Module Code: H9QAF
Long Title Quantitative Analysis for Finance
Title Quantitative Analysis for Finance
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: CORINA SHEERIN
Module Author: CORINA SHEERIN
Departments: School of Business
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 Synthesise data and analyse business problems under conditions of uncertainty, formulate null and alternative hypotheses and exercise critical judgement and discrimination in the resolution of complex problematic situations using hypothesis testing.
LO2 Formulate ideas in an abstract manner using analysis of variance and regression analysis, interpret regression output and critically evaluate the relevance and importance of underlying assumptions in the modelling process.
LO3 Critically evaluate the use of the chi square distribution within the context of financial data, design and conduct tests of hypothesis comparing an observed set of frequencies to an expected distribution and interpret and formulate conclusions based on results.
LO4 Disseminate the components of a time series, select and apply appropriate trend models with necessary adjustments, distinguish between the additive and multiplicative models and evaluate contemporary literature concerning the use of time-series analysis in finance and investment.
LO5 Select from a range of technical analysis indicators and articulate, justify and defend investment recommendations based on chart patterns observed.
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
Hypothesis Testing (Week 1-2)
• Hypothesis Testing: An Introduction • Hypothesis Tests: The Mean (Single Mean, Differences between Mean, Mean Differences) • Hypothesis Tests: Variance (Single Variance, Equality (Inequality) of Two Variances)
Analysis of Variance (Week 3-4)
• The F Distribution • Comparing Population Variances • Analysis of Variance (ANOVA) Assumptions • ANOVA Tests
Multiple Regression and Issues in Regression Analysis (Week 5 -7)
• Assumptions underlying Multiple Regression • Multiple Regression Analysis • Multiple Standard Error of Estimates • Evaluating the Regression Equation • Issues in Regression Analysis: o Multicollinearity o Hetroskedasticity o Serial Correlation
Non Parametric Methods: Chi Squared Distribution (Week 8-10)
• Goodness of Fit Tests: Equal Expected Frequencies • Goodness of Fit tests: Unequal Expected Frequencies • Limitations of Chi Squared Distribution • Contingency Table Analysis
Time Series Analysis (Week 11-12)
• Components of a Time Series Model • Linear and Non Linear Trend Models • Moving Average Models • Seasonality in Time Series models
Technical Analysis (Week 13)
• Principles and Assumptions • Technical and Fundamental Analysis • Technical Analysis Tools
Assessment Breakdown%
Coursework40.00%
End of Module Assessment60.00%

Assessments

Full Time

Coursework
Assessment Type: Project % of total: 40
Assessment Date: n/a Outcome addressed: 1,2
Non-Marked: No
Assessment Description:
The Quantitative Analysis continuous assessment may take the form of a large scale data based project; a technical report; a detailed problem set based assignment which may contain case study data or an in class test. Presentations may also be used in conjunction with any of the afore mentioned assessment methods. The exact nature of the assessment will be decided annually by the programme team bearing in mind contemporary financial issues. The continuous assessment element of this module will assess both theoretical and analytical skills as undertaken on the programme and candidates must demonstrate skills of analysis and interpretation of data regardless of the form of assessment.
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided to students through the use of on-line quizzes and short answer questions. In addition in class problems and discussions will provide an opportunity for formative learning and student feedback to be provided. Provision of individual feedback will be provided individually outside of lecture time or on line through Moodle.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 60
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
The examination will be a minimum of two hours in duration and may include a mix of: short or long problem based questions, vignettes, essay based questions and case study based questions. All questions will be marked according to clarity, structure, contemporary examples (that illustrate points made), reference to materials covered, theories and research in the field. Reference to class material and evidence of outside reading is essential.
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
Practical No Description 3.5 Every Week 3.50
Assignment No Description 2.5 Once per semester 0.21
Independent Learning No Description 4.5 Once per semester 0.38
Total Weekly Contact Hours 3.50
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Practical No Description 3.5 Every Week 3.50
Independent Learning Time No Description 4.5 Once per semester 0.38
Assignment No Description 2.5 Once per semester 0.21
Total Weekly Contact Hours 3.50
 

Module Resources

Recommended Book Resources
  • De Fusco R.A., Pinto J.E., Runkle D.E., and McLeavey D.W. (2007), Quantitative Investment Analysis, Wiley (CFA Institute).
Supplementary Book Resources
  • Kirkpatrick, C.D.. (2012), Time the Markets: Using Technical Analysis to Interpret Economic Data., 2nd. Pearson Education.
  • Lind D.A., Marchal W.G., and Wathen S.A. (2015), Statistical Techniques in Business and Economics, 16th. McGraw Hill.
  • Davison, M.. (2014), Quantitative Finance: A Simulation Based Introduction Using Excel, Chapman and Hall/CRC..
  • Elton, R.J., Gruber, M.J., Brown,S., Goetzmann,W.N.. (2014), Modern Portfolio Theory and Investment Analysis, 9th. Wiley Publications.
  • Koop, G.. (2013), Analysis of Economic Data, Wiley Publications..
  • Jones, C.P.. (2012), Investment Analysis and Management, 12th. Wiley Publications.
  • Jacques, I.. (2013), Mathematics for Economics and Business, 7th. FT Prentice Hall.
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.
  • [Journal], Journal of Finance.
  • [Journal], Journal of Quantitative Finance.
  • [Journal], European Journal of Finance.
  • [Journal], Journal of Economics and Finance.
  • [Journal], Journal of Applied Quantitative Methods.
Discussion Note: