Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2021.8.002
O. S. Suharyo, A. Prabowo, Eko Krisdiono
The Indonesian Navy is the spearhead in maintaining maritime security in Indonesian waters. In carrying out its main tasks, the Indonesian Navy has components of an Integrated Fleet Weapon System in which there are elements of Ships and Naval Bases. To ensure the effectiveness of carrying out operations by ship elements, ship operations are supported by the Naval Base as the organizer of the support function. Naval Base's carrying capacity consists of 5 (five) support functions, including: (1) support for anchoring facilities; (2) support for supply facilities; (3) support for maintenance and repair facilities; (4) support facility maintenance personnel; and (5) support for base development facilities. Naval Base does not yet have its dock to support anchoring facilities for ship operations. In addition to cooperation in the use of the Naval Base anchorage facility, there is also cooperation in port security, both in terms of land and port water aspects. As the number of ship visits at Naval Base Harbor increases, the dock utility increases. The increase in dock utility resulted in a decrease in port services which also resulted in a decrease in the Naval Base Carrying Capacity. To improve port services, Pelindo III implements the port development program contained in the Naval Base Port Master Plan in Permen KP number 792 of 2017. In this study, an analysis of the impact of the Naval Base Port development policy on the carrying capacity of the Naval Base was carried out. The data analysis uses System Dynamics modeling with a simulation period of 30 years in 3 development scenarios, namely short-term scenarios, medium-term scenarios, and long-term scenarios. From the simulation results, it is found that the construction of the Naval Base port affects the Naval Base Carrying Capacity with an average increase of 1.8% in each policy scenario. The increase in Naval Base Carrying Capacity has an effect on increasing Ship Operations by an average of 1.8% and also increasing the Security of Naval Base Harbor by an average of 0.14%. The results of the analysis of this study can be used as consideration for policymaking by the Navy.
{"title":"Decision making of maritime development scenario on the impact of naval base for supporting navy ships operations","authors":"O. S. Suharyo, A. Prabowo, Eko Krisdiono","doi":"10.5267/j.dsl.2021.8.002","DOIUrl":"https://doi.org/10.5267/j.dsl.2021.8.002","url":null,"abstract":"The Indonesian Navy is the spearhead in maintaining maritime security in Indonesian waters. In carrying out its main tasks, the Indonesian Navy has components of an Integrated Fleet Weapon System in which there are elements of Ships and Naval Bases. To ensure the effectiveness of carrying out operations by ship elements, ship operations are supported by the Naval Base as the organizer of the support function. Naval Base's carrying capacity consists of 5 (five) support functions, including: (1) support for anchoring facilities; (2) support for supply facilities; (3) support for maintenance and repair facilities; (4) support facility maintenance personnel; and (5) support for base development facilities. Naval Base does not yet have its dock to support anchoring facilities for ship operations. In addition to cooperation in the use of the Naval Base anchorage facility, there is also cooperation in port security, both in terms of land and port water aspects. As the number of ship visits at Naval Base Harbor increases, the dock utility increases. The increase in dock utility resulted in a decrease in port services which also resulted in a decrease in the Naval Base Carrying Capacity. To improve port services, Pelindo III implements the port development program contained in the Naval Base Port Master Plan in Permen KP number 792 of 2017. In this study, an analysis of the impact of the Naval Base Port development policy on the carrying capacity of the Naval Base was carried out. The data analysis uses System Dynamics modeling with a simulation period of 30 years in 3 development scenarios, namely short-term scenarios, medium-term scenarios, and long-term scenarios. From the simulation results, it is found that the construction of the Naval Base port affects the Naval Base Carrying Capacity with an average increase of 1.8% in each policy scenario. The increase in Naval Base Carrying Capacity has an effect on increasing Ship Operations by an average of 1.8% and also increasing the Security of Naval Base Harbor by an average of 0.14%. The results of the analysis of this study can be used as consideration for policymaking by the Navy.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"9 24 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84072420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2021.8.001
N. Elshaboury
There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.
{"title":"Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms","authors":"N. Elshaboury","doi":"10.5267/j.dsl.2021.8.001","DOIUrl":"https://doi.org/10.5267/j.dsl.2021.8.001","url":null,"abstract":"There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"42 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90273411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2021.8.004
Anima Naik, Pradeep Kumar Chokkalingam
In this paper, we propose the binary version of the Social Group Optimization (BSGO) algorithm for solving the 0-1 knapsack problem. The standard Social Group Optimization (SGO) is used for continuous optimization problems. So a transformation function is used to convert the continuous values generated from SGO into binary ones. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The results obtained by the BSGO algorithm are compared with other binary optimization algorithms. Experimental results reveal the superiority of the BSGO algorithm in achieving a high quality of solutions over different algorithms and prove that it is one of the best finding algorithms especially in high-dimensional cases.
