This paper computes data-driven correlation networks based on the stock returns of international banks and conducts a comprehensive analysis of their topological properties. We first apply spatial-dependence methods to filter the effects of strong common factors and a thresholding procedure to...

This paper computes data-driven correlation networks based on the stock returns of international banks and conducts a comprehensive analysis of their topological properties. We first apply spatial-dependence methods to filter the effects of strong common factors and a thresholding procedure to...

We employ machine learning to develop measures of residential real estate uniqueness from written advertisements. These measures are exogenous from sales prices. We distinguish the effect of market uniqueness (compared to houses for sale at the same time) from the effect of universal uniqueness...

We examine in this paper a critical question in finance: the use of large nonlinear over-parametrized models or simpler models to forecast financial time series and the balance between underfitting and overfitting, the bias-variance trade-off, and the absolute performance in the test set. The...

The volume of papers investigating the relationship between firm-level news and stock prices has shown a marked increase. While the vast majority of these studies use sentiment measures or extensive coding rules to shed light on the implications of qualitative information for financial data, not...

Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or assuming that all agents are identical. While...

Purpose - For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch...

The identification of the business cycle turn points is a challenging task. In this context, the evolution of new data typologies as internet searches data strongly impacted the construction of approaches practical to forecast macroeconomic variables. This work emphasizes the significance of...

Overconfidence behavior, one form of positive illusion, has drawn considerable attention throughout history because it is viewed as the main reason for many crises. Investors' overconfidence, which can be observed as overtrading following positive returns, may lead to inefficiencies in stock...