Journal of Emerging Strategies in New Economics 2022-10-15T07:04:11+00:00 Dr. Ojonugwa Usman Open Journal Systems <p><strong>Journal of Emerging Strategies in New Economics</strong> (ISSN: 2949-8309) is at the forefront of analyzing the economic development of emerging economies in the global context, fostering discussion on research with significant, long-term impact. It explores the causal factors, potential, and limits of economic policy in Eastern Europe, Eurasia, Africa, Asia, Latin America, and the Middle East, projecting possible economic developments in light of growing opportunities. Booming markets, a massive potential for local consumer markets, and abundant low-cost labor make emerging economies key players in international trade and business.</p> <h3><strong>Topics covered include</strong></h3> <ul> <li>Microeconomics </li> <li>Macroeconomics </li> <li>Monetary economics </li> <li>Labour markets </li> <li>Investment </li> <li>Business Economics </li> <li>Industrial policy </li> <li>Central banking </li> <li>Commercial banks </li> <li>Exchange rates </li> <li>Open economy macroeconomics </li> <li>Finance and financial markets </li> <li>International trade </li> <li>Economic integration</li> <li>Economics and Law </li> </ul> <p><strong>Objectives: </strong></p> <p>The objectives of <em>JESNE</em> are to provide a global platform to facilitate communication between policymakers, academics, scholars, international economic organizations, and consultants working on the issues of interest to newly emerging countries. Given a rapidly changing field, <em>JESNE</em> encourages original scientific contributions to policy-relevant analysis and articles which are empirical in nature, with emphasis on the application of modern economic theory and methods of quantitative analysis. It applies a rigid peer-reviewed approach, which aims to publish only very selective, substantive new empirical, methodological, and theoretical research.</p> <h4><strong>Readership: </strong></h4> <p><em>JESNE</em> provides a vehicle to help researchers and policymakers, experts working at international organizations, and academic institutions. The journal provides a forum for discussion of issues of interest to an international readership.</p> <h4><strong>Contents: </strong></h4> <p>Journal of Emerging Strategies in New Economics (ISSN: 2949-8309) publishes original papers, review papers, technical reports, case studies, conference reports, and book reviews.</p> <p><strong>Journal of Emerging Strategies in New Economics</strong> is a nonprofit Journal dedicated to synthesizing and integrating knowledge for the progress of science and the benefit of society.</p> <p>Our articles capture current understanding of a topic, including what is well supported and what is controversial; set the work in historical context; highlight the major questions that remain to be addressed and the likely course of research in upcoming years; and outline the practical applications and general significance of research to society.</p> <p><strong>Journal of Emerging Strategies in New Economics</strong> articles include researchers who want to keep abreast of their field and integrate this information with their own activities; researchers who want an introduction to new fields, with a view to developing an interface between different areas of research; students at all levels who want to gain a thorough understanding of a topic; and business people, journalists, policy makers, practitioners, patients and patient advocates, and other who wish to be informed about developments in research.</p> <p>Contributions to this journal are invited which analyse and discuss well-being, welfare, the nature of the good society, governance and economics, economic psychology; international economics; public finance; health economics; education; economic growth and technological change; political economy, and individual economic motivation, and the associated normative and ethical implications of topics related to economics<br /><br />Editor-in-Chief: </p> <p><strong>Dr. Ojonugwa Usman,</strong> <br />Department of Economics, Istanbul Ticaret University, Istanbul 34445, Turkey<br />Email:</p> PREDICTING THE INSURANCE CLAIM BY EACH USER USING MACHINE LEARNING ALGORITHMS 2022-09-08T08:10:09+00:00 Seshu Kumar Vandrangi <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>Today, data will play a critical role and become a significant wealth creator in the insurance sector. The insurance sector is very significant in today's travel industry. Insurance companies now have more information than ever before. There have been three key eras in the insurance sector over the last 700 years. The manual era lasted from the 15th century to the 1960s, the system age from the 1960s to the 2000s, and now the digital age. H. 2001-20X0. In all three periods, the ultimate corporate objective has been pushed by the core insurance industry's conviction in data analytics to accept evolving technologies in order to enhance its route and concentrate capital. That's all. Inadequate analytical models and algorithms to serve insurance businesses is a big concern in advanced analytics. Only machines are capable of overcoming this obstacle. In this paper, insurance data based on some features are trained and tested over Artificial Neural Networks, Random Forest Regressors, Logistic Regression and predict the charges based on features for predicting the insurance claim.