MSc (Coursework)/PG Diploma in Financial Mathematics 2020

MSc in Financial Mathematics – 2020

1

MSc (Coursework)/PG Diploma in Financial Mathematics – 2020

The MSc/PG Diploma in Financial Mathematics aims to provide a professional

development package for professionals in the discipline of Finance, Insurance, Banks,

Financial Analysis, Financial Consultancy and Financial Simulation sectors. The award

of the degree will provide its recipients with a valuable professional qualification.

Considering new trends in the field of quantitative finance, starting from year 2020 batch,

the program provides two pathways, namely,

Financial Engineering (FE)

Financial Analysis (FA)

Financial Analysis is focused more towards qualitative aspects, and Financial

Engineering is focused on deeper quantitative aspects. Both pathways require core

concepts and tools of financial mathematics in the areas of finance, applied mathematics,

statistics and computer science. They form the common set of courses delivered in

semester I. The split into the two pathways is introduced in semester II based on student

demand. In semester III, a related industry project is introduced to strengthen the

acquired knowledge in the industry setting.

Programme Intended Learning Outcomes

The end of the two years (SLQF Level 9) MSc in Financial Mathematics Degree

holders should be able to:

● ILO I: demonstrate knowledge and proficiency in the terminologies, theories,

concepts, practices and skills specific to the field of finance, financial instruments,

financial markets and financial product development.

● ILO II: display critical awareness of current local/global financial

issues/environments

● ILO III: observe and interpret financial markets to uncover potential

opportunities and construct financial portfolios.

● ILO IV: apply best practices in financial product development / analysis to make

plans, organize projects, monitor outcomes and provide financial leadership.

● ILO V: apply the Standards of Practice and Codes of Conduct of Financial

Practitioners to address ethical challenges within the business environment and

demonstrate intellectual maturity in a global setting.

● ILO VI: practice professionalism and uphold ethical standards and

improve/update skills required for employment and life-long learning.

● ILO VII: effectively communicate & disseminate knowledge, information and

ideas to specialist and a wider society

● ILO VIII: perform independently as well as interdependently

● ILO IX: demonstrate self-direction and originality in tackling and solving

problems and be able to plan and implement tasks at professional levels


2

PART I: PG Diploma

Course Code Course Title Details Notional

hours

FA FE

Semester I

MFM 5041 Applied Finance 30L, 30P, 3C 150 X X

MFM 5042

Optimization Methods for

Finance

30L, 30P, 3C 150 X X

MFM 5043 Financial Products & Pricing 30L, 30P, 3C 150 X X

MFM 5044 Computing for Finance 60P, 2C 100 X X

MFM 5045 Case Study on Financial

Markets

90P, 3C 150 X X

Semester II

MFM 5046 Corporate Finance 30L, 2C 100 X

MFM 5047 Financial Risk Management 30L, 2C 100 X

MFM 5048 Economics for Finance 30L, 2C 100 X

MFM 5049 Financial Reporting and

Analysis

30L, 2C 100 X

MFM 5050 Quantitative Methods in Finance 30L, 30P, 3C 150 X

MFM 5051 Investment Analysis 30L, 30P, 3C 150 X

MFM 5052 Quantitative Risk Analysis 30L, 30P, 3C 150 X

MFM 5053 Financial Econometrics 30L, 30P, 3C 150 X

MFM 5054 Computational Models in

Financial Engineering

60P, 2C 100 X

TOTAL NOTIONAL HOURS

(PG Diploma) – SLQF Level 8

1250 1250

TOTAL CREDITS (PG

Diploma) - SLQF Level 8

25C 25C

PART II: MSc Coursework

Course

Code

Course Title Details Notional

hours

FA FE

Semester III

MFM 5055 Quantitative Finance Project 150P, 5C 500 X

MFM 5056 Financial Analysis Project 150P, 5C 500 X

TOTAL NOTIONAL HOURS

(MSc) - SLQF Level 9

1750 1750

TOTAL CREDITS (MSc

Coursework) - SLQF Level 9

30C 30C


3

Course Code / Title MFM 5041 Applied Finance Credit Value 3

Prerequisites None

Details

Lectures (H) Practical

(H)

