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

Unlabelled: 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.

Navigating the Environmental, Social, and Governance (ESG) landscape: constructing a robust and reliable scoring engine - insights into Data Source Selection, Indicator Determination, Weighting and Aggregation Techniques, and Validation Processes for Comprehensive ESG Scoring Systems.

This white paper explores the construction of a reliable Environmental, Social, and Governance (ESG) scoring engine, with a focus on the importance of data sources and quality, selection of ESG indicators, weighting and aggregation methodologies, and the necessary validation and benchmarking procedures. The current challenges in ESG scoring and the importance of a robust ESG scoring system are addressed, citing its increasing relevance to stakeholders. Furthermore, different data types, namely self-reported data, third-party data, and alternative data, are critically evaluated for their respective merits and limitations.

Digital Finance: Reaching New Frontiers.

Digital Finance must become the center of academic research in finance if the European financial industry is to remain competitive in the future. We argue that the new interdisciplinary field of Digital Finance should be prioritized based on the strategic priorities of the European Union, the needs of the finance industry, and the academic research gaps. Digital Finance as an interdisciplinary field will contribute to the strategic priorities of the European Union, such as financing for growth and jobs, financial stability and supervision, financial education, financing for small and medium-sized enterprises, and combating exclusion and inequality in access to credit.

Audience-Dependent Explanations for AI-Based Risk Management Tools: A Survey.

Artificial Intelligence (AI) is one of the most sought-after innovations in the financial industry. However, with its growing popularity, there also is the call for AI-based models to be understandable and transparent. However, understandably explaining the inner mechanism of the algorithms and their interpretation is entirely audience-dependent.

The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets.

The central research question to answer in this study is whether the AI methodology of Self-Play can be applied to financial markets. In typical use-cases of Self-Play, two AI agents play against each other in a particular game, e.g.

Neural networks and arbitrage in the VIX: A deep learning approach for the VIX.

The Chicago Board Options Exchange Volatility Index (VIX) is considered by many market participants as a common measure of market risk and investors' sentiment, representing the market's expectation of the 30-day-ahead looking implied volatility obtained from real-time prices of options on the S&P 500 index. While smaller deviations between implied and realized volatility are a well-known stylized fact of financial markets, large, time-varying differences are also frequently observed throughout the day. Furthermore, substantial deviations between the VIX and its futures might lead to arbitrage opportunities on the VIX market.