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Quadratic programming (QP) formulations are widely used in optimal investment portfolio selection, a central problem in financial decision-making. In practice, asset allocation decisions operate at two interconnected levels: the strategic level, which allocates the budget across major asset classes, and the tactical level, which distributes the allocation within each class to individual securities or instruments. This study evaluates the Frank–Wolfe (FW) algorithm as a computationally alternative to a QP formulation implemented in CVXPY and solved using OSQP (CVXPY–OSQP solver) for tactical investment portfolio optimization. By iteratively solving a linear approximation of the convex objective function, FW offers a distinct approach to portfolio construction. A comparative analysis was conducted using a tactical portfolio model with a small number of stock assets, assessing solution similarity, computational running time, and memory usage. The results demonstrate a clear trade-off between the two methods. While FW can produce portfolio weights closely matching those of the CVXPY–OSQP solver at lower and feasible target returns, its solutions differ at higher returns near the limits of the feasible set. However, FW consistently achieved shorter execution times and lower memory consumption. This study quantifies the trade-offs between accuracy and efficiency and identifies opportunities to improve FW’s accuracy through adaptive iteration strategies under more challenging optimization conditions.

The mining and energy sector is still the driving force for economic development and community empowerment, especially around mining and energy activities. Therefore, increased investment in the mining and energy sectors needs to be increased and balanced with stricter safety and environmental policies. This paper aims to formulate a quadratic investment portfolio optimization model, and apply it to several stocks in the mining and energy sectors. In this paper, it is assumed that risk is measured using Value-at-Risk (VaR), so that the optimization modeling is carried out using the quadratic investment portfolio approach to the Mean-VaR model with risk-free assets. Furthermore, the model is used to determine the efficient portfolio surface based on several values of risk aversion levels. Based on the results of the analysis, it is found that an efficient portfolio surface has a minimum portfolio return value with an average of 0.766522 and a VaR risk of 0.038687. In addition, the results of the analysis can be concluded that the greater the level of risk aversion, the smaller the VaR value, which is followed by the smaller the portfolio average value.

This study aims to forecast the COVID-19 spread in Indonesia involving vaccination factors using vector autoregressive with exogenous variables (VARX). The COVID-19 spread represented by active, recovered, and death case rate indicators acts as endogenous variables, while the COVID-19 vaccination represented by second-dose vaccination rates acts as exogenous variables. Because the sum of three COVID-19 spread indicators in one day is one, only two indicators with the highest correlation rates are involved in VARX modelling. The other indicator is practically projected by subtracting one from the sum of two indicator projection results. Based on the analysis results, the active and recovered case rates are two indicators chosen in VARX modelling. Using Akaike information criterion, the most suitable VARX model to project the case and recovered case rates are VARX (7, 1). This model is expected to help the Indonesian government project the COVID-19 spread in Indonesia.

Insurance companies are at risk of bankruptcy when their surplus becomes negative, making it necessary to observe the evolution of the surplus over time. In this study, the surplus evolution is assumed to be discrete, based on the fiscal year period. This study examines the surplus model in sharia insurance schemes, focusing on identifying shortcomings in existing models and developing a framework based on community needs. Specifically, this study highlights the potential integration of productive waqf with welfare benefits for fishermen. A systematic review was conducted by collecting scientific works from the Scopus, ScienceDirect, Dimensions, and Google Scholar databases, selected using the PRISMA approach. The results indicate that existing surplus models in sharia insurance schemes remain general and do not address specific community needs, such as fishermen’s welfare. This study provides insights into developing more inclusive and innovative surplus models by integrating productive waqf. These findings are expected to encourage the development of sharia insurance schemes oriented toward sustainability and improving fishermen’s welfare.

The need for Sharia insurance products in the Muslim community continues to significantly increase. Sharia insurance offers sustainability in overcoming the risk of economic loss based on the principles of Islamic law. In addition, Sharia insurance can be a sustainable solution in providing risk management funds. This study aimed to analyze the development and sustainability of Islamic insurance as an alternative form of risk management, as well as its sustainability in the future. The general review is still in the form of the products offered and operational system models in the Sharia insurance industry. The systematic literature review method was used to obtain a visualization and general description of Islamic insurance, employing 774 data articles from 2010 to 2022. From the results of the analysis, it was found that research discussing Islamic insurance has demonstrated significant developments every year. Mitigating risks by offering the principles of Islamic law represents added value for the Islamic insurance industry. In addition, five models of Sharia insurance systems have been introduced and used; namely, the Mudharabah model, the modified Mudharabah model, the Wakalah model, the hybrid model, and the Waqf model. However, the products offered in Sharia insurance are not too numerous and are still focused on individual risk. Based on these results, Sharia insurance will undergo development in the future in terms of both the products offered and risk management. Of course, it can also lead to a transition to the development of sustainable Sharia insurance.