Independent Learning

(H) Notional Hours

30 30 90 150

Rationale This course explores the theoretical aspects of finance and valuation of

money and provides applications

Intended Learning

Outcomes

By the end of the course, students should be able to

● identify and apply basics valuation methods and compute time

value

● value the different cash flows

● apply techniques to price the financial instrument

● use techniques to compare different cash flows

Course Content

The effective rate of interest, the real rate of interest, the force of

interest, nominal rates of interest, the rate of discount, the principle of

equivalence, level cash series, Recursive relations, accumulations,

deferred and conventional level cash series, more general level cash

series, valuing simple projects, financial instrument and their

behavioral properties, fund analysis, Money weighted rate and Time

weighted rate, Excel financial functions and their applications.

Method/s of

Evaluation:

End of semester examination

60%

Continuous Assessment

40 %

References/Readin

g Materials

1. Ross, SA, Westerfield, RW, Jordan, BD, (2002), Fundamentals

of Corporate Finance, 8th edition, McGraw-Hill Publishing

Company.

2. Kellison, SG, (2008), The Theory on Interest, 6th Edition,

Richard D. Irwin Inc.

3. Marek Capinski and Tomasz Zastawniak (2003), Mathematics

for Finance, An introduction to Financial Engineering,

Springer-Verlag London Limited.


4

Course Code / Title MFM 5042 Optimization Methods for Finance Credit Value 3

Prerequisites None

Details


(H)


(H) Notional Hours

30 30 90 150

Rationale Optimization models and methods play an increasingly important role

in financial decisions. This course introduces the approach of

modeling financial decisions as optimization problems and then

developing appropriate optimization methodologies to solve these

problems.

Intended Learning

Outcomes


● model financial optimization problems

● interpret models as mathematical programs

● analyze mathematical programs using optimization

methodology and software

● use analysis to gain insight and make decisions

Course Content

Linear Optimization: Linear Programming, Linear programming

problem, duality, optimality conditions, short review on simplex

method. LP models: Asset/liability cash-flow matching, short-term

financing, dedication, sensitivity analysis for LP, case studies on

constructing a dedicated portfolio. LP models: Asset pricing and

arbitrage, derivative securities and fundamental theorem of asset

pricing, arbitrage detection using LP. Nonlinear Optimization:

Nonlinear Programming, univariate optimization, unconstrained

optimization and constrained optimization, quadratic programming for

portfolio optimization.

Method/s of

Evaluation:


60%


40 %

References/Readin

g Materials

1. Gerard Cornuejols, Reha Tutuncu (2007), Optimization

Methods in Finance, Cambridge University Press.

2. Taha HA (2017), Operations Research, 10th Editions, Pearson-

Prentice Hall.

3. Winston WL, Venkataramanan V (2003), Introduction to

Mathematical Programming, 4th Edition, Brooks/Cole,

Cengage Learning.


5

Course Code / Title MFM 5043 Financial Products & Pricing Credit Value 3

Prerequisites None

Details


(H)


(H) Notional Hours

30 30 90 150

Rationale This course explores financial products of the modern financial market

and the mathematical techniques for product price calculation.

Intended Learning

Outcomes


● identify financial products and value them

● apply techniques to value the products

● design financial products for risk market

Course Content

Introduction to derivatives, complete market, Market risk and credit

risks in the use of derivatives. American and European options, Types

of Trades, Hedgers, Speculators and arbitragers, Hedging with

derivatives, Factors affecting option prices, Strategies with options,

Boundaries with options, One-step Binomial Models, Risk Neutral

valuation, Two-Step Binomial trees, Black Scholes model,

Distribution of returns, volatility, risk neutral pricing, Black-Scholes-

Merton differential equation. Estimating volatility using historical

data, implied volatility, Exotic and path dependent options

Forward and Future Contracts, Futures and forward pricing, Hedging

with futures, Options on stock indices, currencies and futures,

evaluation of future options using a binomial tree, Options on stock

indices, currencies and futures

Method/s of

Evaluation:


60%


40 %

References/Readin

g Materials

1. Hull John, (2008), Options, futures and other derivatives,

International 7th Edn, Pearson Prentice Hall.

