An Overview on Business, Management and Economics Research Vol. 5

Financial measurements have traditionally served as the cornerstone for measuring company performance.  This case study examines the debt status of a company as it strives to expand its manufacturing operations.  The company requires financial money to expand, but its business venture and financial status are regarded risky, therefore it must contact a commercial bank to obtain funds.   A bank lending officer must review the company's financial statements to determine whether a loan is feasible. The lending officer must evaluate significant portions of the firm's financial accounts, do ratio analysis, and then decide whether to extend the loan.

A simple linear model describing a mechanism for the assimilation of new information on traded assets’ prices is introduced. The model is useful in its own right, as it provides a simplified, yet credible, quantitative description of the reality. Further, the model is used as a tool for a theoretical study of market efficiency testing.  This is obtained by modelling certain market conditions under which new information is released and assimilated in asset prices on the one hand, and, on the other hand, by recording what established econometric testing approaches conclude, about the hypothesis of market efficiency. Amongst others it is argued that contrary to the general belief, theoretically a random walk in asset prices, under certain conditions, could be associated with inefficient markets. Furthermore, an enhancement of the battery of statistical tests for market efficiency is proposed by the potential application of specific forms of the suggested linear dynamic model and the possible advantages over the existing techniques are explained. Last but not least, although the conventional terminology of market efficiency classification is used, the proposed model for the assimilation of new information in asset prices is more conducive to a categorical rather than the existing ordinal classification of market efficiency.

Ghana's mining industry's compensation and resettlement laws prohibit speculative development as a risk protection for mining companies. Despite the law, mining communities desire to profit from their speculation. This chapter examines the effect of speculative mining activities and issues of compensation on both the speculators and the mining companies within some mining communities in Ghana. As a qualitative-based research, data was collected purposively using questionnaires, observation, and focus group discussions from 93 people living within communities associated with speculative mining activities. Univariate statistics were used to analyse the data for further descriptive discussion of the key findings. The study revealed that the community folks learned about potential speculative areas through preliminary reconnaissance and preparatory activities of the companies in these areas. The research established that whereas the mining companies were negatively impacted by speculative activities because of increased budgets, community agitations, delays in the commencement of mining activities and threats to resettlement officers' lives during fieldwork; the community folks were also affected by delays in resettlement compensation processing and sometimes losses due to denial in payment. Again, at a 5% significance level, it was established that speculation threatens the sustainability of the mining business because of the plurality of speculative economic activities in the region of operation of mining companies. Even though it is envisaged that speculative mining activities will continue, it has been proposed that to reduce speculative activities in mining communities, there must be increased awareness through civic education to the effect that legally, mining companies are not expected to pay pre-moratorium for speculative economic activities. This may go a long way to improve the relationship between the communities and the mining companies.

This chapter focuses on assessment of determinants of non-performing loans (NPLs) in commercial banks in Kenya. A loan is classified as nonperforming loan when the borrower fails to make scheduled payments of principal or interest for a period of 90 to 180 days. In Kenya, the trend of NPLs increased throughout the 1990s and prior years, causing many banks to fail and others to be placed under statutory supervision. The survivor banks and those founded subsequently discovered that inadequate loan management was a major cause of the severe blow. While the borrowers are to blame, the management of the collapsed institutions must share the blame for adopting poor lending policies, as several lacked smart new techniques of managing NPLs. However, improved credit risk management has reversed the trend of NPLs. Nonetheless, the percentages of gross NPLs to gross loans remain high, potentially leading to the collapse of more banks. The study's aims were to determine whether risk assessment methods, the level of borrowers' lack of awareness of items being supplied, risk management methods, economic conditions, and technical obsolescence all contribute to NPLs. To achieve these goals, primary data was gathered by distributing questionnaires to credit division staff at chosen banks. The information was examined and displayed in tables using the statistical method of spearman's correlation coefficient. The findings revealed that, of the five components evaluated, risk assessment methods had the highest correlation coefficient, making them the primary contributor to NPLs, followed by borrowers' lack of awareness of the products being offered, while risk management methods had the smallest contribution. The findings will give bank management increased knowledge on credit risk exposure and NPL control, resulting in increased profitability.

This chapter explores about YouTube's impact on Generation Z's buying behavior, surveying 200 participants to examine demographics, viewing habits, and trust in YouTuber reviews. Results highlight YouTube's pivotal role; 51% base purchases on reviews, 82.5% find them crucial, and 60% strongly trust YouTubers. Positive comments significantly impact interest, scoring 64.80. Factors influencing consumers include product usefulness (62.73%) and influencer credibility. These findings stress ethical influencer collaborations and cost-effective marketing. As social media evolves, YouTube remains vital for brands connecting with Generation Z.

