies. These strategies outperform traditional duration-convexity hedging by dynamically adjusting hedge ratios in response to evolving market regimes.These contributions collectively advance systematic credit investing by integrating financial expertise with advanced quantitative techniques. The findings offer asset managers practical methodologies for improving credit trading strategies in an increasingly data-driven and complex financial environment.L’évolution du marché du crédit, portée par les réformes réglementaires, l’électronification des passages d'ordre et l’augmentation des données disponibles, a profondément modifié les pratiques des gestionnaires d'actifs. Cette thèse contribue à l’amélioration des stratégies de crédit systématiques en explorant la dynamique du risque de crédit, la conception de modèles d’apprentissage automatique et les techniques de couverture des taux d’intérêt.La première contribution analyse l’impact des annonces d’opérations sur titres—telles que les rachats d’actions, les dividendes et les fusions-acquisitions—sur les spreads des credit default swaps. En quantifiant les mouvements anormaux des spreads autour de ces événements, il a été possible de dissocier l'effet "wealth transfer" de l'effet "signaling", offrant ainsi une meilleure compréhension des mécanismes sous-jacents aux réactions du marché du crédit.La deuxième contribution améliore les modèles de classement d'actifs pour la construction de portefeuilles long-short. Les algorithmes traditionnels d’apprentissage du classement favorisent les actifs les mieux classés au détriment des moins performants, rendant les stratégies neutres au marché moins efficaces. Cette thèse propose des fonctions de perte ajustées afin de renforcer la symétrie du classement sur l’ensemble de la distribution des actifs, améliorant ainsi l’équilibre et la performance des portefeuilles.La troisième contribution propose un modèle de réseau neuronal multitâche unifiant le classement des actifs et la pondération des portefeuilles dans un modèle d’optimisation unique. Contrairement aux méthodes conventionnelles en deux étapes, cette approche produit une construction de portefeuille plus efficiente.Enfin, cette thèse examine la relation entre les marchés actions et des obligations d’entreprise en décomposant leur corrélation en deux composantes : le risque de crédit et la sensibilité aux taux d’intérêt. Puisque ces facteurs varient selon les qualités de crédit et les conditions de marché, deux stratégies adaptatives de couverture des taux d’intérêt sont proposées. Ces stratégies surpassent la couverture traditionnelle "duration-convexity" en ajustant dynamiquement les ratios de couverture selon les régimes de marché.Les contributions de cette thèse offrent aux gestionnaires d’actifs de nouvelles méthodologies afin d'optimiser les stratégies de gestion sur le marché du crédit, dans un environnement financier toujours plus complexe et axé sur les données
Recommended from our members
Efficiency Instead of Justice? ::Searching for the Philosophical Foundations of the Economic Analysis of Law /
No full textEconomic analysis of law is an interesting and challenging attempt to employ the concepts and reasoning methods of modern economic theory so as to gain a deeper understanding of legal problems. According to Richard A. Posner it is the role of the law to encourage market competition and, where the market fails because transaction costs are too high, to simulate the result of competitive markets. This would maximize economic efficiency and social wealth. In this work, the lawyer and economist Klaus Mathis critically appraises Posner's normative justification of the efficiency paradigm from the perspective of the philosophy of law. Posner acknowledges the influences of Adam Smith and Jeremy Bentham, whom he views as the founders of normative economics. He subscribes to Smith's faith in the market as an ideal allocation model, and to Bentham's ethical consequentialism. Finally, aligning himself with John Rawls's contract theory, he seeks to legitimize his concept of wealth maximization with a consensus theory approach. In his interdisciplinary study, the author points out the possibilities as well as the limits of economic analysis of law. It provides a method of analysing the law which, while very helpful, is also rather specific. The efficiency arguments therefore need to be incorporated into a process for resolving value conflicts. In a democracy this must take place within the political decision-making process. In this clearly written work, Klaus Mathis succeeds in making even non-economists more aware of the economic aspects of the law. "Mathis gives a succinct and lucid presentation of the economic theory of law, and of the problems associated with its application as a normative theory in law. At the same time, he rightly draws attention to the advantages associated with this approach, and provides a helpful and thoroughly ambitious introduction to its fundamental principles." Prof. Dr. Jan-R. Sieckmann, Archives for Philosophy of Law and Social Philosophy (ARSP), Vol. 91/2 (2006)
- …