2. Ross S. (2003), Introduction to Mathematical Finance,

Cambridge University Press.

3. Marek Capinski, Tomasz Zastawniak (2011), Mathematics for

Finance: An Introduction to Financial Engineering, Springer


6

Course Code / Title MFM 5044 Computing for Finance Credit Value 2

Prerequisites None

Details


(H)


(H) Notional Hours

None 60 40 100

Rationale This course explores the practical application of electronic

spreadsheets in the aspect of financial mathematics.

Intended Learning

Outcomes


● identify financial products and value them

● apply techniques to value the products

● design financial products for risk market

Course Content

Introduction to formulas for understanding of basic spreadsheets

build-in functions, Functions for financial calculations and decisions.

Do sensitivity analysis with data tables in spreadsheet applications,

Summarizing and visualizing data using build-in functions and pivot

tables, understanding correlations and summary relationships, to

multiple regression tools in spreadsheets, Using Monte Carlo

simulation in spreadsheet, Automating the tasks and Macro

programming.

Method/s of

Evaluation:


50%


50 %

References/Readin

g Materials

1. Wayne L. Winston (2016), Excel 2016 Data Analysis and

Business Modeling, Microsoft Press.

2. Bill Jelen and Tracy Syrstad (2010), VBA and Macros –

Microsoft Excel 2010, Que Publishing.


7

Course Code / Title MFM 5045 Case Study on Financial Market Credit Value 3

Prerequisites MFM 5041, MFM 5042, MFM 5043

Details


(H)


(H) Notional Hours

None 90 60 150

Rationale To provide the hands on experiences in the field of Financial Market

Intended Learning

Outcomes


● apply quantitative methods to real data

● analysis scenarios and judge the current patterns

● prioritize conditions and methods to apply different real cases

● value the given situation based on available

conditions/limitations

Course Content

Individual candidates will be assigned to a Case Study on Financial

Market. Each student will be required to gain thorough knowledge on

a given topic related to the financial market through a set of field

visits/workshops under the guidance of an industrial expertise panel.

After the given period of time, candidates are expected to conduct the

seminar based on their case studies.

Method/s of

Evaluation:

Final Presentation

25%

Viva

25%

Continuous Assessment / Reports

50 %

References/Readin

g Materials

1. Chiradeep Chatterjee (2008), Case Studies on Financial

Markets, The Institute of Chartered Financial Analysts of

India.

2. Tarika Sikarwar (2017), A Handbook of Case Studies in

Finance, Cambridge Scholars Publishing

3. Robert F. Bruner, Kenneth Eades, Michael Schill (2009), Case

Studies in Finance, McGraw-Hill Higher Education.


8

Course Code / Title MFM 5046 Corporate Finance Credit Value 2

Prerequisites MFM 5041, MFM 5043

Details


(H)


(H) Notional Hours

30 None 70 100

Rationale This course explores the theoretical aspects of finance and valuation of

money and provides applications

Intended Learning

Outcomes


● compute the cost of capital and analyze practical applications

● value the taxation of the company

● apply techniques to price the projects

Course Content

Introduction of Corporate finance and financial manager, the goal of

the financial management, Stock valuation, Capital budgeting, Capital

structure, cost of capital, dividend policy, personal and corporation

taxation, Project analysis and evaluation, Return risk and the security

market line, short/long term financing, Measures and assessment of

financial performance.

Method/s of

Evaluation:


60%


40 %

References/Readin

g Materials

1. Ross, SA, Westerfield, RW, Jordan, BD, (2002), Fundamentals

of Corporate Finance, 8th edition, McGraw-Hill Publishing

Company.

2. Kellison, SG, (2008), The Theory on Interest, 6th Edition,

Richard D. Irwin Inc.

3. Marek Capinski and Tomasz Zastawniak (2003), Mathematics

for Finance, An introduction to Financial Engineering,

Springer-Verlag London Limited.