{"title":"Binary social group optimization algorithm for solving 0-1 knapsack problem","authors":"Anima Naik, Pradeep Kumar Chokkalingam","doi":"10.5267/j.dsl.2021.8.004","DOIUrl":"https://doi.org/10.5267/j.dsl.2021.8.004","url":null,"abstract":"In this paper, we propose the binary version of the Social Group Optimization (BSGO) algorithm for solving the 0-1 knapsack problem. The standard Social Group Optimization (SGO) is used for continuous optimization problems. So a transformation function is used to convert the continuous values generated from SGO into binary ones. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The results obtained by the BSGO algorithm are compared with other binary optimization algorithms. Experimental results reveal the superiority of the BSGO algorithm in achieving a high quality of solutions over different algorithms and prove that it is one of the best finding algorithms especially in high-dimensional cases.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"32 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89432465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.7.003
H. Siregar, A. Suroso, H. Siregar, S. Djohar
A pine tree, namely Pinus merkusii is an indigenous species from Indonesia which grows extensively in the Island of Java, Sumatera, and Sulawesi. This plant produces both timber and non-timber forest products (TFP and NTFP). Resin or oleum pine resin, as the main NTFP of Pinus merkusii, becomes the raw material for the gum rosin and turpentine oil industry. Globally, Indonesia is ranked 3rd as a producer of pine products after China and Brazil, in which Perhutani as a State Owned Forestry Enterprise plays a major role in this industry. On average, Perhutani manufactures 65,000 tons of gum rosin and 14,000 turpentine oil per year. Entire volume of both pine products is produced by nine factories with various maximum capacities. Therefore, this research aims to measure efficiency and/or inefficiency score of each factory using data envelopment analysis (DEA) method, which is then complemented by a single bootstrap technique with 2.000 iterations to eliminate bias scores. Cost of raw material, labour, energy, and general affairs are employed as input variables, while the output variables are total revenue and production volume. As result, 27.3% inefficiency (efficiency score = 72.7%) is generally found in all Perhutani’s pine chemical factories. To resolve this inefficiency issue, analytical hierarchy process (AHP) pairwise comparison questionnaire is distributed to 13 expert respondents to determine prioritized operational capability to focus on in optimizing efficiency of production performance. Dimensions of Cost, Quality, Flexibility, Innovation, and Sustainability are selected to construct the AHP questionnaires.
{"title":"Development of efficient strategies to optimize production efficiency: Evidence from Pine chemical industry","authors":"H. Siregar, A. Suroso, H. Siregar, S. Djohar","doi":"10.5267/j.dsl.2022.7.003","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.7.003","url":null,"abstract":"A pine tree, namely Pinus merkusii is an indigenous species from Indonesia which grows extensively in the Island of Java, Sumatera, and Sulawesi. This plant produces both timber and non-timber forest products (TFP and NTFP). Resin or oleum pine resin, as the main NTFP of Pinus merkusii, becomes the raw material for the gum rosin and turpentine oil industry. Globally, Indonesia is ranked 3rd as a producer of pine products after China and Brazil, in which Perhutani as a State Owned Forestry Enterprise plays a major role in this industry. On average, Perhutani manufactures 65,000 tons of gum rosin and 14,000 turpentine oil per year. Entire volume of both pine products is produced by nine factories with various maximum capacities. Therefore, this research aims to measure efficiency and/or inefficiency score of each factory using data envelopment analysis (DEA) method, which is then complemented by a single bootstrap technique with 2.000 iterations to eliminate bias scores. Cost of raw material, labour, energy, and general affairs are employed as input variables, while the output variables are total revenue and production volume. As result, 27.3% inefficiency (efficiency score = 72.7%) is generally found in all Perhutani’s pine chemical factories. To resolve this inefficiency issue, analytical hierarchy process (AHP) pairwise comparison questionnaire is distributed to 13 expert respondents to determine prioritized operational capability to focus on in optimizing efficiency of production performance. Dimensions of Cost, Quality, Flexibility, Innovation, and Sustainability are selected to construct the AHP questionnaires.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"134 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82706633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.6.002
R. A. Rahadi, S. Wiryono, Y. Nainggolan, Kurnia Fajar Afgani, R. Yaman, Ahmad Shazrin Mohamed Azmi, F. Ismail, Jumadil Saputra, D. Rahmawati, Aisyah Moulynia
