Country Economic Conditions Research Articles

SARIMAX–GARCH Model to Forecast Composite Index with Inflation Rate and Exchange Rate Factors

Investors should consider the Indonesia Composite Index (ICI) as a key indicator before making investment decisions, as it reflects the performance of industries and the broader economic growth. In Indonesia, the ICI exhibits fluctuating movements, making accurate forecasting essential for understanding the country's economic conditions, which are closely tied to capital flows, growth, and tax revenues. This study aims to forecast the ICI using the SARIMAX-GARCH model, incorporating macroeconomic factors such as the inflation rate and exchange rate. The findings reveal that both variables significantly impact the ICI, with the model achieving a Mean Absolute Percentage Error (MAPE) of 0.952% for training data and 5.233% for test data. The model's performance is supported by an R² value of 0.9782 and a Mean Squared Error (MSE) of 0.0003. This research not only improves the accuracy of ICI forecasts but also supports Indonesia's 8th Sustainable Development Goal (SDG) for decent work and economic growth. Doi: 10.28991/HIJ-2024-05-03-014 Full Text: PDF

Analysis of Road Infrastructure Problems in Urban City, Karachi

Road infrastructure is a significant part of the economic development of countries. Better performance of road communication system is significant part of all over the world. Developed countries make a significant effort for designing a well-managed traffic arrangement for the individuals. Furthermore, an efficient transport system is the key element for improving the country's economic conditions. Sustainable and efficient traffic management system is the backbone for humanizing the economic growth of developing countries. In case of Pakistan, Karachi is the main industrial hub for whole country's economy and accommodates enormous number of migrants. Due to this reason the population of city is increasing day by day. This paper presents the current situation of road infrastructure troubles in Karachi. The urban city is facing several numbers of issues that were already documented by researchers such as traffic congestion and road safety problems. On an urgent basis an efficient mass transit system is required to reduce the traffic jam conditions. The transport system of Karachi requires instant proper management rules and designed transport facilities. The aim of the paper is to identify the main traffic problems of the large urban city, Karachi, which is faced by the people in their daily life and to suggest recommendations for reducing the current situation related to traffic management strategies. The research methodology for this paper will be based on qualitative analysis of the road infrastructure troubles that are identified by the individuals through interviews and detailed group meetings.

Socio-Economic Determinants of Terrorism in Tunisia

Tunisia has always been affected by terrorism, but since the late 2011s there has been a sharp increase in terrorism perpetrated. The object of this research is to analyze the socio-economic determinants of terrorism using terrorist attacks as a dependent variable and indicators that affect the level of growth in Tunisia such as explanatory variables. The aim of this work is to develop recommendations on how to respond to the state in response to terrorist shocks. The following policy implications were deduced. First, we must increase the level of growth of the country by stabilizing the policy and by controlling governance to show the effectiveness of the results of this study which aims at reducing terrorism and improving the country's economic conditions. Second, in view of the results found in this research, terrorism can stabilize the long-term labor market through investment by putting in place realistic economic policies that are time-bound to encourage the State to create vacancies for the unemployed.

Trend Forecast of Shanghai Stock Exchange Composite Index Based on Monetary Supply and Consumer Price Index

Stock market indices often serve as indicators of a country's economic conditions. Therefore, analysing the trends of stock market indices can assist individuals, institutions, and even governments in comprehending the state of the economy and developing suitable investment strategies or economic policies. However, accurately predicting these indices poses a significant challenge. In recent years, machine learning has displayed remarkable learning capabilities in various industries, making it an intriguing and viable avenue for trend prediction. In this article, we have selected two closely linked data sources, namely monetary supply and consumer price index, which are highly correlated with economic operations. By combining these data with regression models, we have developed an algorithm for predicting China's Shanghai Stock Exchange Composite Index (SSECI). Experimental results illustrate a strong correlation between the collected data and the index, highlighting their value in indicating economic conditions.

