| Module Code: |
H9FINA |
|
Long Title
|
Financial Analytics
|
|
Title
|
Financial Analytics
|
| Module Level: |
LEVEL 9 |
| EQF Level: |
7 |
| EHEA Level: |
Second Cycle |
| Module Coordinator: |
MICHAEL BRADFORD |
| Module Author: |
Simon Caton |
| Departments: |
School of Computing
|
| 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 |
Investigate and evaluate key concepts and financial analytics techniques and assess when to apply such techniques in practical situations |
| LO2 |
Critically assess models used in financial analytics |
| LO3 |
Contextualise, research and utilise analytical models associated with financial data in order to develop strategies for pricing |
| LO4 |
Critically review current research and assess research methods applied in the field of financial analytics |
| 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 |
Module Content & Assessment
| Indicative Content |
|
Introduction
• A review of fundamental financial data analysis techniques
• Emerging financial analytics methods for FinTech
• Selected exemplary case studies
• Evaluating Financial Models
• Core use cases of financial analytics in FinTech and Financial Markets
|
|
Methods for Data Analysis
• Singular value decomposition
• Feature engineering + variable selection
• Dimensionality reduction: Linear discriminant analysis, principal component analysis, barycentric discriminant analysis, multiple correspondence analysis
• Data fusion
|
|
Financial Time Series
• Granger causality
• ARMA, ARIMA, Box-Jenkins Methodology
• Nowcasting and forecasting
|
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Classification Methods for Fintech
• Bayesian statistics and classifiers
• Artificial Neural Networks and Deep Learning
|
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Stochastic Processes
• Random Walks & Martingales
• Binomial Processes
• Brownian Motion
• Poisson, Weinar & Ito Processes
|
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Pricing & Volatility
• Option Pricing & the Black-Scholes Model
• Volatility Estimators (e.g., Garman-Klass, Rodgers-Satchel, Yang-Zhang)
• Garch Model
|
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Risk and Portfolios
• Measurement of Beta, comparative beta, testing market efficiency with regression analysis and with pivot tables.
• Value at Risk measurement: variance covariance, historical simulation, principal component analysis, Monte Carlo simulation.
• Expected Tail Loss: parametric and historic simulation Backtesting of VaR and Expected Tail Loss models
• Mean-variance portfolio selection (mean-/semi-variance portfolios, Back-testing portfolio performance, research on portfolio selection, bond portfolio selection, term structure estimation, capital budgeting).
• Applied machine learning, and big data analytics
|
|
High Performance Computing for FinTech
• Facilitating High Performance FinTech Models
• Big Data Platforms (e.g. h2o.ai)
• Cloud Platforms (e.g. EMR, Watson)
|
| Assessment Breakdown | % |
|---|
| Coursework | 40.00% |
| End of Module Assessment | 60.00% |
AssessmentsFull Time
| Coursework |
| Assessment Type: |
Project |
% of total: |
40 |
| Assessment Date: |
n/a |
Outcome addressed: |
1 |
| Non-Marked: |
No |
Assessment Description:
Learners will undertake a significant team-based analytics project |
|
| End of Module Assessment |
| Assessment Type: |
Terminal Exam |
% of total: |
60 |
| Assessment Date: |
End-of-Semester |
Outcome addressed: |
1,2,3 |
| Non-Marked: |
No |
Assessment Description:
The examination will be a minimum of two hours in duration and may include a mix of: short answer questions, vignettes, essay based
questions and case study based questions.
Marks will be awarded based on clarity, appropriate structure, relevant examples, depth of topic knowledge, and evidence of outside core text reading. |
|
| 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 |
| Lecture |
No Description |
24 |
Every Week |
24.00 |
| Tutorial |
No Description |
24 |
Every Week |
24.00 |
| Independent Learning |
No Description |
202 |
Every Week |
202.00 |
| Total Weekly Contact Hours |
48.00 |
| Workload: Part Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
No Description |
24 |
Every Week |
24.00 |
| Tutorial |
No Description |
24 |
Every Week |
24.00 |
| Independent Learning |
No Description |
202 |
Every Week |
202.00 |
| Total Weekly Contact Hours |
48.00 |
Module Resources
| Recommended Book Resources |
|---|
-
John L. Teall. (1999), Financial Market Analytics, Quorum Books, p.328, [ISBN: 9781567201987].
-
R. Tsay. (2010), Analysis of Financial Time Series, John Wiley & Sons, Hoboken, [ISBN: 9780470414354].
-
Giuseppe Campolieti, Roman N. Makarov.. (2014), Financial mathematics: A Comprehensive Treatment, Boca Raton; CRC Press, [ISBN: 9781439892428].
-
Ansgar Steland. (2012), Financial Statistics and Mathematical Finance: Methods, Models and Applications, Wiley, p.432, [ISBN: 9780470710586].
| | Supplementary Book Resources |
|---|
-
A. Arratia. (2014), Computational Finance: An Introductory Course with R, Atlantis Press, [ISBN: 9789462390690].
-
James Wu, Stephen Coggeshall. (2012), Foundations of Predictive Analytics, Chapman and Hall/CRC, p.337, [ISBN: 9781439869468].
| | This module does not have any article/paper resources |
|---|
| This module does not have any other resources |
|---|
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