Centre for Financial & Management Studies (CeFiMS) - University of London

Individual Professional Courses – IPC  

Quantitative Methods for Financial Management [FM105]

Introduction

Quantitative Methods for Financial Management introduces some of the quantitative methods of financial management which are commonly used by financial analysts, firms’ managers and individual investors. It examines techniques for the valuation of different classes of securities, analyses criteria for guiding investment decisions, considers the measurement of asset risk and return and discusses statistical techniques of forecasting. The MICROFIT computer package is provided for regression analysis and diagnostic procedures.

Aims & Objectives

The aim of the course is to give students confidence and skill in the use of the mathematical and statistical methods used in the analysis of financial instruments and financial markets, including the calculation of financial market yields and prices, frequency distributions, risk and probability, correlation and regression analysis. It teaches not only the relevant theoretical concepts but, in the belief that quantitative techniques can only be learned by doing, the course gives abundant practice in the manipulation of numerical material with problems and exercises.

Resources

Students receive a looseleaf binder containing eight ‘course units’; these texts are carefully structured to provide the main teaching and are equivalent to traditional course lectures, defining and exploring the main concepts and issues, locating these within current economics debate, introducing and linking the further assigned readings and setting out practical exercises for solution. Two obligatory assignments, which are marked by CeFiMS tutors, and a specimen examination paper are also included within the student pack, along with the following:

Textbooks:

Damodar N. Gujarati, Essentials of Econometrics, Second edition, 1998 (International edition), McGraw-Hill Book Company, ISBN0071163069.

Richard A. Brealey and Stewart C. Myers, Principles of Corporate Finance, Sixth Edition, 2000, McGraw-Hill Inc, ISBN0077095650.

Computer Software

MICROFIT, a user-friendly econometrics software programme, which can be run on IBM (PC) compatible computer running MS Windows. A Guide to the installation and use of MICROFIT, with course exercises, is also provided.

Course Timetable

This shows the linkage between the various components of the course and indicates the schedule for reading the texts, submitting assignments, etc.

Course Content

Unit 1 Financial Arithmetic and Valuation of Bonds and Stocks

This first unit introduces the course and the general principles of financial management. It starts by examining the implications of the fact that future cash flows are worth less than an equivalent amount today. This allows the setting up the fundamental formula for the rest of this course, the net present value of a given project. This method is applied to the most common types of financial instruments, stocks and bonds, and shows how their current value can be calculated from this general principle. Since the net present value depends on future cash flows, Unit 1 also touches on how to estimate these using a simple growth formula. Finally, the unit discusses alternative investment criteria, and their merits.

Unit 2 Statistical Concepts and Probability Theory

Unit 2 introduces the elements of the theory of probability, the mathematical theory which deals with uncertainty. The nature of a random variable is characterised, and the mathematical way of describing its behaviour. This is known as the probability distribution function, which could turn out to include ‘too much’ information. The next step is, therefore, to define the mean and standard deviation of a probability distribution, which summarise most of the information needed about the behaviour of the random variable in question. Finally, the unit discusses some important probability distribution functions and some of their properties.

Unit 3 Statistical Inference

The basic principles of statistical inference are introduced in Unit 3. Roughly speaking, this is the process of making inferences from a small sample about the behaviour of much larger populations. Two main approaches are discussed: estimation and hypothesis testing. Both of these methods are explained, and the close relationship between them is examined.

Unit 4 The Classical Linear Regression Model

This unit introduces the concept of regression analysis and explains how it can be used in economics and finance. It explains what is meant by a linear regression model and how its unknown parameters can be estimated by using statistical methods. It explains how to interpret the meaning of the regression parameters, and gives examples of applications in economics and in finance.

Unit 5 Statistical Inference in the Classical Linear Regression Model

Unit 5 develops further the Classical Linear Regression Model (CLRM). It lays out the assumptions on which the CLRM is based, and discusses their statistical and economic meaning. The variance and the standard error of the estimators of the regression parameters are explained and the use of OLS estimators as the best linear unbiased estimators of the regression parameters is demonstrated. Students are taught how to compute confidence intervals and how to perform hypothesis testing. Finally, the unit explains how to measure the goodness of fit of estimation and how to forecast future values of the dependent variable.

Unit 6 The Multiple Linear Regression Model

The multiple linear regression model is examined in Unit 6, which explains how the estimation methods analysed in Units 4 and 5 can be extended to the case where The University has more than one explanatory variable. The unit explains the multiple coefficient of determination and describes how to perform hypothesis testing in the multiple linear regression model. The unit ends by exploring how a coherent strategy for model selection can be developed, and discusses the choice of an appropriate functional form for the regression model.

Unit 7 Topics in the Multiple Linear Regression Model

This unit examines two important topics in econometrics: dummy variables and autocorrelation. Dummy variables explains that they can be used in regression analysis to represent the effect of qualitative variables; it is also explored how dummy variables can be used to assess the possibility of structural breaks in a behavioural relationship. The effects of autocorrelation of the error terms is then considered — how to detect the presence of autocorrelation and what its implications are for econometric estimation. Finally, students are shown how to estimate a regression model when the errors are autocorrelated, and how to write a dynamic model.

Unit 8 Risk Measurement and Investment Decisions

In this final unit, the economic relationship between the returns on an investment and the risk associated with that investment are examined. The theory of probability, which was covered in Units 2 and 3, can be used to establish a formal relationship which is basic for the pricing of financial assets (which are risky by their nature). A more general model, the Capital Asset Pricing Model (CAPM), is developed and the assumptions underlying it are critically discussed, paying special attention to the No Arbitrage Condition. Finally, an alternative pricing model is discussed, the Arbitrage Pricing Theory.

Tuition & Assessment

The course is assessed by two assignments marked by course tutors contracted by the University, and a three-hour examination held in the autumn. The assignments consist of compulsory questions or problems to be solved.