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Title Quantitative Methods for Finance Lecturer Darryl Holden and Robin Alpine Code AG909 Assessment Finance Semester 1 Examination 70% Compulsory Int. Banking & Fin. Compulsory Tutor Darryl Holden and Robin Alpine Weeks 1 – 12 Coursework Investment & Fin. Compulsory Credits 20 Test 30% Int. Accounting & Fin. Compulsory CLASS AIMS This class aims to provide an introduction to statistical techniques that are commonly used in finance, a basic understanding of econometric analysis, and an appreciation of the general role of quantitative methods in finance. It also aims to develop the practical computational skills of participants by offering an opportunity to solve problems using statistical software. The class will focus on the application of statistical techniques in empirical analysis in finance. LEARNING OUTCOMES The class provides opportunities for students to develop and demonstrate knowledge, understanding and skills in the following areas: i) Knowledge Based Outcomes: On completion of the class students will be able to: understand basic calculus and matrix algebra develop and employ the basic statistical concepts and descriptive analysis of data, understand the concepts of random variables and probability distributions of financial data, draw inferences and conclusions from statistical analysis; select appropriate estimation techniques; understand the linear regression model and apply it to financial and economic data evaluate empirical research in finance, and ii) Skills Outcomes: On completion of this class students should be able to demonstrate that they can: describe the major features of data sets and identify their distributional properties, test hypotheses and draw inferences, identify the cause/outcome of relationships between two variables, apply linear regression techniques and interpret the results, and apply basic statistical methods in solving problems. TEACHING AND LEARNING The module will be delivered by a combination of lectures and computer lab (workshop) sessions. Quantitative techniques will be discussed in the context of their application in solving finance problems. Computer lab sessions will provide hands-on experience on applying statistical tools in solving finance problems. Where appropriate, references will made to the use of such techniques in published research papers and/or their use in finance industry. ASSESSMENT A class test will account for 30 percent and the final examination for 70 percent of the assessment. The class test will involve a number of quantitative and short conceptual questions. The test will provide an indication of students’ understanding of relevant analysis and ability to apply the analysis. The final examination will take place in the January diet of examinations and last three hours. It will comprise seven questions divided into two sections (the sections corresponding to sessions 1-7 and sessions 8-12 in the lecture programme detailed below). Answers to four questions, with at least one from each section, are required. Any reassessment will be via a re-sit exam of the same format as the main exam. Class participants will be expected to complete some assignments for workshops and will be provided with feedback on their understanding of the analysis, but these will not be part of the formal assessment scheme. READING There is no recommended textbook for this class. There are typed notes on mathematical techniques, descriptive statistics, and statistics (including regression) on the class MyPlace page. The notes cover essentially the same material as the class lectures and workshops and were prepared by the teaching staff. The notes are not required reading but might be useful for anyone seeking a slightly different, but consistent, presentation of the class material. Web Resources Lecturer to specify Useful Websites: Lecturer to specify in the class LECTURE PROGRAMME Session Lecture Title/Subject/Content 1-2 Basic mathematical techniques: matrix algebra; differentiation; optimisation Descriptive statistics: introduction to the types (time series, cross-section, 3 panel, continuous, discrete) and major sources of data that are commonly used in finance, measures of central tendency & dispersion, and correlation. 4-5 6 7 8-11 12 Probability; discrete and continuous random variables; expectation and variance; joint probability distributions; covariance, correlation, and independence; linear combinations of random variables; the normal, t , 2 and F distributions. Using printed tables and e-tables (excel). Sampling: samples and populations; random sampling; the distribution of the sample mean; point estimation; properties of estimators (unbiasedness, efficiency); confidence interval estimation. Hypotheses testing: null and alternative hypotheses; type one and type two errors; test procedures; applications to single samples, two independent samples, two correlated samples. E-testing (excel) and p values. Simple regression analysis: the mechanics of ordinary least squares (OLS) regression and its assumptions; standard errors; hypothesis tests and confidence intervals; total, explained and residual sums of squares, R2. An introduction to multiple regression analysis. Tests based on the F distribution. The usefulness of dummy variables. Running a regression in excel. Introduction to the usefulness of matrix algebra in regression analysis