Investment Products Research Articles

COVID‐19 pandemic in Ethiopia: Socioeconomic impacts and government recovery measures

AbstractThe study assesses the implications of the COVID‐19 pandemic and the government responses for the economic performance and poverty incidence in Ethiopia for the fiscal years of 2019/20 and 2020/21. It accounts for the impacts of the pandemic on factor productivity, trade costs, export demand, tourism, remittances and foreign direct investment (FDI). An economy‐wide multi‐sectoral model determines impacts at macroeconomic, sectoral, and household levels. A poverty analysis module characterises the effects of the pandemic on food poverty headcount, gap, and severity. Results show that the COVID‐19 impacts could have been significant across all macroeconomic metrics had the government not intervened through fiscal and spending measures. However, much of the recovery through government intervention was driven by agricultural and food processing sectors while output in manufacturing, construction and services sectors continued to be negatively affected. Without government intervention, the food poverty headcount would have increased by about five percentage points. The government measures have mitigated that effect allowing food poverty to reach pre‐COVID‐19 values in 2020/21. Nevertheless, with all measures combined, poor urban households may have been left behind and would require more targeted support to compensate for a significant loss of income.

Analysis of 3D-Printing Layer Height Pattern Effect to ADC12’s Mechanical Properties Investment Casting Results

This study aims to determine the mechanical properties, both hardness and tensile strength of investment casting results from 3D-Printing patterns. In this analysis, the aluminum scrap comes from the piston and will later be Die Casted and become ADC 12. The pattern used Poly Lactid Acid material, then the pattern is printed with varying layer height. After that, analysis was carried out using experimental for surface roughness and hardness of ADC12. The Hardness value is used to get the Tensile strength of ADC12 from empirical aproach. The results of the analysis show that the hardness and tensile strength of the ADC12 casting investment product have decreased due to the increasing value of the surface roughness of the cast product which is influenced by the use of a larger layer thickness.

Composite Supply Index of Investment Products as a Proxy Indicator of Fixed Capital Investment

Composite Supply Index of Investment Products as a Proxy Indicator of Fixed Capital Investment

ECONOMIC EFFICIENCY OF INVESTMENTS IN THE GROWING OF MEDICINAL HERBS AND SPICES

Medicinal herbs are important for human nutrition and industrial processing. The main goal of research is assessment of economic effectiveness of investments in seedlings production and establishment of plantation under mentioned crops. Analysis involves data from the coal mine complex in Pljevlja (Montenegro), while implies dynamic methods such are Net Present Value, (Modified) Internal Rate of Return, and Payback Period. Although research results favored the seedlings production, determined crossover rate indicates some opposite conclusions. Inconsistency in conclusions according to Net Present Value and Internal Rate of Return occurs only for certain range of discount rate. Research also implies sensitive analysis of crossover rate according to changes in volume of invested assets. So, management could make appropriate decisions towards the investments in medicinal plants production. Derived results suppose that investment in production of medicinal herbs seedlings is economically more effective for all discount rates above the crossover rate (6.08%).

부동산 크라우드펀딩 특성으로 인한 투자참여의사가 활성화 방안에 미치는 연구

With the development of IT technology, crowdfunding is growing as a new financing method that connects demanders and suppliers online through the Internet. In addition, efforts to increase assets through investment have become very important, making it inevitable to expand investment in financial investment products that seek higher returns while managing risks to some extent. As a result, loan-type crowdfunding (P2P) is emerging as a new investment alternative.The purpose of this study is to study real estate investment decisions using real estate crowdfunding, and to suggest a direction to expand and revitalize the investment participation of the general public through crowdfunding. As a result of the analysis, investor characteristics, real estate market characteristics, investment product characteristics, and platform characteristics were found in the order, and the higher the importance, the higher the impact on real estate investment decision-making. This served as the basis for being in the spotlight as a new financial product, and in conclusion, first, it is the need for systematic and continuous financial and investment education from the perspective of investors. Second, in terms of platforms, P2P loan brokers must ensure transparency and safety of funds. Third, in terms of investment products, it is necessary to develop financial products, establish marketing and education strategies, and improve the quality of financial counseling services. Fourth, in terms of the real estate market, if the market such as policy and interest rates stabilizes and the real estate market is activated, it will be closer to each other’s goals not only in investment decisions through crowdfunding but also in revitalizing real estate investment participation.

