This study examines portfolio optimization in the Philippine Stock Exchange (PSE) Industrial Sector by integrating Mean-Variance Theory (MVT) with the Security-Potential/Aspiration (SP/A) framework. Using data from 73 industrial firms from 1993 to 2022, expected returns were estimated through a rolling-window approach. Portfolio optimization was performed under varying Risk-Return Factor (RRF) levels, reflecting bearish and bullish investment preferences, while investor decision-making was modeled under a cautiously hopeful condition in which fear and hope were equally weighted. Behavioral probability assignments were generated using different SP/A parameter levels and incorporated into the optimization process. The resulting portfolios were evaluated against a benchmark portfolio and the PSE. Results show that portfolios incorporating behavioral probability assignments achieved superior risk-adjusted performance, with balanced risk-return preferences producing the most favorable outcomes, while highly return-focused portfolios exhibited greater volatility. These findings demonstrate the value of incorporating behavioral factors into portfolio optimization and investment decision-making in emerging markets.

A carbon capture, utilization, and storage (CCUS) retrofit is an irreversible investment whose value depends on when the firm commits, because allowance prices and capture costs move over the investment window. Choosing that date is an optimal stopping problem, for which least-squares Monte Carlo yields an executable timing rule. Capital approval requires more than an executable rule: evidence of how much value that rule gives up against the optimum. A dual certificate supplies that evidence, at the price of a simulation budget of its own. The certificate admits several implementations, and which of them is tightest on a given instance is not known until that budget has been spent; nothing available beforehand orders them. We split the certificate’s inner conditional expectation into two terms: a regression-linear term that is available in closed form under regime-switching lognormal dynamics, and a nonlinear residual that must be simulated. The residual’s share of the two terms’ combined mean absolute magnitude—the residual share—indicates whether the plain hybrid—the certificate with unstratified residual sampling—is competitive with the standard nested estimator. A probe run costing under 2% of a full certification pass is enough to compute that share. On a calibrated 600 MW Chinese coal-fired retrofit, that share, read against a threshold, rules out the plain hybrid before any full-budget run; of the three implementations compared—nested, plain hybrid and stratified hybrid—the stratified hybrid then produces the smallest estimated gap. Even so, the resulting certificate settles less than a capital committee needs. One-sided lower confidence limits lie well above zero, so the trained rule has demonstrable value; the gap between the estimated bounds, however, remains too wide to rank the retrofit against a close alternative. The share therefore governs what certification costs; the width the certificate returns bounds what it can be asked to decide.

This study evaluates a network of five used-car showrooms operated by CV ABC using the Dynamic Capacitated Facility Location Problem without Relocation together with the Huff Model. Demand for 2026 was forecast from 36 months of historical sales and distributed across 13 demand zones according to showroom attractiveness and distance. The model minimizes annual fixed and transportation costs while satisfying demand and branch-capacity constraints. The optimal configuration retains the Mangga Dua, Bintaro, and Bekasi branches and closes the Kemayoran and Pantai Indah Kapuk 2 branches. This configuration reduces total annual cost from IDR 6.906 billion to IDR 3.143 billion, representing a 54.49% saving, while maintaining full demand fulfilment. Sensitivity analysis of demand and the Huff parameters a and b indicates that the selected configuration remains stable under the tested parameter variations, although a 30% demand increase exceeds the available capacity.

This study integrates Security-Potential/Aspiration (SP/A) theory into the Markowitz Mean-Variance (MV) framework to examine how a cautiously hopeful probability assignment shapes portfolio construction in the Other Financial Institutions (OFI) subsector of the Philippine Stock Exchange (PSE). Using daily returns from 1993 to 2022 for thirteen non-bank firms, the study weighted fear and hope equally at four intensities and optimized each across a Risk-Return Factor (RRF) under a fixed daily budget. Before screening, cumulative return peaked at a risk-averse RRF and rose with intensity. After screening, no configuration exceeded the Market Return and no intensity dominated. Portfolio weights followed the RRF rather than the intensity, so the framework serves best as a disciplined way to allocate within the subsector.