Manuel Landajo

Decision Aiding Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case

A comparative study of the performance of a number of classificatory devices, both parametric (LDA and Logit) and non-parametric (perceptron neural nets and fuzzy-rule-based classifiers) is conducted, and a Monte Carlo simulation-based approach is used in order to measure the average effects of sample size variations on the predictive performance of each classifier. The paper uses as a benchmark the problem of forecasting the level of profitability of Spanish commercial and industrial companies upon the basis of a set of financial ratios. This case illustrates well a distinctive feature of many financial prediction problems, namely that of being characterized by a high dimension feature space as well as a low degree of separability. Response surfaces are estimated in order to summarize the results. A higher performance of model-free classifiers is generally observed, even for fairly moderate sample sizes. � 2004 Elsevier B.V. All rights reserved.

Robust neural modeling for the cross-sectional analysis of accounting information

Abstract The performance of robust artificial neural network models in learning bivariate relationships between accounting magnitudes is assessed in this paper. Predictive performances of a number of modeling paradigms (namely, linear models, log-linear structures, classical ratios and artificial neural networks) are compared with regard to the problem of modeling a number of the most outstanding accounting ratio relations. We conduct a large scale analysis, carried out on a representative Spanish data base. Several model fitting criteria are used for each model class (namely, least squares, weighted least squares, least absolute deviations (LAD), and weighted LAD regressions). Hence, besides the standard (least squares-based) version of each model we test a robust (LAD) counterpart, in principle more adequate for distributions strongly affected by outliers, as typically appear in accounting ratio modeling. Our results strongly suggest that classical ratio models, although much used in practical applications, appear to be largely inadequate for predictive purposes, with linear models (both in their least squares and LAD variants) providing much more adequate specifications. In a number of cases, the linear specification is improved by considering flexible non-linear structures. Neural networks, because of their model-free regression capabilities, let us capture generic non-linearities of unknown form in the modeled relations, as well as providing—when properly trained—robust tools for modeling and prediction of general relationships.

Bankruptcy prediction models based on multinorm analysis: An alternative to accounting ratios

In this paper we address the bankruptcy prediction problem and outline a procedure to improve the performance of standard classifiers. Our proposal replaces traditional indicators (accounting ratios) with the output of a so-called multinorm analysis. The deviations of each firm from a battery of industry norms (computed by nonparametric quantile regression) are used as input variables for the classifiers. The approach is applied to predict bankruptcy of firms, and tested on a representative data set of Spanish firms. Results indicate that the approach may provide significant improvements in predictive accuracy, both in linear and nonlinear classifiers.

Flexible quantile-based modeling of bivariate financial relationships: The case of ROA ratio

Ratios used in financial analysis suffer from several drawbacks, and the tools - ranging from linear least-squares regressions to neural networks - suggested as alternatives also have serious disadvantages. We propose an alternative approach, based on quantile regression techniques, which exploits financial information in a more efficient way, not achievable by conventional tools. Our proposal is applied to the ROA (return on assets) ratio, this being one of the most popular ratios among both economic analysts and researchers. An empirical analysis is carried out on real data. Results indicate that the quantile approach provides a more accurate assessment of the financial position of the firm.

A note on model-free regression capabilities of fuzzy systems

Nonparametric estimation capabilities of fuzzy systems in stochastic environments are analyzed in this paper. By using ideas from sieve estimation, increasing sequences of fuzzy rule-based systems capable of consistently estimating arbitrary regression surfaces are constructed. Results include least squares learning of a mapping perturbed by additive random noise in a static-regression context. L/sub 1/ (i.e., least absolute deviation) estimation is also studied, and the consistency of fuzzy rule-based sieve estimators for L/sub 1/-optimal regression surfaces is shown, thus giving additional theoretical support to the robust filtering capabilities of fuzzy systems and their adequacy for modeling, prediction, and control of systems affected by impulsive noise.

Stationarity testing under nonlinear models. Some asymptotic results

Stationarity testing for nonlinear time series models which include several smooth trend components with (possibly) unknown parameters is considered. A pseudo-Lagrange multiplier stationarity test is proposed and its asymptotic behaviour is derived. The limiting null distribution generally depends on the unknown parameters of the model. A bootstrap approach permits this problem to be circumvented and consistency of the bootstrapped test is obtained. The theoretical analysis is complemented with a simulation study which allows us to check the performance of the test in finite samples. The article ends with an empirical application.

Using Nonlinear Quantile Regression for the Estimation of Software Cost

Estimation of effort costs is an important task for the management of software development projects. Researchers have followed two approaches –namely, statistical/machine-learning and theory-based– which explicitly rely on mean/median regression lines in order to model the relationship between software size and effort. Those approaches share a common drawback deriving from their inability to properly incorporate risk attitudes in the presence of heteroskedasticity. We propose a more flexible quantile regression approach that enables risk aversion to be incorporated in a systematic way, with the higher order conditional quantiles of the relationship between project size and effort being used to represent more risk adverse decision makers. A cubic quantile regression model allows consideration of economies/diseconomies of scale. The method is illustrated with an empirical application to a database of real projects. Results suggest that the shapes of higher order regression quantiles may sharply differ from that of the conditional median, revealing that the naive expedient of translating or multiplying some average norm (adding a safety margin to median estimates or including a multiplicative correction factor) is a potentially biased way to consider risk aversion. The proposed approach enables a more realistic analysis, adapted to the specificities of software development databases.

Multiplicative Decomposition of the Change in Aggregate Energy Intensity in the European Countries During the 1995–2010 Period

In this chapter a multiplicative decomposition of the variation of aggregate energy intensity in the European economy (EU27) will be conducted.

Literature Review and Methodology

In order to analyse the historical changes in economic, environmental, socio-economic and energy indicators, it is useful to identify, separate and evaluate the macroeconomic forces that contribute to those changes. Basically, the literature records four paradigms that may be used in order to decompose the change experienced by an indicator. These are (a) econometric analysis, (b) analysis based on aggregate data, (c) index-based analysis (Index Decomposition Analysis, or IDA), and (d) structural analysis (Structural Decomposition Analysis, or SDA).

Mathematical and Statistical Properties of Decomposition Techniques. The Splines Method

As discussed in Chap. 1, the theoretical Divisia index is calculated upon the basis of the continuous time paths of the observed variables. However, only a finite number of discrete observations is available in practice.