Michael Nayat Young

Goods and services are being shifted to be greener in terms of their production. This study investigated the determinant factors that influence pro-environmental green production businesses. An assessment of pro-environmental planned behaviour was used to determine green production. The data were taken from global research studies worldwide based on Scopus indexing to be analyzed with bibliometrics. The collected data consisted of 186 on perceived environmental authority support, 5,501 on perceived environmental concern, 3,755 on pro-environmental attitude (PEATT), 396 on pro-environmental subjective norms, 477 on pro-environmental perceived behavioural control and 407 on green production intention. The co-occurrence network was taken as the input for the partial least squares structural equation model to be formed into six factors. The measurement follows the inner and outer model analyses. The latent variables generated were used as an input for a multilayer perceptron artificial neural network (ANN) with 10 neurons. The bibliometric results show the range of documents from 1975. The structural equation model shows that 5 out of 10 hypotheses are accepted. ANNs predict the normalized importance of each determinant factor, where PEATT is explored as the strongest and most important predictor. Several insights, such as providing incentives, subsidies, rewards, promotion, education, socialization and a deep understanding of benefits for green production, are strongly suggested.

This study proposes a computational decision-support optimization framework for capital allocation under uncertainty using a hybrid Mean–Variance (MV) and Regret–Safety (RS) model. The framework integrates engineering-based multi-objective optimization, probability-distorted scenario generation, and behaviorally parameterized risk constraints to support decisions for moderately risk-averse investors in volatile environments. Using daily data from 30 Philippine Stock Exchange (PSE) blue-chip equities (2020–2022), the system models market uncertainty through simulation and optimizes asset weights using Gurobi. The MV component minimizes portfolio variance for target returns, while the RS component maximizes reliability and safety probability under cumulative prospect theory weighting. Backtesting shows that the MV-0.10 configuration achieved the highest computational efficiency (11.12% cumulative return; 3.96% volatility), whereas RS-0.40 produced the most robust and stable performance trajectory with enhanced regret-resilient allocation. The results highlight that integrating behavioral parameters into optimization enhances decision resilience, reduces performance degradation under uncertainty, and improves human-centered decision quality. The proposed hybrid framework extends industrial engineering approaches in risk modeling, optimization, and decision support for sustainable financial systems in emerging markets.

This study explores the topics of portfolio selection and performance analysis based on the interdisciplinary decision science and optimization lenses to the Philippine service industry. It is a combination of operations research, behavioral finance, and quantitative decision analysis to deal with investment decision-making in the face of uncertainty. Behavioral portfolios are created to embody different types of sentiment by the investor: fearful, cautiously hopeful, hopeful, and equally weighted, in a Safety-First optimization. The time-series evaluation of these portfolios is compared to a benchmark of the market. To address the issue of period effects and heterogeneity of the financial returns series, the research employs statistical hypothesis tests consisting of t-tests, one-sample, and a pair of t-tests in addition to the statistical tests to measure the difference in mean returns of portfolios and compared to the benchmark. The empirical results indicate that several behaviorally adjusted portfolios are significantly higher than the benchmark, aiming to provide positive and statistically significant portfolio-goal differentials. This leads to the conclusion that the found benefits in performance are not necessitated by accident but rather by systematic decision arrangements within the portfolio construction process. The findings also indicate significant trade-offs between risk and returns across behavioral classifications, which implies that important weightings come to play in portfolio performance through investor sentiment and decision attitudes. Through a combination of optimization methods, behavioral modeling, and statistical inference, the study proves that hybrid decision frameworks are able to improve the quality of decision-making in complex and uncertain environments. In general, the study has implications for interdisciplinary decision science because it demonstrates that behaviorally informed optimization enhances the performance and risk management of portfolio. The results confirm the application of combined decision model in investment planning of service industries and market economies.

The study evaluates the optimal portfolio within the investment pool of Holding Firms. Mean-Variance Theory is applied in Modern Portfolio Theory in order to find portfolios with the lowest risk. The framework of portfolio selection involves the identification of the investment pool, return estimation, probability assignment, utilization of the selection model and the evaluation of the portfolio’s performance. The main objective of the study is to compare the performance of Mean-Variance Model Portfolios using Behavioural Probability Assignment and Equal Probability Assignment. 30 years of historical data from the Philippine Stock Exchange in the Holding Firms sector is used in order to estimate returns over 1000 scenarios across an RRF range of 0.1 to 1.0. Cautiously Hopeful SP/A Probability Assignment and Equal Probability Assignment are used. Mean-Variance Model is applied for portfolio selection. Simple statistical techniques are used for the analysis of data pre-cleanup while pairwise t-test comparison and % composition are used for data after outliers are removed.

