The mathematics of banking and finance:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester [u.a.]
Wiley
2006
|
| Schriftenreihe: | Wiley finance series
|
| Schlagworte: | |
| Online-Zugang: | Table of contents only Inhaltsverzeichnis |
| Beschreibung: | XIV, 295 S. graf. Darst. |
| ISBN: | 047001489X 9780470014899 |
Internformat
MARC
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| 100 | 1 | |a Cox, Dennis W. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a The mathematics of banking and finance |c Dennis Cox and Michael Cox |
| 264 | 1 | |a Chichester [u.a.] |b Wiley |c 2006 | |
| 300 | |a XIV, 295 S. |b graf. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley finance series | |
| 650 | 4 | |a Bankalar ve bankacılık - Matematik | |
| 650 | 4 | |a İşletme matematiği | |
| 650 | 4 | |a Bank | |
| 650 | 4 | |a Mathematik | |
| 650 | 4 | |a Business mathematics | |
| 650 | 4 | |a Banks and banking |x Mathematics | |
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| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Cox, Michael |e Verfasser |4 aut | |
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Datensatz im Suchindex
| _version_ | 1804135607053582336 |
|---|---|
| adam_text | Contents Introduction xiii
1 Introduction to How to Display Data and the Scatter Plot 1
1.1 Introduction 1
1.2 Scatter Plots 2
1.3 Data Identification 2
1.3.1 An example of salary against age 2
1.4 Why Draw a Scatter Plot? 3
1.5 Matrix Plots 4
1.5.1 An example of salary against age: Revisited 5
2 Bar Charts 7
2.1 Introduction 7
2.2 Discrete Data 7
2.3 Relative Frequencies 8
2.4 Pie Charts 12
3 Histograms 13
3.1 Continuous Variables 13
3.2 Cumulative Frequency Polygon 14
3.3 Sturges Formula 20
4 Probability Theory 21
4.1 Introduction 21
4.2 Basic Probability Concepts 21
4.3 Estimation of Probabilities 22
4.4 Exclusive Events 22
4.5 Independent Events 22
4.6 Comparison of Exclusivity and Independence 23
4.7 Venn Diagrams 23
4.8 The Addition Rule for Probabilities 24
4.8.1 A simple probability example using a Venn diagram 25
4.9 Conditional Probability 25
4.9.1 An example of conditional probability 26
vi Contents 4.10 The Multiplication Rule for Probabilities 26
4.10.1 A classical example of conditional probability 27
4.11 Bayes Theorem 27
4.11.1 An example of Bayes theorem 28
4.11.2 Bayes theorem in action for more groups 29
4.11.3 Bayes theorem applied to insurance 29
4.12 Tree Diagram 30
4.12.1 An example of prediction of success 30
4.12.2 An example from an American game show:
The Monty Hall Problem 34
4.13 Conclusion 35
5 Standard Terms in Statistics 37
5.1 Introduction 37
5.2 Maximum and Minimum 37
5.2.1 Mean 37
5.2.2 Median 38
5.2.3 Mode 39
5.3 Upper and Lower Quartile 39
5.4 MQMQM Plot 40
5.5 Skewness 41
5.6 Variance and Standard Deviation 41
5.7 Measures for Continuous Data 44
6 Sampling 47
6.1 Introduction 47
6.2 Planning Data Collection 47
6.3 Methods for Survey Analysis 48
6.3.1 Random samples 49
6.3.2 Systematic sampling 49
6.3.3 Stratified sampling 49
6.3.4 Multistage sampling 50
6.3.5 Quota sampling 50
6.3.6 Cluster sampling 50
6.4 How It Can Go Wrong 50
6.5 What Might Be In a Survey? 51
6.6 Cautionary Notes 51
7 Probability Distribution Functions 53
7.1 Introduction 53
7.2 Discrete Uniform Distribution 53
7.2.1 Counting techniques 54
7.2.2 Combination 54
7.2.3 Permutation 55
7.3 Binomial Distribution 55
7.3.1 Example of a binomial distribution 56
Contents vii
7.3.2 Pascal s triangle 56
7.3.3 The use of the binomial distribution 57
7.4 The Poisson Distribution 58
7.4.1 An example of the Poisson distribution 59
7.4.2 Uses of the Poisson distribution 60
7.5 Uses of the Binomial and Poisson Distributions 60
7.5.1 Is suicide a Poisson process? 62
7.6 Continuous Uniform Distribution 64
7.7 Exponential Distribution 66
8 Normal Distribution 67
8.1 Introduction 67
8.2 Normal Distribution 67
