Module Code: H8STATMF
Long Title Statistical Methods for Finance
Title Statistical Methods for Finance
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator:  
Module Author: CORINA SHEERIN
Departments:  
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Apply statistical principles, theories and methods and appreciate how they apply in a range of business decision making situations.
LO2 Recognise and evaluate different types of data and their appropriateness in a range of scenarios.
LO3 Graphically tabulate, summarise and present information in a useful and informative manner suitable for presentation to senior management teams.
LO4 Describe key probability concepts and their application within real world context and hence select and apply probability distributions to utilise within various scenarios and compute probabilities based on practical situations using the, Normal and Binomial distributions.
LO5 Define a sampling distribution of the sample mean and apply the Central Limit theorem in the development of inferences about the population.
LO6 Synthesise, evaluate and interpret relationships between two variables through the use of correlation and regression analysis.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements  
 

Module Content & Assessment

Indicative Content
The Role of Statistics in Finance (Week 1)
• Definition and Role of Statistics • Descriptive versus Inferential Statistics • Primary and Secondary Data • Scales of Measurement
Describing Data: Frequency Tables & Graphics (Week 2-3)
• Frequency Data & Frequency Tables • Graphical Representation of Data: o Bar Charts o Pie Charts o Stem and Leaf Plots o Histograms o Scatter Plots
Describing Data: Measures of Central Tendency (Week 4)
• Mean: Arithmetic versus Geometric • Mode • Median • Calculating the mean of a portfolio
Describing Data: Measures of Dispersion (Week 5-6)
• Range • Mean Absolute Deviation • Variance & Standard Deviation • Skewness • Kurtosis • Calculating the variance and standard deviation of a two stock portfolio • Relationship between risk and return
Probability (Week 7-8)
• The role of probability in financial markets • Approaches to assigning probability • Addition and Multiplication Rule • Conditional Probability: Bayes Theorem, Probability Trees
Probability Distributions (Week 9)
• Normal distribution • Binomial Distribution
Collecting Data (Week 10-12)
• Sampling Methods • Sampling Error • Sampling Distribution of the Sample Mean • Central Limit Theorem
Correlation & Regression (Week 13)
• Correlation Coefficient • Calculating the covariance and correlation between two securities • Coefficient of Determination • Introduction to Regression Analysis
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Project % of total: 50
Assessment Date: n/a Outcome addressed: 3,4,6
Non-Marked: No
Assessment Description:
Learners will be presented with a financial or economic data set and/or case study. Learners will be expected to summarise the data graphically and statistically and must undertake a number of prescribed tests on the data. A number of questions will be presented to the learner and they will be expected to evaluate, combine and synthesise the information and develop and present a detailed report of the findings. Students may be required to undertake a formal presentation defending their findings.
Assessment Type: Assignment % of total: 50
Assessment Date: n/a Outcome addressed: 1,2,3,4,5,6
Non-Marked: No
Assessment Description:
Learners will be given two in class assessments worth 25% each The in class assessments may include a mix of: short answer questions, multiple choice, vignettes and or problem based questions. All questions presented to students will be within the context of financial services and its attendant fields.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lab No Description 4 Every Week 4.00
Independent Learning Time No Description 198 Once per semester 16.50
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lab No Description 4 Every Week 4.00
Independent Learning Time No Description 198 Once per semester 16.50
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Lind D.A., Marchal W.G., and Wathen S.A.. (2015), Statistical Techniques in Business and Economics, 16th. McGraw Hill.
  • Koop G.. (2013), Analysis of Economic Data, Wiley Publications.
Supplementary Book Resources
  • De Fusco R.A., Pinto J.E., Runkle D.E., and McLeavey D.W. (2007), Quantitative Methods for Investment Analysis, Wiley (CFA Institute).
  • Moore, D.S., Notz, W.I., and Fligner, M.A. (2015), The Basic Practice of Statistics, 7th edt. Macmillan Education.
  • Alexander, C.. (2008), Market Risk Analysis Quantitative Methods in Finance, Wiley.
This module does not have any article/paper resources
Other Resources
  • [Website], http://epp.eurostat.ec.europa.eu/.
  • [Website], http://www.ecb.int/home/html/index.en.ht ml.
  • [Website], www.cso.ie.
  • [Website], www.bloomberg.com.
  • [Website], www.reuters.com.
Discussion Note: