Search results for: financial forecasting

Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

Procedia PDF Downloads 146

Authors: Syed Noaman Shah

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The current paper evaluates the effect of external public debt risk on the borrowing cost of private non-financial firms in euro zone. Further, the study also treats the impact of austerity measures on syndicated-loan spreads of private firm followed by euro area member states to revive the economic growth in the region. To test these hypotheses, we follow multivariate ordinary least square estimation method to assess the effect of external public debt on the borrowing cost of private firms. By using foreign syndicated-loan issuance data of non-financial private firms from 2005 to 2011, we attempt to gauge how the private financing cost varies with high levels of sovereign external debt prevalent in the euro zone. Our results suggest significant effect of external public debt on the borrowing cost of private firm. In particular, an increase in external public debt by one standard deviation from its sample mean raises syndicated-loan spread by 89 bps. Furthermore, weak creditor rights protection prevalent in member states deepens this effect. However, we do not find any significant effect of domestic public debt on the private sector borrowing cost. In addition, the results show significant effect of austerity measures on private financing cost, both in normal and in crisis period in the euro zone. In particular, one standard deviation change in fiscal consolidation conditional mean reduces the syndicated-loan spread by 22 bps. In turn, it indicates strong presence of credibility channel due to austerity measures in euro area region.

Keywords: corporate debt, fiscal consolidation, sovereign debt, syndicated-loan spread

Procedia PDF Downloads 396

Authors: Thanh Van Hoang, Tien Yin Chou, Yao Min Fang, Yi Min Huang, Xuan Linh Nguyen

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Reliable information on the spatial distribution of annual rainfall and temperature is essential in research projects relating to urban and regional planning. This research presents results of a classification of temperature and rainfall in the Red River Delta of northern Vietnam based on measurements from seven meteorological stations (Ha Nam, Hung Yen, Lang, Nam Dinh, Ninh Binh, Phu Lien, Thai Binh) in the river basin over a thirty-years period from 1982-2011. The average accumulated rainfall trends in the delta are analysed and form the basis of research essential to weather and climate forecasting. This study employs interpolation based on the Kriging Method for daily rainfall (min and max) and daily temperature (min and max) in order to improve the understanding of sources of variation and uncertainly in these important meteorological parameters. To the Kriging method, the results will show the different models and the different parameters based on the various precipitation series. The results provide a useful reference to assist decision makers in developing smart agriculture strategies for the Red River Delta in Vietnam.

Keywords: spatial interpolation method, ArcGIS, temperature variability, rainfall variability, Red River Delta, Vietnam

Procedia PDF Downloads 311

Authors: Joyly Jill Apud

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Corruption is an all-encompassing issue worldwide. Many attempts have been done to address such cases especially by the government through increasing transparency. The Philippine government increased the mechanism of transparency by opening to public its financial transactions through Full Disclosure Policy – mandating all local governments to post in their websites all financial transactions (Philippine Public Transparency Reporting Project, 2011). For transparency to be fully realized, the challenge lies in creating a mechanism where the constituents are encouraged to engage as social auditors. In line of the said challenge, the study focused in Davao City, Philippines measuring the respondent’s awareness, access and utilization of Full Disclosure Policy (FDP). Particularly, this study determined the significant difference on the awareness, access and utilization of respondents when grouped according to sector and the significant relationship between respondents’ awareness and in the access and utilization of FDP reports. The study used descriptive-correlation, Mean, Anova and Pearson R as statistical treatment. The 120 respondents are from the different sectors of Davao City. These are the Academe, Youth, LGUs, NGOs, Business, and Church groups. The awareness of the respondents was measured in three main categories: Existence of the Policy, Content of the Policy and the Manner of Publication. Access and Utilization of the FDP reports is divided into three: Budget Reports, Procurement Reports and Special Purpose Fund Reports. Results showed that the respondents are moderately aware of the Policy. Though it manifested that the respondents are aware of the disclosure, they are unaware of the Full Disclosure Policy and Full Disclosure Policy Portal. Moreover, the respondents seldom access and utilize all the FDP reports. Further results revealed that there is a significant difference in the awareness and the access and utilization of FDP when grouped according to sector. Moreover, significant relationship in the awareness and the access and utilization of the FDP is evident. It showed that the higher the awareness on FDP, the higher the level of access and utilization on the FDP reports.

Keywords: corruption, e-governance, budget transparency, participation

Procedia PDF Downloads 363

Authors: Alexandru-Ion Marinescu

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There exist a plethora of methods in the scientific literature which tackle the well-established task of credit score evaluation. In its most abstract form, a credit scoring algorithm takes as input several credit applicant properties, such as age, marital status, employment status, loan duration, etc. and must output a binary response variable (i.e. “GOOD” or “BAD”) stating whether the client is susceptible to payment return delays. Data imbalance is a common occurrence among financial institution databases, with the majority being classified as “GOOD” clients (clients that respect the loan return calendar) alongside a small percentage of “BAD” clients. But it is the “BAD” clients we are interested in since accurately predicting their behavior is crucial in preventing unwanted loss for loan providers. We add to this whole context the constraint that the algorithm must yield an actual, tractable mathematical formula, which is friendlier towards financial analysts. To this end, we have turned to genetic algorithms and genetic programming, aiming to evolve actual mathematical expressions using specially tailored mutation and crossover operators. As far as data representation is concerned, we employ a very flexible mechanism – LINQ expression trees, readily available in the C# programming language, enabling us to construct executable pieces of code at runtime. As the title implies, they model trees, with intermediate nodes being operators (addition, subtraction, multiplication, division) or mathematical functions (sin, cos, abs, round, etc.) and leaf nodes storing either constants or variables. There is a one-to-one correspondence between the client properties and the formula variables. The mutation and crossover operators work on a flattened version of the tree, obtained via a pre-order traversal. A consequence of our chosen technique is that we can identify and discard client properties which do not take part in the final score evaluation, effectively acting as a dimensionality reduction scheme. We compare ourselves with state of the art approaches, such as support vector machines, Bayesian networks, and extreme learning machines, to name a few. The data sets we benchmark against amount to a total of 8, of which we mention the well-known Australian credit and German credit data sets, and the performance indicators are the following: percentage correctly classified, area under curve, partial Gini index, H-measure, Brier score and Kolmogorov-Smirnov statistic, respectively. Finally, we obtain encouraging results, which, although placing us in the lower half of the hierarchy, drive us to further refine the algorithm.

Keywords: expression trees, financial credit scoring, genetic algorithm, genetic programming, symbolic evolution

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Authors: Shoaib Yousaf

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The main purpose of this research is to the empirical investigation of the bullwhip effect under sensitivity analysis in the two-tier supply chain. The simulation modeling technique has been applied in this research as a research methodology to see the sensitivity analysis of the bullwhip effect in the rice industry of Pakistan. The research comprises two case studies that have been chosen as a sample. The results of this research have confirmed that reduction in production delay reduces the bullwhip effect, which conforms to the time compressing paradigm and the significance of the reduction in production delay to lessen demand amplification. The result of this research also indicates that by increasing the value of time to adjust inventory decreases the bullwhip effect. Furthermore, by decreasing the value of alpha increases the damping effect of the exponential smoother, it is not surprising that it also reduces the bullwhip effect. Moreover, by reducing the value of time to work in progress also reduces the bullwhip effect. This research will help practitioners and operation managers to reduces the major costs of their products in three ways. They can reduce their i) inventory levels, ii) better utilize their capacity and iii) improve their forecasting techniques. However, this study is based on two tier supply chain, while in reality the supply chain has got many tiers. Hence, future work will be extended across more than two-tier supply chains.

Keywords: bullwhip effect, rice industry, supply chain dynamics, simulation, sensitivity analysis

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Authors: Svetlana Bialkova, Marnix van Gisbergen

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Advanced Virtual Reality (VR) technologies offer compelling multisensory and interactive experiences applicable in various fields from education to entertainment. However, serious VR applications within the financial sector are scarce, and managing ‘real’ business services with(in) VR is a challenge inviting further investigation. The current research addresses this challenge, by exploring the key parameters influencing the VR business power and the development of appropriate VR applications in real financial business. We conducted profound investigation of both B2B and B2C needs, and how these could be met. In three studies, we have approached experts from leading international banks (finance to computer specialists), and their (potential) customers. Study 1 included focus group discussions with experts. First, participants could experience different VR devices such as Samsung Gear VR, then a structured discussion was held. The outcomes are analyzed and summarized in a portfolio. Study 2 further used the portfolio analyzer to profile the management of real business services with(in) VR. Again experts participated, where first being introduced with Samsung Gear, then experiencing it and being interviewed. Based on the outcomes, a survey was developed to interview (potential) customers and test ideas created (Study 3). The results suggest that developing proper system architectures to connect people and to connect devices is crucial for building up powerful business with(in) VR. From one side, connecting devices, e.g., pairing mobile Head Mounted Displays for VR with smart-phones and/or wearable technologies would be appropriate way “to have” customers anywhere, anytime with a brand and/or business. Developing VR Apps, providing detailed real time visualization of performance and infrastructure types could enable 3D VR navigation, 3D contents viewing, but also being opportunity for connecting people in collaborative platforms. The outcomes of the current research are summarized in a model which could be applied to unlock the real business power of VR.

Keywords: business power, B2B, B2C, VR applications

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Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

Procedia PDF Downloads 107

Authors: Tengqin Han

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Delinquency has been a crucial factor in economics throughout the years. Commonly seen in credit card and mortgage, it played one of the crucial roles in causing the most recent financial crisis in 2008. In each case, a delinquency is a sign of the loaner being unable to pay off the debt, and thus may cause a lost of property in the end. Individually, one case of delinquency seems unimportant compared to the entire credit system. China, as an emerging economic entity, the national strength and economic strength has grown rapidly, and the gross domestic product (GDP) growth rate has remained as high as 8% in the past decades. However, potential risks exist behind the appearance of prosperity. Among the risks, the credit system is the most significant one. Due to long term and a large amount of balance of the mortgage, it is critical to monitor the risk during the performance period. In this project, about 300,000 mortgage account data are analyzed in order to develop a predictive model to predict the probability of delinquency. Through univariate analysis, the data is cleaned up, and through bivariate analysis, the variables with strong predictive power are detected. The project is divided into two parts. In the first part, the analysis data of 2005 are split into 2 parts, 60% for model development, and 40% for in-time model validation. The KS of model development is 31, and the KS for in-time validation is 31, indicating the model is stable. In addition, the model is further validation by out-of-time validation, which uses 40% of 2006 data, and KS is 33. This indicates the model is still stable and robust. In the second part, the model is improved by the addition of macroeconomic economic indexes, including GDP, consumer price index, unemployment rate, inflation rate, etc. The data of 2005 to 2010 is used for model development and validation. Compared with the base model (without microeconomic variables), KS is increased from 41 to 44, indicating that the macroeconomic variables can be used to improve the separation power of the model, and make the prediction more accurate.

Keywords: delinquency, mortgage, model development, model validation

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Authors: Jiemiao Chen, Shuoxun Xu

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The main goal of our research is to apply the Vine copula measuring dependency between price, temperature, and precipitation indices to calculate a fair crop insurance premium. This research is focused on Worth, Iowa, United States, over the period from 2000 to 2020, where the farmers are dependent on precipitation and average temperature during the growth period of corn. Our proposed insurance considers both the natural risk and the price risk in agricultural production. We first estimate the distributions of crops using parametric methods based on Goodness of Fit tests, and then Vine Copula is applied to model dependence between yield price, crop yield, and weather indices. Once the vine structure and its parameters are determined based on AIC/BIC criteria and forecasting price and yield are obtained from the ARIMA model, we calculate this crop insurance premium using the simulation data generated from the vine copula by the Monte Carlo Simulation method. It is shown that, compared with traditional crop insurance, our proposed insurance is more fair and thus less costly for the farmers and government.

Keywords: vine copula, weather index, crop insurance premium, insurance risk management, Monte Carlo simulation

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Authors: Kashif Hassan, M.A. Ahanger

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Floods have become more frequent and severe due to effects of global climate change and human alterations of the natural environment. Flood prediction/ forecasting and control is one of the greatest challenges facing the world today. The forecast of floods is achieved by the use of hydraulic models such as HEC-RAS, which are designed to simulate flow processes of the surface water. Extreme flood events in river Jhelum , lasting from a day to few are a major disaster in the State of Jammu and Kashmir, India. In the present study HEC-RAS model was applied to two different reaches of river Jhelum in order to estimate the flood levels corresponding to 25, 50 and 100 year return period flood events at important locations and to deduce flood vulnerability of important areas and structures. The flow rates for the two reaches were derived from flood-frequency analysis of 50 years of historic peak flow data. Manning's roughness coefficient n was selected using detailed analysis. Rating Curves were also generated to serve as base for determining the boundary conditions. Calibration and Validation procedures were applied in order to ensure the reliability of the model. Sensitivity analysis was also performed in order to ensure the accuracy of Manning's n in generating water surface profiles.

Keywords: flood plain, HEC-RAS, Jhelum, return period

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Authors: D. Naranchimeg, G. Ulziisaikhan

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Student dropout problem has not been recently investigated within the Mongolian higher education. A student dropping out is a personal decision, but it may cause unemployment and other social problems including low quality of life because students who are not completed a degree cannot find better-paid jobs. The research aims to determine percentage of at-risk students, and understand reasons for dropouts and to find a way to predict. The study based on the students of the Mongolian National University of Education including its Arkhangai branch school, National University of Mongolia, Mongolian University of Life Sciences, Mongolian University of Science and Technology, Mongolian National University of Medical Science, Ikh Zasag International University, and Dornod University. We conducted the paper survey by method of random sampling and have surveyed about 100 students per university. The margin of error - 4 %, confidence level -90%, and sample size was 846, but we excluded 56 students from this study. Causes for exclusion were missing data on the questionnaire. The survey has totally 17 questions, 4 of which was demographic questions. The survey shows that 1.4% of the students always thought to dropout whereas 61.8% of them thought sometimes. Also, results of the research suggest that students’ dropouts from university do not have relationships with their sex, marital and social status, and peer and faculty climate, whereas it slightly depends on their chosen specialization. Finally, the paper presents the reasons for dropping out provided by the students. The main two reasons for dropouts are personal reasons related with choosing wrong study program, not liking the course they had chosen (50.38%), and financial difficulties (42.66%). These findings reveal the importance of early prevention of dropout where possible, combined with increased attention to high school students in choosing right for them study program, and targeted financial support for those who are at risk.