8.2.1 A simple example of normal probabilities 69
8.2.2 A second example of normal probabilities 69
8.3 Addition of Normal Variables 70
8.4 Central Limit Theorem 70
8.4.1 An example of the Central Limit Theorem 70
8.5 Confidence Intervals for the Population Mean 71
8.5.1 An example of confidence intervals for the population mean 71
8.6 Normal Approximation to the Binomial Distribution 72
8.6.1 An example of the normal approximation to the
binomial distribution 72
8.7 Normal Approximation to the Poisson Distribution 72
8.7.1 An example of fitting a normal curve to the Poisson distribution 73
9 Comparison of the Means, Sample Sizes and Hypothesis Testing 75
9.1 Introduction 75
9.2 Estimation of the Mean 75
9.2.1 An example of estimating a confidence interval for
an experimental mean 76
9.3 Choice of the Sample Size 77
9.3.1 An example of selecting sample size 77
9.4 Hypothesis Testing 77
9.4.1 An example of hypothesis testing 78
9.5 Comparison of Two Sample Means 79
9.5.1 An example of a two sample t test 79
9.6 Type I and Type II Errors 80
9.6.1 An example of type I and type II errors 80
10 Comparison of Variances 83
10.1 Introduction 83
10.2 Chi Squared Test 83
10.2.1 An example of the chi squared test 83
10.3 FTest 85
10.3.1 An example of the F test 85
10.3.2 An example considering the normal distribution 85
viii Contents 11 Chi squared Goodness of Fit Test 91
11.1 Introduction 91
11.2 Contingency Tables 92
11.3 Multiway Tables 94
11.3.1 An example of a four by four table 94
12 Analysis of Paired Data 97
12.1 Introduction 97
12.2 fTest 97
12.3 Sign Test 98
12.4 The U Test 99
12.4.1 An example of the use of the U test 101
13 Linear Regression 103
13.1 Introduction 103
13.2 Linear Regression 103
13.3 Correlation Coefficient 104
13.3.1 An example of examining correlation 105
13.4 Estimation of the Uncertainties 109
13.5 Statistical Analysis and Interpretation of Linear Regression 110
13.6 ANOVA for Linear Regression 110
13.7 Equations for the Variance of a and b 112
13.8 Significance Test for the Slope 112
13.8.1 An example of slope analysis 113
13.8.2 A further example of correlation and linear regression 115
14 Analysis of Variance 121
14.1 Introduction 121
14.2 Formal Background to the ANOVA Table 121
14.3 Analysis of the ANOVA Table 122
14.4 Comparison of Two Causal Means 123
14.4.1 An example of extinguisher discharge times 123
14.4.2 An example of the lifetime of lamps 125
15 Design and Approach to the Analysis of Data 129
15.1 Introduction 129
15.2 Randomised Block Design 129
15.2.1 An example of outsourcing 130
15.3 Latin Squares 131
15.4 Analysis of a Randomised Block Design 132
15.5 Analysis of a Two way Classification 135
15.5.1 An example of two way analysis 137
15.5.2 An example of a randomised block 140
15.5.3 An example of the use of the Latin square 143
16 Linear Programming: Graphical Method 149
16.1 Introduction J49
^ Contents ix
16.2 Practical Examples 149
16.2.1 An example of an optimum investment strategy 149
16.2.2 An example of the optimal allocation of advertising 154
17 Linear Programming: Simplex Method 159
17.1 Introduction 159
17.2 Most Profitable Loans 159
17.2.1 An example of finance selection 164
17.3 General Rules 167
17.3.1 Standardisation 167
17.3.2 Introduction of additional variables 167
17.3.3 Initial solution 167
17.3.4 An example to demonstrate the application of the general rules
for linear programming 167
17.4 The Concerns with the Approach 170
18 Transport Problems 171
18.1 Introduction 171
18.2 Transport Problem 171
19 Dynamic Programming 179
19.1 Introduction 179
19.2 Principle of Optimality 179
19.3 Examples of Dynamic Programming 180
19.3.1 An example of forward and backward recursion 180
19.3.2 A practical example of recursion in use 182
19.3.3 A more complex example of dynamic programming 184
19.3.4 The Travelling Salesman problem 185
20 Decision Theory 189
20.1 Introduction 189
20.2 Project Analysis Guidelines 190