Respon Ekspor Akibat Shock Nilai Tukar Dan Jumlah Uang Beredar di Indonesia

The rupiah exchange rate against the US dollar has always fluctuated, due to the instability of economic conditions both at home and abroad. The macroeconomic conditions of the two countries have an influence on the level of exports, especially in Indonesia. The response to export developments that occur in the country is not only influenced by the rupiah exchange rate with the dollar, the money supply (JUB) in Indonesia is also related to the level of exports that occur. If the JUB increases, the number of exports will also increase. So that the relationship between exports, exchange rates, and the money supply has a close relationship with the country's economic conditions. This study aims to analyze how the response in the level of exports in Indonesia is related to the shocks that occur in the exchange rate between the rupiah and the dollar, and also to the money supply in Indonesia through several variable data such as exports, exchange rates, inflation, BI rate, and money. circulating (M2), using data for 2018:M1-2022:M12 using the Vector Error Correction Model (VECM) analysis method. The results show that the response to inflation (INF), the exchange rate (KURS), the money supply (M2), and the bi rate on exports in Indonesia show that the variable response results are positive and the average is stable.

ANALYSIS OF RETURN RATES FOR ANOMALIES

This study investigates the presence of any purposive movement in the commodity market of expensive metals and energy commodities in the Indian market. Using daily, weekly, and monthly data for gold and crude oil from 2010 to 2020, We discover proof that the commodity market exhibits anomalies. We observed periodic anomalies present due to festivals celebrated. This may have an impact on the country's economic conditions. Given investors' growing propensity to reveal their portfolios to financial vehicles linked to them, this issue is significant for commodities used for speculation, hedging, and investment. The study also observes any significant non-periodic abnormality present for 10 years.

Machine learning-based classifying of risk-takers and risk-aversive individuals using resting-state EEG data: A pilot feasibility study.

Decision-making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers' personality traits (i.e., risk-taking or risk-averse) in recent years. Although there are findings of signal decision-making and brain activity, the implementation of an intelligent brain-based technique to predict risk-averse and risk-taking managers is still in doubt. This study proposes an electroencephalogram (EEG)-based intelligent system to distinguish risk-taking managers from risk-averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time-frequency domain analysis method, was used on resting-state EEG data to extract statistical features. Then, a two-step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features. Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1-measure, indicating that machine learning (ML) models can distinguish between risk-taking and risk-averse managers using the features extracted from the alpha frequency band in 10s analysis window size. The findings of this study demonstrate the potential of using intelligent (ML-based) systems in distinguish between risk-taking and risk-averse managers using biological signals.

The ecosystem services concept in freshwater conservation and restoration

Abstract Intense efforts have focused on ecosystem conservation and restoration (ECR) over the past decades, promoting considerable advances in science and practice in this field. However, the applications of the ecosystem services (ESs) concept are rarely considered in the literature when assessing the success and targets of ECR. The ECR literature was systematically reviewed to analyse the relevance of the ES concept in ECR research published from 2007 to 2019 and to assess how the ES concept is integrated into freshwater ECR. In general, the number of publications focusing on ECR and ESs shows a temporal increase (from 12 to 101 publications, 685 in total). The proportion of publications considering ESs as freshwater ECR targets increased from 33 to 70% until 2019, while the proportion of publications that prioritized ecosystem processes and functions as the target declined from 33 to 16%. Despite the clear upward trend, most studies have focused only on the conceptual framework. In addition, the idea of using market‐based approaches for ESs has spread quickly, but it has still not been broadly accepted. The number of academic researchers who have contributed to this research field varies substantially across different countries. A country's economic conditions and the extent of freshwater withdrawal can affect efforts devoted to studies on freshwater ECR and ESs. Some ecosystems, such as rivers and wetlands, have attracted much more attention than others. Although most studies have discussed ESs in general, the provisioning and regulation of ESs are mainly considered in studies that differentiate specific ES categories. Current limitations in applying the ES concept in ECR across different studies suggest that further collaboration is needed among ecologists, practitioners, stakeholders and policy‐makers to develop a broad mix of approaches for the best and widest use of the ES concept in protecting and enhancing freshwater ecosystems.

