TDRL$: Extracting and Evaluating the Fixed Causal Factors of Financial Markets

Date of Award


Document Type


Degree Name

Master of Science in Machine Learning


Machine Learning

First Advisor

Dr. Kun Zhang

Second Advisor

Dr. Bin Gu


"Stock market analysis has traditionally been driven by the pursuit of profit, with substantial investments made into research seeking lucrative opportunities. Despite a predominant focus on financial gains, a shift towards a deeper understanding of market dynamics is also emerging. This paper aims to contribute to understanding Financial markets by exploring the fixed causal processes within financial market data. In this work, we adapted the existing TDRL framework (a non-linear ICA-based VAE model) to work with financial stock price data and improved its out-of-sample performance compared to the classical Stationary TDRL model. Furthermore, we assess the model's effectiveness in feature reconstruction, predictability, and profitability. This evaluation method will help us not only understand how well the latent factors reconstruct the data but also if these factors have predictive power or profitable potential. Lastly, this work will serve as a framework for future works in this domain."


Thesis submitted to the Deanship of Graduate and Postdoctoral Studies

In partial fulfilment of the requirements for the M.Sc degree in Machine Learning

Advisors: Kun Zhang, Bin Gu

with 2 years embargo period

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