This study aims to analyze the risk and return efficiency from the selected 30 financial firms that are recognized by the Philippine Stock Exchange (PSE) from 1993 to 2022. The Mean-Variance Portfolio is used in order to create an optimal portfolio that will balance the risk and return efficiency. The gathered data from the following financial firms’ documented stocks are meticulously studied compared with the performance of the processed portfolios in the PSE Index (PSEi). The study also underwent descriptive analysis, including a one-sample t-test on paired-return differences, to assess the comparison between the portfolio’s performance and the benchmark. In accordance with this, the top three companies were identified per risk-return factor (RRF) based on their percentage. The results showed a selective difference in the performance of portfolios depending on the level of the risk-return rate. This study suggests that when the RRF level is lower, it is likely beneficial and practical for conservative investors, but it has a lower return, compared with a higher RRF level, it has the potential to have a higher return, with higher risk. This study may serve as a guide for investors or other organizations that will also undergo the same research discussion on portfolio optimization.

Portfolio selection aims to maximize investment returns while maintaining an acceptable level of risk. It aims to maximize returns while keeping risk within acceptable limits. This approach involves choosing investments that yield the highest possible return without exceeding a defined risk threshold. This study explores a framework for building portfolios that could outperform a benchmark, using a safety-first approach and analyzing 30 years of historical returns from nine subsectors within the Service Sector. Back testing indicates that some portfolios from these subsectors have the potential to outperform both the broader Market and the overall Service Sector. However, t-test results do not provide strong statistical evidence that any of these portfolios significantly outperform the Market. The selected portfolios are determined based on their mean and cumulative returns, specifically including Telecommunications (SF1), and Casino and Gaming (SF1). The study identifies a statistically acceptable portfolio for some subsectors, as the t-test results are significant across varying portfolio parameters, while the comparison with the benchmark using the t-test result stated otherwise.

This study proposes a hybrid, data-driven portfolio optimization framework for Philippine blue-chip equities integrating Hierarchical Clustering Analysis (HCA), Association Rule Mining (ARM), and multi-criteria decision-making (MCDM) techniques using Entropy–AHP weighting, TOPSIS efficiency scoring, and VIKOR compromise ranking. Daily price and volume data from 30 Philippine Stock Exchange Index (PSEi) companies, spanning from 2010 to 2022, were analyzed. HCA revealed five statistically strong stock clusters reflecting distinct risk–return–liquidity profiles. ARM uncovered strong intra- and inter-cluster co-movement patterns, confirming behavioral and structural dependencies. Hybrid Entropy–AHP weighting emphasized mean return and downside risk (CVaR) as dominant criteria. TOPSIS and VIKOR rankings showed strong agreement (ρ = 0.94), consistently identifying ICT, SM, ALI, URC, and BPI as top-performing stocks. Portfolio backtesting demonstrated that a concentrated Top-10 strategy maximized cumulative returns, while a Per-Cluster Top-5 strategy achieved superior risk-adjusted stability. Sensitivity analysis across rebalancing frequencies and transaction-cost scenarios indicated that semi-annual rebalancing provides the optimal balance between responsiveness and turnover. Results confirm that combining structural clustering, behavioral rule mining, and multi-criteria optimization improves diversification quality and portfolio robustness in emerging markets.

Plant layout significantly influences manufacturing performance by optimizing the placement of machines and resources to enhance output and minimize operational costs. We redesigned the layout for the steaming line of a food manufacturing facility to improve line efficiency and labor productivity without compromising product quality. We used the define-measure-analyze-design-verify six sigma methodology to identify problems and develop solutions. Layout modifications were validated using ProModel 2016. Results demonstrated reduced process bottlenecks and improved workflow. The results offered actionable insights into food manufacturing and similar industries, promoting the adoption of data-driven, technology-enabled approaches to enhance operational efficiency and productivity.