arXiv research

This paper studies a curious phenomenon in learning energy-based model (EBM) using MCMC. In each learning iteration, we generate synthesized examples by running a non-convergent, non-mixing, and non-persistent short-run MCMC toward the current model, always starting from the same initial distribution such as uniform no…

This study examines the interaction between CDS and stock indices, revealing significant short and long-term impacts.

Study uses artificial counterfactuals to show lockdowns reduced US case and death counts.

A new method learns latent space normalizing flow for approximate inference in generator models.

Machine learning improves measuring climate adaptation impacts.

This paper studies the fundamental problem of learning deep generative models that consist of multiple layers of latent variables organized in top-down architectures. Such models have high expressivity and allow for learning hierarchical representations. Learning such a generative model requires inferring the latent va…

What predicts the evolution over time of subjective well-being? We correlate the trends of subjective well-being with the trends of social capital and/or GDP. We find that in the long and medium run social capital largely predicts the trends of subjective wellbeing in our sample of countries. In the short-term this rel…

Oil markets profoundly influence world economies through determination of prices of energy and transports. Using novel methodology devised in frequency domain, we study the information transmission mechanisms in oil-based commodity markets. Taking crude oil as a supply-side benchmark and heating oil and gasoline as dem…

Classical time series models forecast Bitcoin prices and volatility accurately.

Paper proposes CoopFlow, a two-flow generator for energy-based models.

This study investigates the effects of Markov chain Monte Carlo (MCMC) sampling in unsupervised Maximum Likelihood (ML) learning. Our attention is restricted to the family of unnormalized probability densities for which the negative log density (or energy function) is a ConvNet. We find that many of the techniques used…

We test for the long-run relationship between stock prices, inflation and its uncertainty for different U.S. sector stock indexes, over the period 2002M7 to 2015M10. For this purpose we use a cointegration analysis with one structural break to capture the crisis effect, and we assess the inflation uncertainty based on …

In this paper we introduce a numerical method for nonlinear parabolic PDEs that combines operator splitting with deep learning. It divides the PDE approximation problem into a sequence of separate learning problems. Since the computational graph for each of the subproblems is comparatively small, the approach can handl…

Investor expectations shifted pessimistically during the 2020 stock market crash and recovery.

This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-speci…

This paper examines the short-run relationships between oil prices and GCC stock markets. Since GCC countries are major world energy market players, their stock markets may be susceptible to oil price shocks. To account for the fact that stock markets may respond nonlinearly to oil price shocks, we have examined both l…

New DMEM models forecast volatility combining low- and high-frequency data.

Study shows insurance industry in North Macedonia declined 10% due to COVID-19.

Survival strategies in a market with self-determined prices are closely tied to log-optimal investment.

Study finds short-term wage increases due to COVID-19, contrary to expectations.

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial beha…

This paper investigates the relationship between private and public investment in R&D, while taking into account the effect of several instruments policies such as subsidies and taxes. We design a new look of knowledge spillovers and R&D cooperation to explain the contribution of public and private R&D on growth. We pr…

We propose a continuous-time stock-flow consistent model for inventory dynamics in an economy with firms, banks, and households. On the supply side, firms decide on production based on adaptive expectations for sales demand and a desired level of inventories. On the demand side, investment is determined as a function o…

A new method to learn EBM in latent space for better data modeling.

This work improves VAEs using MCMC methods for better variational bounds.

We consider the problem of sparse phase retrieval from Fourier transform magnitudes to recover the $k$-sparse signal vector and its support $\mathcal{T}$. We exploit extended support estimate $\mathcal{E}$ with size larger than $k$ satisfying $\mathcal{E} \supseteq \mathcal{T}$ and obtained by a trained deep neural net…

Extracting actionable intelligence from distributed, heterogeneous, correlated and high-dimensional data sources requires run-time processing and learning both locally and globally. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on…

We propose a novel approach to sentiment data filtering for a portfolio of assets. In our framework, a dynamic factor model drives the evolution of the observed sentiment and allows to identify two distinct components: a long-term component, modeled as a random walk, and a short-term component driven by a stationary VA…

This paper tackles sampling issues in latent space EBMs by introducing diffusion-based amortization.

Improved likelihood-free inference using preconditioned neural posterior estimation.

The study identifies key factors affecting cryptocurrency prices, including market beta, trading volume, and volatility.

Due to the liberalization of markets, the change in the energy mix and the surrounding energy laws, electricity research is a dynamically altering field with steadily changing challenges. One challenge especially for investment decisions is to provide reliable short to mid-term forecasts despite high variation in the t…

We construct a price impact model between stocks in a correlated market. For the price change of a given stock induced by the short-run liquidity of this stock itself and of the information about other stocks, we introduce a self- and a cross-impact function of the time lag. We model the average cross-response function…

This paper examines how investors mislearn factor risk premia under structural breaks in a misspecified Bayesian framework.

Adaptive rerouting reshapes impacts of maritime chokepoint disruptions

New algorithm improves insurance company's asset allocation decisions.

For environmental problems such as global warming future costs must be balanced against present costs. This is traditionally done using an exponential function with a constant discount rate, which reduces the present value of future costs. The result is highly sensitive to the choice of discount rate and has generated …

This paper proposes a more efficient training method for energy-based models.

Study finds no significant short-term impact on liquidity supply after protocol fees were reduced.

Oil prices affect Russian banks' stability, with negative impacts from decreases.

News sentiment in U.S. economic newspapers has become more persistent over 45 years.

Bayesian ARMA model with directional shifts captures structural breaks in compositional time series.

The study optimizes free trial lengths to boost subscriptions and consumer loyalty.

TRAiL is a linear bandit algorithm that ensures optimal regret and guarantees inference quality.