The Course Outline - Quantitative Foundations 2012to 2013

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Course Outline

Programme:

Cohort: Cohort

Academic Year/Trimester:

2011/2012 Trimester 3 :-September-NOVEMBER

Course Code and Course Name:

INTF 6001 QUANTITATIVE FOUNDATIONS

Course Type: Core

Level: Year 1

Lecturer: Mr. Wayne Munroe

Lecturers e-mail: [email protected]

Lecturers phone #:1868-303-6663

Programme Coordinator: Mr. Arnold Manniram

Prerequisite/co-requisite course (s): None

Involvement of other institutions for delivery: None


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1.0 Vision and Mission of the Lok Jack GSB

The vision of the Lok Jack GSB is to be the best business school in the Caribbean by

2012, serving as a regional centre for business education, training, consulting and

research.

The mission of the School is to improve the international competitiveness of people and

organizations in the Caribbean through business education, training, consulting and

research.

2.0 Quality Statement

Within recent times the global environment is becoming highly competitive and there is

therefore a need for managers to have an understanding of Quantitative Foundations.

The purpose of Quantitative Foundations is to provide participants with a general

overview of financial statistical analysis as it relates to the interpretation of financial

statistical variables in the world of work.

3.0 Course Description

The Quantitative Foundation is taught at top business schools that specialize in finance;

the knowledge of Quantitative Foundation is required at international institutions that are

grounded in research and in the analysis and interpretation of financial and statistical data

in the real world.

The objective of the course Quantitative Foundation is to provide candidates with current

tools required to evaluate and analyze economic and financial statistical variables within

the global environment.


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4.0 Aims

After completing this course in Quantitative Foundation the participants will be able to:

Have an understanding of the usefulness of Quantitative Foundation as it relates tomanagement science as decision making aids.

Performing basic financial statistical and analytical procedures usingQuantitative Foundation

Develop the skill to critically and constructively identify the role, and evaluate theusefulness, of data for business decisions employing Quantitative Foundation

Develop a team building approach to problem solving and to enhance interpersonalcommunication skills.

Simulate and to expose students to the practical realities of the decision-makingenvironment using Quantitative Foundation

Engage in further study of statistics and Quantitative Foundation.


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5.0 Course Outcomes

Outcome Learning & TeachingMethod

Assessment Method

Knowledge Outcomes

By the end of this course

students should be able to:

1. Understand theimportance of

quantitative

business analysis to

industry

2. apply the models ofquantitative analysis

to the real world

3. Interpret the variousmodels of

quantitative business

analysis

The course will be

conducted using :

Lecturerpresentations

Lecture notes Videos On line multiple

choice

On line multiplechoice

Skills Outcomes

By the end of this course,students will possess the

following skill sets:

1. understandquantitative business

analysis

The participants would berequired to

Read the lecture notesand the text before

each class

Participate in class


.


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2. Apply the tools andtechniques of

quantitative business

analysis to

businesses acrossindustries

discussion/arguments

Walk with a scientificcalculator

Ask questions if theydont understand, or

require clarity

Affective Outcomes

(including ethics)

By the end of this course,

students will:

appreciate ethics inmodel formulation

understand thevarious models used

in quantitative

business

apply the knowledgegained to improve

the profitability of an

organisation



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6.0 Class Schedule by topic, required reading etc.

The table describes the organisation of the course by topic, and show how it

integrates into the overall programme.

Date Topic Areas to be covered Required reading

text chapters

Supplemental readings

Wednesday

12th

September

Basic

Statistical

Concepts andfinancial

applications

Define and calculate

measures of centraltendency: the

arithmetic mean, the

median, the mode andother mean concepts

Measures of dispersion:The range, the mean

absolute deviation,population variance and

population standard

deviation, samplevariance and sample

standard deviation,

semi-variance, semi-deviation, and related

concepts, Chebyshevs

inequality, coefficientof variation, the Sharperatio

Chapter 1 S. Arjoon Exer

Wednesday

19th

September

Basic

Statistical

Concepts andits application

to finance

Define the nature of

statistics, Populationsand samples,

Measurement scales

Summarize data using

frequency distributions:

Graph data, Construct ahistogram, the

frequency polygon andthe cumulative

frequency distribution

Chapter 1 S. Arjoon Exer


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Wednesday

26th

September StatisticalConcepts andmarket

Returns

Quartiles, quintiles,

deciles and percentiles,

Quantiles in investmentpractice

Symmetry andskewness in returndistributions.

