Portfolio diversification (PD) is attracting increasing attention, as it is becoming more difficult
to optimize portfolios due to growing financial markets integration. To highlight the evolution …

This article introduces a novel hybrid regime identification-forecasting framework designed
to enhance multi-asset portfolio construction by integrating asset-specific regime forecasts …

Even though large language models such as ChatGPT are not specifically trained for
analyzing asset returns or recommending stocks, it may still provide additional insight into …

We study a dynamic mean‐variance portfolio optimization problem under the reinforcement
learning framework, where an entropy regularizer is introduced to induce exploration. Due to …

This study investigates the benefits of international diversification in the stock markets of the
28 European countries (the EU and the UK) over two five-year periods: a stable period from …

In the era of rapid globalization and digitalization, accurate identification of similar stocks
has become increasingly challenging due to the non-stationary nature of financial markets …

We take inspiration from statistical physics to develop a novel conceptual framework for the
analysis of financial markets. We model the order book dynamics as a motion of particles …

We study continuous-time mean--variance portfolio selection in markets where stock prices
are diffusion processes driven by observable factors that are also diffusion processes yet the …

This paper introduces a multistage stochastic mixed-integer programming model designed
for a goal-based investing (GBI) problem, incorporating the option of goal postponement …

Portfolio optimization is the basic quantitative approach for finding optimal portfolio weights.
It has become increasingly important as portfolio construction involves more and more data …