9

Course Code / Title MFM 5047 Financial Risk Management Credit Value 2


Details


(H)


(H) Notional Hours

30 None 70 100

Rationale This course explores financial risk management techniques and their

applications into financial markets and instruments

Intended Learning

Outcomes


● identify and compute financial risk

● apply suitable risk measures

● quantify financial risk and make decisions

Course Content

Introduction to Risk, Fixed income risk, duration and convexity,

Markowitz risk- return approach for asset allocation of a portfolios,

Efficient frontier, Risk Measures: Value at Risk (VaR), Conditional

Value at Risk (CVaR): Parametric, Historical simulation, Portfolio

analysis and measurements of risk. Equity portfolio risk management

via option strategies.

Method/s of

Evaluation:


60%


40 %

References/Readin

g Materials

1. Philippe Jorion (2009), Financial Risk Manager Hand book

(GARP (Global Association of Risk Professionals)), John

Wiley & Sons.

2. Steven Allen (2003), Financial Risk Management: A

Practitioner's Guide to Managing Market and Credit Risk, John

Wiley & Sons, Ins


10

Course Code / Title MFM 5048 Economics for Finance Credit Value 2

Prerequisites None

Details


(H)


(H) Notional Hours

30 None 70 100

Rationale To provide the advanced knowledge in Economic models, methods

those are essential for economic analysis and economic problem

solving

Intended Learning

Outcomes


● explain the function of market and prices as allocated

mechanisms

● apply the concept of equilibrium to both microeconomics and

macroeconomics

● discuss the application of marginal analysis

● assess the role of the factor market in determining the

allocation of resources

● identify and compare the limits of economic analysis.

Course Content

The basic theory of individual choice, Contingent commodities ,

Insurance and Expected utility, The firm and its goal, Production and

costs, perfect competition when firms have identical cost and non-

identical costs, the basic monopoly model and regulatory responses to

monopoly, price discrimination, Oligopoly models: cournot model,

stackelberg and Bertrand, Demand and supply for factors and

equilibrium in the factor market, Monopsony and monopoly in the

factor market, Inter temporal consumption and production, Investment

decision and Present value, Pareto efficiency and market failure,

Adverse selection, moral hazard , signaling and screening,

Externalities, Public goods

Macroeconomics: IS-LM Model, Unemployment, AD- AS, Inflation,

Phillips curve, Economic growth, Solow model, Exogenous end

endogenous growth theories, open economy, mundel-flemming model,

Theories of consumption, investment, Money demand and Money

supply, Monetary and Fiscal Policy

Method/s of

Evaluation:


60%


40 %

References/Readin

g Materials

1. Katz, ML, Rosen, HS (2005), Microeconomics, McGraw-Hill

Education

2. Mankiw, NG (2010), Macroeconomics, Worth Publishers, NY.


11

Course Code / Title MFM 5049 Financial Reporting and Analysis Credit Value 2

Prerequisites None

Details


(H)


(H) Notional Hours

30 None 70 100

Rationale This course explores the theoretical underpinnings and practical

application of financial statement analysis.

Intended Learning

Outcomes


● discuss the importance of financial statement analysis to user

groups

● identify the basic components of each of the financial

statements

● identify and apply the appropriate techniques of financial

statement analysis

● interpret the results obtained during basic analysis stage

Course Content

Financial statements and the components, Accounting principles,

Importance and limitations of financial statement analysis, Uses and

user groups, Sources of information, Tools and techniques of financial

statement analysis, Financial statement irregularities

Method/s of

Evaluation:


60%


40 %

References/Readin

g Materials

1. Temte, A., Temte, A. (2003). Financial Statement Analysis.

Chicago: Dearborn Trade, A Kaplan Professional Company.

2. Fridson, M., Alvarez, F. (2011). Financial statement analysis: a

practitioner’s guide, fourth edition (4th ed.). Hoboken, N.J:

John Wiley & Sons.


12

Course Code / Title MFM 5050 Quantitative Methods in Finance Credit Value 3

Prerequisites None

Details


(H)


(H) Notional Hours

30 30 90 150

Rationale This course provides the probability and statistical methods to model

finance and to draw quantitative financial discussions.

