677 research outputs found
Production networks and failure avalanches
Although standard economics textbooks are seldom interested in production
networks, modern economies are more and more based upon suppliers/customers
interactions. One can consider entire sectors of the economy as generalised
supply chains. We will take this view in the present paper and study under
which conditions local failures to produce or simply to deliver can result in
avalanches of shortage and bankruptcies across the network. We will show that a
large class of models exhibit scale free distributions of production and wealth
among firms and that metastable regions of high production are highly
localised
Financial distress model prediction using SVM +
Financial distress prediction is of great importance
to all stakeholders in order to enable better decision-making
in evaluating firms. In recent years, the rate of bankruptcy
has risen and it is becoming harder to estimate as companies
become more complex and the asymmetric information between
banks and firms increases. Although a great variety of techniques
have been applied along the years, no comprehensive
method incorporating an holistic perspective had hitherto
been considered. Recently, SVM+ a technique proposed by
Vapnik [17] provides a formal way to incorporate privileged
information onto the learning models improving generalization.
By exploiting additional information to improve traditional
inductive learning we propose a prediction model where data is
naturally separated into several groups according to the size of
the firm. Experimental results in the setting of a heterogeneous
data set of French companies demonstrated that the proposed
model showed superior performance in terms of prediction
accuracy in bankruptcy prediction and misclassification cost.This work was partially supported by Fundacao da Ciencia e Tecnologia' under grant no.PTDC/GES/70168/2006
Feature selection for bankruptcy prediction: a multi-objective optimization approach
In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature
selection in the problem of bankruptcy prediction. The aim is to maximize the accuracy of the
classifier while keeping the number of features low. A two-objective problem - minimization
of the number of features and accuracy maximization – was fully analyzed using two
classifiers, Logistic Regression (LR) and Support Vector Machines (SVM). Simultaneously,
the parameters required by both classifiers were also optimized. The validity of the
methodology proposed was tested using a database containing financial statements of 1200
medium sized private French companies. Based on extensive tests it is shown that MOEA is
an efficient feature selection approach. Best results were obtained when both the accuracy and
the classifiers parameters are optimized. The method proposed can provide useful information
for the decision maker in characterizing the financial health of a company
Enhanced default risk models with SVM+
Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.info:eu-repo/semantics/publishedVersio
Topology of products similarity network for market forecasting
The detection and prediction of risk in financial markets is one of the main challenges of economic forecasting, and draws much attention from the scientific community. An even more challenging task is the prediction of the future relative gain of companies. We here develop a novel combination of product text analysis, network theory and topological based machine learning to study the future performance of companies in financial markets. Our network links are based on the similarity of firms’ products and constructed using the Securities Exchange Commission (SEC) filings of US listed firms. We find that several topological features of this network can serve as good precursors of risks or future gain of companies. We then apply machine learning to network attributes vectors for each node to predict successful and failing firms. The resulting accuracies are much better than current state of the art techniques. The framework presented here not only facilitates the prediction of financial markets but also provides insight and demonstrates the power of combining network theory and topology based machine learning
Will the US Economy Recover in 2010? A Minimal Spanning Tree Study
We calculated the cross correlations between the half-hourly times series of
the ten Dow Jones US economic sectors over the period February 2000 to August
2008, the two-year intervals 2002--2003, 2004--2005, 2008--2009, and also over
11 segments within the present financial crisis, to construct minimal spanning
trees (MSTs) of the US economy at the sector level. In all MSTs, a core-fringe
structure is found, with consumer goods, consumer services, and the industrials
consistently making up the core, and basic materials, oil and gas, healthcare,
telecommunications, and utilities residing predominantly on the fringe. More
importantly, we find that the MSTs can be classified into two distinct,
statistically robust, topologies: (i) star-like, with the industrials at the
center, associated with low-volatility economic growth; and (ii) chain-like,
associated with high-volatility economic crisis. Finally, we present
statistical evidence, based on the emergence of a star-like MST in Sep 2009,
and the MST staying robustly star-like throughout the Greek Debt Crisis, that
