Topic outline

  • Fintech and new technologies



    This course is being lectured in the second period of 2021, on Wednesdays at 14.15-15.45 in AS2, Maarintie 8, and online. Welcome!

    You can also study the course individually year-round.  
    The old name of the course is TU-EV New Technologies in Finance.

    AI



    The course provides a holistic view of the new technologies that are powerfully reshaping and redefining the financial industry. 

    Financial markets are facing new challenges and new opportunities stemming from

    • digitalization and new technologies: fintech, blockchain and cryptocurrencies, high-frequency trading, big data and data analytics, machine learning and artificial intelligence, cybersecurity;
    • new regulations: regulation to open the financial markets for competition, to ease investments, and to stabilize financial markets;
    • rapidly changing global landscape: Fintech startups and large tech companies are challenging traditional banks and financial institutions. 

    The aim of the course is that the students would get both an idea of the multiple possibilities of the new technologies and a basic understanding of some of these technologies. 

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    Schedule: 

    Lectures on Wednesdays at 14.15-15.45

     3.11. Fintech

    10.11. Cryptocurrencies

    17.11. Algotrading and High-frequency trading HFT

    24.11. Machine Learning in Finance

    1.12. Cybersecurity in Finance

    8.12. Quantum computing and Finance


    The course consists of lectures and assignments. The final deadline for all the assignments is 18.12.2021.

    Basically, 1 or 3 credits available. On top of this, 2-3 credits additional credits can be earned by doing individual assignments related to machine learning in finance. Please, register here which version you want to pick. You can change your choice later.

    lecture diary on 5 or 6 lectures, based on the lectures and/or the lecture material. Instructions on the lecture diary will be given after the first lecture. Possible hints for each lecture diary will be given after the corresponding lecture.
    • if you submit each lecture diary within 10 days of the corresponding lecture, 5 lecture diaries will be enough. Otherways, 6 lecture diaries will be required. 
    • the final deadline for the diaries is 18.12. 

    To ensure that the student has understood the technical parts of the lectures, the lecture diary might include very small assignments. The diary is graded Passed/Failed.

    the 1 credit version (lecture diary) AND
    • an essay of 8-10 pages (without figures, pictures, indices, and references) ,
    • or 10 page PowerPoint & video presentation (audio is sufficient), with a 2 page summary of the content,, 
    on some of the topics considered during the course. The work is graded 0-5. 

    Possible topics for the essay are given in the beginning of the course. The list might be completed during the course.

    the 3 credit version AND
    personal computational assignment on machine learning with financial data. To do this assignment, knowledge on both programming and finance is needed—this assignment is carried out very individually.
    In addition, it is possible that there will be an opportunity to try a high-frequency trading platform with cryptocurrency data. 

    There will be a hands-on course in Machine Learning and Finance (TU-EV0008 - Financial Engineering III: Machine Learning) during spring 2022.


    Please find more instructions on page 'Passing the course'. 

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    Lecturer Ruth Kaila
    Assistant Eljas Toepfer
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    The course is open to all students of Aalto University. There are no specific prerequisites.

    The course can be included in the Minor in Financial Engineering, 20-25 credits.







    Please feel free to ask more information on the course, ruth.kailaATaalto.fi.


    WELCOME!

    Ruth and Eljas