International Journal of Quantitative Research and Modeling最新文献

A Scoping Review of Green Supply Chain and Company Performance 绿色供应链与公司绩效的范围审查

International Journal of Quantitative Research and Modeling Pub Date : 2024-04-23 DOI: 10.46336/ijqrm.v5i1.608

E. Ningrum, Arissetyanto Nugroho, D. Darmansyah, N. Ahmar

引用次数: 0

Optimal Portfolio Using Roy’s Safety-First Method on Primary Consumer Goods Sector Stocks 使用罗伊安全第一法评估初级消费品行业股票的最佳投资组合

International Journal of Quantitative Research and Modeling Pub Date : 2024-04-23 DOI: 10.46336/ijqrm.v5i1.641

Estu Putri Dianti, Riaman Riaman, Sukono Sukono

引用次数: 0

Application of Structural Equations Modeling Partial Least Square at the Comparation of the Niveau of Responsibility From Cs and Digics 结构方程模型偏最小二乘法在比较 Cs 和 Digics 的责任水平中的应用

International Journal of Quantitative Research and Modeling Pub Date : 2024-04-23 DOI: 10.46336/ijqrm.v5i1.604

Visca Nadia Pradana, Haposan Sirait

{"title":"Application of Structural Equations Modeling Partial Least Square at the Comparation of the Niveau of Responsibility From Cs and Digics","authors":"Visca Nadia Pradana, Haposan Sirait","doi":"10.46336/ijqrm.v5i1.604","DOIUrl":"https://doi.org/10.46336/ijqrm.v5i1.604","url":null,"abstract":"Banking is an institution that plays a role in increasing economic development and also increasing equitable development. People who are serving users will be more selective in choosing banks so that many banks strive to be superior and more satisfying than other banks. Customer satisfaction can be seen from the role of CS and DigiCS. Customer Service ( CS ) is all actions intended to meet needs and activities by providing services so that each customer's needs are met. Digital Customer Service (DigiCS) is BNI digital banking automation that provides customers with immediate experience when carrying out digital transactions at BNI . The aim of this research is to determine the factors that influence the level of CS and DigiCS customer satisfaction with several variables, namely product quality ( ), service quality ( ), time ( ), convenience/efficiency ( ), and customer satisfaction (Y). The method used in this research is structural equation modeling partial least squares with the help of Microsoft Excel and SmartPLS software with the application of SEM - PLS to analyze the relationship between endogenous latent variables and exogenous latent variables. The results of this research are that for CS customer satisfaction it is found that only the exogenous variable product quality ( ) with its influences indicators customer satisfaction (Y) while for DigiCS customer satisfaction the results are that only the exogenous variable product quality ( ) and the exogenous variable convenience/efficiency ( ) with indicators that influence customer satisfaction (Y).","PeriodicalId":14309,"journal":{"name":"International Journal of Quantitative Research and Modeling","volume":"56 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140666993","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}

引用次数: 0

Enhancing Stock Trend Prediction Using BERT-Based Sentiment Analysis and Machine Learning Techniques 利用基于 BERT 的情绪分析和机器学习技术加强股票走势预测

International Journal of Quantitative Research and Modeling Pub Date : 2024-04-23 DOI: 10.46336/ijqrm.v5i1.567