20.3 Minimax Regret Rule 192
21 Inventory and Stock Control 195
21.1 Introduction 195
21.2 The Economic Order Quantity Model 195
21.2.1 An example of the use of the economic order quantity model 196
21.3 Non zero Lead Time 199
21.3.1 An example of Poisson and continuous approximation 200
22 Simulation: Monte Carlo Methods 203
22.1 Introduction 203
22.2 What is Monte Carlo Simulation? 203
22.2.1 An example of the use of Monte Carlo simulation: Theory of the
inventory problem 203
22.3 Monte Carlo Simulation of the Inventory Problem 205
a i^onienis ^ 22.4 Queuing Problem 208
22.5 The Bank Cashier Problem 209
22.6 Monte Carlo for the Monty Hall Problem 212
22.7 Conclusion 214
23 Reliability: Obsolescence 215
23.1 Introduction 215
23.2 Replacement at a Fixed Age 215
23.3 Replacement at Fixed Times 217
24 Project Evaluation 219
24.1 Introduction 219
24.2 Net Present Value 219
24.2.1 An example of net present value 219
24.3 Internal Rate of Return 220
24.3.1 An example of the internal rate of return 220
24.4 Price/Earnings Ratio 222
24.5 Payback Period 222
24.5.1 Mathematical background to the payback period 222
24.5.2 Mathematical background to producing the tables 223
25 Risk and Uncertainty 227
25.1 Introduction 227
25.2 Risk 227
25.3 Uncertainty 227
25.4 Adjusting the Discount Rate 228
25.5 Adjusting the Cash Flows of a Project 228
25.5.1 An example of expected cash flows 228
25.6 Assessing the Margin of Error 229
25.6.1 An example of break even analysis 229
25.7 The Expected Value of the Net Present Value 231
25.7.1 An example of the use of the distribution approach to the
evaluation of net present value 231
25.8 Measuring Risk 232
25.8.1 An example of normal approximation 234
26 Time Series Analysis 235
26.1 Introduction 235
26.2 Trend Analysis 236
26.3 Seasonal Variations 236
26.4 Cyclical Variations 240
26.5 Mathematical Analysis 240
26.6 Identification of Trend 241
26.7 Moving Average 241
26.8 Trend and Seasonal Variations 242
26.9 Moving Averages of Even Numbers of Observations 244
26.10 Graphical Methods 247
Contents xi
27 Reliability 249
27.1 Introduction 249
27.2 Illustrating Reliability 249
27.3 The Bathtub Curve 249
27.4 The Continuous Case 251
27.5 Exponential Distribution 252
27.5.1 An example of exponential distribution 252
27.5.2 An example of maximum of an exponential distribution 254
27.6 Weibull Distribution 255
27.6.1 An example of a Weibull distribution 256
27.7 Log Normal Distribution 257
27.8 Truncated Normal Distribution 260
28 Value at Risk 261
28.1 Introduction 261
28.2 Extreme Value Distributions 262
28.2.1 A worked example of value at risk 262
28.3 Calculating Value at Risk 264
29 Sensitivity Analysis 267
29.1 Introduction 267
29.2 The Application of Sensitivity Analysis to Operational Risk 267
30 Scenario Analysis 271
30.1 Introduction to Scenario Analysis 271
30.2 Use of External Loss Data 271
30.3 Scaling of Loss Data 272
30.4 Consideration of Likelihood 272
30.5 Anonimised Loss Data 273
31 An Introduction to Neural Networks 275
31.1 Introduction 275
31.2 Neural Algorithms 275
Appendix Mathematical Symbols and Notation 279
Index 285
|
| adam_txt |
Contents Introduction xiii
1 Introduction to How to Display Data and the Scatter Plot 1
1.1 Introduction 1
1.2 Scatter Plots 2
1.3 Data Identification 2
1.3.1 An example of salary against age 2
1.4 Why Draw a Scatter Plot? 3
1.5 Matrix Plots 4
1.5.1 An example of salary against age: Revisited 5
2 Bar Charts 7
2.1 Introduction 7
2.2 Discrete Data 7
2.3 Relative Frequencies 8
2.4 Pie Charts 12
3 Histograms 13
3.1 Continuous Variables 13
3.2 Cumulative Frequency Polygon 14
3.3 Sturges' Formula 20
4 Probability Theory 21
4.1 Introduction 21
4.2 Basic Probability Concepts 21
4.3 Estimation of Probabilities 22
4.4 Exclusive Events 22
4.5 Independent Events 22
4.6 Comparison of Exclusivity and Independence 23
4.7 Venn Diagrams 23
