Predictable rebalancing does not guarantee a profitable front-run.
A plausible institutional flow meets weak futures returns and a stronger, imperfect bond proxy.
Study: The Unintended Consequences of Rebalancing
Portfolio Construction
Research on portfolio weights, diversification, rebalancing, and the gap between backtests and implementation.
Curated by Signal & Evidence. Read our research approach for how we distinguish author findings from house tests.
House adaptation: weak futures-pair result
A backtest spends information every time we change the question.
The Axiomatic Trader connects model complexity, research search and position sizing through explicit assumptions.
Study: The Axiomatic Trader: Latent Regularity, Information Budgets, and the Canonical Form of a Quantitative Investment System
A sparse neural model improves ranking. Size still changes the answer.
Feature structure reduces one model’s parameter burden, but the stronger portfolio claim depends on weighting and costs.
Study: Dependence-Informed Sparse Neural Architecture for Stock Return Prediction
The market’s correlation map can change without giving us a trade.
A reconfiguration index describes a volatility environment. Its premium measure is not a variance-swap payoff.
Study: The Reconfiguration Premium: Co-movement Structure as an Unspanned Dimension of the Variance Risk Premium
A steadier index tracker can still be a worse tracker.
Entropy regularization reduces some portfolio churn. The study’s tables show a trade-off, not universal superiority.
Study: An Entropic Factor Model for Robust Portfolio Replication
Diversification starts with what stocks respond to.
MINGLE builds a graph from shared exposures. The gains are promising, but crisis protection remains limited.
Study: Beyond Co-Movement: Locality by Exposures Enables a Joint Factor-Graph Framework for Portfolio Diversification
Three matrices beat the index. The replication question is harder.
Rank dynamics offer a parsimonious portfolio design, but universe selection and uncertainty qualify the headline.
Study: Are Three Matrices All You Need To Beat the Market? Observable Matrix Dynamics for Portfolio Optimization
Less volatility drag does not make aggressive leverage safe.
A compact neural risk model improves the authors’ backtest. Its drawdowns and universe rules deserve equal billing.
Study: Neural Network-Driven Volatility Drag Mitigation under Aggressive Leverage
Missing prices can turn a risk model into a portfolio problem.
A neural covariance estimator reports lower risk, but the implementation deserves an equally careful audit.
Study: End-to-End Neural Shrinkage of Indefinite Pairwise Correlation Matrices for Small-Cap-Inclusive Portfolios
KellyBoost gets the objective right. That can still mean betting too much.
A direct route from features to portfolio weights exposes the cost of estimation error.
Study: KellyBoost: Growth-Optimal Portfolio Construction with Gradient-Boosted Trees