Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. Given contemporary computing power and potential data collection, many firms, particularly those from the financial sector, wish to use big data. The challenges include capture, curation, storage search, sharing, transfer, data analytics and visualisation.
The primary purpose of this module is to provide the student with an understanding of data analytic approaches in finance. The first part of this module covers predictive analytics, risk modelling and corporate finance. The second part will concentrate on the application of data analytics in high frequency finance, fraud and personal finance.
The main aims of the module are:
1. to enable students to understand the applications of big data and data analytics in finance;
2. to enable students to critically evaluate the techniques above and provide a view on the future direction of innovation in the financial sector.
Objectives
This module enables students to be able:
1. to understand the use of high frequency data in finance and its limitations;
2. to undertake predictive estimations and tests of hypotheses using financial data;
3. to understand how big data can be used in areas of finance such as risk analysis and corporate finance.
4. to critically evaluate how big data and data analytics is changing the financial sector.
The primary purpose of this module is to provide the student with an understanding of data analytic approaches in finance. The first part of this module covers predictive analytics, risk modelling and corporate finance. The second part will concentrate on the application of data analytics in high frequency finance, fraud and personal finance.
The main aims of the module are:
1. to enable students to understand the applications of big data and data analytics in finance;
2. to enable students to critically evaluate the techniques above and provide a view on the future direction of innovation in the financial sector.
Objectives
This module enables students to be able:
1. to understand the use of high frequency data in finance and its limitations;
2. to undertake predictive estimations and tests of hypotheses using financial data;
3. to understand how big data can be used in areas of finance such as risk analysis and corporate finance.
4. to critically evaluate how big data and data analytics is changing the financial sector.
- Module Supervisor: Efthimios Nikolakopoulos
Category: Finance