https://ajpojournals.org/journals/index.php/AJSAS/issue/feed American Journal of Statistics and Actuarial Sciences 2024-05-03T17:14:14+03:00 Journal Admin journals@ajpojournals.org Open Journal Systems <p>American Journal of Statistics and Actuarial Sciences is is an open access journal hosted by AJPO Journals USA LLC. The journal has an International Standard Serial Number (ISSN) of 2958-5244.Statistics and Actuarial Science are two subjects that are closely connected since statistical methods are applied when solving financial insurance problems. Actuarial science on the other hand uses probability mathematics to solve uncertain future problems. The American Journal of Statistics and Actuarial Science deals with fields such as Mathematics, Economics, Finance, Statistics, Probability and Computer Science. The paper is then reviewed by professionals so as to make sure that the journal is clear and comprehensive so as not to compromise the quality. The review remarks are issued within a period of two weeks. This journal is also very popular for its affordable prices which is an advantage to both the existing and upcoming researchers who want to publish their work. The journal is also featured in the google scholar and other journal directories. Therefore, this journal is appropriate for all the actuarial and statistical studies which is then published in both online and printed versions. Its indexed in google scholar, Crossref (DOI), Ebscohost, Research Gate among others.</p> https://ajpojournals.org/journals/index.php/AJSAS/article/view/1995 Time Series Analysis of Unemployment Rates and Its Determinants in the United States 2024-05-03T17:14:14+03:00 Charlie Robinson info@ajpo.org <p><strong>Purpose:</strong> The aim of the study was to assess time series analysis of unemployment rates and its determinants in the United States.</p> <p><strong>Methodology:</strong> This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.</p> <p><strong>Findings:</strong> The study revealed that there is a clear cyclical pattern in unemployment rates, indicating a strong correlation with economic cycles. During periods of economic expansion, unemployment tends to decrease, while it rises during economic downturns. However, the magnitude and duration of these fluctuations vary across different economies and regions.</p> <p><strong>Implications to Theory, Practice and Policy:</strong> Business cycle theory, Philips curve theory and structural unemployment theory may be used to anchor future studies on assessing the time series analysis of unemployment rates and its determinants in the United States. Policymakers should adopt adaptive strategies that acknowledge the dynamic nature of the relationships identified in the time series analyses. Policymakers should consider international collaboration to address the global influences on domestic unemployment rates.</p> 2024-05-03T00:00:00+03:00 Copyright (c) 2024 Charlie Robinson https://ajpojournals.org/journals/index.php/AJSAS/article/view/1993 Impact of Education Policies on Student Performance in Ghana 2024-05-03T16:51:02+03:00 Yaw Osei info@ajpo.org <p><strong>Purpose:</strong> The aim of the study was to assess Impact of education policies on student performance in Ghana.</p> <p><strong>Methodology:</strong> This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.</p> <p><strong>Findings:</strong> The study revealed various insights. Generally, education policies that emphasize high-quality teaching, adequate resources, and equitable access tend to positively influence student outcomes. For instance, policies focused on reducing class sizes, providing professional development for teachers, and implementing evidence-based instructional practices have been associated with improved academic achievement. Furthermore, policies aimed at addressing socioeconomic disparities, such as increasing funding for schools in low-income areas and offering targeted support for disadvantaged students, have shown to narrow achievement gaps. Additionally, policies promoting early childhood education and parental involvement have demonstrated long-term benefits on student performance. &nbsp;</p> <p><strong>Implications to Theory, Practice and Policy:</strong> Social cognitive theory, human capital theory and critical pedagogy may be used to anchor future studies on assessing the education policies on student performance in Ghana. Policymakers and education practitioners should conduct comprehensive impact assessments that go beyond quantitative metrics. Policymakers should prioritize evidence-based decision-making, drawing on rigorous empirical research.</p> 2024-05-03T00:00:00+03:00 Copyright (c) 2024 Yaw Osei https://ajpojournals.org/journals/index.php/AJSAS/article/view/1991 Factors Influencing Stock Market Volatility in the United States 2024-05-03T16:11:51+03:00 Daniel Baker info@ajpo.org <p><strong>Purpose:</strong> The aim of the study was to assess factors influencing stock market volatility in the United States.</p> <p><strong>Methodology:</strong> This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.