FIE453 Big Data with Applications to Finance (E)

• Topics

  Businesses have entered the age of Big Data. Now that computers and the Internet have become so central to modern commerce, businesses are awash with large amounts of data about their customers. Electronic trading has made handling large amounts of data central to financial firms. Big Data has created both challenges and opportunities. The biggest opportunity is to extract useful information from the masses of data using statistics and machine learning. Businesses that can extract such information and act on it automatically have a substantial competitive advantage. Some of the world's most successful companies, such as Google and Facebook, have built themselves around Big Data. Banks and insurance companies have also increased the use of big data and predictive analytics over the last few years to accelerate digitalisation. 

  This course will focus on the applications of predictive analytics and Big Data in finance (as well as other industries where applicable). Examples of such applications include: 

  • Credit scoring: Determining which loans are likely to go bad.
  • Anti-money laundering: Identifying unlawful transactions.
  • Customer analytics: Various propensity models to model eg the likelihood of accepting an offer and the probability of churning.
  • Pricing models: Pricing in non-life insurance and the estimation of risk premium.

  This course will take students to the frontier of big data analysis in finance. Students will learn how to: 

  • Work with large datasets.
  • Handle real-world data issues to enable analysis.
  • Analyze data using the current state-of-the-art algorithms for supervised and unsupervised learning.
  • Understand and avoid overfitting.
  • Apply what they have learned to real data and business problems.

• Learning outcome

  Knowledge

  • Analyze large datasets using both supervised and unsupervised machine learning techniques.

  Skills

  • Analyze data using R.
  • Structure and clean data to make it usable in machine learning algorithms.
  • Handle datasets that are too large to fit in RAM.
  • Apply regression and classification techniques, including linear and logistic regression, to solve supervised machine learning problems.
  • Identify and control for overfitting when developing predictive models.
  • Implement advanced supervised machine learning techniques such as decision trees and boosting.

  General competencies

  • Structure and analyze data in R.
  • Apply machine learning techniques to real-world problems.

• Teaching

  Teaching will be carried out using: 

  • In-person lectures. The lectures will not be recorded
  • Student presentations of final project

• Recommended prerequisites

  Previous knowledge of linear regression is recommended. Some background in finance or experience with R is helpful.

• Credit reduction due to overlap

  None.

• Compulsory Activity

  An individual multiple choice test.

• Assessment

  The course is assessed through a final group project consisting of two components:

  • Written report (60%): A group paper documenting the project work, due December.
  • Presentation (40%): A group oral presentation of the project findings, lasting approximately 15-20 minutes per group.

  All material must be in English. Group size must be between 2-5 students and is subject to approval by the lecturer. Both the report and the presentation must be passed in the same semester. Students who wish to repeat the assessment must submit a new report and present again.

• Grading Scale

  A-F

• Computer tools

  R.

• Literature

  Gareth James, Daniella Witten, Trevor Hastie, Robert Tibshirani (2013). An Introduction to Statistical Learning. Springer Texts in Statistics. 

  Jerome Friedman, Trevor Hastie, Robert Tibshirani (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer

  The two first books are freely available electronically.