the US economy is on track to a recovery.Comment: elsarticle class, includes amsmath.sty, graphicx.sty and url.sty. 68
pages, 16 figures, 8 tables. Abridged version of the manuscript presented at
the Econophysics Colloquim 2010, incorporating reviewer comment
From production networks to geographical economics
Although standard economics textbooks are seldom interested in production networks, modern economies are more and more based upon supplier/ customer interactions. One can consider entire sectors of the economy as generalised supply chains. We will take this view in the present paper and study under which conditions local failures to produce or simply to deliver can result in avalanches of shortage and bankruptcies and in localisation of the economic activity. We will show that a large class of models exhibit scale free distributions of production and wealth among firms and that regions of high production are localised
Path finding on a spherical self-organizing map using distance transformations
Spatialization methods create visualizations that allow users to analyze high-dimensional data in an intuitive manner and facilitates the extraction of meaningful information. Just as geographic maps are simpli ed representations of geographic spaces, these visualizations are esssentially maps of abstract data spaces that are created through dimensionality reduction. While we are familiar with geographic maps for path planning/ nding applications, research into using maps of high-dimensional spaces for such purposes has been largely ignored. However, literature has shown that it is possible to use these maps to track temporal and state changes within a high-dimensional space. A popular dimensionality reduction method that produces a mapping for these purposes is the Self-Organizing Map. By using its topology preserving capabilities with a colour-based visualization method known as the U-Matrix, state transitions can be visualized as trajectories on the resulting mapping. Through these trajectories, one can gather information on the transition path between two points in the original high-dimensional state space. This raises the interesting question of whether or not the Self-Organizing Map can be used to discover the transition path between two points in an n-dimensional space. In this thesis, we use a spherically structured Self-Organizing Map called the Geodesic Self-Organizing Map for dimensionality reduction and the creation of a topological mapping that approximates the n-dimensional space. We rst present an intuitive method for a user to navigate the surface of the Geodesic SOM. A new application of the distance transformation algorithm is then proposed to compute the path between two points on the surface of the SOM, which corresponds to two points in the data space. Discussions will then follow on how this application could be improved using some form of surface shape analysis. The new approach presented in this thesis would then be evaluated by analyzing the results of using the Geodesic SOM for manifold embedding and by carrying out data analyses using carbon dioxide emissions data
Path finding on a spherical self-organizing map using distance transformations
Spatialization methods create visualizations that allow users to analyze high-dimensional data in an intuitive manner and facilitates the extraction of meaningful information. Just as geographic maps are simpli ed representations of geographic spaces, these visualizations are esssentially maps of abstract data spaces that are created through dimensionality reduction. While we are familiar with geographic maps for path planning/ nding applications, research into using maps of high-dimensional spaces for such purposes has been largely ignored. However, literature has shown that it is possible to use these maps to track temporal and state changes within a high-dimensional space. A popular dimensionality reduction method that produces a mapping for these purposes is the Self-Organizing Map. By using its topology preserving capabilities with a colour-based visualization method known as the U-Matrix, state transitions can be visualized as trajectories on the resulting mapping. Through these trajectories, one can gather information on the transition path between two points in the original high-dimensional state space. This raises the interesting question of whether or not the Self-Organizing Map can be used to discover the transition path between two points in an n-dimensional space. In this thesis, we use a spherically structured Self-Organizing Map called the Geodesic Self-Organizing Map for dimensionality reduction and the creation of a topological mapping that approximates the n-dimensional space. We rst present an intuitive method for a user to navigate the surface of the Geodesic SOM. A new application of the distance transformation algorithm is then proposed to compute the path between two points on the surface of the SOM, which corresponds to two points in the data space. Discussions will then follow on how this application could be improved using some form of surface shape analysis. The new approach presented in this thesis would then be evaluated by analyzing the results of using the Geodesic SOM for manifold embedding and by carrying out data analyses using carbon dioxide emissions data
- …