</p> <p><strong>Findings:</strong> The study suggests that various economic, financial, and geopolitical factors play significant roles. Economic indicators such as GDP growth rates, inflation rates, and unemployment levels have been identified as key determinants of stock market volatility. Additionally, financial factors such as interest rates, exchange rates, and credit conditions also exert substantial influence on market volatility. Moreover, geopolitical events, including political instability, trade tensions, and geopolitical conflicts, can trigger significant fluctuations in stock prices.</p> <p><strong>Implications to Theory, Practice and Policy:</strong> Efficient market hypothesis, behavioral finance and market microstructure theory may be used to anchor future studies on assessing the factors influencing stock market volatility in the United States. The empirical studies have highlighted the importance of developing robust risk management strategies for investors. Policymakers should consider the implications of geopolitical events on stock market volatility when formulating economic policies.</p> 2024-05-03T00:00:00+03:00 Copyright (c) 2024 Daniel Baker https://ajpojournals.org/journals/index.php/AJSAS/article/view/1994 Predictive Modeling of Health Insurance Claims in the United States 2024-05-03T17:03:39+03:00 Emily Robinson info@ajpo.org <p><strong>Purpose:</strong> The aim of the study was to assess predictive modeling of health insurance claims in the United States.</p> <p><strong>Methodology:</strong> This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.</p> <p><strong>Findings:</strong> Predictive modeling of health insurance claims has emerged as a crucial tool for insurance companies and healthcare providers to anticipate and manage costs effectively. By analyzing vast amounts of historical claims data, predictive models can forecast future claim volumes, identify high-risk individuals or groups, and predict the financial impact of various healthcare interventions. These models utilize advanced statistical techniques, machine learning algorithms, and data mining approaches to uncover patterns and trends within the data. Key findings suggest that predictive modeling can significantly improve risk assessment accuracy, leading to better resource allocation, fraud detection, and cost containment strategies.</p> <p><strong>Implications to Theory, Practice and Policy:</strong> Health belief model, diffusion of innovation theory and social determinants of health may be used to anchor future studies on assessing the predictive modeling of health insurance claims in the United States. Healthcare institutions and insurers should invest in infrastructure that supports the timely acquisition and processing of relevant data, ensuring the practical application of predictive modeling in day-to-day claims processing. Policymakers should actively collaborate with researchers, industry experts, and regulatory bodies to formulate and enforce ethical standards for the development and implementation of predictive models in health insurance claims.</p> 2024-05-03T00:00:00+03:00 Copyright (c) 2024 Emily Robinson https://ajpojournals.org/journals/index.php/AJSAS/article/view/1992 Bayesian Inference in Assessing Climate Change Impact on Property Insurance Losses in England 2024-05-03T16:28:07+03:00 Oscar Rhys info@ajpo.org <p><strong>Purpose:</strong> The aim of the study was to assess Bayesian inference in assessing climate change impact on property insurance losses in England.</p> <p><strong>Methodology:</strong> This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.</p> <p><strong>Findings:</strong> The study examines the application of Bayesian statistical methods in understanding the effects of climate change on property insurance losses. Through Bayesian inference, researchers can effectively incorporate prior knowledge, such as historical data and expert opinions, with new evidence to estimate the probability distributions of various factors impacting insurance losses. The study finds that Bayesian techniques offer a robust framework for assessing the complex relationship between climate change and property damage, allowing insurers to better quantify and manage risks associated with changing climate conditions. By integrating diverse sources of information and updating models iteratively, Bayesian inference enhances the accuracy and reliability of predictions, enabling insurers to make more informed decisions in adapting to the challenges posed by climate change.</p> <p><strong>Implications to Theory, Practice and Policy:</strong> Bayesian statistics, decision theory and environmental economics may be used to anchor future studies on assessing the Bayesian inference in assessing climate change impact on property insurance losses in England. Tailoring Bayesian models to specific regional challenges and vulnerabilities is crucial for their practical application. Policymakers should actively engage with Bayesian findings to formulate adaptive policies that mitigate the impact of climate change on property insurance losses. &nbsp;</p> 2024-05-03T00:00:00+03:00 Copyright (c) 2024 Oscar Rhys