Nikesh Yadav

{"title":"Enhancing Stock Trend Prediction Using BERT-Based Sentiment Analysis and Machine Learning Techniques","authors":"Nikesh Yadav","doi":"10.46336/ijqrm.v5i1.567","DOIUrl":"https://doi.org/10.46336/ijqrm.v5i1.567","url":null,"abstract":"Predicting stock trends with precision in the ever-evolving financial markets continues to be a formidable challenge. This research investigates an innovative approach that amalgamates the capabilities of BERT (Bidirectional Encoder Representations from Transformers) for sentiment classification (Pang et al., 2002; ?) with supervised machine learning techniques to elevate the accuracy of stock trend prediction. By harnessing the natural language processing process of BERT and its capacity to understand context and sentiment in textual data, coupled with established machine learning methodologies, we aim to provide a robust solution to the intricacies of stock market prediction. By leveraging BERT's natural language processing capabilities, we extract sentiment features from financial news articles. These sentiment scores, combined with traditional financial indicators, form a comprehensive set of features for our predictive model. We aggregate daily net sentiment, among other metrics, and demonstrate its statistically significant predictive efficacy concerning subsequent movements in the stock market. We employed a machine learning model to establish a quantitative relationship between the aggregation of daily net sentiment and trends in stock market movements. Which improved the state-of-the-art performance by 15 percentage points. This research contributes to the ongoing effort to improve stock trend prediction methods, ultimately aiding market participants in making informed investment choices.","PeriodicalId":14309,"journal":{"name":"International Journal of Quantitative Research and Modeling","volume":"11 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140671321","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}

引用次数: 0

The Effect of Company Size and Working Capital on Net Income (An Empirical Study of Manufacturing Sector Companies in the Consumer Goods Industry, Home Appliances Sub-Sector Ladder Registered on the IDX from 2015 to 2020) 公司规模和营运资本对净收入的影响（对 2015 至 2020 年在 IDX 上注册的消费品行业、家用电器子行业阶梯制造业公司的实证研究）

International Journal of Quantitative Research and Modeling Pub Date : 2024-04-23 DOI: 10.46336/ijqrm.v5i1.605

Husaeri Priatna, Iseu Anggraeni, Muhammad Iqbal, Syifa Vidya Sofwan

{"title":"The Effect of Company Size and Working Capital on Net Income (An Empirical Study of Manufacturing Sector Companies in the Consumer Goods Industry, Home Appliances Sub-Sector Ladder Registered on the IDX from 2015 to 2020)","authors":"Husaeri Priatna, Iseu Anggraeni, Muhammad Iqbal, Syifa Vidya Sofwan","doi":"10.46336/ijqrm.v5i1.605","DOIUrl":"https://doi.org/10.46336/ijqrm.v5i1.605","url":null,"abstract":"This study examines how working capital and firm size affect net profit (Empirical Study of Manufacturing Companies in the Consumer Goods Industry Sector, Household Appliances Sub Sector Listed on the IDX for the 2015 – 2020 period). Multiple linear regression analysis was used to determine the effect of two independent variables on one dependent variable. The population in this study is financial reports published by Manufacturing Companies listed on the IDX in the Consumer Goods Industry Sector, Household Appliances Sub-Sector. The sample was taken for six years, from 2015 to 2020, using the Financial Position Report and Profit and Loss Reports to obtain data, Company Size, Working Capital, and Net Income. According to the study's findings, firm size and working capital both have positive and substantial effects on net profit, with the latter having an influence on net profit that is both positive and significant. Other factors that influence Net Profit but are not analysed include the 83.3% outcome of the Coefficient of Determination and the remaining 16.7%. ","PeriodicalId":14309,"journal":{"name":"International Journal of Quantitative Research and Modeling","volume":"94 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670096","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}

引用次数: 0

Optimal Portfolio Using Single Index Model (SIM) For Health Sector Stocks 使用单一指数模型（SIM）优化健康行业股票投资组合

International Journal of Quantitative Research and Modeling Pub Date : 2024-04-23 DOI: 10.46336/ijqrm.v5i1.591

Silvia Wijaya, B. Subartini, Riaman Riaman

引用次数: 0

Investment Portfolio Optimization in Renewable Energy Stocks in Indonesia Using Mean-Variance Risk Aversion Model 利用均值方差风险规避模型优化印尼可再生能源股票的投资组合

International Journal of Quantitative Research and Modeling Pub Date : 2024-04-23 DOI: 10.46336/ijqrm.v5i1.601

Willen Vimelia, Riaman Riaman, Sukono Sukono

引用次数: 0

Calculation of Term Life Insurance Premium Reserves with Fackler Method and Canadian Method 用 Fackler 法和加拿大法计算定期人寿保险保费储备金

