The Mathematics of Banking and Finance

A better title for this book would be "Elementary Statistics for Junior Analysts." It walks readers through data visualization, basic probability, hypothesis testing, regression, analysis of variance, linear programming, Monte Carlo simulation, time series analysis, etc. There is actually very little about banking or finance. There are a few cursory chapters on financial topics like value-at-risk, discounting and project evaluation. But the meat of the book is statistics; finance is garnishing. Of course, throughout the book, examples are contrived to seem financial. This entails little more than sprinkling them with words like "compliance," "trade," or "account." I found no examples that relate in any way to what actually goes on in finance.

 

The treatment of the statistics is cookbookish. Readers are shown how to do specific computations rather than told the underlying theory. This can be confusing at times. For example, the discussion of probability distributions starts with discrete distributions. Rather than try to explain the transition to continuous distributions, the authors simply start talking about uniform and normal distributions. It is as if ten pages were ripped from the book containing that transitional information!

Contents

1. Introduction to How to Display Data and the Scatter Plot

2. Bar Charts

3. Histograms

4. Probability Theory

5. Standard Terms in Statistics

6. Sampling

7. Probability Distribution Functions

8. Normal Distribution

9. Comparison of the Means, Sample Sizes and Hypothesis Testing

10. Comparison of Variances

11. Chi-squared Goodness of Fit Test

12. Analysis of Paired Data

13. Linear Regression

14. Analysis of Variance

15. Design and Approach to the Analysis of Data

16. Linear Programming: Graphical Method

17. Linear Programming: Simplex Method

18. Transport Problems

19. Dynamic Programming

20. Decision Theory

21. Inventory and Stock Control

22. Simulation: Monte Carlo Methods

23. Reliability: Obsolescence

24. Project Evaluation

25. Risk and Uncertainty

26. Time Series Analysis

27. Reliability

28. Value at Risk

29. Sensitivity Analysis

30. Scenario Analysis

31. An Introduction to Neural Networks

Needless to say, if you haven't seen this material before, you will be lost. If you have seen it before, the book may be a handy reminder of how to perform specific computations. On the other hand, you may find Kritzman (2003) more useful. It is better written, heavier on the finance, and lighter on the statistics. [December 14, 2006]

 

For related books, see sections:

Math - Financial Math

Math - Financial Programming

Math - Probability

 

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