| Module Code: |
H8TSFA |
|
Long Title
|
Time Series & Financial Analytics
|
|
Title
|
Time Series & Financial Analytics
|
| Module Level: |
LEVEL 8 |
| EQF Level: |
6 |
| EHEA Level: |
First Cycle |
| Module Coordinator: |
TONY DELANEY |
| Module Author: |
TONY DELANEY |
| Departments: |
School of Computing
|
| Specifications of the qualifications and experience required of staff |
Masters' Degree or PhD in a computing or cognate discipline. May have industry experience also.
|
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Analyse time series using appropriate techniques. |
| LO2 |
Compare and contrast alternative models to assist with forecasting. |
| LO3 |
Source data ethically and communicate forecasts in a comprehensive and professional manner. |
| LO4 |
Apply forecasting techniques to data exhibiting heteroscedacity. |
| LO5 |
Implement quantitative techniques to optimise portfolios, measure performance and value financial assets . |
| LO6 |
Evaluate the role of data analytic approaches in Financial Markets. |
| 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 |
Learners should have attained the knowledge, skills and competence gained from stage 3 of the BSc (Hons) in Data Science
|
Module Content & Assessment
| Indicative Content |
|
Introduction to Time Series Concepts
Decomposition of Time Series. Adjusting for Inflation. Stationarity. Data Transformations. Ethical data sourcing
|
|
Fundamental Time Series Concepts
Mean & Linear Trend models. Random Walk Models. Averaging & smoothing models
|
|
Regression Models with Time Series
Regression models with time series.
|
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ARIMA models I
Non-seasonal ARIMA models. Orders of AR and MA terms. Model estimation
|
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ARIMA models II
Seasonal ARIMA models. Identifying a suitable model
|
|
Autoregressive Conditional Heteroscedacticity I
ARCH (1).
|
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Autoregressive Conditional Heteroscedacticity II
GARCH models.
|
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Portfolio Optimisation
Markowitz portfolio theory. Portfolio risk and return. The diversification effect. Measuring Beta
|
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Performance Measurement in Investment Markets
Performance and risk. Sharpe Index. Treynor’s Measure. Jensen’s Measure. Information Ratio
|
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Financial Indices
Construction of stock market indices. Construction of price indices.
|
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Discounted cash flow models
DCF and bond valuation. Dividend discount models. Relative valuation of equities.
|
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Data Analytic approaches to Financial Markets
Quantitative and high frequency trading. Big data and risk assessment
|
| Assessment Breakdown | % |
|---|
| Coursework | 40.00% |
| End of Module Assessment | 60.00% |
AssessmentsFull Time
| Coursework |
| Assessment Type: |
Formative Assessment |
% of total: |
Non-Marked |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4,5,6 |
| Non-Marked: |
Yes |
Assessment Description:
Formative assessment will be undertaken utilising exercises and short answer questions during certain tutorials.
In class discussions will be undertaken on contemporary topics. Feedback will be provided individually or as a group in oral format. |
|
| Assessment Type: |
Continuous Assessment |
% of total: |
40 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,3,4 |
| Non-Marked: |
No |
Assessment Description:
Learners will be asked to source financial data and undertake a significant forecasting exercise using time series techniques.
The project will assess practical application relating to LO1, LO3 and LO4
Project data should be sourced in an ethical manner and application made for ethical approval where required in accordance with School policy |
|
| End of Module Assessment |
| Assessment Type: |
Terminal Exam |
% of total: |
60 |
| Assessment Date: |
End-of-Semester |
Outcome addressed: |
1,2,3,4,5,6 |
| Non-Marked: |
No |
Assessment Description:
The examination will be in the region of two hours in duration and may include a mix of: theoretical, applied and interpretation questions.
Assessment of LO1, LO3 and LO4 will cover theoretical and conceptual dimensions |
|
| 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.
|
Reassessment Description
The repeat strategy for this module is an examination. All learning outcomes will be assessed in the repeat exam.
|
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 |
| Lecture |
Classroom & Demonstrations (hours) |
24 |
Per Semester |
2.00 |
| Tutorial |
Other hours (Practical/Tutorial) |
24 |
Per Semester |
2.00 |
| Independent Learning |
Independent learning (hours) |
202 |
Per Semester |
16.83 |
| Total Weekly Contact Hours |
4.00 |
Module Resources
| Recommended Book Resources |
|---|
-
Hyndman, R. & Athanasopoulos, G.. (2017), Forecasting: Principles and Practice 2e, O Texts.
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Tsay, R.S.. (2013), An Introduction to analysis of financial data with R, Wiley, New York.
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DeFusco, R., McLeavey, D., Pinto, J. & Runkle, D.. (2015), Quantitative Investment Analysis, Wiley, New Jersey.
| | Supplementary Book Resources |
|---|
-
Brooks, C.. (2008), Introductory Econometrics for Finance (2nd ed), Cambridge University Press, Cambridge.
| | This module does not have any article/paper resources |
|---|
| This module does not have any other resources |
|---|
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