Application of the Support Vector Regression Method with the Grid Search Algorithm to Predict Movement Gold Price

Gold is an investment with the smallest risk because it can be sold anytime and anywhere. In Indonesia, gold bullion as an investment product is known for its purity level of 99.99%, namely gold bullion produced by PT. Aneka Tambang (Antam) through its Precious Metals business unit. Apart from its pure production, Antam gold bullion is easier to resell anytime and anywhere because it has an official certificate from the international gold standardisation institution, namely LBMA (London Bullion Market Association), to more easily estimate the value of gold bullion when sold. To overcome this, predictions of future gold prices are needed. In this research, one of the prediction methods is Support Vector Regression with the Grid Search Algorithm. In this method this method will be used to predict the price of gold, which aims to predict and find out the price of gold one year in the future to produce a level accuracy (MAPE) of 5.43% and the prediction of gold prices increasing from 2023-June-01 to 2024-March-23 while experiencing a decline starting in 2024-March-24. Research by examining the relationship between variables, which emphasises data consisting of numbers so that it is analysed based on statistical procedures using the Support Vector Regression method with data sourced from the daily price of gold bullion through PT. Gallery 24 Pawnshops, North Sumatra. Where this method is very well used in predicting by choosing the best kernel used is the linear kernel because, from these three kernels, the best hyperparameters were obtained for predicting gold price movements using a linear kernel with a division for training and testing data of 60: 40. The MAPE value obtained was 5.43.

리츠(REITs) 배당가능이익에 대한 세제의 문제점 및 개선방안

REITs are indirect real estate investment products that collect funds from investors, invest them in real estate-related securities, and return the profits to investors. In the case of domestically listed REITs, if dividendable profits are generated, more than 90% must be paid as dividends. Through this, corporate tax is exempted. The government has made great efforts to expand the size of the public REITs and public real estate fund markets. Nevertheless, REIT investment is less active in some areas than major overseas countries. Recently, under the Corporate Tax Act, the principle is not to include valuation gains and losses in the tax base. However, arguments have raised that an exception should be recognized for REIT valuation losses. This is because undistributed profits due to valuation losses of REITs, which are indirect investment vehicles, are included in the taxable list, and corporate tax is paid, causing the double taxation problem for REIT investors. Considering this situation, this study examined the problems and improvement measures of the current REIT-related tax system for the growth of Korea's REIT market. In addition, we sought to derive implications for tax support for the REITs market through examples from significant countries. If we want to revitalize the REITs market more than currently, support from government policies is essential. Among them, tax support occupies the most important position. In order to dramatically grow the REITs market, it is necessary to provide bold tax benefits as in other countries. This study raised the need to improve Korea's current REIT tax system, as in the cases of the United States and Japan, and attempted to establish a taxation plan for the REIT tax system suitable for Korea's circumstances. One of the most urgent tasks in revitalizing REIT investment is to quickly resolve the conflicting issues between commercial law and corporate tax law to prevent double taxation of REITs. In addition, the tax support plan presented in this study is expected to minimize tax problems occurring in the REIT market and promote a reasonable taxation plan for the REIT market that is consistent with Korea. The policy alternatives in this study are expected to help use them as primary data when establishing policy alternatives for tax authorities or related ministries in the future.

Enhancing Investment Decisions Through Financial Literacy: The Mediating Role of Income in Medan's Community Using SEM-PLS Modeling

The objective of this research is to elucidate and analyze the extent to which foundational knowledge of investment products and financial analysis influences investment decisions in Medan, incorporating community income enhancement in Medan as a mediating variable. The subjects of this study are entities that utilize green energy, where the independent variables are the basic understanding of investment products and financial analysis, the dependent variable is the investment decision-making in Medan, and the intervening variable is the enhancement of community income in Medan. Data analysis employed the Structural Equation Modeling (SEM) technique utilizing SMART PLS 4.0 software. Data collection methods included questionnaires and observation. The research methodology applied was quantitative descriptive analysis utilizing the SEM method, where data processing with SEM was performed using PLS 4.0 application. The findings indicate that, partially, the variables of basic understanding of investment products and financial analysis significantly affect investment decisions and impact the enhancement of community income. Simultaneously, these variables influence investment decisions through the mediating variable of community income enhancement. A deeper understanding of investment products and financial analysis enables investors to assess whether such conditions can enhance community income, thereby aiding in the decision-making process regarding the allocation of capital for the economic upliftment of the community.