Kurtosis in return

distributions.Using geometric and

arithmetic means

Chapter 2

Lecture notes

S. Arjoon Exer


text chapters


Wednesday

3rd

October

Probability

Concepts

After completing this

section participants

should be able to:

Define and compute

probability, expected

value and variance

Portfolio expected

return and variance of

returnBayes formula,

principles of counting

Chapter 3

Lecture notes

S. Arjoon Exer

Wednesday

11th

OctoberProbability

ConceptsDefine and compute

probability, expected

value and variance.Portfolio expected

return and variance of

return.Bayes formula,

principles of counting

Chapter 4

Lecture notes

S. Arjoon Exer


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Wednesday

17th

October Common

Probability

Distributions

Conduct sampling:

simple randomsampling, stratifiedrandom sampling, time-

series, cross-sectional

dataDistribution of the

sample mean using the

central limit theoremPoint and interval

estimates of the

population mean: point

estimators, confidenceinterval for the

population mean,

selection of the samplesize

More on sampling:

data-mining bias,sample selection bias,

look-ahead bias, time-

period bias

Chapter 5

Lecture notes

S. Arjoon Exer


text chapters


Wednesday

24th

OctoberSampling and

Estimation

Conduct sampling:

simple randomsampling, stratified

random sampling, time-

series, cross-sectionaldata

Distribution of the

sample mean using the

central limit theoremPoint and interval

estimates of the

population mean: pointestimators, confidence

interval for the

Chapter 6

Lecture notes

S. Arjoon Exer


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population mean,

selection of the sample

sizeMore on sampling:

data-mining bias,

sample selection bias,look-ahead bias, time-period bias

Wednesday

31st

October

Hypothesis

TestingConduct hypothesis

testing

Hypothesis testingconcerning the mean:

test concerning a single

mean, test concerningdifference between

means, test concerning

mean differencesHypothesis testing

concerning variance:

tests concerning asingle variance, tests

concerning the equality(inequality) of two

variancesOther issues:nonparametric

interference: testsconcerning correlation

(the spearman rank

correlation coefficient),nonparametric

inference summary

Chapter 7

Lecturer notes

S. Arjoon Exer

Wednesday

7th

November

Correlation

andRegression

Correlation analysis:

scatter plots, correlationanalysis, calculating

and interpreting the

correlation coefficient,limitations of

correlation analysis,

uses of the correlation

Chapter 8Lecture notes

S. Arjoon Exer


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analysis, testing the

significance of the

correlation coefficientLinear regression:

linear regression with

one independentvariable, assumptionsof the linear regression

model, the standard

error of estimate, thecoefficient of

determination,

hypothesis testing,analysis of variance in

a regression with one

independent variable,

prediction of intervals,limitations of

regression analysis

Wednesday

14th

November

Multipleregression and

issues in

regressionanalysis

Define andcalculate the

assumptions of the

multiple linearregression model,

predict the

dependant variablein a multiple

regression model,

test whether all

populationregression

coefficients equal

zero, adjusted R2

Using dummy

variables in

regressions

Violations ofregression

assumptions:

heteroscedasticity,serial correlation,

Multicollinearity

and summary issuesModel specification

and errors inspecification:

Chapter 9

Lecture notes

Exer


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principles of model

specification,

misspecificationfunctional form,

time-series

misspecification(independentvariables correlated

with errors)

Other types of time-series

misspecification]

