The utilisation of Chat Generative Pre-Trained Transformer (ChatGPT) and generative artificial intelligence (GenAI) technologies has started to demonstrate its impact across several domains. The swift shift and widespread implementation of efficient artificial intelligence (AI) present distinct...

Traditional insurance is being threatened as the global business environment becomes more competitive. This paper investigates the influence of competitive intelligence on organizational sustainability of the Nigerian insurance industry. The study employed a cross-sectional survey design in data...

Artificial intelligence, the latest chapter of the technological revolution, has a tremendous potential to change every area of our lives. This article has focused on a specific form of artificial intelligence, namely generative intelligence, which facilitates the generation of content in all...

Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price...

We design an adaptive framework for the detection of illegal trading behavior. Its keycomponent is an extension of a pattern recognition tool, originating from the field of signalprocessing and adapted to modern electronic systems of securities trading. The new methodcombines the flexibility of...

In machine learning and data science literature, clustering is the task of dividing the observations (data points) into several categories in such a way that data points falling into one group are being dissimilar than the data points falling to the other groups such that the variation within a...

In ensemble machine learning we combine decisions of experts to derive at a decision that is better than the individual ones. The process of combining these decisions can be as simple as majority voting or simple averaging or it can be more complicated and involve multiple steps. In this paper...

In this presentation, we analyze the explanatory (in-sample) and predictive (out-of-sample) importance of some of the best known market microstructural features. Our conclusions are drawn over the entire universe of the 87 most liquid futures worldwide, covering all asset classes, going back...

We demonstrate how a machine learning algorithm can be applied to predict and explain modern market microstructure phenomena. We investigate the efficacy of various microstructure measures and show that they continue to provide insights into price dynamics in current complex markets. Some...

Training a Multi-Layer Perceptron (MLP) to achieve a minimum level of MSE is akin to doing Non-Linear Regression (NLR). Therefore, we use available econometric theory and the corresponding tools in R. Only if certain assumptions about the error term in the Data Generating Process are in place,...