Keywords: at risk students, dropout, faculty climate, Mongolian universities, peer climate

Procedia PDF Downloads 383

Authors: Muhammad Naveed

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In this paper, flood issues in Gujranwala and Rawalpindi divisions are discussed as a primary importance as these zones are affected continuously from flooding in recent years, provincial variability of the issue, introduce status of the continuous administration measures, their adequacy and future needs in flood administration are secured. Flood issues in these zones are exhibited by Chenab River Basin, Jhelum Rivers Basin. Some unique problems, related to floods in these divisions is lack of major dams on Chenab and Jhelum rivers and also mismanagement of rivers and canal water like dam break stream, and water signing in Tal zones, are additionally mentioned. There are major Nalaas in these regions like Nalaa Lai of Rawalpindi and Nalaa Daik, Nalaa Palkhu, Nalaa Aik of Gujranwala are major cause of floods in these regions other than rivers. Proper management of these Nalaas and moving of nearby population well in time could reduce impacts from flood in these regions. Progress of different flood administration measures, both auxiliary and non-basic, are discussed. Likewise, future needs to accomplish proficient and fruitful flood management measures in Pakistan are additionally brought up. In this paper, we describe different hard and soft engineering techniques to overcome flood situations in these zones as these zones are more vulnerable due to lack of management in canal and river water. Effective management and use of hard and soft techniques are need of time in coming future for controlling greater flooding in flood risk zones to overcome or minimize people’s death as well as agricultural and financial resources as flood and other natural disasters are a major drawback in the economic prosperity of the country.

Keywords: flood management, rivers, major dams, agricultural and financial loss, future management and control

Procedia PDF Downloads 178

Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

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Authors: Zineb Jibraili, Said Ouhadi, Jorge Arana

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Introduction: Organizational performance is recognized by some theorists as one-dimensional concept, and by others as multidimensional. This concept, which is already difficult to apply in traditional companies, is even harder to identify, to measure and to manage when voluntary organizations are concerned, essentially because of the complexity of that form of organizations such as sport clubs who are characterized by the multiple goals and multiple constituencies. Indeed, the new culture of professionalization and modernization around organizational performance emerges new pressures from the state, sponsors, members and other stakeholders which have required these sport organizations to become more performance oriented, or to build their capacity in order to better manage their organizational performance. The evaluation of performance can be made by evaluating the input (e.g. available resources), throughput (e.g. processing of the input) and output (e.g. goals achieved) of the organization. In non-profit organizations (NPOs), questions of performance have become increasingly important in the world of practice. To our knowledge, most of studies used the same methods to evaluate the performance in NPSOs, but no recent study has proposed a club-specific model. Based on a review of the studies that specifically addressed the organizational performance (and effectiveness) of NPSOs at operational level, the present paper aims to provide a multidimensional framework in order to understand, analyse and measure organizational performance of sport clubs. This paper combines all dimensions founded in literature and chooses the most suited of them to our model that we will develop in Moroccan sport clubs case. Method: We propose to implicate our unified model of evaluating organizational performance that takes into account all the limitations found in the literature. On a sample of Moroccan sport clubs ‘Football, Basketball, Handball and Volleyball’, for this purpose we use a qualitative study. The sample of our study comprises data from sport clubs (football, basketball, handball, volleyball) participating on the first division of the professional football league over the period from 2011 to 2016. Each football club had to meet some specific criteria in order to be included in the sample: 1. Each club must have full financial data published in their annual financial statements, audited by an independent chartered accountant. 2. Each club must have sufficient data. Regarding their sport and financial performance. 3. Each club must have participated at least once in the 1st division of the professional football league. Result: The study showed that the dimensions that constitute the model exist in the field with some small modifications. The correlations between the different dimensions are positive. Discussion: The aim of this study is to test the unified model emerged from earlier and narrower approaches for Moroccan case. Using the input-throughput-output model for the sketch of efficiency, it was possible to identify and define five dimensions of organizational effectiveness applied to this field of study.

Keywords: organisational performance, model multidimensional, evaluation organizational performance, sport clubs

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and PACF. SAS software computed the model parameters using the ML, CLS and ULS methods. The diagnostic checking tests, AIC criterion, RACF graph and RPACF graphs, were used for selected model verification. In this study, the best ARIMA models for Annual Maximum Discharge (AMD) time series was (4,1,1) with their AIC value of 88.87. The RACF and RPACF showed residuals’ independence. To forecast AMD for 10 future years, this model showed the ability of the model to predict floods of the river under study in the Karkheh River Basin. Model accuracy was checked by comparing the predicted and observation series by using coefficient of determination (R²).

