Paper 05: Quantitative Analysis - Traction School of Governance and Business

About Course

The Professional courses are administered at Foundation, Intermediate and Advanced Levels. Each level requires an average of one year, though candidates are advised to provide for an additional one year to meet requirements for internship/ practical experience.

A student must book for a minimum of three papers in a level in any order unless is exempted or has credits.

This course is aimed at persons who wish to qualify and work or practice as investment, securities and financial analysts, portfolio managers, investment bankers, fund managers, consultants on national and global financial markets and related areas.

Course Content

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overview

1. Mathematical Techniques

2. Probability
2.1 Set Theory 2.2 Definition 2.3 Types of sets 2.4 Set description; enumeration and descriptive properties of sets 2.5 Venn diagrams (order - Venn diagrams precede operation of sets) 2.6 Operations of sets; union, intersection, complement and difference 2.7 Probability Theory and Distribution

3. Hypothesis Testing and Estimation
3.1 The arithmetic mean and standard deviation 3.2 Hypothesis tests on the mean (when population standard deviation is unknown) 3.3 Hypothesis tests on proportions 3.4 Hypothesis tests on the difference between two proportions using Z and t statistics 3.5 Chi-Square tests of goodness of fit and independence 3.6 Hypothesis testing using R statistical software

4. Correlation and Regression Analysis
4.1.1 Scatter diagrams 4.1.2 Measures of correlation - product-moment and rank correlation coefficients (Pearson and Spearman) using R software

5. Regression Analysis
5.1.1 Simple and multiple linear regression analysis 5.1.2 Assumptions of linear regression analysis 5.1.3 Coefficient of determination, standard error of the estimate, standard error of the slope, t and F statistics

6. Time series
6.1 Definition of time series 6.2 Components of time series (circular, seasonal, cyclical, irregular/ random, trend) 6.3 Application of time series 6.4 Methods of fitting trend; freehand, semi-averages, moving averages, least-squares methods 6.5 Models - additive and multiplicative models 6.6 Measurement of seasonal variation using additive and multiplicative models 6.7 Forecasting time series value using moving averages, ordinary least squares method and exponential smoothing

7. Linear programming
7.1 Definition of decision variables, objective function and constraints 7.2 Assumptions of linear programming 7.3 Solving linear programming using graphical method 7.4 Solving linear programming using simplex method (basic scenarios)

8 Decision Theory
8.1 Definition 8.2 Decision-making process 8.3 Decision-making environment; deterministic situation (certainty) 8.4 Decision making under risk - expected monetary value, expected opportunity loss, risk using the coefficient of variation, the expected value of perfect information 8.5 Decision trees - sequential decision, the expected value of sample information 8.6 Decision making under uncertainty - maximin, maximax, minimax regret, Hurwicz decision rule, Laplace decision rule.

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