A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Model shows financial turbulence similar to turbulence, with wealth cascading from large to small entities.
New turbulence index using TDA detects financial market transitions.
The paper analyzes financial market turbulence using mathematical physics.
The financial market and turbulence have been broadly compared on account of the same quantitative methods and several common stylized facts they shared. In this paper, the She-Leveque (SL) hierarchy, proposed to explain the anomalous scaling exponents deviated from Kolmogorov monofractal scaling of the velocity fluctu…
In agreement with the recent research findings in the econophysics, we propose that the nonlinear dynamic chaos can be generated by the turbulent capital flows in both the quantitative easing transmission channels and the transaction networks channels, when there are the laminar turbulent capital flows transitions in t…
This paper examines cryptocurrency integration with traditional markets, showing how network structure and turbulence influence cross-asset spillovers.
We develop a framework especially suited to the autocorrelation properties observed in financial times series, by borrowing from the physical picture of turbulence. The success of our approach as applied to high frequency foreign exchange data is demonstrated by the overlap of the curves in Figure (1), since we are abl…
We analyze whether the prediction of the fractal markets hypothesis about a dominance of specific investment horizons during turbulent times holds. To do so, we utilize the continuous wavelet transform analysis and obtained wavelet power spectra which give the crucial information about the variance distribution across …
Causal-NECO VaR improves financial risk assessment under market turbulence.
Financial volatility risk and its relation to a business cycle-related intrinsic time is addressed through a multiple round evolutionary quantum game equilibrium leading to turbulence and multifractal signatures in the financial returns and in the risk dynamics. The model is simulated and the results are compared with …
Improved eigenvalue distribution method for financial data.
A new approach to the understanding of complex behavior of financial markets index using tools from thermodynamics and statistical physics is developed. Physical complexity, a magnitude rooted in Kolmogorov-Chaitin theory is applied to binary sequences built up from real time series of financial markets indexes. The st…
A new approach to the understanding of the complex behavior of financial markets index using tools from thermodynamics and statistical physics is developed. Physical complexity, a magnitude rooted in the Kolmogorov-Chaitin theory is applied to binary sequences built up from real time series of financial markets indices…
The inversion formula for conservative multifractal measures was unveiled mathematically a decade ago, which is however not well tested in real complex systems. In this Letter, we propose to verify the inversion formula using high-frequency turbulent financial data. We construct conservative volatility measure based on…
Novel framework uses causality for financial forecasting.
Multiplicative random cascade model naturally reproduces the intermittency or multifractality, which is frequently shown among hierarchical complex systems such as turbulence and financial markets. As described herein, we investigate the validity of a multiplicative hierarchical random cascade model through an empirica…
We describe tests validating progress made toward acceleration and automation of hydrodynamic codes in the regime of developed turbulence by three Deep Learning (DL) Neural Network (NN) schemes trained on Direct Numerical Simulations of turbulence. Even the bare DL solutions, which do not take into account any physics …
We investigate whether fractal markets hypothesis and its focus on liquidity and invest- ment horizons give reasonable predictions about dynamics of the financial markets during the turbulences such as the Global Financial Crisis of late 2000s. Compared to the mainstream efficient markets hypothesis, fractal markets hy…
Deep learning compares turbulence models in plasma physics.
We present a model of financial markets originally proposed for a turbulent flow, as a dynamic basis of its intermittent behavior. Time evolution of the price change is assumed to be described by Brownian motion in a power-law potential, where the `temperature' fluctuates slowly. The model generally yields a fat-tailed…
Paper predicts turbulent flows using physics-informed deep learning.
GyroSwin models plasma turbulence with neural nets, reducing costs and capturing neglected nonlinearities.
Study on complex tori foliations and flat geometries.
Neural surrogates speed up 5D gyrokinetic simulations of plasma turbulence.
NN-Turb generates turbulent velocity statistics using neural networks.
Transfer learning improves chaotic dynamics predictions with less data.
Multifractality is ubiquitously observed in complex natural and socioeconomic systems. Multifractal analysis provides powerful tools to understand the complex nonlinear nature of time series in diverse fields. Inspired by its striking analogy with hydrodynamic turbulence, from which the idea of multifractality originat…
Physics-informed ML models improve turbulence understanding in fusion plasmas.
A neural network models pressure-Hessian from local velocity gradients in turbulent flows.
Neural networks predict flow and elastic stresses in viscoelastic turbulence.
xVAE models extreme turbulence events in turbulent flows.
Researchers use quantum chaos and RMT to analyze turbulence, revealing unique scaling laws.
Study predicts turbulent electric fields in fusion plasmas using deep learning.
Generative adversarial networks improve subgrid modeling in turbulent reactive flows.
Adversarial reinforcement learning optimizes microswimmers' path-planning in turbulent flows.
Neural network predicts turbulence near-wall regions efficiently.
Catastrophic events, though rare, do occur and when they occur, they have devastating effects. It is, therefore, of utmost importance to understand the complexity of the underlying dynamics and signatures of catastrophic events, such as market crashes. For deeper understanding, we choose the US and Japanese markets fro…
Three physics-constrained regression exercises for image velocimetry and turbulence modeling.
Three ways synchronization in financial markets can cause contagion, using models of decision-making and oscillators.
This paper proposes a multi-scale Markov-Switching GARCH model for EUR/USD volatility.
No projective structure found on foliations of elliptic curves.
Convolutional networks predict turbulence from wall quantities.
The paper examines how randomness in forex returns increases during financial crises.
Physics-guided reinforcement learning optimizes swimming in turbulent flows.
HFNO enhances interpretability of turbulent flows through parallel wavenumber bin processing.
The concepts of scale invariance, self-similarity and scaling have been fruitfully applied to the study of price fluctuations in financial markets. After a brief review of the properties of stable Levy distributions and their applications to market data we indicate the shortcomings of such models and describe the trunc…
The recent financial crisis has stressed the need to understand financial systems as networks of interdependent countries, where cross-border financial linkages play the fundamental role. It has also been emphasized that the relevance of these networks relies on the representation of changes follow-on the occurrence of…
In complex systems such as turbulent flows and financial markets, the dynamics in long and short time-lags, signaled by Gaussian and fat-tailed statistics, respectively, calls for a unified description. To address this issue we analyze a real dataset, namely, price fluctuations, in a wide range of temporal scales to em…