EHV-1 Pathogenesis: Current in vitro Models and Future Perspectives

Primary infection and pathogenesis of equine herpesvirus type 1 (EHV-1) require an intricate interaction of virus with the mucosal epithelium, mononuclear cells and the vascular endothelium. Studies on EHV-1 have been facilitated by the development of different in vitro models that recapitulate the in vivo tissue complexity. The available in vitro assays can be categorized into (i) models mimicking the epithelium-peripheral blood mononuclear cell (PBMC) interaction, which include ex vivo mucosal (nasal and vaginal) explants and equine respiratory epithelial cells (EREC) cultures; and (ii) PBMC-endothelium mimicking models, including flow chamber and contact assays. These in vitro models have proven their worth in attempts to recapitulate the in vivo architecture and complexity, produce data relevant to natural host infection, and reduce animal use due to in vivo experiments. Although horse models are still needed for certain experiments, e.g., EHV-1 myeloencephalopathy or vaccination studies, available in vitro models can be used to obtain highly valuable data on virus-host tissue interactions. Microfluidic based 3D culture system (e.g., horse-on-a-chip) could be a potential upgraded version of these in vitro models for future research

Share buybacks:A theoretical exploration of genetic algorithms and mathematical optionality

This article exclusively formulates and presents three innovative hypotheses related to the execution of share buybacks, employing Genetic Algorithms (GAs) and mathematical optimization techniques. Drawing on the foundational contributions of scholars such as Osterrieder, Seigne, Masters, and Guéant, we articulate hypotheses that aim to bring a fresh perspective to share buyback strategies. The first hypothesis examines the potential of GAs to mimic trading schedules, the second posits the optimization of buyback execution as a mathematical problem, and the third underlines the role of optionality in improving performance. These hypotheses do not only offer theoretical insights but also set the stage for empirical examination and practical application, contributing to broader financial innovation. The article does not contain new data or extensive reviews but focuses purely on presenting these original, untested hypotheses, sparking intrigue for future research and exploration. JEL Classification: G00.</p

Generative Adversarial Networks in finance: an overview

Modelling in finance is a challenging task: the data often has complex statistical properties and its inner workings are largely unknown. Deep learning algorithms are making progress in the field of data-driven modelling, but the lack of sufficient data to train these models is currently holding back several new applications. Generative Adversarial Networks (GANs) are a neural network architecture family that has achieved good results in image generation and is being successfully applied to generate time series and other types of financial data. The purpose of this study is to present an overview of how these GANs work, their capabilities and limitations in the current state of research with financial data, and present some practical applications in the industry. As a proof of concept, three known GAN architectures were tested on financial time series, and the generated data was evaluated on its statistical properties, yielding solid results. Finally, it was shown that GANs have made considerable progress in their finance applications and can be a solid additional tool for data scientists in this field

The Volatility of Long-Term Bond Returns:Persistent Interest Shocks and Time-Varying Risk Premiums

We develop an almost affine term-structure model with a closed-form solution for factor loadings in which the spot rate and the risk price are fractionally integrated processes with different integration orders. This model is used to explain two stylized facts. First, predictability of longterm excess bond returns requires sufficient volatility and persistence in the risk price. Second, the large volatility of long-term bond returns requires persistence in the spot rate. Decomposing long-term bond returns, we find that the expectations component from the level factor is more volatile than returns themselves and that the risk premium correlates negatively with level-factor innovations

Examining share repurchase executions:insights and synthesis from the existing literature

This literature review aims to address the critical knowledge gap in the field of share repurchase executions, a financial activity involving companies repurchasing trillions of dollars' worth of their own shares. The significance of understanding these mechanisms and their impact is underscored by their potential influence on the global economy. The paper employs a comprehensive analysis of existing literature, focusing on share repurchase mechanisms and motivations. It scrutinizes both open-market repurchases and Accelerated Share Repurchase contracts. Methodological approaches in current research, such as the use of partial differential equations and tree methods, are also evaluated. The review reveals that the execution phase of share repurchases remains largely unexplored. Unanswered questions persist about trading schedules, implications, costs, broker and corporate performance, and psychological effects of beating a buyback benchmark. Additionally, the review identifies significant limitations in current research methodologies. The paper advocates for the application and development of more advanced tools like machine learning and artificial intelligence to address these gaps. It also suggests potential areas for future research, including the role of technology in share repurchase execution, psychological factors influencing corporate buybacks, and the development of performance metrics for brokers and corporations. The review serves not only to highlight existing gaps in literature but also to suggest avenues for future research that could fundamentally enhance our understanding of share repurchase executions. JEL classification: G1, G12, G14, G02, G4.</p

Share buybacks: a theoretical exploration of genetic algorithms and mathematical optionality

This article exclusively formulates and presents three innovative hypotheses related to the execution of share buybacks, employing Genetic Algorithms (GAs) and mathematical optimization techniques. Drawing on the foundational contributions of scholars such as Osterrieder, Seigne, Masters, and Guéant, we articulate hypotheses that aim to bring a fresh perspective to share buyback strategies. The first hypothesis examines the potential of GAs to mimic trading schedules, the second posits the optimization of buyback execution as a mathematical problem, and the third underlines the role of optionality in improving performance. These hypotheses do not only offer theoretical insights but also set the stage for empirical examination and practical application, contributing to broader financial innovation. The article does not contain new data or extensive reviews but focuses purely on presenting these original, untested hypotheses, sparking intrigue for future research and exploration.JEL ClassificationG00