Models withqualitative

dependent variables

Wednesday

21st

November

Time-series

AnalysisChallenges of

working with timeseries

Trend models:

linear trend models,log-linear trend

models, trend

models and testingfor correlated errors

Autoregressive

(AR) Time-seriesmodels: covariance-

stationary series,

detecting serially

correlated errors inan autoregressive

model, mean

reversion, multi-period forecasts and

the chain rule of

forecasting,

comparing forecastmodel performance,

instability of

regressioncoefficients

Random walks and

units roots: randomwalks, the unit test

of non-stationarityMoving averages

Chapter 10Lecture notes

Exer


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time series models:

smoothing past

values with an nperiod moving

average, moving

average time seriesmodel forforecasting

Seasonality in time

series modelsAutoregressive

moving average

modelsAutoregressive

conditional

heteroscedasticity

modelsRegression with

more than one time

seriesOther issues in time

series

Suggested steps intime series

forecasting

Wednesday

28th

November

Portfolio

Concepts

Mean-variance

analysis: the

minimum-variance

frontier and relatedconcepts, extension

to the three-asset

case, determiningthe minimum-

0variance frontier

for many assets,

diversification andportfolio size,

portfolio choice

with a risk-freeasset, the capital

asset pricing model,

mean-varianceportfolio choice

rules-anintroduction

Chapter 11 Exer


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Practical issues in

mean-variance

analysis: estimatinginputs for mean-

variance

optimization,instability in theminimum-variance

frontier

Multifactor models:factors and types of

multifactor models,

the structure ofmacroeconomic

factor models,

arbitrage pricing

theory and thefactor model, the

structure of

fundamental factormodels, multifactor

models in current

practice,applications.

7.0 Details of Assessment

Coursework 40%

Final Examination 60%

Total - 100%

Multiple choice questions will be administered.

A total of not more than ten will be administered after the third, sixth, ninth and twelfthweek in order to assist the progress of the class. All forty questions will be of equal weight

and will be conducted ONLINE:

8.0 Dates for coursework assignments

Assignment Attempting Interval Dates


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20 multiple choice questions at the end

of classes 1- 6

17th October to 24th October

20 multiple choice questions at the

end classes 7-11

Tuesday 21to 29th November

9.0 Rubrics

Excuses

1. Excuses relating to matters under the control of the student will not beaccepted. These include, but are not limited to, bad time management,

computer problems, inability to locate information, personal commitments

such as holidays, political or social activities. Technical problems relatedto passwords or internet access will NOT be accepted as excuses.

2. Where extenuating circumstances are claimed for missing theexamination, these must be confirmed by documentary evidence at the

time.(For example, medical certificate in the case of illness, employer

letter in the case of unavoidable business commitment, etc). Foreign

travel is generally not accepted as an excuse for missing the examination

3. All requests for missing the examination must be approved by theProgramme Director in advance of the deadline for missing the

examination.

4. Where extenuating circumstances are accepted, no penalty will beapplied. Assignments approved for late submission received after marking

of coursework is complete will not be recorded as failures and the student

will be allowed to redo the multiple choice examination.

5. Notice of extenuating circumstances will be included in the student's file.

All appeals or cases falling outside these guidelines will be referred to the

Academic Director.

11.0 Required Text

Name of text: Quantitative Investment Analysis

ISBN#: 13 978 0 470 05220 4Publisher: J Wiley

Edition: 2nd

Available at: UWI Book shop

Additional reading

Name of text: A guide to understanding statistics by Mr. S. Arjoon

ISBN#: 12-89-1597


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Publisher: IOB

Edition: 1st

Available at: UWI Book shop


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12.0 Other Requirements for the course

Scientific calculator

Financial Calculator Statistical tables

Lecturer signature: ______________________________________________

Programme Directors signature: __________________________________

Date submitted _________________________________________________

Signature of Executive Director _____________________ Date: ____________________

The Course Outline - Quantitative Foundations 2012to 2013

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