Course Details in 2022/23 Session

Module Title	LM Big Data Management in Finance
School	Birmingham Business School
Department	Finance
Module Code	07 36320
Module Lead	Dr. Anandadeep Mandal
Level	Masters Level
Credits	20
Semester	Semester 2
Pre-requisites
Co-requisites
Restrictions	None
Contact Hours	Lecture-30 hours Seminar-6 hours Practical Classes and workshops-4 hours Guided independent study-160 hours Total: 200 hours
Exclusions
Description	This module is about a core set of modern analytical techniques that are often used to manage Big Data in finance. First, students will be taught on the module unsupervised machine learning data management techniques that are often used in finance (e.g. clustering algorithms). Second, students will also be taught Deep Learning techniques that are often used to manage Big Data in finance, including Neural Networks. Third, Reinforcement Learning techniques and their potential applications to portfolio selection and trading strategies will also be covered in the module. Throughout, this course spends a significant amount of time on practical applications of theories introduced along with their role in promoting ethical and sustainable businesses, financial systems and economies.
Learning Outcomes	By the end of the module students should be able to: Critically apply a range of techniques to manage Big Data and carry out projects by using data obtained from Birmingham Business School’s Trading Room that have access to Bloomberg, WRDS, ORBIS Bankfocus, COMPUSTAT, CRSP, Datastream, etc. Demonstrate critical knowledge and understanding of Deep Learning Techniques and Reinforcement Learning Techniques. Critically apply a range of Big Data management techniques to solve problems in finance (such as in asset management, portfolio choice, trading strategies, etc.). Design, collect, disseminate and manage data in an ethical and responsible manner.
Assessment	36320-01 : Groupwork 1 Computer-based Project : Coursework (50%) 36320-02 : Groupwork 2 Video Exercise : Coursework (50%)
Assessment Methods & Exceptions	Assessment: An individual computer-based project containing about 2,000 words (or equivalent) (50%) and A 15-minutes video on a big data management exercise (50%) Reassessment: A 4,000 word Individual Assignment (100%)
Other
Reading List