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...

The purpose of this study is to build a system that automatically generates an optimal strategy (AGOS) which can solve the business problem of maximizing the acceptance rate without taking more risks in loan approval process. This paper formulates the finding an optimal approval strategy of a...

The stock market is a highly complex non-linear and chaotic system that is influenced by political, economic and psychological factors. Therefore, it seems very difficult to check this system. More scientific studies with an emphasis on the accurate identification of stock price behavior led to...

Inspired with the research, we present an enhanced Deep Neural Network (DNN) framework that combines Symbolic Genetic Algorithm (SGA) with Long-Short Term Memory (LSTM) in predicting the cross-sectional price returns using 245 fundamental indicators in China. The study addresses the challenges...

In this era of rapid digital revolution within the financial sector, our paper offers a cutting-edge approach to the intricacies of share buyback programs by employing Genetic Algorithms (GAs). Navigating the complexity of financial market operations, we leverage artificial intelligence to craft...