Application of Quantitative Techniques for the Prediction of Bank Acquisition Targets.

By: Zopounidis, ConstantinContributor(s): Pasiouras, Fotios | Tanna, Sailesh KumarMaterial type: TextTextSeries: Series on Computers and Operations Research SerPublisher: Singapore : World Scientific Publishing Co Pte Ltd, 2005Copyright date: ©2005Description: 1 online resource (293 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9789812703200Subject(s): Bank mergers -- Econometric models | Prediction theoryGenre/Form: Electronic books.Additional physical formats: Print version:: Application of Quantitative Techniques for the Prediction of Bank Acquisition TargetsDDC classification: 332.1 LOC classification: HG1722.P37 2005Online resources: Click to View
Contents:
Intro -- Contents -- Preface -- 1. Banks M&As: Motives and Evidence -- 1.1 Overview -- 1.2 M&As Trends in the European Union -- 1.3 Reasons and Motives for Banks M&As -- 1.3.1 Firm level motives and external factors of M&As -- 1.3.1.1 Firm level motives -- Synergy -- Economies of Scale -- Economies of Scope -- Increased Market Power -- The Market for Bank Control - Inefficient Management Replacement -- Risk Diversification -- Capital Strength -- Agency Motives (Managerial Motives) -- Hubris Motives -- 1.3.1.2 External factors -- Deregulation and Laws -- Technological Developments -- Globalisation -- Shareholder Pressures -- Introduction of the Euro -- Macroeconomic Conditions -- 1.3.2 Practitioners' views on reasons for banks M&As -- The European Central Bank Report (2000) -- The Group of Ten Report (2001) -- 1.4 Studies on Banks' M&As -- 1.4.1 Characteristics of banks involved in M&As -- 1.4.2 Premium paid on banks' M&As -- 1.4.3 Evidence on merged banks' operating performance -- Univariate T-Test -- Efficiency Studies -- Overall Assessment o f Overating Performance Studies -- 1.4.4 Evidence on merged banks' stock market performance -- Overall Assessment of Event Studies -- 1.4.5 Evidence on European banks M&As -- Vander Vennet (I 996) -- Tourani Rad and Van Beek (I 999) -- Cybo-Ottone and Murgia (2000) -- Huizinga, Nelissen and Vander Vennet (2001) -- Beitel and Schiereck (2001) -- Beitel, Schiereck, Wahrenburg (2002) -- Diaz, Olalla, Azofra (2004) -- Lepetit, Patry, Rous (2004) -- Dunis and Klein (200.5) -- 2. Studies on the Prediction of Acquisition Targets -- 2.1 Introduction -- 2.2 Studies Employing Statistical Techniques -- Simkowitz and Monroe (1971) -- Tzoannos and Samuels (1972) -- Stevens (1973) -- Belkaoui (1978) -- Wansley and Lane (I 983) -- Rege (I 984) -- Bartley and Boardman (1 986) -- Bartley and Boardman (I 990).
Barnes (I 990) -- Kira and Morin (I 993) -- 2.3 Studies using Econometric Techniques -- Harris, Stewart, Carleton (I 982) -- Dietrich and Sorensen (I 984) -- Palepu (1986) -- Ambrose and Megginson ( I 992) -- Walter (I 994) -- Meador, Church and Rayburn (1 996) -- Powell (I 997) -- Barnes (1998) -- Kim and Arbel (1998) -- Cudd and Duggal (2000) -- Powell (2001) -- 2.4 Other Studies -- Slowinski, Zopounidis, Dimitras (I 997) -- Zanakis and Zopounidis (1997) -- Fairclough and Hunter (1 998) -- Cheh, Weinberg and Yook (I 999) -- Barnes (2000) -- Zopounidis and Doumpos (2002) -- Espahbodi and Espahbodi (2003) -- Tartari, Doumpos, Baourakis, Zopounidis (2003) -- Doumpos, Kosmidou, Pasiouras (2004) -- 2.5 Conclusions -- 3 . Methodological Framework for the Development of Acquisition Targets Prediction Model -- 3.1 Introduction -- 3.2 Sampling Considerations -- 3.2.1 Definition of acquisitions -- 3.2.2 Training and testing samples -- 3.2.2.1 Holdout sample -- 3.2.2.2 Resampling techniques -- 3.2.2.3 Population drift -- 3.2.3 Proportion of acquired and non-acquired firms in sample -- (i) Groups' proportions in training sample -- (ii) Groups' proportions in testing sample -- (iii) Matching criteria -- 3.3 Variables Selection Process -- 3.3.1 Financial theory and human judgement -- 3.3.2 Univariate statistical tests of mean differences -- 3.3.3 Factor analysis -- 3.4 Method Selection -- 3.4.1 Discriminant analysis -- 3.4.2 Logit analysis -- 3.4.3 UTilitks Additives DIScriminantes (UTADIS) -- 3.4.4 Multi-group Hierarchical DIScrimination (MHDIS) -- 3.4.5 Classification and Regression Trees (CART) -- 3.4.6 Nearest neighbours -- 3.4.7 Support Vector Machines (SVMs) -- 3.5 Aspects of Model Evaluation -- 3.5.1 The classification matrix -- (i) Absolute comparison performance -- (ii) Comparative performance -- 3.5.2 Issues to consider when choosing evaluation measure.
3.5.3 Out-of-sample performance - model's testing -- 3.6 Conclusion -- 4 . Data and Preliminary Analysis -- 4.1 Introduction -- 4.2 Data Sources -- (i) Acquired banks -- (ii) Non-acquired banks -- 4.3 Samples Construction -- Training Sample A -- Training Sample B -- Testing Sample A -- Testing Sample B -- 4.4 Identification of Candidate Variables -- Capital Ratios -- Management Performance -- Profit Efficiencv -- Cost Efficiency -- Liquidity Ratios -- Growth -- Size -- Market Share -- 4.5 Financial Variables and Country (Industry) Adjustment -- 4.6 Variables Reduction Process -- 4.6.1 Descriptive statistics -- 4.6.2 Univariate test of two groups mean differences -- 4.6.3 Correlation analysis -- 4.7 Conclusion -- 5 . Development of Acquisitions Prediction Models -- 5.1 Introduction -- 5.2 Summary of Results of Model Comparisons -- 5.3 Development and Evaluation of Prediction Models -- 5.3.1 Discriminant analysis -- (i) Cut-offrobability equal to 0.5 -- (ii) Cut-offpoint two - Acquisition probabilities distribution and Minimization of errors -- 5.3.2 Logit analysis -- (i) Cut-of probability equal to 0.5 -- (ii) Cut-offpoint two - Acquisition probabilities distribution and Minimization of errors -- 5.3.3 UTADIS -- 5.3.4 MHDIS -- 5.3.5 CART -- 5.3.6 Nearest neighbours -- 5.3.7 SVMs -- 5.4 Conclusions -- 6 . Integration of Prediction Models -- 6.1 Introduction -- 6.2 Integrated (Multi-Classifiers) Models -- 6.2.1 Majority voting -- 6.2.2 Stacked generalization approach -- 6.3 Development and Evaluation of Integration Models -- 6.3.1 Application of the majority voting rule -- 6.3.2 Application of stacked generalization approach -- 6.3.3 Comparison of majority voting and stacked models -- 6.4 Conclusions -- 7 . Conclusions -- 7.1 Introduction -- 7.2 Why Prediction Models for EU Banks -- 7.3 Summary ofthe Findings.
7.4 Why Classification Results Differ Across Methods? -- 7.5 Suggestions for Further Research -- Bibliography -- Index.
Summary: Key Features:Extensive discussion of the current trends of M&As in bankingComprehensive analysis of the motives of M&AsExtensive presentation and comparison of several statistical and computational methodologies for analyzing and predicting M&AsEmpirical results on the use of existing methodologies using real-world data on M&As.
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
No physical items for this record

