Classifying Firms’ Performance Using Data Mining Approaches

Superior prediction and classification in determining company’s performance are major concern for practitioners and academic research in providing useful or important information to the shareholders and potential investors for investment decision. Generally, the normal practice to analysed firm’s performance are based on financial indicators reported in the company’s annual report including the balance sheet, income and cash flow statements. In this work, a few popular and important benchmarking machine learning techniques for the data mining including neural networks, support vector machine, rough set theory, discriminant analysis, logistic regression, decision table, sequential minimal optimization and decision tree have been tested as to classify firm’s performance. The data mining techniques produce high classification rate that is more than 92%. This work also has reduced total number of ratios to be evaluated due to long processing time and large processing resources. Finally, the CA/TA, S/TA, E/TA, GM, FC, PBT/TA, and EPS have been considered for of the final reduced financial ratios. The results show that the 7 reduced ratios are comparable as the common 24 ratios. And to the still produce high classification rate and able classify the firm’s performance

Research on Innovation and Strategic Risk Management in Manufacturing Firms

Chinese manufacturing companies in Bangladesh are committed to achieving optimal investment policy for investors in different industries. The purpose of this research is the strategic risk assessment; where the research approach involves collecting qualitative data through questionnaire survey and compute variables with programmed Rough Set Theory. Researchers have identified a set of key internal and external strategic uncertainties and also accessed the most important attributes from strategic risks. Here, Sector regulation, changing the tax law and organizational governance as the most degree of risk factors in strategic risk analysis. Overall, the focus of our research is to identify strategic risk attributes and proposed a risk assessment framework by demonstrating empirical study analysis of specific industries in Bangladesh those are directly invested by Chinese investors

The identification of acquisition targets in the EU banking industry: An application of multicriteria approaches

In this paper we develop classification models for the identification of acquisition targets in the EU banking industry, incorporating financial variables that are mostly unique to the banking industry and originate from the CAMEL approach. A sample of 168 non-acquired banks matched with 168 acquired banks is used over the period 1998-2002, covering 15 EU countries. We compare and evaluate the relative efficiency of three multicriteria approaches, namely MHDIS, PAIRCLAS, and UTADIS, with all models developed and tested using a 10-fold cross validation approach. We find that the importance of the variables differs across the models. However, on the basis of univariate test and the results of the models we could state that in general after adjusting for the country where banks operate, acquired banks are less well capitalized and less cost and profit efficient. The results show that the developed models can achieve higher classification accuracies than a naïve model based on random assignments. Nevertheless, there is fair amount of misclassification that is hard to avoid given the nature of the problem, showing that as in previous studies for non-financial firms, the identification of acquisitions targets in banking is a difficult task. © 2006 Elsevier Inc. All rights reserved

Statistical techniques vs. SEES algorithm : an application to a small business environment

The aim of this research is to compare the accuracy of a rule induction classifier system –Quinlan’s SEE5– with linear discriminant analysis and logit. The classification task chosen is the differentiation of the most efficient companies from the least efficient ones on the basis of a set of financial variables. The sample consists of a database containing the annual accounts of the companies located in the Principality of Asturias (Spain), which are mainly small businesses. The main results indicate that SEE5 outperforms logit, but it is not clearly better than discriminant analysis. However, SEE5 models suffer from bigger increases in error rates when tested with validation samples. Another interesting finding is that in SEE5 systems both the number of variables selected and the number of rules inferred grow when sample size increases.El objetivo de esta investigación es comparar la precisión de un sistema de clasificación por reglas inductivas (SEE5, de Quinlan) con discriminación de análisis y logística. La tarea de clasificación elegida es la diferenciación entre las compañías más y menos eficientes en base a una serie de variables financieras. La muestra consiste en una base de datos que contiene las cuentas anuales de las compañías localizadas en el Principado de Asturias (España), que mayormente se trata de negocios pequeños. Los principales resultados indican que SEE5 supera la logística, pero no es claramente mejor que un análisis discriminatorio. Sin embargo, los modelos SEE5 padecen un aumento en los ratios de error cuando se prueban con muestras de validación. Otro hallazgo interesante es que en los sistemas SEE5 tanto el número de variables seleccionadas como el número de reglas inferidas aumentan cuando el tamaño de la muestra es mayor