Keywords: time series modelling, stochastic processes, ARIMA model, Karkheh river

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Authors: Nasrin Bakhshizadeh, Ashkan Forootan

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A major problem that affects the quality control of polymer in the industrial polymerization is the lack of suitable on-line measurement tools to evaluate the properties of the polymer such as melt and density indices. Controlling the polymerization in ordinary method is performed manually by taking samples, measuring the quality of polymer in the lab and registry of results. This method is highly time consuming and leads to producing large number of incompatible products. An online application for estimating melt index and density proposed in this study is a neural network based on the input-output data of the polyethylene production plant. Temperature, the level of reactors' bed, the intensity of ethylene mass flow, hydrogen and butene-1, the molar concentration of ethylene, hydrogen and butene-1 are used for the process to establish the neural model. The neural network is taught based on the actual operational data and back-propagation and Levenberg-Marquart techniques. The simulated results indicate that the neural network process model established with three layers (one hidden layer) for forecasting the density and the four layers for the melt index is able to successfully predict those quality properties.

Keywords: polyethylene, polymerization, density, melt index, neural network

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Authors: Jinyan Li, Simon Fong, Raymond Wong, Mohammed Sabah, Fiaidhi Jinan

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Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method.

Keywords: Imbalanced dataset, meta-heuristic algorithm, SMOTE, big data

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Authors: Pradeep Kulshrestha, Maulana Ayoub Ali

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The concept of financial inclusion has been tabled in the whole world where practitioners, academicians, policy makers and economists are working hard to look for the best possible opportunities in order to enable the whole society to be in the banking cycle. The Islamic banking system is considered to be one of the said opportunities. Countries like the United Kingdom, United States of America, Malaysia, Saudi Arabia, the whole of the United Arab Emirates and many African countries have accommodated the aspect of Islamic banking in the conventional banking system as one of the financial inclusion strategies. This paper tries to analyse the current state of affairs of the Islamic Banking system in Tanzania in order to understand the improvement of the provision of Islamic banking products and services in the said country. The paper discusses the historical background of the banking system in Tanzania, the level of penetration of banking products and services and the coming of the Islamic banking system in the country. Furthermore, the paper discusses banking regulatory bodies, legal instruments governing banking operations as well as number of legal challenges facing Islamic banking operations in the country. Following a critical literature review, the paper discovered that there is no legal instrument which talks about the introduction and provision of Islamic banking system in Tanzania. Furthermore, the Islamic banking system was considered as a banking product which is absolutely incorrect because Islamic banking is considered to be as a banking system of its own. In addition to that, it has been discovered that lack of a proper regulatory system and legal instruments to harmonize the conventional and Islamic banking systems has resulted in the closure of one Islamic window in the country, which in the end affects the credibility of the newly introduced banking system. In its conclusive remarks, the paper suggests that Tanzania should work on all legal challenges affecting the smooth operations of the Islamic banking system. This can be in a way of adopting various Islamic banking legal models which are used in countries like Malaysia and others, or a borrowing legal harmonization process which has been adopted by the UK, Uganda, Nigeria and Kenya.

Keywords: Islamic banking, Islamic windows, regulations, banks

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Authors: Sophia Anastasiou, Cosmas Nathanailides, Fotini Kakali, Panagiotis Logothetis, Gregorios Kanlis

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The method and rearing conditions of aquaculture may very between different regions and aquaculture sites. Globally, the Aquaculture industry faces a challenge to develop aquaculture methods which safeguard the economic viability of the company, the welfare of farmed fish and final product quality and sustainable development of aquaculture. Marine fish farms in Greece operate in different locations and farmed fish are exposed to a variety of rearing conditions. This paper investigates the variability of product quality and the financial performance of different marine fish farms operating in West Greece. Production parameters of gilthead sea bream fish farm such as feeding regimes, mortalities, fish densities were used to calculate the economic efficiency of six different aquaculture sites from West Greece. Samples of farmed sea bream were collected and lipid content, microbial load and filleting yield of the samples were used as quality criteria. The results indicate that Lipid content, filleting yield and microbial load of fish originating from different fish farms varied significantly with improved quality exhibited in fish farms which exhibited improved Feed conversion rates and lower mortalities. Changes in feeding management practices such as feed quality and feeding regimes have a significant impact on the financial performance of sea bass farms. Fish farms which exhibited improved feeding conversion rates also exhibited increased profitability. Improvements in the FCR explained about 13.4 % of the difference in profitability of the different aquaculture sites. Lower mortality and higher growth rates were also exhibited by the fish farms which exhibited improved FCR. It is concluded that best feeding management practices resulted in improved product quality and profitability.

Keywords: fish quality, aquaculture management, feeding management, profitability

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Authors: Mohammad Lemar Zalmai, Osman Hurol Turkakin, Cemil Akcay, Ekrem Manisali

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Most of the construction projects are exposed to time and cost overruns due to various factors and this is a major problem. As a solution to this, the Earned Value Management (EVM) method is considered. EVM is a powerful and well-known method used in monitoring and controlling the project. EVM is a technique that project managers use to track the performance of their project against project baselines. EVM gives an early indication that either project is delayed or not, and the project is either over budget or under budget at any particular day by tracking it. Thus, it helps to improve the management control system of a construction project, to detect and control the problems in potential risk areas and to suggest the importance and purpose of monitoring the construction work. This paper explains the main parameters of the EVM system involved in the calculation of time and cost for construction projects. In this study, the project management software Primavera P6 is used to deals with the project monitoring process of a seven-storeyed (G+6) faculty building whose construction is in progress at Istanbul, Turkey. A comparison between the planned progress of construction activities and actual progress is performed, and the analysis results are interpreted. This case study justifies the benefits of using EVM for project cash flow analysis and forecasting.