International Journal of Quantitative Research and Modeling Pub Date : 2024-04-23 DOI: 10.46336/ijqrm.v5i1.589

Khalilah Razanah Zakirah, B. Subartini, Riaman Riaman

引用次数: 0

Investment Portfolio Optimization In Infrastructure Stocks Using The Mean-VaR Risk Tolerance Model 利用均值-VaR 风险容忍度模型优化基础设施类股票的投资组合

International Journal of Quantitative Research and Modeling Pub Date : 2024-04-23 DOI: 10.46336/ijqrm.v5i1.602

Arla Aglia Yasmin, Riaman Riaman, Sukono Sukono

{"title":"Investment Portfolio Optimization In Infrastructure Stocks Using The Mean-VaR Risk Tolerance Model","authors":"Arla Aglia Yasmin, Riaman Riaman, Sukono Sukono","doi":"10.46336/ijqrm.v5i1.602","DOIUrl":"https://doi.org/10.46336/ijqrm.v5i1.602","url":null,"abstract":"Infrastructure a crucial role in economic development and the achievement of Sustainable Development Goals (SDGs), with investment being a key activity supporting this. Investment involves the allocation of assets with the expectation of gaining profit with minimal risk, making the selection of optimal investment portfolios crucial for investors. Therefore, the aim of this research is to identify the optimal portfolio in infrastructure stocks using the Mean-VaR model. Through portfolio analysis, this study addresses two main issues: determining the optimal allocation for each infrastructure stock and formulating an optimal stock investment portfolio while minimizing risk and maximizing return. The methodology employed in this research is the Mean-VaR approach, which combines the advantages of Value at Risk (VaR) in risk measurement with consideration of return expectations. The findings indicate that eight infrastructure stocks meet the criteria for forming an optimal portfolio. The proportion of each stock in the optimal portfolio is as follows: ISAT (2.74%), TLKM (33.894%), JSMR (3.343%), BALI (0.102%), IPCC (5.044%), KEEN (14.792%), PTPW (25.863%), and AKRA (14.219%). The results of this study can serve as a foundation for better investment decision-making.","PeriodicalId":14309,"journal":{"name":"International Journal of Quantitative Research and Modeling","volume":"83 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140667568","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}

引用次数: 0

Analysis Volatility Spillover of Stock Index in ASEAN (Case Study: Indonesia, Singapore, Malaysia) 东盟股指波动溢出分析（案例研究：印度尼西亚、新加坡、马来西亚）

International Journal of Quantitative Research and Modeling Pub Date : 2024-04-23 DOI: 10.46336/ijqrm.v5i1.603

Kirana Fara Labitta, Dwi Susanti, Sukono Sukono

{"title":"Analysis Volatility Spillover of Stock Index in ASEAN (Case Study: Indonesia, Singapore, Malaysia)","authors":"Kirana Fara Labitta, Dwi Susanti, Sukono Sukono","doi":"10.46336/ijqrm.v5i1.603","DOIUrl":"https://doi.org/10.46336/ijqrm.v5i1.603","url":null,"abstract":"Every country has its own income, including ASEAN countries such as Indonesia, Singapore, and Malaysia. One source of national income can come from stocks, which can be measured by the stock index. The income of each country depends on each other and can be influenced by a phenomenon, such as the Covid-19 pandemic. The Covid-19 pandemic can also cause volatility spillover. This research aims to analyze volatility spillover in ASEAN countries (Indonesia, Singapore, and Malaysia) before and during Covid-19 by looking at the effects of asymmetric volatility. Volatility spillover testing in this study uses the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model, starting with creating a time series model and then modeling the residuals from that model, then finding the estimated parameter results of asymmetric volatility effects. The results of this study indicate that during the period before Covid-19, there is volatility spillover for Indonesia and Malaysia. Then, during the Covid-19 period, there is volatility spillover for Indonesia and Malaysia, for Indonesia and Singapore, and for Singapore and Malaysia.","PeriodicalId":14309,"journal":{"name":"International Journal of Quantitative Research and Modeling","volume":"83 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140670396","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}

引用次数: 0