Intended Learning

Outcomes


● discuss basic probability theory

● define random variables and solve univariate and multivariate

probability problems

● apply the concepts of expectation, variance, covariance and

correlation to solve problems

● use methods in statistical inference to solve financial problems

Course Content

Basic statistics and probability theory, random variables, univariate

and multivariate probability distributions-continuous and discrete,

expectation-conditional and unconditional, variance, covariance,

variance-covariance matrices, correlation, random matrices, sampling

and estimation, confidence intervals, F-test, t-Test, Chi-Squared Test,

Hypothesis testing and ANOVA, MATLAB and Excel functions to

solve probabilistic problems in finance

Method/s of

Evaluation:


60%


40 %

References/Readin

g Materials

1. Sheldon M. Rose (2014), Introduction to Probability and

Statistics for Engineers and Scientists, Fifth Edition, Elsevier.

2. Alan J. Izenman (2008), Modern Multivariate Statistical

Techniques, Springer.

3. Jay L. Devore and Kenneth N. Berk (2012), Modern

Mathematical Statistics with Applications, Springer.


13

Course Code / Title MFM 5051 Investment Analysis Credit Value 3


Details


(H)


(H) Notional Hours

30 30 90 150

Rationale This course provides basic concepts of financial decision marking

techniques

Intended Learning

Outcomes


• compute and analyze the impact of interest rate variation.

• compute the bond price and analyze variability

• apply the valuation methods to identify feasibility of the given

project

Course Content

Introduction to financial analysis, pricing a bond and sensitivity of it,

zero coupon bonds and their features, par yield, spot rates, forward

rates, term structure of the interest rate, yield rate, duration and

convexity of the bond, fund analysis, valuing real cash flows, valuing

random cash flows, Excel computation and solvers.

Method/s of

Evaluation:


60%


40 %

References/Readin

g Materials

1. R Stephen G. Kellison (2009), The Theory of Interest, 3rd

Edition, McGraw-Hill Irwin.

2. Perry H. Beaumont (2004), Financial Engineering Principles:

A Unified Theory for Financial Product Analysis and

Valuation, John Wiley & Sons, Inc.

3. Marek Capinski &Tomasz Zastawniak (2003), Mathematics

for Finance: An Introduction to Financial Engineering,

Springer.


14

Course Code / Title MFM 5052 Quantitative Risk Analysis Credit Value 3


Details


(H)


(H) Notional Hours

30 30 90 150

Rationale This course provides basic concepts of financial decision making

techniques

Intended Learning

Outcomes


● compute financial risk

● value suitable risk measures

● model and quantify financial risk

● quantify credit risk

● model operational risk

Course Content

Introduction to risk, financial risk, credit risk and operational risk,

Risk measures, Risk return tradeoff. Risk modelling and

quantification. Fixed income, equity and option market risk analysis

and strategies, Yield curve and forward curve modelling, duration and

convexity, Value at Risk: Parametric approach, historical simulation

and monte-carlo simulation. Credit Risk Modelling: Altman’s model

and Application of credit Matrix approach. Operational Risk: Basel II

(Basel-II) framework.

Method/s of

Evaluation:


60%


40 %

References/Readin

g Materials

1. Donald R. Van Deventer, Kenji Imai, Mark Mesler (2014),

Advanced Financial Risk Management, John Wiley & Sons,

Ins.

2. Philippe Jorion (2009), Financial Risk Manager Handbook

(GARP (Global Association of Risk Professionals)), John

Wiley & Sons.

3. Steven Allen (2003), Financial Risk Management: A

Practitioner's Guide to Managing Market and Credit Risk, John

Wiley & Sons, Ins.


15

Course Code / Title MFM 5053 Financial Econometrics Credit Value 3

Prerequisites None

Details


(H)


(H) Notional Hours

30 30 90 150

Rationale This course provides basic concepts of financial decision making

techniques

Intended Learning

Outcomes


● estimate linear relationships among financial variables and

derive conclusions based on estimated models

● model and forecast univariate and multivariate financial time

series

● model and forecast financial volatility

Course Content

Statistical Properties of Financial Returns, Regression analysis and

Applications in Finance, Maximum Likelihood Estimation, Univariate

Time Series and Applications to Finance, Vector Autoregressive

Models and Cointegration, Modelling Volatility – Conditional

Heteroscedastic Models, Modelling Volatility and Correlations –

Multivariate GARCH Models.