Keywords: earned value management (EVM), construction cost management, construction planning, primavera P6, project management, project scheduling

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Authors: E. U. Dampage, Ovindi D. Bandara, Vinushi S. Waraketiya, Samitha S. R. De Silva, Yasiru S. Gunarathne

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The knowledge of water inflow figures is paramount in decision making on the allocation for consumption for numerous purposes; irrigation, hydropower, domestic and industrial usage, and flood control. The understanding of how reservoir inflows are affected by different climatic and hydrological conditions is crucial to enable effective water management and downstream flood control. In this research, we propose a method using a Long Short Term Memory (LSTM) Artificial Neural Network (ANN) to assist the aforesaid decision-making process. The Kotmale reservoir, which is the uppermost reservoir in the Mahaweli reservoir complex in Sri Lanka, was used as the test bed for this research. The ANN uses the runoff in the Kotmale reservoir catchment area and the effect of Sea Surface Temperatures (SST) to make a forecast for seven days ahead. Three types of ANN are tested; Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and LSTM. The extensive field trials and validation endeavors found that the LSTM ANN provides superior performance in the aspects of accuracy and latency.

Keywords: convolutional neural network, CNN, inflow, long short-term memory, LSTM, multi-layer perceptron, MLP, neural network

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Authors: Chakrit Chotamonsak

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In this study, the Weather Research and Forecasting (WRF) model was used as regional climate model to dynamically downscale the ECHAM5 Global Climate Model projection for the regional climate change impact on air quality–related meteorological conditions in the upper northern Thailand. The analyses were focused on meteorological variables that potentially impact on the regional air quality such as sea level pressure, planetary boundary layer height (PBLH), surface temperature, wind speed and ventilation. Comparisons were made between the present (1990–2009) and future (2045–2064) climate downscaling results during majority air pollution season (dry season, January-April). Analyses showed that the sea level pressure will be stronger in the future, suggesting more stable atmosphere. Increases in temperature were obvious observed throughout the region. Decreases in surface wind and PBLH were predicted during air pollution season, indicating weaker ventilation rate in this region. Consequently, air quality-related meteorological variables were predicted to change in almost part of the upper northern Thailand, yielding a favorable meteorological condition for pollutant accumulation in the future.

Keywords: climate change, climate impact, air quality, air pollution, Thailand

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Authors: Chen Shuwen, Nunthapin Chantachaimongkol

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Since the world has moved into a transitional period, known as globalization; the business environment is now more complicated than ever before. Corporate information has become a matter of great importance for stakeholders, in order to understand the current situation. As a result of this, the concept of corporate governance has been broadly introduced to manage and control the affairs of corporations while businesses are required to disclose both financial and non-financial information to public via various communication channels such as the annual report, the financial report, the company’s website, etc. However, currently there are several other issues related to asymmetric information such as moral hazard or adverse selection that still occur intensively in workplaces. To prevent such problems in the business, it is required to have an understanding of what factors strengthen their transparency, accountability, fairness, and responsibility. Under aforementioned arguments, this paper aims to propose a conceptual framework that enables an investigation on how corporate governance mechanism influences disclosure efficiency of listed companies in the Association of Southeast Asia Nations (ASEAN) and the factors that should be considered for further development of good behaviors, particularly in regards to voluntary disclosure practices. To achieve its purpose, extensive reviews of literature are applied as a research methodology. It is divided into three main steps. Firstly, the theories involved with both corporate governance and disclosure practices such as agency theory, contract theory, signaling theory, moral hazard theory, and information asymmetry theory are examined to provide theoretical backgrounds. Secondly, the relevant literatures based on multi- perspectives of corporate governance, its attributions and their roles on business processes, the influences of corporate governance mechanisms on business performance, and the factors determining corporate governance characteristics as well as capability are reviewed to outline the parameters that should be included in the proposed model. Thirdly, the well-known regulatory document OECD principles and previous empirical studies on the corporate disclosure procedures are evaluated to identify the similarities and differentiations with the disclosure patterns in the ASEAN. Following the processes and consequences of the literature review, abundant factors and variables are found. Further to the methodology, additional critical factors that also have an impact on the disclosure behaviors are addressed in two groups. In the first group, the factors which are linked to the national characteristics - the quality of national code, legal origin, culture, the level of economic development, and so forth. Whereas in the second group, the discoveries which refer to the firm’s characteristics - ownership concentration, ownership’s rights, controlling group, and so on. However, because of research limitations, only some literature are chosen and summarized to form part of the conceptual framework that explores the relationship between corporate governance and the disclosure practices of listed companies in ASEAN.