Based on Baidu Index and GBDT Shanghai Index rise and fall forecast

The stock market reflects the country's economic conditions, and it is of great significance to have a good prediction effect on the stock market. But with the rapid rise of the Internet, big data, and machine learning, the prediction of the stock market trend is not limited to the traditional methods and data sets. The trend of the stock market is not only dependent on itself but also affected by some other factors. Therefore, based on the machine learning model, this paper studies the prediction of investors' attention to the Shanghai Composite Index trend. This paper crawled the relevant index data from the website of Baidu Index based on the selected keywords. The correlation coefficient is used to select the keyword data with the best lag order and data type and as the model's input data. Through the establishment of LSTM, LASSO, RF, and GBDT models, the rise and fall of the Shanghai Composite Index are predicted. That is to say. The paper takes the accuracy of the rise and fall prediction as the judgment standard. GBDT model has the best prediction effect on the Shanghai Stock Exchange Index and can best explain the rise and fall of the Shanghai Stock Exchange Index. So, people can use this research to buy stocks before they rise and sell them before they fall.

The efficiency of municipal waste management systems in the environmental context in the European Union

Performance criteria of non-energy materials hold an important place in the system of indicators of the green growth strategy. Management of generated municipal waste and its effectiveness depend to a large extent on the ratio of the amount of incinerated, recycled or composted waste to the amount taken to landfills. This ratio is tied to the level of economic development of the country. The conducted research revealed several regularities in the management of generated municipal waste. As the country's economic conditions improve the amount of generated municipal waste increases while the share sent to landfills decreases, and vice versa ‒ less economically developed European Union countries are characterized by a lower amount of generated waste, but a larger amount of it sent to landfills. Multi-criteria methods were used in order to compare countries according to the efficiency of their municipal waste management system in an environmental context. Such assessment entails combining the components into one summarizing measure and taking into account the impact of these components on the environment at the same time. The ranking of the countries revealed that higher efficiency is found in the more economically developed countries of the European Union even though the amount of municipal waste generated in them is higher too. It also became apparent that as it grew, the efficiency gradually decreased.

A study on the impact of selected macro-economic factors on share price movement of certain vital sectors

Investment decisions are highly influenced by macroeconomic variables as changes in macroeconomic variables effect stock markets differently according to the country economic conditions and government policies. The study contributes by determining the effect of various macroeconomic variables on stock prices this study will be useful for the investors who might be able to identify some basic economic variables that they should focus on while investing in stock market and will have an advantage to make their own suitable investment decisions.

Credit Guarantee the National Economic Recovery Program (PEN) for MSMEs: A Case Study of MSMEs for Bank Pembangunan Daerah DIY

In recent months the world has been shocked by a deadly virus, namely Coronavirus Disease 2019 or better known as the COVID-19 virus, which causes a health crisis and an economic crisis. National Economic Recovery or PEN is one of the policies to improve the country's economic conditions due to the COVID-19 pandemic which has a systemic impact. The impact affects almost all sectors, including the Micro, Small and Medium Enterprises sector. This study aims to analyze credit guarantees on MSME income in Daerah Istimewa Yogyakarta (DIY). This study uses a qualitative descriptive method, with UMKM recipients of PEN program assistance through Bank BPD DIY as respondents. The collection technique was through interviews and questionnaires with a total sample of 30 respondents and selected based on the purposive sampling method. The rate of return and the questionnaire that can be processed is 100%. From the results of interviews that have been carried out, it was concluded that in general new working capital credit guarantees help entrepreneurs in recovering MSME businesses due to being affected by COVID-19. The results of this study are in accordance with the Government's target that the assistance in credit guarantees helps micro-enterprises to increase their business income.

The Philosophy of the Educational System in Singapore and who was behind this Success

This study aimed to identify the philosophy of the Singapore's educational system and to explain the reasons for its success. The results revealed that Singapore's educational philosophy is linked to the pragmatic philosophy, which is based on educating a creative generation with self-expression freedom and is tied to the country's economic, cultural, and social conditions. It emphasizes that the individual is society's most important component. In light of the results, the researchers recommend undertaking more research into the philosophy of Singapore's educational system, disseminating the findings, and alerting the relevant authorities of Singaporean philosophy. This is to take steps toward implementing this philosophy in Jordan, as well as to perform an evaluation study of the educational system's philosophy in the Hashemite Kingdom of Jordan.

