Deep Learning with Applications to Finance (not offered)

FIE458 Deep Learning with Applications to Finance (not offered)

Autumn 2022

Spring 2023
  • Topics

    Deep learning is the latest frontier in using computers to analyze data. Most of the most glamorous advances in AI, such as voice recognition and automatic translation, are made possible by deep learning.

    In this class, we will explore whether deep learning can be used to predict financial markets. Deep learning requires immense amounts of data, so we choose a market where such data is readily available: cryptocurrency markets, such as Bitcoin.

    Cryptocurrency markets are a very promising area of application, because data is readily available and because the market is dominated by small retail investors who are more likely to engage in predictable patterns.


    • Multilayer perceptrons
    • Convolutional neural networks
    • LSTM networks
    • Autoencoders


    • Cryptocurrency data.
    • Other data as time permits, such as Twitter data.

  • Learning outcome


    • Students should understand the principles of deep learning.
    • Students should understand the dangers of overfitting and how to avoid them.
    • Students should understand the structure of the cryptocurrency market and the investment possibilities.


    • Students should be able to develop a deep learning model, and the trade-offs between different models.
    • Students should be able to use financial data to fit and evaluate a deep learning model.

    General competencies

    • Students should be able to analyze financial data using a deep learning library.
    • Students should be to able to communicate their findings to investment professionals.
    • Students should be able to form an opinion about issues in both deep learning and the cryptocurrency markets.

  • Teaching


    Accommodations will be made to allow for taking the class online.

  • Recommended prerequisites

    Prior experience with R or Python recommended.

    Prior experience with linear regression is helpful.

  • Assessment

    The class is graded on a portfolio of class presentations (approximately 35%), a group final paper (approximately 60%) and peer review (approximately 5%). One grade is given for the entire portfolio. All material must be in English.

    For class presentations, students will be expected to present multiple times through the semester, either singly or in groups. Group size must be approved by the lecturer. Presentations can be held online in the exact same format, in case restrictions related to Covid-19 demands it.

    The final 5% is peer review for all group work. At the end of the term, students will evaluate the contribution of his/her team members. The peer review requires you to behave in a responsible and respectful manner. If I deem a student to deviate from such behavior, I may overrule the peer review.

    The grade awarded may not be appealed due to the nature of the assessments.

  • Grading Scale


  • Computer tools

    A laptop with R or Python, and the Keras deep learning library.

  • Literature

    Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016). Deep Learning. MIT Press

    The book is available electronically.


ECTS Credits
Teaching language

Spring. Not offered spring 2022.

Course responsible

Associate Professor Walter Pohl, Department of Finance, NHH.