Time series anomaly detection plays a critical role in various applications, from finance to industrial monitoring. Effective models need to capture both the inherent characteristics of time series data and the unique patterns associated with anomalies. While traditional forecasting-based and...

The Efficiency-Adjusted Deferred Acceptance (EADA) mechanism addresses the Pareto inefficiency of the celebrated Deferred Acceptance (DA) algorithm by assigning every student to a weakly more preferred school. However, it remains uncertain which and how many students do not see an improvement in...

Extensive evidence shows exposure to ambient PM2.5 is associated with a wide range of poor health outcomes. But few studies examine genuinely long-run pollution exposures in nationally representative data. This study does so, exploiting longitudinally-linked Census data for Northern Ireland,...

This paper examines the economic impact of Ireland's partition, assessing market access losses using detailed geospatial data and multimodal transport network analysis. The study reveals that partition significantly reduced market access on both sides of the border, contributing to population...

The COVID-19 pandemic highlighted the vulnerabilities women encounter in labor markets worldwide. We investigate the potential of social protection measures in mitigating declines in women's labor market participation. Specifically, we look at the Indian context, where lockdowns spurred a...

I evaluate the impact of abortion policies in sub-Saharan Africa to understand possible consequences from a reduced international support for women's rights following the overturn of Roe v. Wade. I find that decriminalizing abortion reduces fertility through two complementary channels. For...

This study explores how market power and financial flexibility shape corporate investment policies among U.S. large and mature corporations, by estimating firm-specific, time-varying investment-to-added-value sensitivities. We find that firms with market power exhibit lower investment...

We test the out-of-sample trading performance of model-free reinforcement learning (RL) agents and compare them with the performance of equally-weighted portfolios and traditional mean-variance (MV) optimization benchmarks. By dividing European and U.S. indices constituents into factor datasets,...

With the advent of Big Data, managing large-scale portfolios of thousands of securities is one of the most challenging tasks in the asset management industry. This study uses an evolutionary multi objective technique to solve large-scale portfolio optimisation problems with both long-term listed...

We test the out-of-sample trading performance of model-free reinforcement learning (RL) agents and compare them with the performance of equally-weighted portfolios and traditional mean-variance (MV) optimization benchmarks. By dividing European and U.S. indices constituents into factor datasets,...