Zobrazeno 1 - 10
of 197
pro vyhledávání: '"Sarlin, Peter"'
Autor:
Luukkonen, Risto, Burdge, Jonathan, Zosa, Elaine, Talman, Aarne, Komulainen, Ville, Hatanpää, Väinö, Sarlin, Peter, Pyysalo, Sampo
The pretraining of state-of-the-art large language models now requires trillions of words of text, which is orders of magnitude more than available for the vast majority of languages. While including text in more than one language is an obvious way t
Externí odkaz:
http://arxiv.org/abs/2404.01856
In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the mos
Externí odkaz:
http://arxiv.org/abs/1706.09627
Autor:
Forss, Thomas, Sarlin, Peter
To understand the relationship between news sentiment and company stock price movements, and to better understand connectivity among companies, we define an algorithm for measuring sentiment-based network risk. The algorithm ranks companies in networ
Externí odkaz:
http://arxiv.org/abs/1706.05812
Autor:
Rönnqvist, Samuel, Sarlin, Peter
Publikováno v:
Neurocomputing, 264, 2017
While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting r
Externí odkaz:
http://arxiv.org/abs/1603.05670
Autor:
Mezei, József, Sarlin, Peter
This paper proposes RiskRank as a joint measure of cyclical and cross-sectional systemic risk. RiskRank is a general-purpose aggregation operator that concurrently accounts for risk levels for individual entities and their interconnectedness. The mea
Externí odkaz:
http://arxiv.org/abs/1601.06204
Autor:
Rönnqvist, Samuel, Sarlin, Peter
News is a pertinent source of information on financial risks and stress factors, which nevertheless is challenging to harness due to the sparse and unstructured nature of natural text. We propose an approach based on distributional semantics and deep
Externí odkaz:
http://arxiv.org/abs/1507.07870
Autor:
Holopainen, Markus, Sarlin, Peter
This paper presents first steps toward robust models for crisis prediction. We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most
Externí odkaz:
http://arxiv.org/abs/1501.04682
Autor:
Mezei, Jozsef, Sarlin, Peter
The policy objective of safeguarding financial stability has stimulated a wave of research on systemic risk analytics, yet it still faces challenges in measurability. This paper models systemic risk by tapping into expert knowledge of financial super
Externí odkaz:
http://arxiv.org/abs/1412.5452
Probabilistic topic modeling is a popular and powerful family of tools for uncovering thematic structure in large sets of unstructured text documents. While much attention has been directed towards the modeling algorithms and their various extensions
Externí odkaz:
http://arxiv.org/abs/1409.5623