</p> </div> </div> </div> 2022-09-08T00:00:00+00:00 Copyright (c) 2022 Journal of Emerging Strategies in New Economics PREDICTIONS OF CUSTOMER BEHAVIOUR OVER ECOMMERCE WEBSITES AND ANTICIPATING THEIR INTENTION 2022-09-08T08:22:10+00:00 Novokhatska Anastasiia <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>This paper presents a real-time behavioural analytics solution for online consumers that consists of two modules that estimate visitor purchase intent and website desertion probability at the same time. The first module predicts a visitor's purchase intent using aggregated page view statistics acquired during a visit as well as certain session and user information. The collected features are fed into classifiers such as random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP). To increase classifier performance and scalability, use oversampling and feature selection pre-processing processes. The findings reveal that MLP calculated with a robust backpropagation method with weight backtracking outperforms RF and SVM in terms of accuracy and F1 score. Another discovery is that, although clickstream data gathered from online navigation patterns transmit vital information about visitor buy intent, session information bases include unique information about purchasing inclinations. In the second module, everyone estimate the likelihood of a visitor's desire to exit the website without completing a purchase using just sequential clickstream data. Everyone trains a long short-term memory-based recurrent neural network to provide a sigmoidal output indicating the predicted horizon. When used together, the modules detect visitors who are ready to make a purchase but are likely to depart the site within the forecast time and take the necessary steps to enhance website abandonment and buy conversion rates to do. Our findings indicate the viability of employing clickstream and session information data to forecast purchase intent in virtual retail environments in an accurate and scalable manner.</p> </div> </div> </div> 2022-09-08T00:00:00+00:00 Copyright (c) 2022 STOCK PRICE PREDICTION FROM NEWS HEADLINES USING MACHINE LEARNING MODELS 2022-09-08T08:24:19+00:00 Ahmed J. Obaid <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>It's a tremendously intriguing and exciting issue to forecast and speculate on stock market values, especially worldwide company values. This article uses economic news received from businesses to discuss changes in stock prices and projections of stock values. Pay attention to business news headlines and assess headline sentiment using a number of tactics. The Neural Network reconstructs sentiment outcomes with changes in equities over the same period by using BERT as a benchmark and comparing the findings with three other tools. Compared to the other two tools, BERT and RNN are substantially more accurate since they can recognise emotional values without the neutral component. Establish when changes in stock values occur by contrasting these findings with stock value fluctuations over the same time period using sentiment analysis of economic news articles. The impact of sentimental value on changes in sentimental stock market value was also shown to vary significantly amongst the various models.</p> </div> </div> </div> 2022-09-08T00:00:00+00:00 Copyright (c) 2022 STOCK PRICE PREDICTION USING TIME SERIES FORECASTING BY MACHINE LEARNING MODELS 2022-09-08T08:27:51+00:00 Sajaratuddur Lelya Hilda <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>It is a highly obvious fact that, the stock market is a fickle beast, and making forecasts may be difficult. Stock prices are impacted by both economic and non-economic variables. Refers to several essential physical, psychological, rational, and so on factors. The stock price is predicted using the autoregressive integrated migration Average (ARIMA) model in this research article. have a model for predicting stock prices. Create and disseminate obtained inventory data from Yahoo Finance on a regular basis. The experimental findings show that ARIMA models may be used to accurately estimate inventory levels and short-term pricing.</p> </div> </div> </div> 2022-09-08T00:00:00+00:00 Copyright (c) 2022 GDP PREDICTION FOR COUNTRIES USING MACHINE LEARNING MODELS 2022-09-08T08:40:58+00:00 Gurunadh Velidi <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>The significance of GDP is demonstrated by the fact that it offers data on the size and functioning of an economy. Real GDP growth is frequently used as a measure of the state of the economy as a whole. Real GDP growth is typically regarded as a positive indicator of the state of the economy. The goal of this research is to forecast global GDP and determine its annual growth rate using machine learning. Since GDP varies from year to year, it is crucial to understand the status and importance of the variables that influence a country's GDP now. If you depict this in a diagram, it will be simpler to grasp. Additionally, the significance of the impacted parameters in determining GDP is calculated in this research-based study. Viewers may easily compare the GDP growth rates of other nations as well as their best and worst performance predictions. In this study, by using a variety of machine learning algorithms to analyse the global GDP.</p> </div> </div> </div> 2022-09-07T00:00:00+00:00 Copyright (c) 2022