1,721,033 research outputs found
Learning Machines Supporting Bankruptcy Prediction
Full text linkIn many economic applications it is desirable to make future predictions about the financial status of a company. The focus of predictions is mainly if a company will default or not. A support vector machine (SVM) is one learning method which uses historical data to establish a classification rule called a score or an SVM. Companies with scores above zero belong to one group and the rest to another group. Estimation of the probability of default (PD) values can be calculated from the scores provided by an SVM. The transformation used in this paper is a combination of weighting ranks and of smoothing the results using the PAV algorithm. The conversion is then monotone. This discussion paper is based on the Creditreform database from 1997 to 2002. The indicator variables were converted to financial ratios; it transpired out that eight of the 25 were useful for the training of the SVM. The results showed that those ratios belong to activity, profitability, liquidity and leverage. Finally, we conclude that SVMs are capable of extracting the necessary information from financial balance sheets and then to predict the future solvency or insolvent of a company. Banks in particular will benefit from these results by allowing them to be more aware of their risk when lending money.Support Vector Machine, Bankruptcy, Default Probabilities Prediction, Profitability
Estimation of Default Probabilities with Support Vector Machines
Full text linkPredicting default probabilities is important for firms and banks to operate successfully and to estimate their specific risks. There are many reasons to use nonlinear techniques for predicting bankruptcy from financial ratios. Here we propose the so called Support Vector Machine (SVM) to estimate default probabilities of German firms. Our analysis is based on the Creditreform database. The results reveal that the most important eight predictors related to bankruptcy for these German firms belong to the ratios of activity, profitability, liquidity, leverage and the percentage of incremental inventories. Based on the performance measures, the SVM tool can predict a firms default risk and identify the insolvent firm more accurately than the benchmark logit model. The sensitivity investigation and a corresponding visualization tool reveal that the classifying ability of SVM appears to be superior over a wide range of the SVM parameters. Based on the nonparametric Nadaraya-Watson estimator, the expected returns predicted by the SVM for regression have a significant positive linear relationship with the risk scores obtained for classification. This evidence is stronger than empirical results for the CAPM based on a linear regression and confirms that higher risks need to be compensated by higher potential returns.Support Vector Machine, Bankruptcy, Default Probabilities Prediction, Expected Profitability, CAPM.
Estimating Probabilities of Default With Support Vector Machines
Full text linkThis paper proposes a rating methodology that is based on a non-linear classification method, the support vector machine, and a non-parametric technique for mapping rating scores into probabilities of default. We give an introduction to underlying statistical models and represent the results of testing our approach on German Bundesbank data. In particular we discuss the selection of variables and give a comparison with more traditional approaches such as discriminant analysis and the logit regression. The results demonstrate that the SVM has clear advantages over these methods for all variables tested.Bankruptcy, Company rating, Default probability, Support vector machines.
A Microeconomic Explanation of the EPK Paradox
Full text linkSupported by several recent investigations the empirical pricing kernel paradox might be considered as a stylized fact. In Chabi-Yo et al. (2008) simulation studies have been presented which suggest that this paradox might be caused by regime switching of stock prices in financial markets. Alternatively, we want to emphasize a microeconomic view. Based on an economic model with state dependent utilities for the financial investors we succeed in explaining the paradox by changes of the risk attitudes. Theoretically, the change behaviour is compressed by the pricing kernels. As a starting point for empirical insights we shall develop and investigate inverse problems in terms of data fits for estimated basic values of the pricing kernel.Pricing kernel, representative agent, empirical pricing kernel, epk paradox, state dependent utilities, switching points
Empirical Pricing Kernels and Investor Preferences
Full text linkThis paper analyzes empirical market utility functions and pricing kernels derived from the DAX and DAX option data for three market regimes. A consistent parametric framework of stochastic volatility is used. All empirical market utility functions show a region of risk proclivity that is reproduced by adopting the hypothesis of heterogeneous individual investors whose utility functions have a switching point between bullish and bearish attitudes. The inverse problem of finding the distribution of individual switching points is formulated in the space of stock returns by discretization as a quadratic optimization problem. The resulting distributions vary over time and correspond to different market regimes.Utility function, Pricing Kernel, Behavioral Finance, Risk Aversion, Risk Proclivity, Heston model.
The Bologna Process: How Student Mobility Affects Multi-Cultural Skills and Educational Quality
Full text linkWe analyze the two goals behind the European Bologna Process of increasing student mobility: enabling graduates to develop multi cultural skills and increasing the quality of universities. We isolate three effects: 1) a competition effect that raises quality; 2) a free rider effect that lowers quality; 3) a composition effect that influences the relative strengths of the two previous effects. The effects lead to a trade off between the two goals. Full mobility may be optimal, only when externalities are high. In this case, student mobility yields inef- ficiently high educational quality. For moderate externalities partial mobility is optimal and yields an inefficiently low quality of education.Student mobility, Quality of higher education, Multicultural skills, Bologna Process
Barrier Option Hedging under Constraints: A Viscosity Approach
Full text linkWe study the problem of finding the minimal initial capital needed in order to hedge without risk a barrier option when the vector of proportions of wealth invested in each risky asset is constraint to lie in a closed convex domain. In the context of a Brownian diffusion model, we provide a PDE characterization of the super-hedging price. This extends the result of Broadie, Cvitanic and Soner (1998) and Cvitanic, Pham and Touzi (1999) which was obtained for plain vanilla options, and provides a natural numerical procedure for computing the corresponding super-hedging price. As a by-product, we obtain a comparison theorem for a class of parabolic PDE with relaxed Dirichet conditions involving a constraint on the gradient.Super-replication, barrier options, portfolio constraints, viscosity solutions
Time Dependent Relative Risk Aversion
Full text linkRisk management and the thorough understanding of the relations between financial markets and the standard theory of macroeconomics have always been among the topics most addressed by researchers, both financial mathematicians and economists. This work aims at explaining investors’ behavior from a macroeconomic aspect (modeled by the investors’ pricing kernel and their relative risk aversion) using stocks and options data. Daily estimates of investors’ pricing kernel and relative risk aversion are obtained and used to construct and analyze a three-year long time-series. The first four moments of these time-series as well as their values at the money are the starting point of a principal component analysis. The relation between changes in a major index level and implied volatility at the money and between the principal components of the changes in relative risk aversion is found to be linear. The relation of the same explanatory variables to the principal components of the changes in pricing kernels is found to be log-linear, although this relation is not significant for all of the examined maturities.risk aversion, pricing kernels, time dependent preferences
Technological Choice under Organizational Diseconomies of Scale
Full text linkWith adverse selection, diseconomies of scale associated with hierarchies may induce the implementation of a second-best technology. This occurs whenever rents to lower tiers of the hierarchy increase faster than total surplus. This is more likely with longer hierarchies.Adverse Selection, Hierarchies, Technology
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