BERTFOREX: Cascading Model for Forex Market Forecasting Using Fundamental and Technical Indicator Data Based on BERT

Foreign exchange (Forex) rate forecasting is presently pursued by many researchers as it plays an important role in financial technology and business. The challenge of Forex research lies in its characteristics, fluctuation, non-linearity, and random walk phenomena. Several related studies generate forecasting signals using fundamental data (FD) and technical indicator data (TI) to support Forex. FD is an indicator of country economic conditions, while TI shows the price pattern-based signal. However, these two indicators pose two major limitations on their deployment. First, modeling a sequential neural network causes gradient vanishing and information loss. Second, FD exerts a significant impact on currency price upon its quarterly update and release. The second limitation is known as FD releasing problem. Moreover, Forex forecasting using FD and TI is typically conducted in an equal aggregation manner, resulting in inaccurate prediction due to unequal data changing frequency. In this work, we propose a cascading model for forex market forecasting using FD and TI based on BERT (BERTFOREX). The processing step of BERTFOREX are as follows: 1) BERT is applied to FD for extracting the hidden patterns over the designated period. 2) These extracted hidden patterns of FD are aggregated as additional weights to TI since FD frequency changes slower than that of TI. 3) BERT is again applied to extract the aggregated pattern within TI and FD over other influencing days. 4) To demonstrate the efficiency of BERTFOREX, the aggregated pattern is fed into a simple neural network for forecasting. The proposed method outperforms other methods in terms of percentage of correct signals, sensitivity, specificity, precision, and negative predictive value.

Dampak Pembatasan Kegiatan Masyarakat Terhadap Pertumbuhan Ekonomi: Suatu Kajian

This study aims to review various policies taken by the government when the government considers priorities balance between handling Covid-19, namely by implementing restrictions on community activities and the country's economic conditions. This research is a type of qualitative research with descriptive method. Descriptive method was chosen because the research was conducted with current events related to current conditions. The results of this study indicate that Indonesia has not implemented significant policies while still implementing normal human inflows from abroad. Either, a new policies’s the end of PSBB and changes to the Implementation of Community Activity Restrictions (PPKM) which was launched by the Chair of KPCPEN on January 7, 2021 with the first locus in Java and Bali. Observing the economic crisis history that hit Indonesia, many parties will be grateful, considering that the impact of the Covid-19 pandemic on the economy was no more severe than the monetary crisis in 1998. Currently, the government has adopted six strategies for Indonesia's economic transformation: (1) Competitive Human Resources; (2) Economic Sector Productivity includes Industrialization, (3) Green Economy includes a low carbon economy, blue economy, and energy transition aimed at meeting increasing energy needs, opening up more sustainable green job opportunities, and supporting the reduction of greenhouse gas emissions; (4) Digital Transformation; (5) Domestic economic integration; and (6) Relocating the National Capital as a new source of growth and balancing the economy between regions

Analysis of United States Quantitative Easing Policy on Real Output in Indonesia

This study discusses analysis of United States quantitative easing policy on real output in Indonesia. QE policy not only affects US economy but also influences the economic indicators of other countries, especially Indonesia countries with increasingly integrated market conditions. At present the Indonesia economy has been very open, so that policies originating from abroad can affect the country's economic conditions. The possibility of global spillover against non-conventional monetary policies such as QE. It is using the Vector Autoreggresion (VAR) methods to see the effect of QE policy. The data is time series for the 1999Q1-2016Q4. This study will analyze the impact of macroeconomic variables such as interest rates, money supply and inflation on GDP. The results of this study indicate that the implementation of the QE policy has an impact on the rate of GDP growth in each country of  Indonesia.