4.8 The Addition Rule for Probabilities 24
4.8.1 A simple probability example using a Venn diagram 25
4.9 Conditional Probability 25
4.9.1 An example of conditional probability 26
vi Contents 4.10 The Multiplication Rule for Probabilities 26
4.10.1 A classical example of conditional probability 27
4.11 Bayes'Theorem 27
4.11.1 An example of Bayes' theorem 28
4.11.2 Bayes' theorem in action for more groups 29
4.11.3 Bayes'theorem applied to insurance 29
4.12 Tree Diagram 30
4.12.1 An example of prediction of success 30
4.12.2 An example from an American game show:
The Monty Hall Problem 34
4.13 Conclusion 35
5 Standard Terms in Statistics 37
5.1 Introduction 37
5.2 Maximum and Minimum 37
5.2.1 Mean 37
5.2.2 Median 38
5.2.3 Mode 39
5.3 Upper and Lower Quartile 39
5.4 MQMQM Plot 40
5.5 Skewness 41
5.6 Variance and Standard Deviation 41
5.7 Measures for Continuous Data 44
6 Sampling 47
6.1 Introduction 47
6.2 Planning Data Collection 47
6.3 Methods for Survey Analysis 48
6.3.1 Random samples 49
6.3.2 Systematic sampling 49
6.3.3 Stratified sampling 49
6.3.4 Multistage sampling 50
6.3.5 Quota sampling 50
6.3.6 Cluster sampling 50
6.4 How It Can Go Wrong 50
6.5 What Might Be In a Survey? 51
6.6 Cautionary Notes 51
7 Probability Distribution Functions 53
7.1 Introduction 53
7.2 Discrete Uniform Distribution 53
7.2.1 Counting techniques 54
7.2.2 Combination 54
7.2.3 Permutation 55
7.3 Binomial Distribution 55
7.3.1 Example of a binomial distribution 56
Contents vii
7.3.2 Pascal's triangle 56
7.3.3 The use of the binomial distribution 57
7.4 The Poisson Distribution 58
7.4.1 An example of the Poisson distribution 59
7.4.2 Uses of the Poisson distribution 60
7.5 Uses of the Binomial and Poisson Distributions 60
7.5.1 Is suicide a Poisson process? 62
7.6 Continuous Uniform Distribution 64
7.7 Exponential Distribution 66
8 Normal Distribution 67
8.1 Introduction 67
8.2 Normal Distribution 67
8.2.1 A simple example of normal probabilities 69
8.2.2 A second example of normal probabilities 69
8.3 Addition of Normal Variables 70
8.4 Central Limit Theorem 70
8.4.1 An example of the Central Limit Theorem 70
8.5 Confidence Intervals for the Population Mean 71
8.5.1 An example of confidence intervals for the population mean 71
8.6 Normal Approximation to the Binomial Distribution 72
8.6.1 An example of the normal approximation to the
binomial distribution 72
8.7 Normal Approximation to the Poisson Distribution 72
8.7.1 An example of fitting a normal curve to the Poisson distribution 73
9 Comparison of the Means, Sample Sizes and Hypothesis Testing 75
9.1 Introduction 75
9.2 Estimation of the Mean 75
9.2.1 An example of estimating a confidence interval for
an experimental mean 76
9.3 Choice of the Sample Size 77
9.3.1 An example of selecting sample size 77
9.4 Hypothesis Testing 77
9.4.1 An example of hypothesis testing 78
9.5 Comparison of Two Sample Means 79
9.5.1 An example of a two sample t test 79
9.6 Type I and Type II Errors 80
9.6.1 An example of type I and type II errors 80
10 Comparison of Variances 83
10.1 Introduction 83
10.2 Chi Squared Test 83
10.2.1 An example of the chi squared test 83
10.3 FTest 85
10.3.1 An example of the F test 85
10.3.2 An example considering the normal distribution 85
viii Contents 11 Chi squared Goodness of Fit Test 91
11.1 Introduction 91
11.2 Contingency Tables 92
11.3 Multiway Tables 94
11.3.1 An example of a four by four table 94
12 Analysis of Paired Data 97
12.1 Introduction 97
12.2 fTest 97
12.3 Sign Test 98
12.4 The U Test 99
12.4.1 An example of the use of the U test 101
13 Linear Regression 103
13.1 Introduction 103
13.2 Linear Regression 103
13.3 Correlation Coefficient 104
13.3.1 An example of examining correlation 105
13.4 Estimation of the Uncertainties 109
13.5 Statistical Analysis and Interpretation of Linear Regression 110
13.6 ANOVA for Linear Regression 110
13.7 Equations for the Variance of a and b 112
