Exploring outliers in global economic dataset having the impact of COVID-19 pandemic

Outlier is a value that lies outside most of the other values in a dataset. Outlier exploration has a huge importance in almost all the industry applications like medical diagnosis, credit card fraudulence and intrusion detection systems. Similarly, in economic domain, it can be applied to analyse many unexpected events to harvest new knowledge like sudden crash of stock market, mismatch between country's per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest. These situations can arise due to several reasons, out of which the present COVID-19 pandemic is a leading one. This motivates the present researchers to identify a few such vulnerable areas in the economic sphere and ferret out the most affected countries for each of them. Two well-known machine-learning techniques DBSCAN and Z-score are utilised to get these insights, which can serve as a guideline towards improving the overall scenario subsequently. Copyright © 2023 Inderscience Enterprises Ltd.

Impact of Disruptive Technologies on Customer Experience Management In ASEAN: A Review

The financial services sector in the Association of South East Asian Nations (ASEAN) region has seen significant growth, driven by digitalization and the rise of fintech firms. Financial services accounted for about 8% of the overall Gross Domestic Product (GDP) at around 3 Trillion in 2021 [1]. While the GDP contracted slightly due to the COVID-19 pandemic, the overall outlook over the next five years remains positive.To further boost this growth, and foster innovation, regulators across ASEAN are establishing foundations for open finance, as is clear from policies in Singapore [2], the Philippines [3], and Indonesia [4].The main objectives of the open finance framework are to offer integrated financial services by making customer experiences that are fully digital, frictionless, empathetic, and anticipatory to customer needs.Customers today are more digitally empowered, expect personalized service, and often maintain relationships with multiple retail banks. As such, Customer Experience (CX) management is a top priority for retail banks to ensure overall brand recall, customer loyalty, and growth.This however also poses a new challenge to incumbent banks, as they need to embark on complex digital transformation journeys to stay relevant and competitive with due consideration for costs and accrued benefits.In this context, this study explores the impact of cloud, Artificial Intelligence (AI), and digital channels, collectively referred to as disruptive technologies, on customer experience management.It does so by critically examining existing literature on the evolution of digital technologies, their applications for customer engagement and the consequent impact on customer behaviours, and customer experience measures such as the Customer Satisfaction Score (CSAT) and Net Promoter Score (NPS). Based on the review, the study identifies opportunities for future research in the form of research questions, which include factors like experience quality, behaviour traits, and customer segmentation attributes that impact customer experience.The study contributes by providing insights to retail banks on key factors to consider while embarking on digital transformation projects to improve customer experience. While the study focuses on retail banking, its contributions could be beneficial to adjacent financial services like lending and insurance in ASEAN. © 2022 IEEE.

Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data

The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response to the pandemic have directly and drastically decreased human socioeconomic activities. To quantify and assess the extent of the pandemic's impact on the economy of Hebei Province, China, nighttime light (NTL) data, vegetation information, and provincial quarterly gross domestic product (GDP) data were jointly utilized to estimate the quarterly GDP for prefecture-level cities and county-level cities. Next, an autoregressive integrated moving average model (ARIMA) model was applied to predict the quarterly GDP for 2020 and 2021. Finally, economic recovery intensity (ERI) was used to assess the extent of economic recovery in Hebei Province during the pandemic. The results show that, at the provincial level, the economy of Hebei Province had not yet recovered;at the prefectural and county levels, three prefectures and forty counties were still struggling to restore their economies by the end of 2021, even though these economies, as a whole, were gradually recovering. In addition, the number of new infected cases correlated positively with the urban NTL during the pandemic period, but not during the post-pandemic period. The study results are informative for local government's strategies and policies for allocating financial resources for urban economic recovery in the short- and long-term. © 2022 by the authors.

Resilience Index Framework for the Construction Industry in Developing Countries

The construction industry in many developing countries is considered the main engine for economic growth. Quantification of the resilience of the construction industry in developing economies is essential for stakeholders and decision makers. Many researchers have attempted to quantify the construction industry's resilience in the context of developed economies;however, there is lack of established measures of such quantification in developing countries. This paper proposes a framework for the composition of an index that quantifies the resilience of the construction industry in developing countries. The proposed framework is demonstrated in the context of three developing countries: Rwanda, Egypt, and Turkey. The index is composed of measures such as the construction value added to a country's gross domestic product (GDP) and employment in construction. Principal Component Analysis (PCA) is utilized for weighting and aggregation of the individual variables. Studying the causal relationship between construction growth and economic development from 1971 to 2022, results show that construction growth leads to economic development in each of the three countries. Results of the proposed index values indicate that the construction industry in each of the three countries demonstrated increased resilience by sustaining both its outputs and its employment generation aspect in the two years following the coronavirus pandemic in 2019. Quantification of the construction industry's resilience in countries where the construction growth leads to the economic growth would provide a crucial insight for stakeholders and decision makers. © 2023 American Society of Civil Engineers.