Method/s of

Evaluation:


60%


40 %

References/Readin

g Materials

1. Brooks, C (2019), Introductory Econometrics for Finance, 4th

Edition, Cambridge University Press.

2. Alexander, C. (2001). Market Models: A Guide to Financial

Data Analysis. John, Wiley & Sons.


16

Course Code / Title MFM 5054 Computational Models in Financial

Engineering Credit Value 2

Prerequisites MFM 5044

Details


(H)


(H) Notional Hours

None 60 40 100

Rationale This course provides theoretical and practical knowledge, on building

and using, computational models based on soft programming

techniques

Intended Learning

Outcomes


● compare hard computing methods with soft computing

methods and choose the appropriate method for solving a given

problem

● describe theoretical aspect of Artificial Neural Networks

(ANN) their advantages and limitations

● implement a simple Feed Forward Network in Excel and

MATLAB or similar software and use ANN to solve real

world classification and prediction problems

● describe how Genetic Algorithms (GA) functions and

implement a GA program to solve optimization problems

● use Monte-Carlo simulations to solve appropriate problems

Course Content

Introduction to MATLAB (ML) programming, Introduction to

Artificial Neural Networks (ANN), Single Layer Networks, Multilayer

Networks, Different learning rules, Advantages and limitations of

ANN. Preprocessing and post processing of data, Using ANN to solve

real world problems, Introduction to Genetic Algorithms (GA) with

advantages, disadvantages and limitations, Encoding data to genes,

Cross overs, mutations and other generation creation techniques,

Different selection methods, Solving TSP and knapsack problems,

Using ML to implement GA to solve problems, Different hybrid

mechanism, Implement a GA ANN hybrid, Advantages of a hybrid,

Introduction to Monte Carlo simulations (MC), its applications, Using

ML to implement a MC to model real world problems

Method/s of

Evaluation:


50%


50 %

References/Readin

g Materials

1. Kevin L. Priddy, Paul E. Keller (2005), Artificial Neural

Networks: An Introduction, ,SPIE Publications.

2. Zbigniew Michalewicz (2009), Genetic Algorithms + Data

Structures = Evolution Programs, Springer-Verlag Berlin

Heidelberg

3. Randy L. Haupt, Sue Ellen Haupt (2004), Practical Genetic

Algorithms, Wiley-Interscience


17

Course Code / Title MFM 5055 Quantitative Finance Project Credit Value 5

Prerequisites None

Details


(H)


(H) Notional Hours

20 30 450 500

Rationale This course provides opportunities to utilize gained

theoretical/practical knowledge and experiences to solve real

quantitative finance problems in real environment settings

Intended Learning

Outcomes


● design models to quantify future behavior

● modify existing models appropriately

● develop simulation models to demonstrate financial behavior

● collaborate with existing models/methods

● write reports to demonstrate models and their features

● develop effective presentations

Course Content

Individual candidates will be assigned guided study on quantitative

finance topics. Each student will be required to study/develop/modify

model/s and simulation technique/s on a given topic related to

quantitative finance under the guidance of a supervisor and or

industrial mentor appointed by the department.

After the given six months period of time, candidates are expected to

conduct the four seminars (proposal / literature / methodology / basic

results) in each six weeks’ time based on their studies. The end of the

period students are supposed to submit reports according to given

guidelines and make final presentation.

Method/s of

Evaluation:

Final Presentation

20%

Viva

20%

Report

30 %

Continuous

Assessment (Four

presentations)

30%

References/Readin

g Materials

1. Scott P. Mason, Robert C. Merton, Andre F. Perold, Peter

Tufano (2005),Cases in Financial Engineering: Applied

Studies of Financial Innovation, Prentice Hall.