Studies into the detection of buried objects (particularly optical fibres) in saturated sediment. Part 2: design and commissioning of test tank

This report is the second in a series of five, designed to investigate the detection oftargets buried in saturated sediment, primarily through acoustical or acoustics-relatedmethods. Although steel targets are included for comparison, the major interest is intargets (polyethylene cylinders and optical fibres) which have a poor acousticimpedance mismatch with the host sediment. This particular report details theconstruction of a laboratory-scale test facility. This consisted of three maincomponents. Budget constraints were an over-riding consideration in the design.First, there is the design and production of a tank containing saturated sediment. Itwas the intention that the physical and acoustical properties of the laboratory systemshould be similar to those found in a real seafloor environment. Particularconsideration is given to those features of the test system which might affect theacoustic performance, such as reverberation, the presence of gas bubbles in thesediment, or a suspension of particles above it. Sound speed and attenuation wereidentified as being critical parameters, requiring particular attention. Hence, thesewere investigated separately for each component of the acoustic path.Second, there is the design and production of a transducer system. It was the intentionthat this would be suitable for an investigation into the non-invasive acousticdetection of buried objects. A focused reflector is considered to be the most costeffectiveway of achieving a high acoustic power and narrow beamwidth. Acomparison of different reflector sizes suggested that a larger aperture would result inless spherical aberration, thus producing a more uniform sound field. Diffractioneffects are reduced by specifying a tolerance of much less than an acousticwavelength over the reflector surface. The free-field performance of the transducerswas found to be in agreement with the model prediction. Several parameters havebeen determined in this report that pertain to the acoustical characteristics of the waterand sediment in the laboratory tank in the 10 – 100 kHz frequency range.Third, there is the design and production of an automated control system wasdeveloped to simplify the data acquisition process. This was, primarily, a motordrivenposition control system which allowed the transducers to be accuratelypositioned in the two-dimensional plane above the sediment. Thus, it was possible forthe combined signal generation, data acquisition and position control process to be coordinatedfrom a central computer.This series of reports is written in support of the article “The detection by sonar ofxdifficult targets (including centimetre-scale plastic objects and optical fibres) buriedin saturated sediment” by T G Leighton and R C P Evans, written for a Special Issueof Applied Acoustics which contains articles on the topic of the detection of objectsburied in marine sediment. Further support material can be found athttp://www.isvr.soton.ac.uk/FDAG/uaua/target_in_sand.HTM

The Use of Rough Set Theory in Determining the Preferences of the Customers of an Insurance Agency

In today’s market environment a fierce competition is being experienced. It can be clearly stated that the businesses that determine the customer profiles well and manufacture related products in accordance with the requests/needs of the customers gain superiority over their rivals. Within this scope, this fact is also an important issue for the companies that are trying to keep up with other competitors in the insurance sector. In this study, this critical problem of EPD which is an agency of Allianz Insurance was solved by using Rough Set Theory (RST) method. Ten condition attributes (i.e. age, gender, etc.) were examined in the study. Decision attribute is the variable of the insurance type which includes individual retirement, health and life insurances. With the method of RST, a set of rules were identified which may help in developing strategies that will bring in new customers to EPD while keeping present ones. The attained results were presented to the executives of EPD. The executives have re-determined their marketing strategies in compliance with these results and exercised these strategies accordingly. Feedbacks from the executives indicated that the RST helps in facilitating the development of marketing strategies based on the characteristics of the customers and determining their profiles. Keywords: Rough set theory, customer’s profile, insurance, decision rule

Development of bank acquisition targets prediction models

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Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world

The prediction of bank acquisition targets with discriminant and logit analyses: Methodological issues and empirical evidence

Summarization: This paper uses discriminant and logit analyses to develop prediction models to identify bank acquisition targets. We consider several methodological issues, such as whether the choice of the estimation technique, the selection of variables, the use of raw versus industry relative data, the train-and-test sampling scheme, and the criteria for model evaluation affect the predictive accuracy of the developed models. Both estimation methods generate remarkably similar model performance rankings, while differences are revealed in the relative importance of variables when using raw versus industry relative data. We find that in most cases there is a fair amount of misclassification, consistent with previous studies in non-financial sectors, which is hard to avoid given the nature of the problem.Presented on: Research in International Business and Financ