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1.
Collective dynamics of stock market effciency
Luiz G. A. Alves, Higor Y. D. Sigaki, Matjaž Perc, Haroldo V. Ribeiro, 2020, izvirni znanstveni članek

Opis: Summarized by the efcient market hypothesis, the idea that stock prices fully refect all available information is always confronted with the behavior of real-world markets. While there is plenty of evidence indicating and quantifying the efciency of stock markets, most studies assume this efciency to be constant over time so that its dynamical and collective aspects remain poorly understood. Here we defne the time-varying efciency of stock markets by calculating the permutation entropy within sliding time-windows of log-returns of stock market indices. We show that major world stock markets can be hierarchically classifed into several groups that display similar long-term efciency profles. However, we also show that efciency ranks and clusters of markets with similar trends are only stable for a few months at a time. We thus propose a network representation of stock markets that aggregates their short-term efciency patterns into a global and coherent picture. We fnd this fnancial network to be strongly entangled while also having a modular structure that consists of two distinct groups of stock markets. Our results suggest that stock market efciency is a collective phenomenon that can drive its operation at a high level of informational efciency, but also places the entire system under risk of failure.
Ključne besede: collective dynamics, social physics, econophysics, stock market
Objavljeno v DKUM: 14.01.2025; Ogledov: 0; Prenosov: 2
.pdf Celotno besedilo (2,58 MB)
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2.
Learning physical properties of liquid crystals with deep convolutional neural networks
Higor Y. D. Sigaki, Ervin K. Lenzi, Rafael S. Zola, Matjaž Perc, Haroldo V. Ribeiro, 2020, izvirni znanstveni članek

Opis: Machine learning algorithms have been available since the 1990s, but it is much more recently that they have come into use also in the physical sciences. While these algorithms have already proven to be useful in uncovering new properties of materials and in simplifying experimental protocols, their usage in liquid crystals research is still limited. This is surprising because optical imaging techniques are often applied in this line of research, and it is precisely with images that machine learning algorithms have achieved major breakthroughs in recent years. Here we use convolutional neural networks to probe several properties of liquid crystals directly from their optical images and without using manual feature engineering. By optimizing simple architectures, we fnd that convolutional neural networks can predict physical properties of liquid crystals with exceptional accuracy. We show that these deep neural networks identify liquid crystal phases and predict the order parameter of simulated nematic liquid crystals almost perfectly. We also show that convolutional neural networks identify the pitch length of simulated samples of cholesteric liquid crystals and the sample temperature of an experimental liquid crystal with very high precision.
Ključne besede: liquid crystal, neural network, artificial intelligence, soft matter
Objavljeno v DKUM: 20.11.2024; Ogledov: 0; Prenosov: 2
.pdf Celotno besedilo (1,94 MB)
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