13.8 Significance Test for the Slope 112
13.8.1 An example of slope analysis 113
13.8.2 A further example of correlation and linear regression 115
14 Analysis of Variance 121
14.1 Introduction 121
14.2 Formal Background to the ANOVA Table 121
14.3 Analysis of the ANOVA Table 122
14.4 Comparison of Two Causal Means 123
14.4.1 An example of extinguisher discharge times 123
14.4.2 An example of the lifetime of lamps 125
15 Design and Approach to the Analysis of Data 129
15.1 Introduction 129
15.2 Randomised Block Design 129
15.2.1 An example of outsourcing 130
15.3 Latin Squares 131
15.4 Analysis of a Randomised Block Design 132
15.5 Analysis of a Two way Classification 135
15.5.1 An example of two way analysis 137
15.5.2 An example of a randomised block 140
15.5.3 An example of the use of the Latin square 143
16 Linear Programming: Graphical Method 149
16.1 Introduction J49
^ Contents ix
16.2 Practical Examples 149
16.2.1 An example of an optimum investment strategy 149
16.2.2 An example of the optimal allocation of advertising 154
17 Linear Programming: Simplex Method 159
17.1 Introduction 159
17.2 Most Profitable Loans 159
17.2.1 An example of finance selection 164
17.3 General Rules 167
17.3.1 Standardisation 167
17.3.2 Introduction of additional variables 167
17.3.3 Initial solution 167
17.3.4 An example to demonstrate the application of the general rules
for linear programming 167
17.4 The Concerns with the Approach 170
18 Transport Problems 171
18.1 Introduction 171
18.2 Transport Problem 171
19 Dynamic Programming 179
19.1 Introduction 179
19.2 Principle of Optimality 179
19.3 Examples of Dynamic Programming 180
19.3.1 An example of forward and backward recursion 180
19.3.2 A practical example of recursion in use 182
19.3.3 A more complex example of dynamic programming 184
19.3.4 The'Travelling Salesman'problem 185
20 Decision Theory 189
20.1 Introduction 189
20.2 Project Analysis Guidelines 190
20.3 Minimax Regret Rule 192
21 Inventory and Stock Control 195
21.1 Introduction 195
21.2 The Economic Order Quantity Model 195
21.2.1 An example of the use of the economic order quantity model 196
21.3 Non zero Lead Time 199
21.3.1 An example of Poisson and continuous approximation 200
22 Simulation: Monte Carlo Methods 203
22.1 Introduction 203
22.2 What is Monte Carlo Simulation? 203
22.2.1 An example of the use of Monte Carlo simulation: Theory of the
inventory problem 203
22.3 Monte Carlo Simulation of the Inventory' Problem 205
a i^onienis ^ 22.4 Queuing Problem 208
22.5 The Bank Cashier Problem 209
22.6 Monte Carlo for the Monty Hall Problem 212
22.7 Conclusion 214
23 Reliability: Obsolescence 215
23.1 Introduction 215
23.2 Replacement at a Fixed Age 215
23.3 Replacement at Fixed Times 217
24 Project Evaluation 219
24.1 Introduction 219
24.2 Net Present Value 219
24.2.1 An example of net present value 219
24.3 Internal Rate of Return 220
24.3.1 An example of the internal rate of return 220
24.4 Price/Earnings Ratio 222
24.5 Payback Period 222
24.5.1 Mathematical background to the payback period 222
24.5.2 Mathematical background to producing the tables 223
25 Risk and Uncertainty 227
25.1 Introduction 227
25.2 Risk 227
25.3 Uncertainty 227
25.4 Adjusting the Discount Rate 228
25.5 Adjusting the Cash Flows of a Project 228
25.5.1 An example of expected cash flows 228
25.6 Assessing the Margin of Error 229
25.6.1 An example of break even analysis 229
25.7 The Expected Value of the Net Present Value 231
25.7.1 An example of the use of the distribution approach to the
evaluation of net present value 231
25.8 Measuring Risk 232
25.8.1 An example of normal approximation 234
26 Time Series Analysis 235
26.1 Introduction 235
26.2 Trend Analysis 236
26.3 Seasonal Variations 236
26.4 Cyclical Variations 240
26.5 Mathematical Analysis 240
26.6 Identification of Trend 241
26.7 Moving Average 241
26.8 Trend and Seasonal Variations 242