This chapter provides research on the top 50 of the Fortune 500 companies to research potential for material deficiency. Such a material and significant deficiency in the reporting of corporate assets might identify a real need to provide financial statement stakeholders with a “fair” knowledge of the value of corporate human capital. Corporate social responsibility is a business model by which companies make a concerted effort to operate in ways that enhance rather than degrade society and the environment. The machinery, equipment, inventory, and other assets of a twenty-first-century corporation have no actual economic value unless they are used. Corporations continue to disregard human value, despite the accounting profession's advice. Nonetheless, there is a growing recognition that people account for the majority of business productive capacity when compared to the current and fixed assets reflected in company financial accounts. There is a need to provide stakeholders with a “fair” knowledge of the value of corporate human capital to provide a higher standard of transparency and accountability in international financial reporting and to provide the basis for research into the sustainability and potential expansion of growth in the world economy. The knowledge foundation for wise and efficient investments in human capital will come from a suitable standard for human capital valuation. Investments that are both profitable and efficient will be especially beneficial to governments, the service sector, and individuals looking to advance European development. In fact, the legitimacy of financial statements that omit "the most valuable" business assets is being questioned by the Organization for Economic Cooperation and Development (OECD). Recently, the OECD has noted that human capital may be measurable “by the output potential of specific competencies”; “the fruits of (corporate) investment”; the objective measurement of the market “rental” price of human capital; and lastly, perhaps self-evident when it comes to physical capital, the output potential of corporate investment in their human capital inventory. The extraordinary failure of financial experts to focus on analyzing their human capital needs is perhaps not surprising, when the focus on production has proved to be so successful. Indeed, the very success of the intellect which has generated productivity is the very strongest evidence that a similar focus on human capital is likely to bring even more business success and prosperity.

This chapter analyzes a specific method of selection to classify the most suitable foreign country for the commercial promotion of the Treviso Radicchio Rosso PGI. Study also compares some selected countries in order to draw up the ranking of five ideal nations. The concept of agribusiness as a theoretical framework is since its inception naturally associated with the notion of value chain. Back in mid-20th century, increasing bonds between consumer experience and agricultural production had already been identified.

The specific analysis considers many variables, chosen with a view to selecting eligible markets, and ultimately draws up a consistent ranking of the five best nations. A planned approach to international trade development requires an analysis of the different foreign markets so as to identify and select the countries that offer the best opportunities. Using the Overall Market Opportunity Index (OMOI) technique, the most potential foreign market is identified. The most pertinent signs for each of the seven categories that are used to evaluate their appeal are highlighted in this. According to the research, Denmark is the top market for Treviso Radicchio segmentation methods. Accordingly, policy and business implications are addressed, and opportunities for future research into emerging related issues are suggested. The rapid changes in behaviours, the increasingly urgent nutritional needs, a growing multitude of young people forecast unprecedented economic impacts in demand. These factors are associated with the desires to taste typical Italian food, with customs extremely receptive with respect to modern marketing strategies, facilitated by new distribution technologies. Enhancing the ranking analysis techniques that may be applied to choose global agribusiness goals for Made in Italy enterprises will increase their competitiveness globally.

This chapter proves that polynomial models are effective in predicting intraday movement of financial assets. We apply a Polynomial Auto Regression (PAR) model to intraday price data of four major crypto currencies forecast intraday price volatility and convert the model into a real-time profitable automated trading system. Financial risk refers to the uncertainty of investment results that is affected by one or more random factors, including operational risk, credit risk, exchange rate risk and market risk. A PAR model was constructed to fit crypto currencies' behavior and to attempt to predict their short-term trends and trade them profitably. We employed machine learning (ML) techniques to train our system utilizing minutes' worth of data for six months, and then we used it to carry out lucrative trading and report for the next six months. The buy and hold (B&H) approach was significantly outperformed by our system for each of the four crypto currencies that were studied, according to the results. Results show that our system's best performances were achieved trading Ethereum and Bitcoin and worse trading Cardano. Our trading system used six months of training to identify the best fit polynomial model for the examined cryptocurrencies. Moreover, the system has optimized the percentage deviation from the prediction line that will guide the trade entrance. The highest net profit (NP) for Bitcoin trades was 15.58%, achieved by using 67 minutes bars to form the prediction model, compared to -44.8% for the B&H strategy. Trading Ethereum, the system generated 16.98% NP, compared to -33.6% for the B&H strategy, 61 minutes bars. Moreover, the highest NPs achieved trading Binance Coin (BNB) and Cardano were 9.33% and 4.26%, compared to 0.28% and -41.8% for the B&H strategy, respectively. Furthermore, the system better predicted Ethereum and Cardano uptrends than downtrends while it better predicted Bitcoin and BNB downtrends than uptrends. Additionally, for every cryptocurrency, the system found a distinct optimal arrangement. Additionally, the algorithm performed better on long trades for Ethereum and Cardano than on short transactions, and better on short trades for Bitcoin and BNB than on long trades.

This chapter investigates the mediating role of problem representation on the relationship between knowledge capability of fraud investigators and fraud risk assessment within Nigeria deposit money banks. Extensive literature in auditing, forensic accounting, banking and related fields was reviewed for thorough understanding of past, present and future trends in the study area. Nigeria Deposit Insurance Corporation (NDIC) annual reports (2014–2017) suggest that the country’s deposit money banks (DMBs) have been exposed to escalating incidence of fraud and forgeries. This has hampered their capacity, efficiency and contributions to national economic growth and development. This study examines the capability and competence of DMB regulators to prevent and detect fraud and, at the same time, develop an effective mechanism to do so. The study employed quantitative methodology. Data was collected using questionnaires administered on 350 forensic accountants and bank examiners working with the Central Bank of Nigeria (CBN), NDIC and 15 randomly selected DMBs in Lagos. Data collected were analyzed using descriptive statistics and multivariate regression. Key results include significant positive relationship between the study variables and reduction in fraud incidence in DMBs. This result has implications for existing accounting literature on knowledge capability and competence as vehicles for fraud prevention and detection in DMBs in Nigeria. The study recommends an integrative approach to bank examination in which both forensic accountants and bank examiners are involved as permanent fixtures in fraud prevention and detection in DMBs. This will guarantee a fraud-free DMB sector.