Sustainable Development and Eradication of Poverty in Nigeria: Institutional Investors as a Primary Tool

In response to the growing challenge of inequality around the world, the United Nations in 2015 adopted the 2030 agenda for sustainable development, a set of seventeen (17) sustainable development goals (SDGs). Chief amongst them is the goal to reduce inequalities among and within countries by reducing income inequalities: SDG1. To enable effective implementation of the goal, the United Nations calls for a global partnership, involving the public and private sectors. Placing its core emphasis on the Nigerian private sector, this article examines, from a doctrinal and analytical perspective, the strategic contributions institutional investors in Nigeria can make in combating inequality. The article identifies ‘Responsible Investment’, a growing international phenomenon, as one of the veritableinstruments that can be engaged in bridging the inequality gap prevalent in the country

Time Sensitive Interviews with Suspects, Witnesses, and Informants: Challenges and Opportunities

Obtaining information quickly is crucial in many law enforcement, security, and military operations, particularly in time-sensitive scenarios such as terrorist attacks, hostage situations, or dynamic operational contexts. This article examines the challenges inherent in the task of eliciting time-sensitive information, focusing on the difficulties faced by both interviewers and interviewees in high-pressure or time-limited situations. We review current legislative provisions for urgent interviews and identify a significant gap in empirical research on effective methodologies for information gathering in such scenarios. Here we argue for the adoption of rapport-based approaches, supported by empirical evidence, to improve the efficacy of time-sensitive elicitation. In particular, we examine the adaptability of the ORBIT model of communication and the recently developed rapport-based Time-Critical Questioning (TCQ) protocol for this context. Research to date indicates that rapport-based strategies, that emphasize clear and adaptive communication, foster focus and cooperation and increase the yield of actionable intelligence in time-sensitive situations. Finally, we outline a roadmap for future research and practice, encouraging collaborative efforts to develop evidence-based practice and training that address the unique challenges of time-sensitive interviews and enhance operational outcomes

Predictive and Prescriptive Analytics for Strategic Financial Decisions: Seasoned Equity Offerings, Stock Splits, Pandemic effects, and Investment Decisions

Scholars in the intersection of operational research, strategy, and finance have extensively examined the effects of event studies in finance, especially that of a strategic nature, such as that of planned as well as unexpected corporate events and respective abnormal returns on the stock market. Nonetheless, there is still a research gap on the extent of the forecastability of this abnormal behaviour, especially when predictions may provide crucial information to both investors and issuers, and therefore drive effectively investment decisions. In this study we forecast the value effect of SEOs and Stock Splits, across developed and emerging economies. The selection of these nations, namely the United States (benchmark), Brazil, and India, was based on their Gross Domestic Product (GDP) and the impact of their stock markets on economic growth. Data consist of 2,043 strategic financial decisions with historical information from the New York Stock Exchange (NYSE), Bombay Stock Exchange (BSE), National Stock Exchange of India (NSE) and Brazil Stock Exchange (B3) from 2010 to 2020. Linear regression (benchmark), random forests, gradient boosting machines, support vector regression and neural networks methods are empirically evaluated, with non-linear models performing better than the benchmark. A trading simulation is also incorporated to complement model outcomes and determine whether these predictions could be capitalised through effective decision making in the investment spectrum. Finally, the effects of the COVID-19 pandemic were also analysed for SEOs in the NYSE, and significant differences were discovered in March and April 2020. Results indicate how negative abnormal returns were exacerbated by COVID-19’s systemic impact during March and rebounded in April

Volatility Spillovers Between Financial Markets and Cryptocurrencies

This paper analyses the relationships between the volatilities of five major stock markets (S&P 500, CAC 40, DAX, FTSE 100, and Nikkei 225) and five cryptocurrencies (Bitcoin, Dash, Ethereum, Monero, and Ripple), (WTI), and gold. The GARCH model, which describes the volatility of financial assets and cryptocurrencies, was used. A significant and higher volatility spillover was observed across these market pairs. The conditional correlation between Bitcoin and other cryptocurrencies is time-varying, but the conditional correlations between crypto-currencies and gold and all assets are negative during the period (2017-2018) and positive. At the beginning of the COVID-19 crisis, the conditional correlation between cryptocurrencies, stock indices, and WTI increased, which confirms the impact of COVID-19 related contagion between them.Our findings show that cryptocurencies and gold are considered hedges for the international investors during the period 2017-2018

Predicting Motor-Vehicle Deaths Using Machine Learning: Proposed 8E-Model

This study conducts a comprehensive time series analysis of motor-vehicle fatalities in the USA spanning from 2019 to 2021, revealing a troubling upward trajectory. Factors such as over-speeding, impaired driving, reduced road traffic enforcement during the pandemic, and instances of driving under the influence have significantly contributed to the surge in fatal crashes during this period. Utilizing the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, this research forecasts the trajectory of motor vehicle deaths in the USA. The forecast suggests a continuation of the upward trend, emphasizing the urgency of addressing the escalating fatalities. In response to the burgeoning global trend of increasing accidents and fatalities, this study advocates for the implementation of broader preventive measures worldwide. Proposed strategies encompass the crucial role of policy implementation and road safety measures in curbing the rising toll of road accidents, particularly in the USA.Furthermore, this study extends the existing 7E model (Education, Engineering, Enforcement, Exposure, Examination of Competence and Fitness, Emergency Response, and Evaluation) by introducing the eighth ‘E’—Empathy—in the context of road safety. This augmentation creates the 8E model, offering a more encompassing framework adaptable on a global scale. The inclusion of empathy underscores the significance of considering human emotions, behaviours, and societal impact in crafting effective road safety initiatives