When to switch from pasture to compost barn? An economic analy-sis of four scenarios in Minas Gerais, Brazil

In Brazil, producers are migrating from pasture production systems to compost barns in search of greater profitability. These decisions are taken without an analysis of the cost, economic feasibility, and risks involved in this change. Our study investigates the economic feasibility and risk of implementing the compost barn system and compares it to pasture production in the state of Minas Gerais’ southern region. Four scenarios were developed using data from literature and a panel of experts. They inform the production margin, the evaluated production system, investment data, revenue data and cost data. The scenarios were evaluated using a Monte Carlo simulation model. All scenarios had negative minimum values for NPV and IRR, indicating the probability of not being economically viable and demonstrating the risks involved in the dairy activity. However, scenarios 2 (Pasture, from 501 to 1,000 liters/day) and 4 (Compost Barn, from 1,000 to 2,000 liters/day) had positive means for NPV, with the highest probability of occurring values greater than zero. The analysis of the IRR result reinforces the better performance presented by scenarios 2 and 4, with greater probabilities of positive return and, consequently, lower risk. Scenarios 1 (Pasture, up to 500 liters/day) and 3 (Compost Barn, from 501 to 1,000 liters/day) can be discarded by the producer as an investment alternative.

Firm-Level and Macroeconomic Determinants of R D Investmentsin Turkey

Purpose–The purpose of this study is to examine firm-level and macroeconomic determinants of R&D investments in Turkey. Design/Methodology/Approach–This study employs two groups of variables to explain R&D investments of publicly traded firms in Turkey. The first group contains firm-level financial indicators, containing liquidity, profitability, tangibility, productivity and financial leverage. The second group includes macroeconomic variables such as inflation rates, industrial production, economic growth, foreign trade and foreign direct investments. To exhibit the impact of two groups of factors, this study applies twopanel data regression models. Findings–The findings indicate that macroeconomic variables do not explain firm-level R&D investments. The only significant variable is inflation rates, which have an adverse effect on R&D decisions. On the other hand, firm-level financial variables explain the variation in R&D investments quite well. Liquidity and financial leverage significantly and positively influence innovation and research while profitability has a negative impact on them.Discussion–The results indicate that firm-level R&D expenditures are mostly robust to macroeconomic conditions, while internal determinants are more significant in explaining R&D behavior

EKSPLORASI TEORI DISRUPSI DIGITAL CLAYTON CHRISTENSEN DAN MAQASHID SYARIAH TERHADAP INOVASI PERBANKAN SYARIAH DI ERA DIGITAL

This research holistically explains the innovation of Islamic banking in the digital era, where, in the last decade, the digital revolution has changed the way financial institutions operate, triggering challenges and opportunities for Islamic banking. Through in-depth desk research with qualitative research based on analytical descriptive analysis to highlight the complex challenges and innovations faced by Islamic banking accompanied by Clayton Christensen's Digital Disruption theoretical framework and Maqashid Syariah concept. This research highlights the various challenges Islamic banking faces, ranging from integrating digital technology with Sharia principles to competition with conventional financial institutions, the information gap and the need for competent human resources. On the other hand, innovations in Islamic banking products in the digital era include using technologies such as mobile banking, blockchain, and AI to improve services and operational efficiency and expand access to finance through innovative financing and investment products. Analysis based on Clayton Christensen's Digital Disruption theory reveals how Islamic banking adopts technology to offer more efficient and personalised solutions to customers, while the Maqashid Shariah perspective shows how such innovations fulfil the objectives of Islamic law, such as safeguarding religion, soul, mind, offspring, and property, as well as protecting the environment. Overall, Islamic banking product innovation in the digital era illustrates the synergy between technology and Sharia principles to create a fair, inclusive, sustainable financial ecosystem. By continuously developing solutions that meet the needs of modern society while maintaining the integrity of Sharia values, Islamic banking is at the forefront of providing added value to customers and society while opening up new economic growth opportunities.

Private and Public Investment: Are they Substitute or Complementary in Driving Economic Growth in Nigeria?

The paper investigates the relationship between government and private investment with a view to ascertaining their complementarity or substitutability in driving economic growth in Nigeria for the period of 1981-2021. To achieve the objective, nonlinear autoregressive distributed lags (ARDL) models and nonlinear Granger causality tests were employed to analyze annual secondary data which were sourced from the Central Bank of Nigeria Statistical Bulletin. Findings show that government investment expenditure displayed a positive and long-term influence on private investment, suggesting a “crowding-in” effect where increased public investment stimulates private investment thereby fostering overall economic growth. Economic adversities or reduced public investment exerted a substantial negative impact on private investment, underscoring the complementarity of government investment expenditure in driving economic growth in Nigeria. Also, the nonlinear Granger causality tests reveal unidirectional relationship as government investment expenditure causes private investment across various embedding dimensions, emphasizing the positive influence of government investment on private investment. The findings generally suggest the need for balanced fiscal policies, prioritizing productive government investments, encouraging private investment, controlling inflation, and making time-sensitive policy decisions to promote economic growth in Nigeria.

