Behavioural Finance & Big Data
Module overview As part of the Behavioural Finance
Key benefits ■ Gain a fundamental understanding of big data ■ Understand financial market anomalies ■ Gain a Postgraduate Award upon successful completion.
Faculty On this module you will gain insights from the likes of Dr Greg Davies (Head of Behavioural Science, Oxford Risk), Eryk Walczak (Senior Research Data Scientist, Bank of England), Marianne Polman (Dutch Central Bank), Remy Jansen (Dutch Central Bank) and Brunello Rosa (CEO, Rosa & Roubini).
Module structure ■ The module lasts for 17 weeks and culminates in an assessment ■ Module materials are organised into 10 lessons – each lasting one week ■ You can expect to spend around 11 hours studying per lesson ■ Lessons include reading materials, video interviews, discussion points, reflection activities ■ There are four wbsLive online sessions with your tutor.
section of the module, you will develop an understanding of key deviations from rationality that have been documented in behavioural science, and learn how these deviations can affect the economy, and in particular central banks’ analyses across their macroeconomic, macroprudential and microprudential responsibilities. As part of the Big Data section of the module, you will be introduced to recent advances in data science, the basics in big data analytics and you will have the opportunity to discuss current debates on the validity of the descriptive, predictive and prescriptive claims of big data analytics. Who would benefit from this module? This module would benefit those working in financial regulation authorities, central banks as well as investment banks and rating agencies as well as those working as Asset Managers, Reserves Managers, or those involved in projects requiring due dilligence.
Key topics covered During this module, you will cover the following.
Topics
“ I recently received a promotion at work and the knowledge and skills I am gaining from the programme are invaluable for my current role and my future progression.”
Households delegate financial decisions to intermediaries. Can such delegation distort financial markets? Regression including penalised regression / regularisation: Lasso and ridge regression
What models, including prospect theory, do people use to evaluate risky situations?
Decision heuristics: How do people form expectations about future outcomes?
Key info
Eromosele Pax Alenkhe Bank Examiner, Nigeria (current participant)
k nearest neighbours (KNN) for classification and regression
Cross validation (bias-variance trade-off) and scoring, ROC curves
■ This module will run between October 2021 and February 2022 ■ This module features a two-week induction period prior to starting
Clustering, unsupervised learning, and text analysis
Artifical intelligence and neural networks
Decision trees
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wbs.ac.uk/go/banking
wbs.ac.uk/go/banking
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