2. Gianluca Fusai, Andrea Roncoroni (2008), Implementing

Models in Quantitative Finance: Methods and Cases (Springer

Finance), Springer-Verlag Berlin Heidelberg

3. Wolfgang Härdle, Cathy Yi-Hsuan Chen, Ludger Overbeck

(2017), Applied Quantitative Finance, Springer-Verlag Berlin

Heidelberg


18

Course Code / Title MFM 5056 Financial Analysis Project Credit Value 5

Prerequisites None

Details


(H)


(H) Notional Hours

20 30 450 500

Rationale This course provides opportunities to utilize gained

theoretical/practical knowledge and experiences to analysis financial

situations in real settings

Intended Learning

Outcomes


● design the analysis tools to quantify current behavior

● modify existing analyzing tools appropriately

● develop simulation models to demonstrate financial behavior

● collaborate with existing financial tools

● write reports to demonstrate analysis tools and their features

● develop effective presentations

Course Content

Individual candidates will be assigned guided study on financial

analysis topics. Each student will be required to study/develop/modify

analysis tools and simulation technique/s on a given topic related to

financial analysis under the guidance of a supervisor and or industrial

mentor appointed by the department.

After the given six months period of time, candidates are expected to

conduct the four seminars (proposal / literature / methodology / basic

results) in each six weeks’ time based on their studies. The end of the

period students are supposed to submit reports according to given

guidelines and make final presentation.

Method/s of

Evaluation:

Final Presentation

20%

Viva

20%

Report

30 %

Continuous

Assessment (Four

presentations)

30%

References/Readin

g Materials

1. Harold Bierman Jr (Cornell) (2017), Case Studies for

Corporate Finance, World Scientific.

2. K. MidgleyR. G. Burns (2015),Case Studies in Business

Finance and Financial Analysis, Springer


19

Table1: Course vs Programme ILOs Map

Course

Code

ILO

I

ILO

II

ILO

III

ILO

IV

ILO

V

ILO

VI

ILO

VI I

ILO

VIII

ILO

IX MFM 5041 H L M H M M M M M MFM 5042 H L L H L M M M M MFM 5043 H M M H M M M M M MFM 5044 H L L H L H M M M MFM 5045 H H H H H H H H H MFM 5046 H L M H M H M L M MFM 5047 H M H H M M M L M MFM 5048 H M L H M M M L M MFM 5049 H H L H H M M M M MFM 5050 H M M H L M M L M MFM 5051 H M H H H H M L M MFM 5052 H H H H M M M L M MFM 5053 H M M H L M M L M MFM 5054 H M L H L H M H M MFM 5055 H H H H H H H H H MFM 5056 H H H H H H H H H

Table 2: Courses vs Level Descriptors Learning Outcomes (SLQF) (1, 2,..12 denote Learning Outcomes given in SLQF- see Table3)

Course

Code 1 2 3 4 5 6 7 8 9 10 11 12

MFM 5041 H H H L H L H H M M L L MFM 5042 H H H L H L H H M M L L MFM 5043 H H H L H L H H M M L L MFM 5044 H H H L H L H H M M L L MFM 5045 H H H H H H H H H H H H MFM 5046 H H H L H L H H M M L L MFM 5047 H H H L H L H H M M L L MFM 5048 H H H L H L H H M M L L MFM 5049 H H H H H H H H M M L L MFM 5050 H H H L H L H H M M L L MFM 5051 H H H L H L H H M M L L MFM 5052 H H H H H H H H M M L L MFM 5053 H H H L H L H H M M L L MFM 5054 H H H H H H H H H H H H MFM 5055 H H H H H H H H H H H H MFM 5056 H H H H H H H H H H H H


20

H Highly correlated

M Moderately correlated

L Correlated

Table 3: Categories of Learning Outcomes (SLQF)

No Categories of Learning Outcomes Core Area

1 Subject / Theoretical Knowledge Knowledge

2 Practical Knowledge and Application

3 Communication

Skills

4 Teamwork and Leadership

5 Creativity and Problem Solving

6 Managerial and Entrepreneurship

7 Information Usage and Management

8 Networking and Social Skills

9 Adaptability and Flexibility Attitudes, Values,

Professionalism and Vision

for life 10 Attitudes, Values and Professionalism

11 Vision for Life

12 Updating Self / Lifelong Learning Mind-set and Paradigm

Documents

MSc (Coursework)/PG Diploma in Financial Mathematics 2020