Forecasting ESG Stock Indices Using a Machine Learning Approach

As the demand for investment products tied to environmental, social and governance (ESG) concerns rises, ESG stock indices have been established. These indices aim to aid investors in navigating and assessing the risks associated with firms based on ESG factors and potential investment returns. The objective of the article is to predict ESG stock indices using a machine learning approach. We use daily data of Dow Jones Sustainability Index (DJSI) World, DJSI Asia Pacific and DJSI Emerging Market from 2018 to 2022 as samples. Two-layer ensemble model – combination of support vector machine (SVM), random forest (RF), long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms – is employed to forecast the indices. The results show that the ensemble model accurately forecasts the indices, with the prediction line closely matching the actual values. It gives the implication that investors are able to improve investment decisions, assist in managing investment risk, and optimize their portfolio diversification. Meanwhile, policymakers are able to anticipate economic trends, inflation and interest rates, assisting in the development of successful economic policies. This research article presents a machine learning approach for predicting ESG stock indices. The proposed model combines SVM, RF, LSTM and GRU algorithms to create a powerful two-layer ensemble model that outperforms individual models. The results show that the ensemble model accurately forecasts ESG stock indices, with the prediction line closely matching the actual values. The model offers insights into the behaviour of different algorithms, highlighting their strengths and limitations. The proposed model can guide decision-making processes, support investment strategies, and ultimately contribute to advancing sustainable investment practices.

PENGARUH DANA PIHAK KETIGA PERBANKAN SYARIAH TERHADAP PERTUMBUHAN EKONOMI DI INDONESIA: A Theoritical Approach

Economic growth is an important determinant of individual income levels and the growth rate of a particular region, measured through GDP. The NPF ratio serves as a metric to assess credit quality or the bank's competence in managing credit risk. DPK includes funds obtained from the public in the form of investment products. The methods used in this article include a qualitative approach involving a literature study or library-based research. The purpose of this investigation involves analyzing the prevailing theories through a comparative examination of the literature pertaining to the research results. The literature used in this study includes research findings and reviews derived from nationally recognized scholarly articles in accordance with financial management. All articles used in this study were sourced from the electronic data search engines ResearchGate and Google Scholar. Through the analysis of the determinants of Islamic banking (NPF and DPK), a significant correlation was identified. The impact of NPF on economic growth manifests in its capacity to increase borrowers' ability to repay loans, while simultaneously incurring an escalation of loan risk. Similarly, the effect of Third Party Funds on economic growth is illustrated by the important role that economic growth plays in the capitalization of Islamic banks, consequently exerting a major influence on the size of third party funds attracted by Islamic banks. Based on the aforementioned findings, it is highly recommended that researchers proceed to conduct a comprehensive literature review to explore additional independent variables (such as Return on Assets and Capital Adequacy Ratio) that impact economic growth during the Covid-19 pandemic.

Optimal production and carbon reduction investment decision for manufacturers with leasing–selling strategies considering financing

AbstractFor a capital‐constrained manufacturer, in the context of carbon reduction, whether products should be sold or leased and which financing mode is more beneficial are usually debated. By considering Carbon Emission Permits Repurchase Financing (CEPRF) and Green Credit Financing (GCF), the optimal decision‐making models with different strategies are presented. The Karush–Kuhn–Tucker theorem is applied and the results are compared. Meanwhile, the influence of initial capital and interest rates on the manufacturer's decision‐makings, profits, and environment are explored. The results show that: under the selling strategy, when the initial capital is small, GCF is better for the manufacturer; otherwise, CEPRF is better; under the leasing strategy, both optimal production and carbon reduction investment with CEPRF are lower than that with GCF, but their profits are higher. With CEPRF, the leasing strategy would bring more economic and environmental benefits to the manufacturer; with GCF, the selling strategy would result in more economic and environment benefits.

Evaluation of polyhydroxyalkanoate recovery from food waste by sequencing batch biofilm reactor with high mixed microbial biomass

In this study, the enrichment sequencing batch biofilm reactor (SBBR) and conventional enrichment reactor (SBR) were established, and a new strategy was developed to achieve higher biomass yield and polyhydroxyalkanoate (PHA) production from food waste than those achieved through the conventional method. After the 10th day of an expanded cultivation period, the respective active biomass concentrations in SBR and SBBR were 5.16 and 13.92 g/L, indicating a significant improvement in the biomass yield of mixed microbial culture in the SBBR. The PHA accumulation test revealed that maximum PHA concentration in SBR and SBBR reached 6.60 and 12.70 g/L, respectively, after 80 days of enrichment, indicating that compared to SBR, the SBBR could more effectively improve the PHA accumulation capacity. Meganema, Thauera, and Paracoccus were the dominant PHA-accumulating bacteria in the SBR and SBBR. Additionally, the volumetric productivity of the SBBR was 3.22 g COD/L/d, twice that of the SBR. This study provides new strategies and insights for improving PHA production and reducing the costs of PHA production and reactor investment.