The property is a unique product that cannot be contrasted with other commercial products due to pricing conditions. Property price determination is one of the crucial aspects of property development activities because of the profit margin made by the developer and the purchasing preferences. This study attempts to extend the literature that has largely focused on factors of housing prices in developed markets and provided recent evidence of housing price determinants in two countries (i.e., Indonesia and Malaysia). Thus, this study examines the factors affecting housing prices in Jakarta Metropolitan Region and Greater Kuala Lumpur. A quantitative approach was used involving two countries, namely Indonesia and Malaysia. The data was collected using a survey questionnaire through purposive sampling. A total of 100 respondents (Indonesia) and 134 respondents (Malaysia) participated in this study. The data was analyzed using descriptive (frequency) and inferential statistics (chi-square test and multinomial regression). The results indicated that housing location, property funding, and health have a significant effect on residential property prices in Indonesia. Besides that, the results displayed that housing physical design, home design and construction, developer and real estate products, development concepts, housing location, property funding, social status, health, law provisions, and external factors do not affect residential property price in Malaysia. Despite being neighbors, Indonesia and Malaysia have distinct economic and landscape characteristics. Furthermore, considering Indonesia has a higher number of Covid-19 cases than Malaysia, significant information on how the pandemic has affected the demand, cost, and pricing of residential housing in Jakarta and Kuala Lumpur will be provided. The findings of this study will provide recommendations to investors, buyers, and policy about the residential housing industry's prospects for growth in emerging nations following the pandemic.
{"title":"Determining the factors influencing residential property price: A comparative study between Indonesia and Malaysia","authors":"R. A. Rahadi, S. Wiryono, Y. Nainggolan, Kurnia Fajar Afgani, R. Yaman, Ahmad Shazrin Mohamed Azmi, F. Ismail, Jumadil Saputra, D. Rahmawati, Aisyah Moulynia","doi":"10.5267/j.dsl.2022.6.002","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.6.002","url":null,"abstract":"The property is a unique product that cannot be contrasted with other commercial products due to pricing conditions. Property price determination is one of the crucial aspects of property development activities because of the profit margin made by the developer and the purchasing preferences. This study attempts to extend the literature that has largely focused on factors of housing prices in developed markets and provided recent evidence of housing price determinants in two countries (i.e., Indonesia and Malaysia). Thus, this study examines the factors affecting housing prices in Jakarta Metropolitan Region and Greater Kuala Lumpur. A quantitative approach was used involving two countries, namely Indonesia and Malaysia. The data was collected using a survey questionnaire through purposive sampling. A total of 100 respondents (Indonesia) and 134 respondents (Malaysia) participated in this study. The data was analyzed using descriptive (frequency) and inferential statistics (chi-square test and multinomial regression). The results indicated that housing location, property funding, and health have a significant effect on residential property prices in Indonesia. Besides that, the results displayed that housing physical design, home design and construction, developer and real estate products, development concepts, housing location, property funding, social status, health, law provisions, and external factors do not affect residential property price in Malaysia. Despite being neighbors, Indonesia and Malaysia have distinct economic and landscape characteristics. Furthermore, considering Indonesia has a higher number of Covid-19 cases than Malaysia, significant information on how the pandemic has affected the demand, cost, and pricing of residential housing in Jakarta and Kuala Lumpur will be provided. The findings of this study will provide recommendations to investors, buyers, and policy about the residential housing industry's prospects for growth in emerging nations following the pandemic.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"39 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87121892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.7.004
A. Golab, E. S. Gooya, A. A. Falou, Mikael Cabon
Project management has a fundamental role in national development, industrial development, and economic growth. Schedule management is also one of the knowledge areas of project management, which includes the processes employed to manage the timely completion of the project. This paper deals with the Resource-Constrained Project Scheduling Problem (RCPSP), which is a part of schedule management. The objective of the problem is to optimize and minimize the project duration while constraining the resource quantities during project scheduling. There are two important constraints in this problem, namely resource constraints and precedence relationships of activities during project scheduling. Many methods such as exact, heuristic, and meta-heuristic have been developed by researchers to solve the problem, but there is a lack of investigation of the problem using methods such as neural networks and machine learning. In this article, we develop a multi-layer feed-forward neural network (MLFNN) to solve the standard single- mode RCPSP. The advantage of this method over evolutionary methods or metaheuristics is that it is not necessary to generate numerous solutions or populations. The developed MLFNN learns based on eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, percentage of remaining work, etc., which are calculated at each step of project scheduling, and identified priority rules, which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter out an unscheduled activity from the list of eligible activities and schedule all activities of the project according to the given project constraints. Finally, we investigate the performance of the presented approach using the standard benchmark problems from PSPLIB.
