Jul 2026· International Journal of Financial Engineering· 0 citations· 22 references
Abstract
This paper proposes an Exponentially Weighted Moving Mean–Variance (EMMV) model that discounts older data with a forgetting factor. Unlike traditional asset-level smoothing, the EMMV aggregates portfolio-level moments across rolling windows, yielding stable, adaptive allocations. Hyperparameters are selected objectively via a genetic algorithm with walk-forward cross-validation. Using 30 U.S. stocks from 2010 to 2025, the model achieves a buy-and-hold Sharpe ratio of 1.49 — more than double that of the equally weighted portfolio — and a rebalanced Sharpe ratio of 0.69 after transaction costs, outperforming all benchmarks. Robustness analysis confirms consistently high performance over a wide parameter range, and bootstrap tests verify significant or near-significant improvements. The framework provides a practical, data-driven tool for dynamic portfolio selection in nonstationary markets.
The results suggest that including a covariance noise or normalization term in the traditional expected value and variance-based approach to portfolio management helps reduce the impact of estimation error and market volatility on portfolio performance.
Zichun Fu· Journal of Innovation and De...· 0 citations
This research examines a naive rolling Mean-Variance Optimization (MVO) strategy for multi-stock allocation, in which expected returns and covariances are estimated solely from trailing historical data and re-estimated on a monthly basis. The central aim is to test empirically whether this deliberately simple approach...
Yu-Tong Li· Advances in Economics, Manag...· 0 citations
We investigate a computable and empirically implementable framework for continuous-time mean--variance optimal portfolio selection with random market coefficients. The market model is built on a tractable multifactor stochastic volatility structure, which captures state-dependent risk premia, stochastic volatility, and...
Zhecheng Huang, Guojiang Shao, Lei Wang et al.· 0 citations
This paper develops a model-driven analytical framework for portfolio optimization under a multidimensional Black–Scholes model with time-varying parameters, where both the drift and volatility functions evolve linearly over time. Within this framework, explicit closed-form expressions are derived for the covariance ma...
Touch Toem, S. Rujivan, Angelo E. Marasigan· Mathematics· 0 citations
We compare three families of Value-at-Risk and expected shortfall estimators—rolling historical simulation, GARCH(1,1) filtered historical simulation (FHS), and a walk-forward multi-quantile LSTM—on identical, strictly out-of-sample footing across thirty-nine developed, emerging, and frontier equity markets over 2005–2...