The Effects of Foreign Direct Investment on Economic Growth (Gross Domestic Product) in Tanzania

This study aims to analyse the effects of Foreign Direct Investment on Economic Growth (Gross Domestic Product) in Tanzania. The World Bank’s data on foreign direct investment and gross domestic product from 1988 to 2021 was employed in order to achieve the purpose of the study. The Vector Autoregressive Regression (VAR) Model was selected, in which a neoclassical growth model was applied within the implementation of this model. According to the findings of the research that was carried out, the Gross Domestic Product was primarily influenced by its prior values, which suggests that the economy is capable of some degree of self-sufficiency in terms of driving changes in the economy. According to the findings, foreign direct investment had a relatively minimal direct impact on gross domestic product, which suggests that other factors played a more significant role in influencing economic performance. There was no bidirectional causal relationship between gross domestic product and foreign direct investment; they both functioned independently of one another. It has been established that endogenous shocks have a significant role in driving changes in both GDP and FDI, which underscores the impact that internal forces have on the economic system. The importance of taking into account internal factors while conducting an analysis of economic growth has been brought to light as a result of these findings, which have contributed to a better understanding of the complex relationship that exists between GDP and FDI.

Basic Farmland Construction in China: A cost‐effective investment for agricultural productivity and disaster resistance

AbstractStarting in 2006, China launched a project to carry out Basic Farmland Construction in 116 representative counties. The objective of this project was to enhance agricultural productivity. China invested 30 billion yuan in this project from 2006 to 2013. Using a difference‐in‐differences approach, this study estimates that Basic Farmland Construction increased per capita agricultural output by 6.3%, crop sown area by 5%, and agricultural mechanization by 7.3%. The total benefits of the project far exceeded the total investments. By incorporating with rainfall shocks, this study finds that the project could mitigate most of the damage from droughts and floods.

Suitability of risk assessment tools used during the portfolio recommendation process

Orientation: Investors are overwhelmed with Collective Investment Schemes (CIS) portfolio options.Research purpose: The study set out to assess the emerging themes considered essential and prevalent in establishing a risk profile, establishing a framework for risk assessment tools, and evaluating existing risk assessment tools used in the industry.Motivation for the study: In 2020, newly amended legislation confirmed the importance of a risk assessment. This legislation made it mandatory and compulsory for financial planners to conduct a risk profile analysis concerning investment products in South Africa. Consequently, CIS have implemented various risk assessment tools to establish the investor risk profile.Research approach/design and method: Documentary research methodology was employed, where secondary data were analysed qualitatively. Twelve risk assessment tools were collected, and the established framework was used to evaluate existing risk assessment tools.Main findings: A sound theoretical framework for risk assessment tools, which is comprehensive in nature and include demographic factors, risk categorisation, and regulatory factors, was established. The results indicate that current tools used in the industry are inconsistent and do not address all the factors required to establish an investor risk profile.Practical/managerial implications: The study is significant in that it guides the financial services industry into the elements of assessment in establishing an investor risk profile.Contribution/value-add: A risk profile analysis is compulsory for financial planning. Financial planners and policymakers can consider the established framework to implement a standardised, comprehensive, holistic, risk assessment tool.

Explaining Youth Unemployment in Malaysia. The Auto-Regressive Distributive Lag (ARDL) Approach

This study examines the influence of macroeconomic factors, namely Gross Domestic Product (GDP), Inflation, Population and Foreign Direct Investment (FDI) on Youth Unemployment in Malaysia from 1991 to 2021 using time series analysis. The data was obtained from World Bank Data and analyzed using EViews software. The time series data was conducted using the Augmented Dickey-Fuller (ADF) test. The Auto-Regressive Distributive Lag (ARDL) approach to cointegration was then employed to determine the short- and long-term analysis of the series. The ARDL bound test analysis indicates that there is a cointegration relationship between macroeconomic factors and youth unemployment. The results suggest that economic growth, inflation, and population have a negative and significant impact on youth unemployment, while foreign direct investment has a positive but insignificant effect on youth unemployment in the long term. Upon analyzing the short-term outcome, it was observed that all factors exhibited a negative correlation and exerted a substantial impact on youth unemployment’s rate.