Huawei in Venezuela: a review of 2006–2019

PurposeThe aim of this article is to describe Huawei's internationalization process in Venezuela and show how socio-political and economic conditions helped to expedite the company's development in this Latin American nation between 2006 and 2019. Through this internationalization process, Huawei participated in a large technological transition in Venezuelan telecommunications.Design/methodology/approachThis research uses an integrative approach, developing a quasi-case study from a review of the academic literature, contemporary news stories and institutional and practitioner documents.FindingsThe review indicates that Huawei was engaged in business with the Venezuelan phone company before its renationalization. Secondly, Huawei's internationalization was a beneficiary of the increased relations between the Venezuelan and Chinese governments, mainly through “oil for loans/goods” agreements. Lastly, this internationalization process includes wholly owned subsidiaries, direct export, greenfield and government joint ventures.Practical implicationsThis research provides an understanding to other firms and strategists about the benefits of strong bilateral economic relationships between home and host countries.Originality/valueThis paper is among the first academic articles that describe the internationalization process of Huawei in Venezuela. Considering the host country's changing political and economic conditions during the last 20 years, such research may provide a perspective for considering other Chinese business expansions in Venezuela and Latin America.

Prospect Prediction Model Of Indonesian Telematics Medium Large Size Enterprises Using Deep Learning Approach

Analysis of business prospects is an important part of predicting a country's economic conditions. Currently, the prediction of prospects for medium-big sized enterprises (MLE) in the telematics sector has not been widely researched and represented as a factor of economic development in Indonesia. In fact, in accordance with the development of the Industrial Revolution 4.0, the telematics sector business is one of the pillars that is a priority to be developed in Indonesia. The main purpose of this study is to construct the prediction model for prospects in the Indonesian telematics LME sector using a deep learning approach. We used data from the 2016 National Economic Census as many as 2500 preprocessed data. The deep learning approach in this study used a multilayer perceptrón (MLP) architecture, 17 attributes, 3 hidden layers and 5 target classes. The attributes in question include province, business owner education, legal entity status, length of operation, business network, total assets, business lava, number of workers, difficulties, partnerships, marketing innovations, comparison of profit with the previous year, and development plans. The target class of prospects are excellent, good, neutral, bad and very bad. The optimal results were achieved in epoch 50 conditions with a learning reate of 0.2 and an accuracy rate of 98.80%. Based on the prediction model, this business prospect can be used as a reference for the development of MLE in the telematics sector in Indonesia. This prospect model still lacks visualization and attribute analysis that affects the classification of prospects for Indonesian telematics MLE. Research development opportunities can be carried out through the integration of the whitebox model in the deep learning model and complementing a web-based graphical user interface (GUI) to make it easier for stakeholders to develop strategies based on the strength of attributes that affect the prospects for MLE Telematics Indonesia. This is expected to boost the competitiveness of the prospects for Indonesian telematics MLE.

Impacts of South Korea’s Political Events in 2017 Towards KRX 100

The capital market is a means for investors to invest in the long term and diversify, spread ownership of the company, and even create a healthy business climate through information disclosure. Political phenomena and events that occur in a country greatly affect the sensitivity of the capital market and can affect investors' decisions in investing in the capital market, because the political situation of a country affects the country's economic conditions. This study was determined of abnormal return and trading volume activity of South Korea’s political events. It has a ten windows period, amounts of samples were 98 companies of KRX 100 by purposive sampling technique. Analysis techniques used one-sample t-test and paired difference test. The result showed there is an abnormal return, a significant negative abnormal return difference, and no significant trading volume activity before and after South Korea’s political events. It means South Korea’s market is said to be efficient in a strong form because it is not influenced by political information and news that occurred in 2017.

Analysis of United States Quantitative Easing Policy on Real Output in Indonesia

This study discusses analysis of United States quantitative easing policy on real output in Indonesia. QE policy not only affects US economy but also influences the economic indicators of other countries, especially Indonesia countries with increasingly integrated market conditions. At present the Indonesia economy has been very open, so that policies originating from abroad can affect the country's economic conditions. The possibility of global spillover against non-conventional monetary policies such as QE. It is using the Vector Autoreggresion (VAR) methods to see the effect of QE policy. The data is time series for the 1999Q1- 2016Q4. This study will analyze the impact of macroeconomic variables such as interest rates, money supply and inflation on GDP. The results of this study indicate that the implementation of the QE policy has an impact on the rate of GDP growth in each country of Indonesia