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Thesis defences

PhD Oral Exam - Bahareh Amirshahi, Business Administration

Three Essays on Cryptocurrency Analytics and Forecasting by Using Deep Learning Models

Date & time
Monday, June 3, 2024
9 a.m. – 12 p.m.

This event is free


School of Graduate Studies


Nadeem Butt


John Molson Building
1450 Guy
Room 11.316

Wheel chair accessible


When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.


Since the emergence of Bitcoin in 2008 as the first cryptocurrency, digital assets have become favored investment options worldwide. Understanding and predicting the behavior of cryptocurrency markets are essential for effective risk management and investment decisions. However, the rapid fluctuations in these markets make accurate predictions a challenging task. In this thesis, key aspects of cryptocurrency market analytics are explored from three different angles. The recurring theme in each study involves proposing hybrid prediction models by combining a feature extractor component with deep learning models to enhance prediction performance.

The first study focuses on predicting cryptocurrency volatility, an underexplored area despite extensive studies in financial markets. By combining traditional econometrics methods with deep learning models, we forecast daily volatility with improved accuracies. The findings revealed that deep learning models not only enhance the accuracy of traditional models but also exhibit superior forecasting when combined with such models in a hybrid approach.

In the second study, we address the challenge of predicting cryptocurrency price values. Recognizing the impracticality of a universal model due to unique cryptocurrency characteristics, we propose a flexible architecture tailored for each cryptocurrency. Additionally, we explore the impact of sentiment data from Twitter posts on prediction accuracy, employing state-of-the-art pre-trained language models in an ensemble manner for more robust sentiment analysis.

The third study focuses on predicting the direction of cryptocurrency prices. We propose an innovative approach by combining data denoising techniques with machine learning methods, aiming to generate high-quality data for prediction models and achieve significantly higher accuracies. Notably, we assess the proposed approach across distinct periods: before, during, and after the COVID-19 pandemic, filling a critical gap in research regarding predictive models during crisis periods.

This research helps in developing highly accurate prediction models that can aid investors seize profitable opportunities and avoid potential losses. By analyzing over 25 cryptocurrencies, collectively representing 75% of the total market capitalization, this study offers a comprehensive perspective beyond the conventional Bitcoin-centric approach.

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