Our Scientific Publications
Characterizing Financial Market Coverage using AI
Jean Marie Tshimula, D’Jeff K. Nkashama, Patrick Owusu, Marc Frappier, Pierre-Martin Tardif, Froduald Kabanza, Armelle Brun, Jean-Marc Patenaude, Shengrui Wang, Belkacem Chikhaoui
Published on ArXiv – 2023
The paper uses AI to analyze thousands of YouTube videos from Bloomberg and Yahoo Finance, extracting financial insights through speech-to-text transcription and natural language processing. This helps identify key market trends, influential entities, and evolving narratives—offering valuable tools for financial analysis and investment decision-making.
Neural Forecasting at Scale
Philippe Chatigny , Shengrui Wang , Jean-Marc Patenaude , Boris N. Oreshkin
Published on ArXiv – 2021
This paper introduces N-BEATS(P), a scalable and memory-efficient deep learning architecture for univariate time series forecasting that significantly reduces computational costs while maintaining state-of-the-art accuracy. Its relevance in finance lies in its demonstrated ability to perform zero-shot forecasting on financial datasets—such as stocks and ETFs—enabling rapid, cost-effective deployment of predictive models across diverse financial instruments without retraining.
Spatiotemporal neural networks for forecasting financial time series
Philippe Chatigny, Jean-Marc Patenaude, Shengrui Wang
Published in the International Journal of Approximate Reasoning – Dec 2020
This paper proposes STANN, a spatiotemporal adaptive neural network that improves long-term forecasting of multivariate financial time series by dynamically adjusting its autoregressive order using an attention mechanism. Applied to investment fund data, STANN significantly enhances forecasting accuracy and supports more effective autonomous trading strategies in finance.