Keywords: corporate governance, disclosure practice, ASEAN, listed company

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Authors: Krizette Ladera

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Fathers are there not only to provide the financial stability of a family but a father is also there to provide the love and support that usually people would see as the mother’s responsibility. To describe the lived experiences and how fathers make sense of their lived experiences with their children who have cerebral palsy is the main objective of the study. A qualitative research using a thematic analysis was used for the study. The qualitative research focused on the personal narratives, self-report and expression of the participant’s memory in terms of how they tell their stories. The interpretative phenomenological analysis was used to focus on the experience of the participants on how they will describe their experiences, and to also add on that the IPA will also attempt to describe and explain the meaning of human experiences using interview, specifically on the father who have a child that suffers from cerebral palsy. For the sampling technique, the snowball technique was used to gather participants from the referral of other participants. The five non-randomly selected fathers will be served as the participants for the research. A self-made interview with an open-ended question was used as the research instrument; it includes profiling of the respondent as well as their experiences in taking care of their child that suffers from cerebral palsy. In analyzing a data, the researcher used the thematic analysis where in the interview was made into a transcript, then it was organized and divided themes. After that, the relations of each themes, was identified and it was later documented and translated into written text format using thematic grouping. Finally, the researcher analyzed each data according to its themes and put it in a table to be presented in the result section of the study And as for the result of the study, the researcher was able to come up with the four (4) main themes that most of the participants experienced and those are: The experiences in finding out about the condition of the Child, disclosing the condition of the child to the family and its emotional effect, The experiences of living the day of day realities in providing the physical, financial, emotional and a well balanced environment to the child, and the religious perspectives of the fathers. Along with those four (4) themes comes the subtheme which explains the themes in a more detailed explanation.

Keywords: cerebral palsy, children, fathers, lived experiences

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Authors: Hideaki Endo, Mika Goto

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The global outbreak of the COVID-19 pandemic has seriously affected railway businesses. The number of railway passengers decreased due to the decline in the number of commuters and business travelers to avoid crowded trains and a sharp drop in inbound tourists visiting Japan. This has affected not only railway businesses but also related businesses, including hotels, leisure businesses, and retail businesses at station buildings. In 2021, the companies were divided into profitable and loss-making companies. This division suggests that railway companies, particularly loss-making companies, needed to decrease operational inefficiency. To measure the impact of COVID-19 and discuss the sustainable management strategies of railway companies, we examine the cost inefficiency of Japanese listed railway companies by applying stochastic frontier analysis (SFA) to their operational and financial data. First, we employ the stochastic frontier cost function approach to measure inefficiency. The cost frontier function is formulated as a Cobb–Douglas type, and we estimated parameters and variables for inefficiency. This study uses panel data comprising 26 Japanese-listed railway companies from 2005 to 2020. This period includes several events deteriorating the business environment, such as the financial crisis from 2007 to 2008 and the Great East Japan Earthquake of 2011, and we compare those impacts with those of the COVID-19 pandemic after 2020. Second, we identify the characteristics of the best-practice railway companies and examine the drivers of cost inefficiencies. Third, we analyze the factors influencing cost inefficiency by comparing the profiles of the top 10 railway companies and others before and during the pandemic. Finally, we examine the relationship between cost inefficiency and the implementation of efficiency measures for each railway company. We obtained the following four findings. First, most Japanese railway companies showed the lowest cost inefficiency (most efficient) in 2014 and the highest in 2020 (least efficient) during the COVID-19 pandemic. The second worst occurred in 2009 when it was affected by the financial crisis. However, we did not observe a significant impact of the 2011 Great East Japan Earthquake. This is because no railway company was influenced by the earthquake in this operating area, except for JR-EAST. Second, the best-practice railway companies are KEIO and TOKYU. The main reason for their good performance is that both operate in and near the Tokyo metropolitan area, which is densely populated. Third, we found that non-best-practice companies had a larger decrease in passenger kilometers than best-practice companies. This indicates that passengers made fewer long-distance trips because they refrained from inter-prefectural travel during the pandemic. Finally, we found that companies that implement more efficiency improvement measures had higher cost efficiency and they effectively used their customer databases through proactive DX investments in marketing and asset management.

