Long Title:Financial Data Analysis
Language of Instruction:English
Module Code:H8FDA
Credits: 10
NFQ Level:LEVEL 8
Field of Study: Computer use
Module Delivered in 1 programme(s)
Module Coordinator: Simon Caton
Module editor: MICHAEL BRADFORD
Teaching and Learning Strategy: The learning strategy involves the use of lectures, tutorials, case studies, paper reviews and practical work as appropriate. Lectures will include active learning components such as paired discussion, problem solving, and class feedback. Practical sessions will comprise of group work and individual learning. Learners will also have access to research publications as required. Analysis of datasets will be carried out through the implementation of practical techniques associated with the underpinning theoretical aspects of financial analysis. Tutorial sessions will be utilised for review of research papers, core methods and case studies.
Learning Environment: Learning will take place in classroom or lab environments as appropriate. In lab environments, each student will have access to IT resources. Learners will have access to library resources and to faculty outside of the classroom where required. Module materials will be placed on Moodle, the colleges LMS.
Module Description: The aim of this module is to provide an in-depth coverage of core methods commonly employed in the analysis of nancial data. Furthermore, learners will apply these techniques in real-world contexts utilising appropriate tools for analysing financial data sets.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Investigate core techniques for financial data analysis
LO2 Evaluate and assess models used in financial data analysis
LO3 Utilise analytical models associated with financial data in order to develop strategies for FinTech use cases
LO4 Review current research and research methods to derive meaning from financial data
Pre-requisite learning
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
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 

Module Content & Assessment

Indicative Content
Introduction
Financial data analysis in FinTech  Core use cases  Available tools
Linear Time Series Analysis and Its Applications to Financial Data
Stationarity  Correlation and Autocorrelation  White Noise and Linear Time Series  Simple AR, MA and ARMA models Seasonality  Regression models with Time Series  Time Series Errors
Conditional Heteroscedastic Models
Noting and detecting volatility  Model building  ARCH and GARCH  Random Coefficient Autoregressive Models  Stochastic Volatility Models
Non-linear Models
Non-linear Models  Tests of non-linearity  Modelling  Forecasting
Multivariate Time Series Analysis & Its Applications to Financial Data
Weak Stationarity and Cross-Correlation Matrices  Vector Autoregressive and Moving Average Models  Reparameterization  Higher Dimensional Volatility Models  Factor-Volatility Models  Multivariate t Distribution
Micro-structure Models and Pricing
Micro-structure measures (e.g. Bid-ask Spreads, Liquidity, etc.  Linear and non-linear Models for price changes  Options  Continuous-Time Stochastics Processes  Black Scholes  Value at Risk
Markov Chain Monte Carlo Methods
Markov Chain Simulation  Sampling  Stochastic Volatility Models  Linear Regression with Time Series Errors  Markov Switching Models  Missing Values and Outliers
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%
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
Should learners not achieve a 40% pass mark, they will either sit a repeat terminal exam, or undertake an assessment that assesses all learning outcomes.

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

 

Module Workload

This module has no Full Time workload.
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 Hours 250.00
Total Weekly Learner Workload 250.00
Total Weekly Contact Hours 48.00
 

Module Resources

Recommended Book Resources
  • Giuseppe Campolieti, Roman N. Makarov., Financial mathematics, Boca Raton; CRC Press [ISBN: 1439892423]
  • Ansgar Steland, Financial Statistics and Mathematical Finance, Wiley [ISBN: 0470710586]
  • Analysis of Financial Time Series, John Wiley & Sons Hoboken [ISBN: 0470414359]
  • John L. Teall, Financial Market Analytics, Quorum Books [ISBN: 1567201989.]
Supplementary Book Resources
  • James Wu, Stephen Coggeshall, Foundations of Predictive Analytics, Chapman and Hall/CRC [ISBN: 1439869464.]
  • Arratia, Argimiro 2014, Computational Finance: An Introductory Course with R, Atlantis Press [ISBN: 946239069X]
This module does not have any article/paper resources
This module does not have any other resources
 

Module Delivered in

Programme Code Programme Semester Delivery
HDFINTECH HDip FinTech 2 Core Subject