{"title":"A multilayer feed-forward neural network (MLFNN) for the resource-constrained project scheduling problem (RCPSP)","authors":"A. Golab, E. S. Gooya, A. A. Falou, Mikael Cabon","doi":"10.5267/j.dsl.2022.7.004","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.7.004","url":null,"abstract":"Project management has a fundamental role in national development, industrial development, and economic growth. Schedule management is also one of the knowledge areas of project management, which includes the processes employed to manage the timely completion of the project. This paper deals with the Resource-Constrained Project Scheduling Problem (RCPSP), which is a part of schedule management. The objective of the problem is to optimize and minimize the project duration while constraining the resource quantities during project scheduling. There are two important constraints in this problem, namely resource constraints and precedence relationships of activities during project scheduling. Many methods such as exact, heuristic, and meta-heuristic have been developed by researchers to solve the problem, but there is a lack of investigation of the problem using methods such as neural networks and machine learning. In this article, we develop a multi-layer feed-forward neural network (MLFNN) to solve the standard single- mode RCPSP. The advantage of this method over evolutionary methods or metaheuristics is that it is not necessary to generate numerous solutions or populations. The developed MLFNN learns based on eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, percentage of remaining work, etc., which are calculated at each step of project scheduling, and identified priority rules, which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter out an unscheduled activity from the list of eligible activities and schedule all activities of the project according to the given project constraints. Finally, we investigate the performance of the presented approach using the standard benchmark problems from PSPLIB.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"6 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74445312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2021.11.001
G. Kumar, A. Ramaa, M. Shilpa
In most of the machining processes, the complexity arises in the selection of the right process parameters, which influence the machining process and output responses such as machinability and surface roughness. In such situations, it is important to estimate the inter-relationships among the output responses. One such method, Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to study the inter-relationships of the output responses. Estimation of proper weights is also crucial where the output responses are conflicting in nature. In the current study, DEMATEL technique is used for estimating the inter-relationships for output responses in machining of EN 24 alloy under dry conditions. CRiteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights and finally the optimal selection of machining parameters is carried out using Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The model developed guides the decision maker in selection of precise weights, estimation of the inter relationships among the responses and selection of optimal process parameters.
{"title":"Parametric analysis of dry machining process using a novel integrated multi-attribute decision making approach","authors":"G. Kumar, A. Ramaa, M. Shilpa","doi":"10.5267/j.dsl.2021.11.001","DOIUrl":"https://doi.org/10.5267/j.dsl.2021.11.001","url":null,"abstract":"In most of the machining processes, the complexity arises in the selection of the right process parameters, which influence the machining process and output responses such as machinability and surface roughness. In such situations, it is important to estimate the inter-relationships among the output responses. One such method, Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to study the inter-relationships of the output responses. Estimation of proper weights is also crucial where the output responses are conflicting in nature. In the current study, DEMATEL technique is used for estimating the inter-relationships for output responses in machining of EN 24 alloy under dry conditions. CRiteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights and finally the optimal selection of machining parameters is carried out using Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The model developed guides the decision maker in selection of precise weights, estimation of the inter relationships among the responses and selection of optimal process parameters.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"21 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76523989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.2.004
V. H. Tran, Khoa Dang Duong, Trung Nam Nguyen, Van Ngoc Pham
The study provides the forecasts of expected returns based on cross-sectional estimates from the Fama-Macbeth regressions in the United Kingdom. We collected the data of listed firms on the London Stock Exchange on the DataStream from January 1980 to December 2020. We analyze the data sample by employing three cross-sectional models' ten-year rolling estimates of Fama-Macbeth slopes. The empirical findings demonstrate that an investor can derive a composite estimate of the expected return by integrating various company-specific variables in real-time. Model 1 indicates that the expected-return estimates have a predictive slope for future monthly returns of 95.07%, with a standard error of 0.1981. Moreover, model 2 and model 3 report the predictability of returns are 77.57% and 76.94%. In short, our empirical evidence suggests that investors and stakeholders may consider using model 1 to estimate the cost of equity due to its simplicity and effective prediction capability. Our findings are consistent with trade-off theory and prior literature.
