Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade and have transformative impact on numerous industry sectors …

Recently, the popularity of using the expressive power of quantum computing to solve
known, challenging problems has increased remarkably. This study aims to develop a clear …

We construct a robust stochastic discount factor (SDF) summarizing the joint explanatory
power of a large number of cross-sectional stock return predictors. Our method achieves …

Social responsibility investment (SRI) has attracted worldwide attention for its potential in
promoting investment sustainability and stability. We developed a three-step framework by …

We develop results for the use of Lasso and post‐Lasso methods to form first‐stage
predictions and estimate optimal instruments in linear instrumental variables (IV) models …

Academic research relies extensively on macroeconomic variables to forecast the US equity
risk premium, with relatively little attention paid to the technical indicators widely employed …

portfolio selection requires an estimator of the covariance matrix of returns. To address this
problem, we promote a nonlinear shrinkage estimator that is more flexible than previous …

We analytically compare two dominant methodologies for the construction of an ESG factor:
the time-series (ratings used to sort stocks) and cross-sectional (ratings used to weight …

The portfolio optimization model has limited impact in practice because of estimation issues
when applied to real data. To address this, we adapt two machine learning methods …

This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make
rolling one‐minute‐ahead return forecasts using the entire cross‐section of lagged returns …