Keywords: COVID-19 pandemic, stochastic frontier analysis, railway sector, cost efficiency

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Authors: Mária Csernoch, Piroska Biró

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The legends about “user-friendly” and “easy-to-use” birotical tools (computer-related office tools) have been spreading and misleading end-users. This approach has led us to the extremely high number of incorrect documents, causing serious financial losses in the creating, modifying, and retrieving processes. Our research proved that there are at least two sources of this underachievement: (1) The lack of the definition of the correctly edited, formatted documents. Consequently, end-users do not know whether their methods and results are correct or not. They are not aware of their ignorance. They are so ignorant that their ignorance does not allow them to realize their lack of knowledge. (2) The end-users’ problem-solving methods. We have found that in non-traditional programming environments end-users apply, almost exclusively, surface approach metacognitive methods to carry out their computer related activities, which are proved less effective than deep approach methods. Based on these findings we have developed deep approach methods which are based on and adapted from traditional programming languages. In this study, we focus on the most popular type of birotical documents, the text-based documents. We have provided the definition of the correctly edited text, and based on this definition, adapted the debugging method known in programming. According to the method, before the realization of text editing, a thorough debugging of already existing texts and the categorization of errors are carried out. With this method in advance to real text editing users learn the requirements of text-based documents and also of the correctly formatted text. The method has been proved much more effective than the previously applied surface approach methods. The advantages of the method are that the real text handling requires much less human and computer sources than clicking aimlessly in the GUI (Graphical User Interface), and the data retrieval is much more effective than from error-prone documents.

Keywords: deep approach metacognitive methods, error-prone birotical documents, financial losses, human and computer resources

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Authors: P. Wongthongtham, K. Coutinho, A. MacCarthy

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Layer-One.X (L1X) blockchain provides the infrastructure layer, and decentralised applications can be created on the L1X infrastructure. L1X tokenomics are important and require a proportional balance between token distribution, nurturing user activity and engagement, and financial incentives. In this paper, we present research in progress on L1X tokenomics describing key concepts and implementations, including token velocity and value, incentive scheme, and broad distribution. Particularly the economic design of the native token of the L1X blockchain, called HeartBit (HB), is presented.

Keywords: tokenisation, layer one blockchain, interoperability, token distribution, L1X blockchain

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Authors: Bahram Soltani, Louai Ghazieh

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The responsibility has a big importance further to the financial crisis and the various pressures, which companies face their duties. The main objective of this study is to explain the variation of mechanisms of the responsibility of the manager in the company among the advanced capitalist economies. Then we study the impact of these mechanisms on the performance and the value creation in European companies. To reach our goal, we established a final sample composed on average of 284 French, British and German companies quoted in stock exchanges with 2272 annual reports examined during the period from 2005 to 2012. We examined at first the link of causalities between the determining-mechanisms bound to the company such as the characteristics of the board of directors, the composition of the shareholding and the ethics of the company on one side and the profitability of the company on the other side. The results show that the smooth running of the board of directors and its specialist committees are very important determinants of the responsibility of the managers who impact positively the performance and the value creation in the company. Furthermore, our results confirm that the presence of a solid ethical environment within the company will be effective to increase the probability that the managers realize ethical choices in the organizational decision-making. At the second time, we studied the impact of the determining mechanisms bound to the function and to the profile of manager to know its relational links, his remuneration, his training, his age and his experiences about the performance and the value creation in the company. Our results highlight the existence of a negative relation between the relational links of the manager, his very high remuneration and the general profitability of the company. This study is a contribution to the literature on the determining mechanisms of company director's responsibility (Accountability). It establishes an empirical and comparative analysis between three influential countries of Europe, to know France, the United Kingdom and Germany.

Keywords: leaders, company’s performance, accountability mechanisms, corporate governance, value creation of firm, financial crisis

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Authors: Mabel Usunobun Olanipekun, Henry Ogbemudia Omoregbee

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Anomalies such as water pipeline and hydraulic or petrochemical pipeline network leakages and bursts have significant implications for economic conditions and the environment. In order to ensure pipeline systems are reliable, they must be efficiently controlled. Wireless Sensor Networks (WSNs) have become a powerful network with critical infrastructure monitoring systems for water, oil and gas pipelines. The loss of water, oil and gas is inevitable and is strongly linked to financial costs and environmental problems, and its avoidance often leads to saving of economic resources. Substantial repair costs and the loss of precious natural resources are part of the financial impact of leaking pipes. Pipeline systems experts have implemented various methodologies in recent decades to identify and locate leakages in water, oil and gas supply networks. These methodologies include, among others, the use of acoustic sensors, measurements, abrupt statistical analysis etc. The issue of leak quantification is to estimate, given some observations about that network, the size and location of one or more leaks in a water pipeline network. In detecting background leakage, however, there is a greater uncertainty in using these methodologies since their output is not so reliable. In this work, we are presenting a scalable concept and simulation where a pressure-driven model (PDM) was used to determine water pipeline leakage in a system network. These pressure data were collected with the use of acoustic sensors located at various node points after a predetermined distance apart. We were able to determine with the use of correlation difference to determine the leakage point locally introduced at a predetermined point between two consecutive nodes, causing a substantial pressure difference between in a pipeline network. After de-noising the signal from the sensors at the nodes, we successfully obtained the exact point where we introduced the local leakage using the correlation difference model we developed.

Keywords: leakage detection, acoustic signals, pipeline network, correlation, wireless sensor networks (WSNs)

Procedia PDF Downloads 71