In the dynamic world of finance, accurately predicting stock market movements is a pressing challenge. Algorithmic trading rose from 50% in 2012 with the amount of $23,226,924 million to 85% in 2023 with the total amount of $46,199,811.4 million. The rapid rise of algorithmic trading is largely attributed to advancements in technology, which enable high-speed data analysis, precision in trade execution, and the ability to capitalize on market opportunities 24/7. In addition, stock markets are playing a central role in the economic sustainability of our society. These factors combined with advancements in Artificial Intelligence (AI) and Machine Learning (ML) have significantly fueled the growth of algorithmic trading. AI and ML technologies now play a crucial role in developing sophisticated trading algorithms by efficiently analyzing market structures and improving the prediction of key market factors. Increasingly, academics and financial institutions are integrating AI techniques, such as machine learning and neural networks, into financial markets, enhancing both accuracy and speed in trading. Research shows that only 1% of traders can profit net of fees due to lack of information, trading disciplines, and decision making based on emotions. To address this issue thoroughly, it is crucial to undertake comprehensive research to identify the various factors involved in stock prices and their complicated interrelationship. Nevertheless, the absence of consensus on factors, datasets, and prediction methodologies impedes progress in developing precise, stock price forecasts. This research seeks to significantly advance a long-term resolution for predicting stock prices by offering an exhaustive analysis of the factors, data sources, and prediction techniques associated with stock trading.

Assessing the performance of nursing homes during pandemics such as COVID-19 is critically important, particularly in light of an aging global population and the heightened need for long-term care. This urgency has led to a heightened global emphasis on optimizing nursing home resources. To address this objective, we developed a hybrid method that integrates Data Envelopment Analysis (DEA) with Machine Learning (ML) techniques to improve and predict the performance of these facilities. We applied this innovative approach to over 500 nursing homes across Pennsylvania. Given the complex regulatory and funding environments, with significant variations across regions, we performed a comparative efficiency analysis using DEA across three Pennsylvania regions: West, East, and Central. Once we identified the sources of inefficiency, we suggested actionable solutions to improve these facilities. We further utilized ML techniques to predict the efficiency of nursing homes. Our results showed that the number of citations, complaints, COVID-19 cases, and COVID-19–related deaths are critical factors affecting nursing home efficiency. Comprehensive approaches to address these factors include refining staff training programs, adopting regular feedback mechanisms, enhancing regulatory compliance, strengthening infection control practices, and managing resources effectively.

Metaverse is the next advancement of the Internet that could usher in unprecedented opportunities for sustainability, resilience, and growth in our society. However, it could also expose us to significant challenges in security, privacy, governance, and ethical concerns. Additionally, the Metaverse is complex and transformations to it could be prone to failures – an unfortunate reality considering that many ICT and digital transformation projects have failure rates as high as 70–90%. This paper proposes an innovative ‘e-factory’ model for planning, engineering, and management of sustainable Metaverse solutions to address the huge challenges of security, governance, compliance, and failure rates. A working prototype of such an e-factory is currently operational and is being used to support work with the United Nations as well as research and teaching assignments at academic institutions. Main results based on short case studies are shared and future research directions are discussed.

Keywords: SDGs, AI, Blockchain, digital transformation, strategic planning, SPACE, enterprise architecture, enterprise integration, information systems, systems engineering, engineering management

The metaverse has emerged as the next frontier of digital transformation. Immersive applications bridge the gap between physical and digital operations, paving the way for innovation and a greater degree of user engagement. However, organizations struggle with developing strategies to reach their optimal potential. Metaverse initiatives are still in their infancy and lack structured metrics and frameworks to measure and benchmark the maturity of an immersive application. There is a need for a framework that can evaluate immersive applications, identify areas for improvement, and align these with the strategic goals of the organization. This paper presents a structured framework, based on six key factors that influence immersion quality, and offers a four-stage maturity model for each of these factors. Using this framework, we evaluate a couple of immersive applications. This preliminary research aims to provide a foundation for future research, helping organizations maximize their presence in the metaverse and maintain a competitive edge in the years to come.

Keywords: Metaverse, Immersive systems analysis, Strategic planning, Digital Transformation, Computer Aided Planning

This policy brief goes beyond AI and suggests that the metaverse is a much more powerful technology bundle that could usher in unprecedented opportunities while also exposing us to unimaginable challenges. The brief starts with an overview of the Metaverse as a technology bundle that integrates AI, Blockchains, IoTs, Cloud, Web3.0, and AR/VR to possibly accelerate progress in key SDGs. In addition, other technology bundles that could further accelerate global B2B trade and other targeted SDGs are introduced. The paper also recommends challenges and appropriate policies and approaches for the benefit of multiple SDGs.