Financial Dynamics of Listed Banks in Pakistan: Exploring the Interplay between Cost-Income Ratio, Capital Adequacy, and Performance Metrics

/in /by

This study delves into the relationship between the Cost-Income Ratio, Capital Adequacy, and the performance of listed banks in Pakistan. Drawing data from 2014 to 2022 annual reports, the Generalized Method of Moments (GMM) in STATA version 18 is employed for analysis. The findings disclose a negative connection between capital adequacy and performance, particularly return on assets (ROA) and return on equity (ROE). While the correlation lacks statistical significance for ROA, it becomes significant in the context of ROE. Additionally, a statistically significant negative correlation is identified between the cost-income ratio and both ROA and ROE. Total equity debt displays a negative relationship, achieving significance concerning ROA. Bank size demonstrates a significant negative correlation with both ROA and ROE. GDP exhibits a positive link, significant only with ROE. These findings contribute valuable insights into the dynamics of financial indicators influencing bank performance in the Pakistani context.

Forecasting Dead Oil Viscosity Using Machine Learning Processes for Niger Delta Region

/in /by

Prediction of Dead oil viscosity using experimental measurements is highly exorbitant and time consuming, hence the use of forecasting models. Dead oil viscosity is a very important PVT parameter that solve numerous reservoir engineering problems and one of the most required factors for enhanced oil recovery processes. This study utilized two machine learning algorithms of Artificial   Neural Network (ANN) and Support Vector Machine (SVM) to predict dead oil viscosity. A total number of 243 data set was obtained from PVT report from Niger-Delta, out of which, 70% were used to train the models, 15% for testing and 15% for validation.  Quantitative and qualitative analysis was carried out to compare the performance and reliability of the new developed machine learning models with some selected empirical correlations. The result revealed that the Artificial Neural Network Outperformed Support Vector Machine (SVM) as well as common dead oil viscosity empirical correlations with the best rank of 0.144, highest correlation coefficient of 0.984, Mean Absolute Error (Ea) of 0.205, with a better performance plot, followed by Support Vector Machine model with correlation coefficient of 0.926, Mean Absolute Error (Ea) of 0.199 and the rank of 0.176. The new developed Artificial Neural Network model can potentially replace the empirical models for dead oil viscosity predictions for Niger Delta region.

Proposed Knowledge Management System for the Time Efficient in R&D Department of PT Automotive Lumina

/in /by

The automotive industry has grown post-Covid 19, positively impacting related sectors like car lighting. Due to high demand, PT Automotive Lumina, a lamp manufacturer specializing in OEM car lights, needs better time management. Responding to RFQs for new car models poses significant challenges due to delays caused by dependence on Juoku Technology. To tackle this, the company initiated an independent R&D process, but the need for more effective knowledge management worsens the situation, affecting competitiveness in a rapidly changing market.

This research addresses two key questions: What Knowledge Management system can be proposed for time efficiency in the R&D Department of PT Automotive Lumina? and How to implement the suggested Knowledge Management system for time efficiency in the R&D Department of PT Automotive Lumina? Qualitative research methods were employed to study these questions, including interviews with key stakeholders. Analysis revealed eight sub-causes contributing to the observed problems, including a lack of skilled personnel, ineffective knowledge management, reliance on Juoku’s R&D schedule, rapid technological advancements, inefficient processes, limited decision-making authority, limited testing equipment in the R&D department, and most importantly, the absence of a knowledge management system. Several solutions to these challenges are presented based on the SECI, PPT framework, and Core, Advanced, and Innovative knowledge framework. Direct training, knowledge capture, and collaboration tools are among the planned knowledge management programs at PT Automotive Lumina. Preparation procedures were conducted in November and December 2023, with the proposed solution implementation set to begin in February 2024. Following deployment, knowledge management activities will be evaluated, and necessary modifications will be made for future improvements. PT Automotive Lumina aims to enhance time efficiency and support independent R&D through improved knowledge management procedures.