26.9 Moving Averages of Even Numbers of Observations 244
26.10 Graphical Methods 247
Contents xi
27 Reliability 249
27.1 Introduction 249
27.2 Illustrating Reliability 249
27.3 The Bathtub Curve 249
27.4 The Continuous Case 251
27.5 Exponential Distribution 252
27.5.1 An example of exponential distribution 252
27.5.2 An example of maximum of an exponential distribution 254
27.6 Weibull Distribution 255
27.6.1 An example of a Weibull distribution 256
27.7 Log Normal Distribution 257
27.8 Truncated Normal Distribution 260
28 Value at Risk 261
28.1 Introduction 261
28.2 Extreme Value Distributions 262
28.2.1 A worked example of value at risk 262
28.3 Calculating Value at Risk 264
29 Sensitivity Analysis 267
29.1 Introduction 267
29.2 The Application of Sensitivity Analysis to Operational Risk 267
30 Scenario Analysis 271
30.1 Introduction to Scenario Analysis 271
30.2 Use of External Loss Data 271
30.3 Scaling of Loss Data 272
30.4 Consideration of Likelihood 272
30.5 Anonimised Loss Data 273
31 An Introduction to Neural Networks 275
31.1 Introduction 275
31.2 Neural Algorithms 275
Appendix Mathematical Symbols and Notation 279
Index 285 |
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| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV021750299 |
| illustrated | Not Illustrated |
| index_date | 2024-07-02T15:32:06Z |
| indexdate | 2024-07-09T20:43:11Z |
| institution | BVB |
| isbn | 047001489X 9780470014899 |
| language | English |
| lccn | 2006001400 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014963525 |
| oclc_num | 63178779 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-824 DE-703 DE-521 |
| owner_facet | DE-473 DE-BY-UBG DE-824 DE-703 DE-521 |
| physical | XIV, 295 S. graf. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley finance series |
| spelling | Cox, Dennis W. Verfasser aut The mathematics of banking and finance Dennis Cox and Michael Cox Chichester [u.a.] Wiley 2006 XIV, 295 S. graf. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley finance series Bankalar ve bankacılık - Matematik İşletme matematiği Bank Mathematik Business mathematics Banks and banking Mathematics Finanzmathematik (DE-588)4017195-4 gnd rswk-swf Finanzmathematik (DE-588)4017195-4 s DE-604 Cox, Michael Verfasser aut http://www.loc.gov/catdir/toc/ecip067/2006001400.html Table of contents only HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014963525&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Cox, Dennis W. Cox, Michael The mathematics of banking and finance Bankalar ve bankacılık - Matematik İşletme matematiği Bank Mathematik Business mathematics Banks and banking Mathematics Finanzmathematik (DE-588)4017195-4 gnd |
| subject_GND | (DE-588)4017195-4 |
| title | The mathematics of banking and finance |
| title_auth | The mathematics of banking and finance |
| title_exact_search | The mathematics of banking and finance |
| title_exact_search_txtP | The mathematics of banking and finance |
| title_full | The mathematics of banking and finance Dennis Cox and Michael Cox |
| title_fullStr | The mathematics of banking and finance Dennis Cox and Michael Cox |
| title_full_unstemmed | The mathematics of banking and finance Dennis Cox and Michael Cox |
| title_short | The mathematics of banking and finance |
| title_sort | the mathematics of banking and finance |
| topic | Bankalar ve bankacılık - Matematik İşletme matematiği Bank Mathematik Business mathematics Banks and banking Mathematics Finanzmathematik (DE-588)4017195-4 gnd |
| topic_facet | Bankalar ve bankacılık - Matematik İşletme matematiği Bank Mathematik Business mathematics Banks and banking Mathematics Finanzmathematik |
| url | http://www.loc.gov/catdir/toc/ecip067/2006001400.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014963525&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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