arXiv research

Quantum walk model captures asymmetry and bimodality in long-term financial returns.

Firm growth process in the developing economies is known to produce divergence in their growth path giving rise to bimodality in the size distribution. Similar bimodality has been observed in wealth distribution as well. Here, we introduce a modified kinetic exchange model which can reproduce such features. In particul…

Proposes new loss functions for better handling bimodal predictive uncertainty.

We find stationary distributions in a financial model with trends and mean-reversion.

A new method generates synthetic data with realistic marginal distributions.

A new model generates samples with a succinct common representation using Wyner's common information.

Associating image regions with text queries has been recently explored as a new way to bridge visual and linguistic representations. A few pioneering approaches have been proposed based on recurrent neural language models trained generatively (e.g., generating captions), but achieving somewhat limited localization accu…

Filtering data with a pre-trained model improves multimodal contrastive learning performance.

Improved speech emotion recognition using pre-trained language models.

This work improved clustering methods by analyzing various datasets and dendrograms.

This study examines how earnings announcements affect option volatility and pricing.

Bayesian model improves data-efficiency in reinforcement learning.

Deep learning speeds up pressure prediction in carbon storage reservoirs.

New methods improve prediction regions for high-dimensional data.

Improved financial market calibration reveals large excess volatility.

WES improves neural network regression by stretching distribution error.

Bayesian neural networks with latent variables are scalable and flexible probabilistic models: They account for uncertainty in the estimation of the network weights and, by making use of latent variables, can capture complex noise patterns in the data. We show how to extract and decompose uncertainty into epistemic and…

A simple spin system is constructed to simulate dynamics of asset prices and studied numerically. The outcome for the distribution of prices is shown to depend both on the dimension of the system and the introduction of price into the link measure. For dimensions below 2, the associated risk is high and the price distr…

Deep networks achieve linear separability through progressive folding of data in higher dimensions.

Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representations that can process and relate information from multiple modalities. In this paper, we propose two methods for unsupervised learning of j…

The American economy can be thought of as a highly connected random network in terms of both its technological and informational connections. The cumulative size of economic recessions, the fall in output from peak to trough, is analysed for the US economy 1900-2002. A least squares fit of an exponential relationship b…

Trend and Value are pervasive anomalies, common to all financial markets. We address the problem of their co-existence and interaction within the framework of Heterogeneous Agent Based Models (HABM). More specifically, we extend the Chiarella (1992) model by adding noise traders and a non-linear demand of fundamentalis…

New model corrects bias in crowdsourced ratings for diverse items.

The two phase behavior in financial markets actually means the bifurcation phenomenon, which represents the change of the conditional probability from an unimodal to a bimodal distribution. In this paper, the bifurcation phenomenon in Hang-Seng index is carefully investigated. It is observed that the bifurcation phenom…

This work learns shared word embeddings for acoustic and phonetic sequences.

Spatially aware ESN detects anomalies in chaotic time series.

cKAM improves adaptive sampling by incorporating a cyclical stepsize scheme.

Detects corruption in agentic models during execution.

Simple baselines improve multimodal utterance learning.

New framework embeds physics in coarse-grained models without big data.

Much research has been conducted arguing that tipping points at which complex systems experience phase transitions are difficult to identify. To test the existence of tipping points in financial markets, based on the alternating offer strategic model we propose a network of bargaining agents who mutually either coopera…

AMF-VI uses adaptive mixtures of flows for robust VI across diverse distributions.

A new model that combines economic growth rate fluctuations at the microscopic and macroscopic level is presented. At the microscopic level, firms are growing at different rates while also being exposed to idiosyncratic shocks at the firm and sector level. We describe such fluctuations as independent Lévy-stable fluctu…

Efficiently tests two distributions with few label queries.

Improved simulation of phase transitions using hierarchical autoregressive networks.

Proposes a flexible framework for implied volatility surfaces with random parameters.

Study shows bifurcating price dynamics in ASME with traders.

A new mathematical framework for multimodal learning.

Proposes a new normalization method for deep neural networks in financial forecasting.

Bayesian model enhances phenotype discovery in asthma EHRs.

A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN-PCA method is inspired by recent developments in computer vision using deep learning. CNN-PCA can be viewed as a generalization of an ex…

Cross-sectional signatures of market panic were recently discussed on daily time scales in [1], extended here to a study of cross-sectional properties of stocks on intra-day time scales. We confirm specific intra-day patterns of dispersion and kurtosis, and find that the correlation across stocks increases in times of …

A new coordinate system for SPD matrices simplifies computations and generative modeling.

How big is the risk that a few initial failures of nodes in a network amplify to large cascades that span a substantial share of all nodes? Predicting the final cascade size is critical to ensure the functioning of a system as a whole. Yet, this task is hampered by uncertain or changing parameters and missing informati…

The market events of 2007-2009 have reinvigorated the search for realistic return models that capture greater likelihoods of extreme movements. In this paper we model the medium-term log-return dynamics in a market with both fundamental and technical traders. This is based on a Poisson trade arrival model with variable…

Suppose one buys two very similar stocks and is curious about how much, after some time T, one of them will contribute to the overall asset, expecting, of course, that it should be around 1/2 of the sum. Here we examine this question within the classical Black and Scholes (BS) model, focusing on the evolution of the pr…

Modified lognormal distribution with flexible tails for skewed data.

The FastICA algorithm is one of the most popular iterative algorithms in the domain of linear independent component analysis. Despite its success, it is observed that FastICA occasionally yields outcomes that do not correspond to any true solutions (known as demixing vectors) of the ICA problem. These outcomes are comm…