Commodification Study in Socio-Economic Perspective (Study Case: Ponggok Tourism Water Village, Klaten, Central Java)

/in /by

This article aims to describe commodification from the socio-economic perspective the local people in Ponggok Tourism Water Village. It employs a descriptive method to summarize data and information. Several indicators include employment opportunities, income local people, cultural life and social conflict through analysis. The results show that an expansion of employment opportunities, an increase in income and the standard of living of local people. There is a change in a person’s socioeconomic status which is indirectly a result of his involvement in the tourism sector. It cannot be denied that it has also had a negative impact. Not a few residents from outside the area migrate and there are conflicts between residents. Local people life is experiencing changes towards westernization. Ponggok Village was previously known as a village that was far from hedonistic life, but now it is different following the times. Packaging local culture has also become something of global value.

The Dual Dance: Strategies for Success in Profit and Cash Flow Management

/in /by

This comprehensive guide delves into the intricate dynamics of managing profit and cash flow, offering financial managers a strategic roadmap for achieving a delicate balance. Beginning with an exploration of the symbiotic relationships between profit and cash flow, the guide provides insights into common misconceptions and pitfalls that can hinder success. Practical strategies, including working capital management, efficient inventory controls, and rigorous cost management, are meticulously outlined to empower managers in their quest for financial equilibrium. The guide advocates for the integration of cutting-edge tools and technologies, emphasizing the importance of financial forecasting, cash flow projections, and the adoption of accounting software. Moreover, it underscores the significance of balancing long-term and short-term goals, building resilience in economic downturns, and addressing regulatory compliance and financial risk management. Continuous monitoring and adaptation, facilitated through the implementation of Key Performance Indicators and regular financial health checks, are highlighted as essential practices for sustained success. In conclusion, the guide serves as a compass, guiding financial managers through the dynamic landscape of profit and cash flow, transforming challenges into opportunities for enduring prosperity.

A Generic Approach to Entity Resolution Mechanisms for Big Data on Real World Match Problems in the Global Oil and Gas Sector

/in /by

Complex challenges are facing the global oil and gas industry. Oil prices are dropping due to OPEC production level, US oil boom, and other factors. Many experts believe that prices of oil will remain low for years at equilibrium of around $40-50 (Blumberg, 2018; Walls and Zheng 2018; Azar, 2019). Although 2019 oil price is expected to average at $65 with a further decline at $62 by 2020 (Amadeo, 2019; Kasim, 2019). Also, newly commercial resources are extremely expensive to develop, as massive capital investments are required. This research intends to develop a comprehensive entity resolution framework that has the ability to search across multiple databases with disparate forms, tame large amounts of data very quickly, efficiently resolving multiple entities into one, as well as finding hidden connections without human intervention. Putting in place a system to manage these entities will not only help to better assign resources, but to do so in a more expedient fashion. Although the necessary information is mostly already available within the oil and gas companies, it is spread around different company areas and application. Entity resolution will helps to aggregate these data, identify and exploit connection between entities and offer holistic all-in-one information that can helps to identify and deal with potential risk. We therefore present such an evaluation of existing implementations on challenging real-world match tasks. We consider approaches both with and without using machine learning to find suitable parameterization and combination of similarity functions. In addition to approaches from the research community we also consider a state-of-the-art commercial entity resolution implementation. Our results indicate significant quality and efficiency differences between different approaches. We also find that some challenging resolution tasks such as matching product entities from Opec database are not sufficiently solved with conventional approaches based on the similarity of attribute values.

Evaluation of Onion peels as Feed Additive on Blood Profile Broiler

/in /by

The purpose of this study was to assess how adding garlic peel (Allium sativum L.), shallot peel (Allium ascalonicum L.), and onion peel (Allium cepa L.) as additives to the blood profile. The material used was 189 one-day-old commercial broiler chicken (unsexing) and assigned to nine treatment T0(-): basal diet, T0(+): basal diet + antibiotic (zinc bacitracin 0.1%), T1: basal diet + 0.5 % garlic peel, T2: basal diet + 0.5 % shallot peel, T3: basal diet + 0.5 % onion peel, T4: basal diet + 0.25 % garlic peel and 0.25% shallot peel, T5: basal diet + 0.25 %. garlic peel + 0,25% onion peel, T6: basal diet + 0.25% shallot peel + 0.25% onion peel, T7: basal diet + 0.167% garlic peel + 0.167% shallot peel + 0.167% onion peel. The observed variables are hemoglobin, hematocrit, erythrocytes, and leukocytes. The data are analyzed using Analysis of Variance (ANOVA). The study results show that adding three garlic powders has no effect (P>0.05) on hemoglobin, hematocrit, erythrocytes dan leukocytes. In summary, Onion peel flour can be used as a feed additive to enhance the immune system.

