Mr Alberto Pallotta

Hourly Academic

Alberto Pallotta

  • School Faculty of Business and Law

  • Department Accounting, Finance & Economics

  • Location London

Research activities

My research primarily focuses on Quantitative Finance, with specialized interests in portfolio optimization, volatility prediction, and quantitative trading strategies. I have published several papers that highlight the application of Machine Learning to trading algorithms and the use of Graph Theory in portfolio optimization.


Current Teaching

  • Computational Finance (ECS3556): Where I explore complex computational methods in finance, equipping students with the skills to apply these techniques in practical scenarios.
  • Quantitative Methods (ECS1003): This module introduces students to essential quantitative tools necessary for economic and financial analysis.
  • Part of Advanced Econometrics (ECS3003): I contribute to this module by focusing on advanced econometric techniques used in financial modelling and prediction.

    • Biography

    I hold a degree in Engineering and a Msc in Artificial Intelligence.

     My career began in finance, where I assumed various roles before co-founding the London Trading Institute. 

    I have also served as a DeFi advisor for Tendermint, the American company that developed Cosmos, one of the largest blockchains globally.

     My expertise in quantitative finance and blockchain led me to contribute as a reviewer to the Law Commission's consultation on digital asset regulation and collaborate with the Digital, Culture, Media and Sport Select Committee (DCMS). I am deeply passionate about finance, artificial intelligence, and mathematics. 

    Currently, I am the Head of R&D at a Swiss asset management firm and teach two quantitative finance-oriented modules at Middlesex University.

    Publications

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