Intro -- Contents -- Preface -- 1. Banks M&As: Motives and Evidence -- 1.1 Overview -- 1.2 M&As Trends in the European Union -- 1.3 Reasons and Motives for Banks M&As -- 1.3.1 Firm level motives and external factors of M&As -- 1.3.1.1 Firm level motives -- Synergy -- Economies of Scale -- Economies of Scope -- Increased Market Power -- The Market for Bank Control - Inefficient Management Replacement -- Risk Diversification -- Capital Strength -- Agency Motives (Managerial Motives) -- Hubris Motives -- 1.3.1.2 External factors -- Deregulation and Laws -- Technological Developments -- Globalisation -- Shareholder Pressures -- Introduction of the Euro -- Macroeconomic Conditions -- 1.3.2 Practitioners' views on reasons for banks M&As -- The European Central Bank Report (2000) -- The Group of Ten Report (2001) -- 1.4 Studies on Banks' M&As -- 1.4.1 Characteristics of banks involved in M&As -- 1.4.2 Premium paid on banks' M&As -- 1.4.3 Evidence on merged banks' operating performance -- Univariate T-Test -- Efficiency Studies -- Overall Assessment o f Overating Performance Studies -- 1.4.4 Evidence on merged banks' stock market performance -- Overall Assessment of Event Studies -- 1.4.5 Evidence on European banks M&As -- Vander Vennet (I 996) -- Tourani Rad and Van Beek (I 999) -- Cybo-Ottone and Murgia (2000) -- Huizinga, Nelissen and Vander Vennet (2001) -- Beitel and Schiereck (2001) -- Beitel, Schiereck, Wahrenburg (2002) -- Diaz, Olalla, Azofra (2004) -- Lepetit, Patry, Rous (2004) -- Dunis and Klein (200.5) -- 2. Studies on the Prediction of Acquisition Targets -- 2.1 Introduction -- 2.2 Studies Employing Statistical Techniques -- Simkowitz and Monroe (1971) -- Tzoannos and Samuels (1972) -- Stevens (1973) -- Belkaoui (1978) -- Wansley and Lane (I 983) -- Rege (I 984) -- Bartley and Boardman (1 986) -- Bartley and Boardman (I 990).

