P. Desforges and N. Morizet, IPAG Research Seminar, November 17, 2022.
Research Paper #1: N. Morizet, M. Rizzato, D. Grimbert and G. Luta, ``A Pilot Study on the Use of Generative Adversarial Networks for Data Augmentation of Time Series'', MDPI AI 2022.
Abstract: Data augmentation is needed to use Deep Learning methods for the typically small time series datasets. There is limited literature on the evaluation of the performance of the use of Generative Adversarial Networks for time series data augmentation. We describe and discuss the results of a pilot study that extends a recent evaluation study of two families of data augmentation methods for time series (i.e., transformation-based methods and pattern-mixing methods), and provide recommendations for future work in this important area of research.
Download: https://www.mdpi.com/2673-2688/3/4/47
Research Paper #2: P. Desforges and C. Geissler, ``Analysis of the Relevance of Sentiment Data for the Prediction of Excess Returns in a Multi-Asset Framework'', Journal of Forecasting 2022.
Abstract: In this study, we look at the relevance of sentiment data for the prediction of excess returns in a multi-asset analysis. We start by initial exploratory data analysis in order to assess the pertinence of the sentiment data. We then compare the performance of rule-based algorithms with and without the sentiment data. The data considered is provided by Ravenpack. Finally, we explore the economic relevance of the forecast model in a long-only and long-short context. Inclusion of sentiment data leads to encouraging results.
Download: TBA