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2 edition of Predicting likely takeover targets using multivariate discriminant analysis found in the catalog.

Predicting likely takeover targets using multivariate discriminant analysis

Denise Waldron

Predicting likely takeover targets using multivariate discriminant analysis

by Denise Waldron

  • 379 Want to read
  • 3 Currently reading

Published by University College Dublin in Dublin .
Written in English

    Subjects:
  • Consolidation and merger of corporations -- Forecasting.,
  • Discriminant analysis.,
  • Multivariate analysis.

  • Edition Notes

    Thesis (M.B.A.) - University College Dublin, 1997.

    Statementby Denise Waldron.
    The Physical Object
    Paginationviii, 107p. :
    Number of Pages107
    ID Numbers
    Open LibraryOL17209869M

    One multivariate technique that is commonly used is discriminant function analysis. This paper compares and contrasts the two purposes of discriminant analysis, prediction and description. Using a heuristic data set, a conceptual explanation of both techniques is provided with emphasis on which aspects of the computer printouts are essential for the interpretation of each type of discriminant by: 2. explanatory variables ranged from 1 to In addition to discriminant analysis, other techniques used were logit analysis, probit analysis, regression analysis, and neural network. Altman’s () model is the oldest and the most widely cited model using multivariate discriminant analysis (MDA) to predict corporate Size: KB.

    Multivariate data analysis is widely employed to classify this type of data. There are two well-known models (Multivariate Logistic regression (MLR) and linear discriminant analysis (LDA)) to predict relations between two or more groups, using a set of predictors. Alkarkhi et al. [1] and Krieng [6].Author: Hanaa Elgohari. Discriminant Analysis Muscular Dystrophy Linear Discriminant Analysis Mahalanobis Distance Canonical Variate These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

    Chapter Discriminant Analysis Introduction Discriminant Analysis finds a set of prediction equations based on independent variables that are used to classify individuals into groups. There are two possible objectives in a discriminant analysis: finding a predictive equation. The prediction of corporate failure, based on a specific model, is of vital importance to a large body of individuals. The two statistical methodologies which enjoy prominence in the literature when establishing these models are multivariate discriminant analysis and logistic regression by: 6.


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Predicting likely takeover targets using multivariate discriminant analysis by Denise Waldron Download PDF EPUB FB2

COMPUSTAT data it 1 1 and 18 are used for net income, common equity and preferred equity, respectively. Palepu, Predicting takeover targets 33 The ratio is computed and averaged over a period of four years prior to the year from which an observation is by: Barnes, Paul, “The Prediction of Takeover Targets in the U.K.

by means of Multiple Discriminant Analysis.” Journal of Business Finance and Account 73–84, (). Google ScholarCited by: Stevens () defended multiple discriminant analysis as a model that was well suited to many financial problems where the dependent variable is dichotomous.

However, most of the studies conducted in the s and s switched to logistic regression models for predicting takeover by: The identification of U.K. takeover targets using published historical cost accounting data Some empirical evidence comparing logit with linear discriminant analysis and raw financial ratios with industry-relative ratios In takeover prediction only a small fraction of firms are likely to be targets.

The use of a sample with an equal number Cited by: Target Prediction in M&A Activities in the Croatian Banking Sector. predicting takeover targets such as neural.

multiple discriminant analysis and logistic. compared with the traditional statistical techniques of discriminant analysis and logistic regression. Numerous problems associated to ANN application in target prediction at the undeveloped capital markets and predictive capabilities of ANN are analysed in section 3.

The conclusions of the study are presented in section 4. Size: KB. #2. Multiple Discriminant Analysis. It is used for compressing the multivariate signal so that a low dimensional signal which is open to classification can be produced.

Quadratic Discriminant Analysis. In this type of analysis, your observation will be classified in the forms of the group that has the least squared distance. Discriminant analysis is used to predict the probability of belonging to a given class (or category) based on one or multiple predictor variables.

It works with continuous and/or categorical predictor variables. Previously, we have described the logistic regression for two-class classification problems, that is when the outcome variable has two possible values (0/1, no/yes, negative/positive).5/5(2).

Choosing between logistic regression and discriminant analysis. Journal of the American Statistical Association, 73(), Using multivariate statistics. An example of discriminant analysis is using the performance indicators of a machine to predict whether it is in a good or a bad condition.

Benefits of Discriminant Analysis Discriminant Analysis. The Prediction of Takeover Targets in the UK by Means of Multiple Discriminant Analysis Article in Journal of Business Finance & Accounting 17(1) - 84 December with 41 Reads.

This paper uses discriminant and logit analyses to develop prediction models to identify bank acquisition targets. Our study complements the aforementioned studies by developing discriminant and logit models of prediction, while undertaking a systematic comparison of the two sets of models by addressing several methodological issues, such as the approach to select variables, the use of raw versus industry relative data, and other issues involved in the evaluation process for predicting bank acquisition by: Xin Xin, Jianhua Hu, Liangyuan Liu, On the oracle property of a generalized adaptive elastic-net for multivariate linear regression with a diverging number of parameters, Journal of Multivariate Analysis, /,(), ().Cited by: Part of the Meteorological Monographs book series (METEOR, volume 4) Maurice G., A course in multivariate analysis.

New York, Hafner Publishing Co., Miller R.G. () Statistical Prediction by Discriminant Analysis. In: Statistical Prediction by Discriminant Analysis. Meteorological Monographs, vol by: The only model that provides significantly correct predictions for both acquired and non-acquired firms in either the calibration or holdout sample is a linear discriminant function with six.

Discriminant analysis is a form of multivariate prediction of group membership (similar to multinomial logistic regression analysis), using latent variables. In Graph 1, individuals are characterized on two variables – income (the x variable, in £s per month) and their educational qualifications (the y variable – the higher the value.

This paper illustrates the use of a multicriteria decision aid technique for the development of a model for the prediction of acquisition targets. A sample of 76 UK firms acquired over the period – matched with 76 non-acquired firms is used to develop a model that discriminates between acquired and non-acquired firms.

Back-testing results on the discriminating ability of the model Cited by: An Overview and Application of Discriminant Analysis in Data Analysis Alayande, (i.e., with a categorical target variable), not for regression.

The target variable may have two or more categorical data. The first stage in any multivariate analysis of this nature should be a factor analysis Cited by: 1. Linear discriminant analysis with Tanagra – Reading the results Data importation We want to perform a linear discriminant analysis with Tanagra.

We open the “” file into Excel, we select the whole data range and we send it to Tanagra using the “” Size: 1MB. Insolvency Prediction Model Using Multivariate Discriminant Analysis and Artificial Neural Network for the Finance Industry in New Zealand Kim-Choy Chung Department of Marketing, University of Otago, P O Dunedin, New Zealand Tel: E .An Application of Logit Analysis to Prediction of Merger Targets J.

Kimball Dietrich, University of Southern California Eric Sorensen, University of Arizona Logit estimation is applied to predicting the probability that a given firm will be a merger target.

Care is Cited by: takeover attempt. Methodology Multivariate Discriminant Analysis Discriminant analysis is a statistical technique that can be employed to classify objects (targets of tender offers) into one or more mutually exclusive categories (such as a good or poor takeover candidate).

This classification is based on various individual char.