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This article discusses the growing importance of artificial intelligence (AI) and machine learning (ML) in the field of Mathematical Finance. The authors highlight the establishment of an online seminar series on "Machine Learning in Finance" to facilitate the exchange of ideas during the COVID-19 pandemic. The special issue of the journal features nine papers that cover a range of topics, including reinforcement learning, stochastic differential equation models, financial networks, market-making algorithms, GANs, optimal control problems, and conditional distribution computation. The authors hope that this collection of papers will contribute to the intersection of mathematical finance and machine learning and inspire future research in this field. [Extracted from the article]

BACKGROUND: Drought stress (DS) is the main abiotic stress that maize suffers during its whole growth period, and maize is also sensitive to DS. It had been demonstrated that DS could improve the quality of normal maize starch. However, waxy maize, which has special properties, has not been explored in depth, which limits the breeding and cultivation of waxy maize varieties and the application of waxy maize starch. Therefore, in this study, we investigated the effects of DS on the biosynthesis, structure, and functionality of waxy maize starch. RESULTS: The results showed that DS decreased the expression level of SSIIb, SSIIIa, GBSSIIa, SBEI, SBEIIb, ISAII, and PUL, but increased the expression level of SSI and SBEIIa. DS did not change the average chain length of amylopectin, while increased the relative content of fa chains (RCfa) and decreased the RCfb1 and RCfb3. Furthermore, DS decreased the amylose content, amorphous lamellar distance da, semi‐crystalline repeat distance, and average particle size, whereas it increased the relative crystallinity, crystalline distance dc, the content of rapidly digested starch in the uncooked system and resistant starch content in both the uncooked and cooked system. Conclusions: For waxy maize, DS could raise the relative expression level of SSI and SBEIIa, thus increasing RCfa. The larger number of RCfa could create steric hindrance, which can lead to producing more resistant starch in waxy maize starch. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The rapid changes in the finance industry due to the increasing amount of data have revolutionized the techniques on data processing and data analysis and brought new theoretical and computational challenges. In contrast to classical stochastic control theory and other analytical approaches for solving financial decision‐making problems that heavily reply on model assumptions, new developments from reinforcement learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial environments. This survey paper aims to review the recent developments and use of RL approaches in finance. We give an introduction to Markov decision processes, which is the setting for many of the commonly used RL approaches. Various algorithms are then introduced with a focus on value‐ and policy‐based methods that do not require any model assumptions. Connections are made with neural networks to extend the framework to encompass deep RL algorithms. We then discuss in detail the application of these RL algorithms in a variety of decision‐making problems in finance, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo‐advising. Our survey concludes by pointing out a few possible future directions for research. [ABSTRACT FROM AUTHOR]

We analyze a class of stochastic differential games of singular control, motivated by the study of a dynamic model of interbank lending with benchmark rates. We describe Pareto optima for this game and show how they may be achieved through the intervention of a regulator, whose policy is a solution to a singular stochastic control problem. Pareto optima are characterized in terms of the solutions to a new class of Skorokhod problems with piecewise‐continuous free boundary. Pareto optimal policies are shown to correspond to the enforcement of endogenous bounds on interbank lending rates. Analytical comparison between Pareto optima and Nash equilibria provides insight into the impact of regulatory intervention on the stability of interbank rates. [ABSTRACT FROM AUTHOR]