Forecasting of GDP (Gross Domestic Product) per Capita Using (ARIMA) Data-Driven Intelligent Time Series Predicting Approach

Gross Domestic Product is the aggregate value of all final services and products generated by the country during the measurement period, including private inventories, paid-in capital expenditures, government purchases, personal consumption, and the balance of international commerce. During the Pandemic period of the last two years, the COVID-19 outbreak has caused chaos in the worldwide economy. Sickness outbreaks, supply-chain disruptions, and, more recently, inflation have made policymaking exceedingly difficult. This research aims to forecast GDP (Gross Domestic Product) per capita for the coming years while also examining historical and present trends in India. This study's objective is to forecast India's future GDP per capita over ten years, from 2021 to 2030, using ARIMA. According to a study, India's GDP per capita has been growing during the last 10 years, and this movement is likely to last over the following ten years. © 2022 IEEE.

COVID-19 Crisis: Forecasting the Arab World Economy Performance Using the ARIMA Model

This paper predicts Coronavirus Disease (COVID-19)'s potential influence on the Arab country's economy by using the Autoregressive Integrated Moving Average (ARIMA) model. The world bank offers data of the Arab countries' Gross Domestic Product (GDP) over the period 1960-2019. As we show up at the pinnacle of the COVID-19 pandemic, quite possibly the most critical inquiry going up against us is: what is the potential impact of the progressing crisis on the Arab countries' economic improvement rate? The results have shown that the GDP growth is approximately -3.8% to 1.5% for 2021 and 2022, respectively. The referenced outcomes show that pandemic status significantly affects the Arab world economy special after the energy demand decline, which prompts a fall in oil price. In spite of the fact that the Arab world's financial development is growing again, it is not most likely going to re-visitation of business as usual for quite a while to come. © 2022 Newswood Limited. All rights reserved.

Note: ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights

Policymakers often make decisions based on GDP, unemployment rate, industrial output, etc. The primary methods to obtain or estimate such information are resource-intensive. In order to make timely and well-informed decisions, it is imperative to come up with proxies for these parameters, which can be sampled quickly and efficiently, especially during disruptive events like the COVID-19 pandemic. We explore the use of remotely sensed data for this task. The data has become cheaper to collect than surveys and can be available in real-time. In this work, we present Regional GDP-NightLight (ReGNL), a neural network trained to predict GDP given the nightlights data and geographical coordinates. Taking the case of 50 US states, we find that ReGNL is disruption-agnostic and can predict the GDP for both normal years (2019) and years with a disruptive event (2020). ReGNL outperforms time-series ARIMA methods for prediction, even during the pandemic. © 2022 ACM.

Integrated Lean Logistics-Warehousing model to reduce Lead Time in an SME of food sector: A research in Peru

The food industry represented 2019 3.2% of the Gross Domestic Product (GDP) in Peru, in addition to presenting high employability being that the sector contained 7.4% of the Economically Active Population (EAP) [1]. However, due to the world crisis caused by COVID 19, this sector has been harmed in the year 2020 because the GDP of the accommodation and restaurants sector decreased by 50.2% compared to the previous year [2]. For that reason, strengthening this industry is considered crucial. One of the most critical links in the management of the supply chain of material, this problem results in the late delivery of orders to their customers, which would generate significant monetary losses and a bad reputation. In this sense, the present research proposes an integral improvement model based on the combination of tools such as the 5S ordering of raw material under a multi-criteria ABC approach, FEFO, MRP, Forecasting, and BPM, whose objective is to reduce the lead time. The model was validated through a simulation in Arena 16.1, where a reduction in delivery time and the frequency of extra purchases in 7.2% y 50%. © 2022 ACM.

Machine Learning Techniques to Analyze Pandemic-Induced Economic Outliers

Outliers, or outlying observations, are values in data, which appear unusual. It is quite essential to analyze various unexpected events or anomalies in economic domain like sudden crash of stock market, mismatch between country’s per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest to find the insights for the benefit of humankind. These situations can arise due to several reasons, out of which pandemic is a major one. The present COVID-19 pandemic also disrupted the global economy largely as various countries faced various types of difficulties. This motivates the present researchers to identify a few such difficult areas in economic domain, arises due to the pandemic situation and identify the countries, which are affected most under each bucket. Two well-known machine-learning techniques DBSCAN (density based clustering approach) and Z-score (statistical technique) are utilized in this analysis. The results can be used as suggestive measures to the administrative bodies, which show the effectiveness of the study. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Risk Management at Small and Medium-Sized Enterprises Under the Coronavirus Pandemic