Barnes (I 990) -- Kira and Morin (I 993) -- 2.3 Studies using Econometric Techniques -- Harris, Stewart, Carleton (I 982) -- Dietrich and Sorensen (I 984) -- Palepu (1986) -- Ambrose and Megginson ( I 992) -- Walter (I 994) -- Meador, Church and Rayburn (1 996) -- Powell (I 997) -- Barnes (1998) -- Kim and Arbel (1998) -- Cudd and Duggal (2000) -- Powell (2001) -- 2.4 Other Studies -- Slowinski, Zopounidis, Dimitras (I 997) -- Zanakis and Zopounidis (1997) -- Fairclough and Hunter (1 998) -- Cheh, Weinberg and Yook (I 999) -- Barnes (2000) -- Zopounidis and Doumpos (2002) -- Espahbodi and Espahbodi (2003) -- Tartari, Doumpos, Baourakis, Zopounidis (2003) -- Doumpos, Kosmidou, Pasiouras (2004) -- 2.5 Conclusions -- 3 . Methodological Framework for the Development of Acquisition Targets Prediction Model -- 3.1 Introduction -- 3.2 Sampling Considerations -- 3.2.1 Definition of acquisitions -- 3.2.2 Training and testing samples -- 3.2.2.1 Holdout sample -- 3.2.2.2 Resampling techniques -- 3.2.2.3 Population drift -- 3.2.3 Proportion of acquired and non-acquired firms in sample -- (i) Groups' proportions in training sample -- (ii) Groups' proportions in testing sample -- (iii) Matching criteria -- 3.3 Variables Selection Process -- 3.3.1 Financial theory and human judgement -- 3.3.2 Univariate statistical tests of mean differences -- 3.3.3 Factor analysis -- 3.4 Method Selection -- 3.4.1 Discriminant analysis -- 3.4.2 Logit analysis -- 3.4.3 UTilitks Additives DIScriminantes (UTADIS) -- 3.4.4 Multi-group Hierarchical DIScrimination (MHDIS) -- 3.4.5 Classification and Regression Trees (CART) -- 3.4.6 Nearest neighbours -- 3.4.7 Support Vector Machines (SVMs) -- 3.5 Aspects of Model Evaluation -- 3.5.1 The classification matrix -- (i) Absolute comparison performance -- (ii) Comparative performance -- 3.5.2 Issues to consider when choosing evaluation measure.

3.5.3 Out-of-sample performance - model's testing -- 3.6 Conclusion -- 4 . Data and Preliminary Analysis -- 4.1 Introduction -- 4.2 Data Sources -- (i) Acquired banks -- (ii) Non-acquired banks -- 4.3 Samples Construction -- Training Sample A -- Training Sample B -- Testing Sample A -- Testing Sample B -- 4.4 Identification of Candidate Variables -- Capital Ratios -- Management Performance -- Profit Efficiencv -- Cost Efficiency -- Liquidity Ratios -- Growth -- Size -- Market Share -- 4.5 Financial Variables and Country (Industry) Adjustment -- 4.6 Variables Reduction Process -- 4.6.1 Descriptive statistics -- 4.6.2 Univariate test of two groups mean differences -- 4.6.3 Correlation analysis -- 4.7 Conclusion -- 5 . Development of Acquisitions Prediction Models -- 5.1 Introduction -- 5.2 Summary of Results of Model Comparisons -- 5.3 Development and Evaluation of Prediction Models -- 5.3.1 Discriminant analysis -- (i) Cut-offrobability equal to 0.5 -- (ii) Cut-offpoint two - Acquisition probabilities distribution and Minimization of errors -- 5.3.2 Logit analysis -- (i) Cut-of probability equal to 0.5 -- (ii) Cut-offpoint two - Acquisition probabilities distribution and Minimization of errors -- 5.3.3 UTADIS -- 5.3.4 MHDIS -- 5.3.5 CART -- 5.3.6 Nearest neighbours -- 5.3.7 SVMs -- 5.4 Conclusions -- 6 . Integration of Prediction Models -- 6.1 Introduction -- 6.2 Integrated (Multi-Classifiers) Models -- 6.2.1 Majority voting -- 6.2.2 Stacked generalization approach -- 6.3 Development and Evaluation of Integration Models -- 6.3.1 Application of the majority voting rule -- 6.3.2 Application of stacked generalization approach -- 6.3.3 Comparison of majority voting and stacked models -- 6.4 Conclusions -- 7 . Conclusions -- 7.1 Introduction -- 7.2 Why Prediction Models for EU Banks -- 7.3 Summary ofthe Findings.

7.4 Why Classification Results Differ Across Methods? -- 7.5 Suggestions for Further Research -- Bibliography -- Index.

Key Features:Extensive discussion of the current trends of M&As in bankingComprehensive analysis of the motives of M&AsExtensive presentation and comparison of several statistical and computational methodologies for analyzing and predicting M&AsEmpirical results on the use of existing methodologies using real-world data on M&As.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

There are no comments on this title.

to post a comment.

Powered by Koha