ARIMA and Exponential Smoothing Models in Forecasting the Macau Property Price Index During COVID-19

The real estate market plays an important role in the economies of many countries, and the future trend of the market has long been a topic of concern to both academics and practitioners. This paper attempts to study the effectiveness and superiority of two univariate time series models, Autoregressive Integrated Moving Average  (ARIMA) model and exponential smoothing, to forecast the Macau residential property price index during the COVID-19 pandemic. Based on 1- and 2-year holdout samples during the shock of COVID-19 in Macau, the results show that the out-of-sample forecasting performances of both models are better than the baseline model of classical decomposition. There is also evidence that the ARIMA models outperform the Winters three-parameter exponential smoothing models in the two out-of-sample periods. Therefore, in the context of unprecedented events such as COVID-19, the ARIMA method is more effective than the Winters exponential smoothing method in making rapid and accurate adjustments when the Macau residential property price index is significantly affected. Our findings provide important implications for relevant government departments, home buyers and sellers, and property market participants in their selections of reliable models to forecast future property market behavior

Predictive Dynamics in Cryptocurrency Trading: Unraveling Behavioral and Psychological Influences

The rapid expansion of cryptocurrency trading has become a defining feature of contemporary financial markets, attracting a constantly growing group of participants, now surpassing 106 million worldwide. This research focuses on the psychological and behavioral foundations of trading behaviors, investigating how individual psychological states and lifestyle choices impact cryptocurrency trading activities. Using Ordinary Least Squares (OLS) regression, we examine the influence of various factors such as Loneliness, Negative Emotions, Fear of Missing Out (FOMO), Socialization, Healthy Lifestyle Habits, Entertainment Spending, and Sense of Achievement on the frequency of cryptocurrency trades. Our study also includes an analysis of gender differences through Levene’s T-test, thereby increasing the depth of our predictive model. The results of this study aim to fill a gap in existing literature by quantifying the degree to which individual psychological profiles and behaviors can predict trading activities, thereby providing detailed insights into the emotional and cognitive dimensions of the digital trading world. This research not only contributes to the field of behavioral finance but also provides a foundation for developing strategic interventions tailored to various trader segments, ultimately fostering a deeper understanding of the complex dynamics that characterize the crypto market’s volatile landscape

CASE COMMENT – What is Dishonestly For? Mistaking the Normativity of an Honesty Claim

If the mental element of a crime required no more than objective fault, then objective mistakes as to the normative standard of honesty, impropriety etc would inculpate. There is a tension here between the doctrine that “ignorance of the criminal law is no excuse” and the constitutional right not to be subject to ex post facto law making. Because evaluations by fact finders about the normative wrongness of conduct (ie the medical operation was normatively well below the average norms of medical care or the conduct was dishonest against the norms of honesty) only become apparent after the fact the defendant is not able to search the published offences to find the actus reus of such an offence, which they must be able to do if the doctrine that ignorance of the criminal law is no excuse is to apply to them. In the case of mistakes about normative standards, when the mental element requires D to have a subjective state of mind in respect to the normative standard, the constitutional right against ex post facto law making takes precedence over the rule that ignorance of the criminal law is no excuse. It is because crimes of negligence such as gross negligence manslaughter do not require D to have subjective fault in relation to the norms that D has failed live up to, that D’s ignorance of those norms is considered to be ignorance of the criminal law per se. Under R v Ghosh what is honest is an objective normative question, but D can make a subjective mistake about the norms of honestly since those norms are not set out in law as is required by the doctrine that “ignorance of the criminal law is no excuse”. The latter doctrine does not excuse ignorance, but that is on the condition that the “law” was “discoverable” (i.e. existed in case law or statute and was online or otherwise published) had D attempted to know it in advance of doing the proscribed act

New Global Governance and Overarching Frameworks: Reimagining the Rule of Law, AI and ESG for the Betterment of the World

The advancement of digital technologies, particularly in Artificial Intelligence (AI), the geopolitical fragmentation of Environment, Social, and Governance (ESG) with a lack of mandatory international governance, calls for increased global cooperation and integration in overarching central conceptual and of action frameworks. As humanity faces critical environmental challenges—such as climate extremes and biodiversity loss and wars—the disparities between rich and poor become more evident and the planet displays its illness. Addressing these challenges requires collective social change, underpinned by shared operating systems, open-source models, and quality data. Humanity’s fragmented relationshipwith nature highlights the need for a robust global governance system. As AI and ESG matters transcend national borders, there is a growing need for internationalframeworks, such as the involvement of the International Court of Justice (ICJ) to resolve disputes and the rule of law, both at national and international levels to be interconnected, ensuring that legal frameworks complement each other. A shift toward “sust-AI-nability,” grounded in human reason, science- and fact-based, with values- and risk-based must coordinate cooperation, essential for managing global challenges, foster meaningful transformation, and advance the United Nations’ Sustainable Development Goals (SDGs)