The economic prosperity of many countries of the world directly depends on the share of the gross domestic product, which is made up of products and services of small and medium-sized enterprises. Currently, new risks associated with the coronavirus pandemic have emerged in the external environment of their functioning. This study identifies and analyzes these risks, identifies the main measures that contribute to their leveling, supplements the classification of internal and external risks that influence the economic sustainability of small and medium-sized enterprises (SMEs) significantly. The developed conceptual aspects of risk management at SMEs enable to identify, minimize and effectively prevent the impact of risks that can negatively affect the main business processes, considering the peculiar functioning of enterprises. At the same time, an emphasis is made on the dual influence of risks, because small and medium-sized enterprises can open new positive opportunities and directions for business development in the crisis. It was concluded that timely and reliable identification of possible risk situations will help the management of small and medium-sized enterprises to organize or adjust business processes affected by the coronavirus pandemic, ensure the sustainability of activities in the risky economic environment and stay in occupied market niches. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Challenges for expatriates in japan to live as consumers

Japan's population as of March 1, 2021was 125,480,00, according to an estimate by the Statistics Bureau, Ministry of Internal Affairs and Communications;a decrease of approximately 480,000 or minus 0.38%, compared to the previous year. This is the eleven consecutive annual decline and the largest margin of decrease on record. As of June 1, 2020, the number of foreign residents in Japan rose to 2,885,904 with the increase of 56,488 or 2.0 percentages by Statistics of Japan, a portal site for Japanese Government Statistics. In addition, the number of visiting Japan in 2019 was boosted up to 31.9 million with the increase 2.2% compared to the last year. Besides, the number of foreign tourists visiting Japan in 2019 announced by the Japan Tourism Agency increased 6.5% from the previous year to US$48.1 billion that represents one percent of Japan's gross domestic product. The Government of Japan announced to keep opening its door to foreigners both for residence and visiting, especially emphasizing on the former. The COVID-19 has been continuing its spread across the world with more than 60 million confirmed cases in 190 countries. This pandemic with restricted scopes of behavior mandates have disrupted the consumer habits of their lifestyles. The recent attack of the pandemic has revealed that it has a huge influence on a variety of businesses in Japan, blocking out the flow of global supply chains and the inflow of people traffic. More seriously, negatively were affecting sales and profits of companies in Japan. This paper aims to focus on the aspects of expatriates' lives in Japan and then clarifies the issues to be addressed, resulting in some tips for developing the economy of Japan. The main factor of the difficulties to live in Japan is due to the policy of the Government of Japan. However, there could exist other reasons for them to be in trouble in Japan for their daily lives that were emerging from the research. The secondary data collected by ASMARQ Co. was adopted to measure each degree of livability. The outcome of the analysis indicates comprehensive understandings of and useful insights into future business in Japan. © 2021 ACM.

A Statistical Analysis of Impact of COVID19 on the Global Economy and Stock Index Returns.

The outbreak of pandemic COVID-19 across the world has completely disrupted the political, social, economic, religious, and financial structures of the world. According to the data of April 22nd, 2020, more than 4.6 million people have been screened, in which the infection has made more than 2.7 million people positive, in which 182,740 people have died due to infection. More than 80 countries have closed their borders from transitioning countries, ordered businesses to close, instructed their populations to self-quarantine, and closed schools to an estimated 1.5 billion children. The world's top ten economies such as the United States, China, Japan, Germany, United Kingdom, France, India, Italy, Brazil, and Canada stand on the verge of complete collapse. In addition, stock markets around the world have been pounded, and tax revenue sources have fallen off a cliff. The epidemic due to infection is having a noticeable impact on global economic development. It is estimated that by now the virus could exceed global economic growth by more than 2.0% per month if the current situation persists. Global trade may also fall from 13 to 32% depending on the depth and extent of the global economic slowdown. The full impact will not be known until the effects of the epidemic occurred. This research analyses the impact of COVID-19 on the economic growth and stock market as well. The aim of this research is to present how well COVID-19 correlated with economic growth through gross domestic products (GDP). In addition, the research considers the top five other tax revenue sources like S&P500 (GPSC), Crude oil (CL = F), Gold (GC = F), Silver (SI = F), Natural Gas (NG = F), iShares 20 + Year Treasury Bond (TLT), and correlate with the COVID-19. To fulfill the statistical analysis purpose this research uses publically available data from yahoo finance, IMF, and John Hopkins COVID-19 map with regression models that revealed a moderated positive correlation between them. The model was used to track the impact of COVID 19 on economic variation and the stock market to see how well and how far in advance the prediction holds true, if at all. The hope is that the model will be able to correctly make predictions a couple of quarters in advance, and describe why the changes are occurring. This research can support how policymakers, business strategy makers, and investors can understand the situation and use the model for prediction.