The purpose of this research is to classify time-series data on the number of daily COVID-19 cases based on the dynamics. This research aims to evaluate the effectiveness of community activity restrictions in suppressing the number of new cases of COVID-19 in cities and regencies in West Java. We performed time-series clustering on daily positive case data for COVID-19 in 27 cities and regencies in West Java Province, Indonesia for this study. The k-medoids clustering algorithm was used for clustering, with shape-based lock step measures, specifically, the cross correlation-based distance. We used daily new infected cases data for COVID-19 in 27 cities and regencies in West Java Province during the worst situation. We used data from 1 July 2021 to 31 September 2021 and from 1 January 2022 to 31 May 2022, during the Emergency Community Activity Restriction period (PPKM). According to our findings, the optimal number of clusters that could be formed from the data we had was 4 clusters for the first period and 2 clusters for the second period, with silhouette value of 0.2633 and 0.6363, respectively. For the first period, we discovered that PPKM was successful in clusters 1 and 2, namely in 25 cities/districts in West Java, except for Bogor and Depok, while for the second period, we found PPKM to be effective in reducing the number of COVID-19 cases throughout cities and regencies in West Java. This shows there is an improvement from the implementation of PPKM in the first period. We also found that the cluster that was formed was not only influenced by the effectiveness of the PPKM, but also by geography. The closer a city is to a hotspot region for the spread of COVID-19, the earlier the increase in the number of new COVID-19 cases will occur.

The risk of natural disasters has increased over the last few decades, leading to significant economic losses across the globe. In response, research related to the risk of economic loss due to natural disasters has continued to develop. At present, insurance remains the best solution for funding such losses. The purpose of this study is to analyse the development of insurance as an alternative for sustainable economic recovery after natural disasters. The data used are articles obtained from several sources indexed by Scopus and Google Scholar. The search resulted in a final database of 266 articles, culled from a total of 813 articles before the final selection was made. The articles used are publications from 2000–2021 (including 21 database periods), to which we applied a systematic literature review method. Identification and evaluation of the articles was carried out through visualization of their content, development of disaster risk insurance, and availability of disaster risk insurance by country and type. The identification results show that the relationship between the word “insurance”, according to visualization using the VOSviewer software, has a relationship with other clusters including the words “disaster”, “disaster insurance”, “risk”, “natural disaster”, “study”, “recovery”, and “disaster risk financing”. The 266 articles studied show that there was an annual increase in the number of published scientific papers over the period 2000–2021. The types of disaster risk insurance, based on a review of the articles, include agricultural insurance, flood insurance, property insurance, earthquake insurance, crop insurance, and natural disaster insurance. In addition, of the six types of disaster risk insurance, three have been discussed the most in the last five years, namely, agricultural, flood, and property insurance. The increase in the number of scientific publications discussing these three types of disaster risk insurance has been influenced by climate change. Climate change causes a significant increase in the potential for disasters and is accompanied by an increased risk of loss. This review is expected to provide information and motivation for researchers related to the development and importance of disaster risk insurance research. Research in the risk sector for disaster losses due to climate change should be continued in the future in order to help fund economic recovery, especially throughout the insurance sector. With continuous research on disaster risk insurance, it is hoped that the resulting information can be more effective in determining insurance risk and in helping local economies and communities recover after the advent of a disaster. With the availability of funds for post-disaster recovery, the regional economy affected by the disaster can be immediately restored and recovered from adversity.

Fish stocking in inland fisheries involves a prey–predator interaction model so that the number of fish stocked affects optimal and sustainable yields. It is very important to make mathematical modeling to optimize inland fisheries management which is part of the blue economy. Currently, studies that focus on predator–prey mathematical modeling in inland fisheries, especially those related to insurance are lacking. The bibliometric database was taken from Google Scholar, Dimensions, Science Direct, and Scopus in the 2012–2022 research years. After further processing, it is displayed on the PRISMA diagram and visualized on VOSviewer to display the update of this research topic. As blue economy sustainability, the management of fisheries sector needs to be reviewed deeply. In this study, the assumptions of the predator–prey mathematical model are made to obtain the equilibrium point, maximum sustainable yield (MSY), and catch per unit effort (CPUE) values. These results can be used to calculate fisheries insurance as a strategy for optimizing sustainable fishermen’s income.

Understanding generational differences in herding behavior is crucial for policymakers, financial educators, and market regulators, particularly in emerging markets where retail investor participation is rapidly growing. This study investigates the influence of herding behavior on investment decision-making among Generations X, Y, and Z in Indonesia, as well as the moderating role of investment experience. Using a multi-group structural equation modeling (SEM) approach with data from 1293 retail investors, the research compares behavioral tendencies across cohorts. Results reveal that herding behavior has a positive and significant impact on investment decision-making in all generations, with the strongest effect observed in Generation X, followed by Generation Z and Generation Y. Investment experience significantly weakens herding behavior’s influence for Generation X but shows no significant moderating effect for Generations Y and Z, suggesting that psychological and social influences, particularly from digital platforms, may outweigh experiential learning in younger cohorts. These findings align with behavioral finance theory, which explains herding as a cognitive and emotional bias heightened by market uncertainty. The results provide practical implications for designing targeted financial education programs and regulatory measures to promote independent decision-making and reduce susceptibility to biased market information, especially among younger generations in digitally driven investment environments.

Gross Domestic Product (GDP) is one indicator for measuring a country’s economic growth. However, the increase in GDP and population growth are affecting CO2 emissions. This study analyses the effects of GDP and population density on CO2 emissions in Indonesia. To this end, it used the Cobb-Douglas model, and parameter estimation using Ant Colony Optimisation algorithm. The analysis of the results reveals that GDP and population density influence CO2 emissions in Indonesia significantly, and significantly follows the Cobb-Douglas model with increasing return to scale characteristics. Thus, an increase in GDP and population density will lead to increased CO2 emissions in Indonesia.