arXiv research

Anti-correlated noise improves machine learning model generalization.

This paper introduces anti-correlation networks to study China's stock market.

We discuss stochastic modeling of volatility persistence and anti-correlations in electricity spot prices, and for this purpose we present two mean-reverting versions of the multifractal random walk (MRW). In the first model the anti-correlations are modeled in the same way as in an Ornstein-Uhlenbeck process, i.e. via…

With the random matrix theory, we study the spatial structure of the Chinese stock market, American stock market and global market indices. After taking into account the signs of the components in the eigenvectors of the cross-correlation matrix, we detect the subsector structure of the financial systems. The positive …

Proposes a method to generate private synthetic data in a decentralized setting using correlated noise.

In this paper we use wavelet concepts to show that correlation coefficient between two financial data's is not constant but varies with scale from high correlation value to strongly anti-correlation value This studies is important because correlation coefficient is used to quantify degree of independence between two va…

TS-Fault benchmarks TSF models against structural faults.

A challenging problem in the study of complex systems is that of resolving, without prior information, the emergent, mesoscopic organization determined by groups of units whose dynamical activity is more strongly correlated internally than with the rest of the system. The existing techniques to filter correlations are …

This paper analyzes correlations in patterns of trading of different members of the London Stock Exchange. The collection of strategies associated with a member institution is defined by the sequence of signs of net volume traded by that institution in hour intervals. Using several methods we show that there are signif…

For the London Stock Exchange we demonstrate that the signs of orders obey a long-memory process. The autocorrelation function decays roughly as $τ^{-α}$ with $α\approx 0.6$, corresponding to a Hurst exponent $H \approx 0.7$. This implies that the signs of future orders are quite predictable from the signs of past orde…

In this article we analyse linear correlation and non-linear dependence of traded volume, $v$, of the 30 constituents of Dow Jones Industrial Average at different value scales. Specifically, we have raised $v$ to some real value $α$ or $β$, which introduces a bias for small ($α, β<0$) or large ($α, β>1$) values. Our r…

Based on the Log-Periodic Power Law (LPPL) methodology, with the universal preferred scaling factor $λ\approx 2$, the negative bubble on the oil market in 2014-2016 has been detected. Over the same period a positive bubble on the so called commodity currencies expressed in terms of the US dollar appears to take place w…

We consider the Nordic electricity spot market from mid 1992 to the end of year 2000. This market is found to be well approximated by an anti-persistent self-affine (mean-reverting) walk. It is characterized by a Hurst exponent of $H\simeq 0.41$ over three orders of magnitude in time ranging from days to years. We argu…

Study proposes pricing mechanism for cryptocurrency options.

We propose an artificial market model based on deterministic agents. The agents modify their ask/bid price depending on past price changes. The temporal development of market price fluctuations is calculated numerically. A probability density function of market price changes has power law tails. Autocorrelation coeffic…

We find that when measured in terms of dollar-turnover, and once $β$-neutralised and Low-Vol neutralised, the Size Effect is alive and well. With a long term t-stat of $5.1$, the "Cold-Minus-Hot" (CMH) anomaly is certainly not less significant than other well-known factors such as Value or Quality. As compared to marke…

The dynamics of market prices is described as the evolution of opinions in the trading community regarding future market behavior. The price then is a function of the voting process of the market players in favor to raise or reduce the value of a stock. The model presented in this paper is suited for pricing of options…

Stock prices are observed to be random walks in time despite a strong, long term memory in the signs of trades (buys or sells). Lillo and Farmer have recently suggested that these correlations are compensated by opposite long ranged fluctuations in liquidity, with an otherwise permanent market impact, challenging the s…

New attacks inflate earnings while reducing fraud scores, potentially millions at stake.

Improved sampling efficiency for inverse problems using variance-reduced diffusion methods.

Study shows different price correlations in European electricity markets.

In this paper we have analyzed scaling properties of time series of stock market indices (SMIs) of developing economies of Western Balkans, and have compared the results we have obtained with the results from more developed economies. We have used three different techniques of data analysis to obtain and verify our fin…

New method identifies common cause in causal insufficiency, revealing complex phase transitions.

We investigate the two components of the total daily return (close-to-close), the overnight return (close-to-open) and the daytime return (open-to-close), as well as the corresponding volatilities of the 2215 NYSE stocks from 1988 to 2007. The tail distribution of the volatility, the long-term memory in the sequence, a…

We examine volatility of an Indian stock market in terms of aspects like participation, synchronization of stocks and quantification of volatility using the random matrix approach. Volatility pattern of the market is found using the BSE index for the three-year period 2000-2002. Random matrix analysis is carried out us…

Identifying behavior that is relatively invariant under different conditions is a challenging task in far-from-equilibrium complex systems. As an example of how the existence of a semi-invariant signature can be masked by the heterogeneity in the properties of the components comprising such systems, we consider the exc…

We study properties of the cross-sectional distribution of returns. A significant anti-correlation between dispersion and cross-sectional kurtosis is found such that dispersion is high but kurtosis is low in panic times, and the opposite in normal times. The co-movement of stock returns also increases in panic times. W…

The study examines order flow in financial markets using fractional Lévy stable motion.

Study shows XRP price correlates with transaction network metrics.

We analyze the sequence of time intervals between consecutive stock trades of thirty companies representing eight sectors of the U. S. economy over a period of four years. For all companies we find that: (i) the probability density function of intertrade times may be fit by a Weibull distribution; (ii) when appropriate…

Market impact is a key concept in the study of financial markets and several models have been proposed in the literature so far. The Transient Impact Model (TIM) posits that the price at high frequency time scales is a linear combination of the signs of the past executed market orders, weighted by a so-called propagato…

Study examines how crypto arbitrage affects XRP price and network correlation.

A new test validates ensemble models against the null hypothesis.

Modeling financial markets with a novel order flow model.

A novel circuit motif uses sister cells for inference with correlated priors.

Geometric stability measures neural network robustness, distinguishing from similarity metrics.

Measures three types of noise in LLM evaluations.

New method uncovers hidden groups in CDS market not tied to standard industry classifications.

The study investigates noise effects on parameter estimation for Ornstein-Uhlenbeck processes.

Noise in SGD affects overparameterized models, favoring sparse solutions.

L2R learns to denoise images without needing noise distribution knowledge.

Interpolating label noise makes models vulnerable to adversarial attacks.

NR-GANs learn clean images from noisy data.

We present a noise-injected version of the Expectation-Maximization (EM) algorithm: the Noisy Expectation Maximization (NEM) algorithm. The NEM algorithm uses noise to speed up the convergence of the EM algorithm. The NEM theorem shows that injected noise speeds up the average convergence of the EM algorithm to a local…

BeGIN benchmarks GNNs for instance-dependent label noise in graphs.

Enhanced consistency bounds derived for classification under a new noise condition.

Proposes a progressive label correction method for feature-dependent label noise.

New methods for handling time-varying label noise in time series classification.