arXiv research

Average Oracle outperforms DCC+NLS in portfolio optimization.

Study uses copulas and DCC-GARCH for multivariate risk analysis of VaR and CVaR.

DCC separates marginal estimation from dependence modeling for improved classification accuracy.

Two methods are proposed to filter correlations in DCC-GARCH residuals for foreign exchange rates.

Simplifies NL models by approximating them as LPV systems and identifying NL subterms.

The NL score optimizes speaker recognition tasks.

Introduces NL bialgebras combining Lie and Nijenhuis structures.

We provide conditions for the existence and the unicity of strictly stationary solutions of the usual Dynamic Conditional Correlation GARCH models (DCC-GARCH). The proof is based on Tweedie's (1988) criteria, after having rewritten DCC-GARCH models as nonlinear Markov chains. Moreover, we study the existence of their f…

The study analyzes macroeconomic factors affecting copper futures volatility and long-term correlation with S&P 500.

The study uses DCC for financial market analysis, revealing hidden correlations.

This work improves density estimation by characterizing pdf complexity using NL-spectrum.

Softmax is found ineffective for NL block, leading to improved performance.

Study reveals dynamic linkage between Peanut and Soybean Oil futures markets.

Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keyword-based queries. We propose a reinforcement-learning-driven translation model framework able to 1) learn the translation from NL expressions to que…

The abstract describes a new manifold structure for NLS type equations.

The study shows how trade uncertainty affects stock-bond correlations over time.

Paper characterizes and represents pairwise causal background knowledge for improved causal inference.

A nonlinear wave alternative for the standard Black-Scholes option-pricing model is presented. The adaptive-wave model, representing 'controlled Brownian behavior' of financial markets, is formally defined by adaptive nonlinear Schrödinger (NLS) equations, defining the option-pricing wave function in terms of the stock…

Autonomous reinforcement learning agents, like children, do not have access to predefined goals and reward functions. They must discover potential goals, learn their own reward functions and engage in their own learning trajectory. Children, however, benefit from exposure to language, helping to organize and mediate th…

We propose a new cognitive framework for option price modelling, using quantum neural computation formalism. Briefly, when we apply a classical nonlinear neural-network learning to a linear quantum Schrödinger equation, as a result we get a nonlinear Schrödinger equation (NLS), performing as a quantum stochastic filter…

A new method simplifies noisy data filtering for CNNs.

Model predicts global financial market risks and asset allocation.

We consider the learning from noisy labels (NL) problem which emerges in many real-world applications. In addition to the widely-studied synthetic noise in the NL literature, we also consider the pseudo labels in semi-supervised learning (Semi-SL) as a special case of NL. For both types of noise, we argue that the gene…

Machine learning predicts ship performance changes over time.

Study analyzes how COVID-19 impacts crypto and stock market volatility.

This study shows how trade policy uncertainty affects stock-T bill correlations.

Physics-Informed Neural Networks (PINNs) benchmarked against clinical estimator and reveal parameter identifiability

An important feature of successful supervised machine learning applications is to be able to explain the predictions given by the regression or classification model being used. However, most state-of-the-art models that have good predictive power lead to predictions that are hard to interpret. Thus, several model-agnos…

Program synthesis from natural language (NL) is practical for humans and, once technically feasible, would significantly facilitate software development and revolutionize end-user programming. We present SAPS, an end-to-end neural network capable of mapping relatively complex, multi-sentence NL specifications to snippe…

This paper uses a geometric approach to understand how normalization layers affect neural network optimization.

The aim of this paper is to investigate the relations between Seifert manifolds and (1,1)-knots. In particular, we prove that every orientable Seifert manifold with invariants {Oo,0|-1;(p,q),...,(p,q),(l, l-1)} has a cyclically presented fundamental group and, moreover, it is the n-fold strongly-cyclic covering of the …

The study shows that several properties are not profinite invariants.

This paper improves image super-resolution by integrating cross-scale non-local attention.

ETF approval boosts Bitcoin's correlation with equities, stabilizes with gold, and maintains negative correlation with fiat currencies.

Develops a new framework for joint portfolio risk forecasting.

Dynamic classifier chains with XGBoost reduces multi-label classification costs and improves label dependency handling.

Recent advances in randomized incremental methods for minimizing $L$-smooth $μ$-strongly convex finite sums have culminated in tight complexity of $\tilde{O}((n+\sqrt{n L/μ})\log(1/ε))$ and $O(n+\sqrt{nL/ε})$, where $μ>0$ and $μ=0$, respectively, and $n$ denotes the number of individual functions. Unlike incremental me…

In the present work we analyse the dynamics of indirect connections between insurance companies that result from market price channels. In our analysis we assume that the stock quotations of insurance companies reflect market sentiments which constitute a very important systemic risk factor. Interlinkages between insur…

Non-linear shrinkage isn't optimal for portfolio optimization, especially when asset dependence is non-stationary.

Proves uniqueness of solutions for a nonlocal Liouville equation with finite Q-curvature.

Road extraction from very high resolution satellite (VHR) images is one of the most important topics in the field of remote sensing. In this paper, we propose an efficient Non-Local LinkNet with non-local blocks that can grasp relations between global features. This enables each spatial feature point to refer to all ot…

This letter explores the behavior of conditional correlations among main cryptocurrencies, stock and bond indices, and gold, using a generalized DCC class model. From a portfolio management point of view, asset correlation is a key metric in order to construct efficient portfolios. We find that: (i) correlations among …

Recently, a novel adaptive wave model for financial option pricing has been proposed in the form of adaptive nonlinear Schrödinger (NLS) equation [Ivancevic a], as a high-complexity alternative to the linear Black-Scholes-Merton model [Black-Scholes-Merton]. Its quantum-mechanical basis has been elaborated in [Ivancevi…

New algorithms solve nonconvex-concave minimax problems without parameter knowledge.

Improved LDA using a nonlinear covariance estimator for better performance.

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven…

Adaptive wave model for financial option pricing is proposed, as a high-complexity alternative to the standard Black--Scholes model. The new option-pricing model, representing a controlled Brownian motion, includes two wave-type approaches: nonlinear and quantum, both based on (adaptive form of) the Schrödinger equatio…

Paper compares econometric models with machine learning for energy forecasting.