Georgios Papaioannou

Chanyeol Choi, Yoon Kim, Yu Yu et al.

Natural language processing (NLP) has been widely used in quantitative finance, but traditional methods often struggle to capture rich narratives in corporate disclosures, leaving potentially informative signals under-explored. Large language models (LLMs) offer a promising alternative due to their ability to extract nuanced semantics. In this paper, we ask whether semantic signals extracted by LLMs from corporate disclosures predict alpha, defined as abnormal returns beyond broad market movements and common risk factors. We introduce a simple framework, LLM as extractor, embedding as ruler, which extracts context-aware, metric-focused textual spans and quantifies semantic changes across consecutive disclosure periods using embedding-based similarity. This allows us to measure the degree of metric shifting -- how much firms move away from previously emphasized metrics, referred as moving targets. In experiments with portfolio and cross-sectional regression tests against a recent NER-based baseline, our method achieves more than twice the risk-adjusted alpha and shows significantly stronger predictive power. Qualitative analysis suggests that these gains stem from preserving contextual qualifiers and filtering out non-metric terms that keyword-based approaches often miss.

Xinghong Fu, Yanhong Li, Georgios Papaioannou et al.

Learning time series foundation models has been shown to be a promising approach for zero-shot time series forecasting across diverse time series domains. Insofar as scaling has been a critical driver of performance of foundation models in other modalities such as language and vision, much recent work on time series foundation modeling has focused on scaling. This has resulted in time series foundation models with hundreds of millions of parameters that are, while performant, inefficient and expensive to use in practice. This paper describes a simple recipe for learning efficient foundation models for zero-shot time series forecasting that are orders of magnitude smaller. We show that large-scale transformers are not necessary: small hybrid models that interleave long convolution and linear RNN layers (in particular DeltaNet layers) can match the performance of larger transformer-based models while being more than a hundred times smaller. We also describe several data augmentation and inference strategies that further improve performance. This recipe results in Reverso, a family of efficient time series foundation models for zero-shot forecasting that significantly push the performance-efficiency Pareto frontier.

Stamos Katsigiannis, Georgios Papaioannou, Dimitris Maroulis

Nowadays, real-time video communication over the internet through video conferencing applications has become an invaluable tool in everyone's professional and personal life. This trend underlines the need for video coding algorithms that provide acceptable quality on low bitrates and can support various resolutions inside the same stream in order to cope with limitations on computational resources and network bandwidth. In this work, a novel scalable video coding algorithm based on the contourlet transform is presented. The algorithm utilizes both lossy and lossless methods in order to achieve compression. One of its most notable features is that due to the transform utilised, it does not suffer from blocking artifacts that occur with many widely adopted compression algorithms. The proposed algorithm takes advantage of the vast computational capabilities of modern GPUs, in order to achieve real-time performance and provide satisfactory encoding and decoding times at relatively low cost, making it suitable for applications like video conferencing. Experiments show that the proposed algorithm performs satisfactorily in terms of compression ratio and speed, while it outperforms standard methods in terms of perceptual quality on lower bitrates.