We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically focusing on price processes with semi-Markov and Hawkes Jump-Diffusion dynamics. We begin by discussing the basics of RL and the deep RL framework used; we deployed the...

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin's Q ratio, market capitalization, return on assets, leverage, and...

Predicting and trading assets in the global financial market represents a complex challenge driven by the dynamic and volatile nature of the sector. This study proposes a day trading strategy that optimizes asset purchase and sale parameters using differential evolution. To this end, an...

We construct a model of bubbles where an asset can be used as collateral primarily due to higher-order uncertainty: while both a lender and a borrower know that the intrinsic value of the asset is low, they may still believe that a “greater fool” exists who will purchase it at a much higher...

This paper examines the dynamic relationships between speculative activities, commodity returns, and macroeconomic conditions across five sectors compassing 29 commodities. Using weekly data spanning from January 2000 to July 2023, we construct comprehensive measures of commodity market...