{"title":"Forecasting the cross-sectional stock returns: Evidence from the United Kingdom","authors":"V. H. Tran, Khoa Dang Duong, Trung Nam Nguyen, Van Ngoc Pham","doi":"10.5267/j.dsl.2022.2.004","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.2.004","url":null,"abstract":"The study provides the forecasts of expected returns based on cross-sectional estimates from the Fama-Macbeth regressions in the United Kingdom. We collected the data of listed firms on the London Stock Exchange on the DataStream from January 1980 to December 2020. We analyze the data sample by employing three cross-sectional models' ten-year rolling estimates of Fama-Macbeth slopes. The empirical findings demonstrate that an investor can derive a composite estimate of the expected return by integrating various company-specific variables in real-time. Model 1 indicates that the expected-return estimates have a predictive slope for future monthly returns of 95.07%, with a standard error of 0.1981. Moreover, model 2 and model 3 report the predictability of returns are 77.57% and 76.94%. In short, our empirical evidence suggests that investors and stakeholders may consider using model 1 to estimate the cost of equity due to its simplicity and effective prediction capability. Our findings are consistent with trade-off theory and prior literature.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"31 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77780408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.4.003
Valentín J. Calderon-Contreras, Jhony Ostos, Wilmer Florez-Garcia, Harold D. Angulo-Bustinza
In order to identify the determinants that influence the credit risk of Peruvian municipal savings banks, this quantitative research uses a nonexperimental design and a longitudinal sample to analyze monthly data corresponding to macroeconomic variables and microfinance institutions’ internal variables from 2011 to 2020. Using multiple linear regression, the results show that the interest rate, unemployment rate, and liquidity ratio positively influence the credit risk of Peruvian municipal savings banks; the study also shows that gross domestic product, efficiency of administrative expenses, solvency, and coverage of provisions exert a negative influence on credit risk. It is concluded that seven of the eight independent variables studied influence the credit risk of Peruvian municipal savings banks; only the inflation variable does not significantly influence credit risk.
{"title":"Determinants of credit risk: A multiple linear regression analysis of Peruvian municipal savings banks","authors":"Valentín J. Calderon-Contreras, Jhony Ostos, Wilmer Florez-Garcia, Harold D. Angulo-Bustinza","doi":"10.5267/j.dsl.2022.4.003","DOIUrl":"https://doi.org/10.5267/j.dsl.2022.4.003","url":null,"abstract":"In order to identify the determinants that influence the credit risk of Peruvian municipal savings banks, this quantitative research uses a nonexperimental design and a longitudinal sample to analyze monthly data corresponding to macroeconomic variables and microfinance institutions’ internal variables from 2011 to 2020. Using multiple linear regression, the results show that the interest rate, unemployment rate, and liquidity ratio positively influence the credit risk of Peruvian municipal savings banks; the study also shows that gross domestic product, efficiency of administrative expenses, solvency, and coverage of provisions exert a negative influence on credit risk. It is concluded that seven of the eight independent variables studied influence the credit risk of Peruvian municipal savings banks; only the inflation variable does not significantly influence credit risk.","PeriodicalId":38141,"journal":{"name":"Decision Science Letters","volume":"4 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73729369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5267/j.dsl.2022.2.001
A. Tijani, A. Ahmadi
In this article, we analyse whether the management structure of a company plays a role in the sustainability of companies. More specifically, we study the impact of occupied outside directors, outside directors sitting on several boards of directors, on the environmental, social and governance (ESG) performance of the company. We collect information about board characteristics, information about the board and management from MSCI ESG Research and financial information from Compustat. The study collects data based on panel data, which ranges from 2014 to 2020. The final sample consists of 550 US companies over a five-year period and contains 3850 firm-year observations. The study finds a positive relationship between busy outside directors and ESG performance. Busy outside directors have a positive impact not only on the overall ESG score, but also on individual ESG components. The environmental score is most affected by busy external directors, while the governance score appears to be little affected. Contrary to the theory that busy outside directors are overly engaged and degrade the fixed value, the findings support the theory that busy outside directors improve a company's sustainability performance because of their engagement, experience and the ESG performance.
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