Applying Warren Buffet’s Investment Strategy to Indonesia Stock Market

/in /by

In the past five years, there has been a significant surge in local Indonesian investors, predominantly comprising millennials and Gen Z, contributing to the positive growth of the stock market. However, a concerning phenomenon known as “herding bias” has been observed, highlighting the tendency of young investors to follow prevailing trends without fully understanding the intricacies and risks associated with their investments. This paper aims to address this issue by enhancing public awareness of stocks and minimizing associated risks, focusing specifically on value investing as an effective strategy. The research utilizes a ten-year period, selecting samples from companies listed in IDX30 through Warren Buffett’s screening test. This test incorporates key factors such as Conservative Debt, Price-to-Earnings Ratio (P/E Ratio), Price-to-Book Ratio (P/BV Ratio), Debt to Equity Ratio (D/E Ratio), and Return on Equity (ROE). Following the sample selection, the paper calculates the intrinsic value of chosen stocks, determines the margin of safety, estimates expected returns, and evaluate the risk-adjusted performance. Additionally, qualitative factors like understanding the business, market conditions, and internal company factors are considered. The findings indicate that Warren Buffett’s investment strategy can be successfully applied to Indonesian stocks. Investors are advised to select stocks based on careful consideration of financial ratios, analyzing annual reports, calculating intrinsic values, determining margins of safety, and assessing expected returns alongside associated risks. This research provides valuable insights that can serve as a practical reference for stock investment decisions, particularly for stocks with high market capitalization. By promoting a comprehensive understanding of value investing, this paper aims to empower young Indonesian investors to make informed and strategic choices, thereby mitigating the impact of herding bias in the local investment landscape.

Impact of Related Lending on Bank Health: Case Study in Indonesia Banking Industry

/in /by

Related lending is a critical driver of banks’ health, particularly on its profitability and risks profile. As banks engage in related lending activities, they face challenges in managing profitability and assessing various risks, including systemic and credit risks. Nevertheless, the banking literature presents divided views on this: the information view and the looting view. The information view posits that related lending could enhance bank profitability and reduce risks through improved information symmetry between banks and borrowers. Conversely, the looting view theorizes that related lending may deteriorate banks’ performance, reducing profitability and increasing risks, primarily due to the misallocation of resources and the prioritization of personal interests by banks’ insiders.

The challenges of related lending have been intensified by the global crisis of the COVID-19 pandemic. Empirical research indicate that banks tend to increase lending to related parties by up to 20% during economic difficulties, with more significant effects in emerging economy such as Indonesia. This trend is reflected in increasing related lending ratio and deteriorating financial indicators of publicly listed Indonesian banks, such as declining profitability ratios of return on assets (ROA) and net interest margin (NIM), as well as increasing risk ratios of higher non-performing loans (NPL) during the pandemic’s onset.

Therefore, this study will investigate the impact of related lending on bank health of publicly listed Indonesian banks across two critical periods, before crisis (2013-2019) and during the crisis due to the pandemic (2020-2022). By employing a quantitative approach through regression analysis, this study will be able to assess the relationship between bank profitability and risk ratios with their corresponding variables. The aim is to provide empirical evidence on whether related lending enhance or impair bank performance in terms of profitability and risk, particularly under the economic strains brought by the pandemic.

The Determination of Maximum Flow Rate in Well X Layer Y Field Z

/in /by

The field development target which usually in field X are mostly carried out in gas reservoirs over time, in several candidate wells gas reserves have started to run low so that the remaining oil reserves and pressure in the reservoir naturally decrease or natural depletion. This is due to continuous production of gas reserves. One of the wells that has the biggest potential for oil reserves in field X is well A9. In this study, the maximum flow rate was determined in the A9 layer A well in field X with a skin value of 10 using manual calculations. From manual calculations on the condition of well X with skin 10 Qmax Well X from the IPR plot results with manual calculations is 25.87 bbl/day with a PI value obtained 0.009371 stb/d/psi meanwhile in well condition X without skin the Qmax value obtained is 40.12 bbl/day with a skinless J value of 0.01457 stb/d/psi. From the determination of IPR on ECRIN, the Qmax value in well X without skin is 43.08 bbl/day, meanwhile in the condition of well X with skin 10 the Qmax value obtained is 25.84 bbl/day