| Nazish Shahid†, Jiawei Guo‡, Haoyang Liu§
Abstract: To determine whether portfolio strategy outcomes are dataset-dependent or reflect broader trends, this study examines two distinct asset sets. We evaluate five allocation models—Equal Weighting, Maximum Sharpe Ratio, Inverse Variance, Markowitz Mean-Variance, and Robust Optimization—using two 15-stock groups derived from the Dow Jones Industrial Average. Strategy effectiveness is measured through a standardized backtest incorporating a 40-day warm-up window and regular rebalancing schedules. Evaluation criteria include historical returns, risk metrics, turnover frequency, drawdowns, and execution fees. Rather than relying on a single backtest, this work specifically tests whether strategic performance remains uniform across different asset pools. The findings demonstrate broad structural consistency across both tested groups, though localized shifts occur in secondary strategy return rankings. Although the Equal Weighted model produces the top cumulative return, the Inverse Variance approach reliably delivers superior downside protection by minimizing volatility, curtailing drawdowns, and maintaining steady position weights. Conversely, optimization-driven techniques incur greater rebalancing activity and transaction expenses without yielding better out-of-sample performance. These results indicate that risk-focused, resilient allocation frameworks offer more dependable portfolio behavior than models dependent on historical return forecasts. Keywords: portfolio allocation, backtesting, portfolio optimization, equal weighting, inverse variance strategy, maximum Sharpe ratio, robust optimization, risk management, diversification, Dow Jones Industrial Average (DJIA). MSC: 91G10, 90C90, 62P05. DOI 10.56082/annalsarscimath.2026.4.305 Read full article *Accepted for publication on October 08, 2026 |
PUBLISHED in Annals Academy of Romanian Scientists Series on Mathematics and Its Application,
ISSN ONLINE 2066 – 6594
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