Develops Heuristic Portfolio Optimization (HPO) as an information-restricted projection of Markowitz/tangency solution
problem Practitioners allocate capital with forecast-light rules like equal weight, inverse volatility, risk parity, HRP, and RA-HRP
method Implies-return principle and fixed-tree cluster-Sharpe recursion
result Formalizes HPO maps, proves defect equals squared inefficiency, and identifies nodewise alphas as policy-gradient coordinates
New methods incorporate alpha signals into portfolio construction, improving performance.
problem Signal-blindness in existing portfolio construction methods.
method Introduces three methods: HRP-μ, HRP-Σμ, and CRISP. result CRISP at intermediate γ consistently outperforms other methods. Study applies HRP to Latin American markets, showing smoother risk-return profile.
problem Lack of empirical analyses of HRP in Latin American markets.
method Hierarchical Risk Parity (HRP) with hierarchical clustering and recursive bisection.
result HRP portfolio outperforms Max Sharpe portfolio in NUAM markets, with smoother risk-return profile.
Paper connects two portfolio methods, HRP and Minimum Variance, revealing their underlying similarity.
problem Inability to universally adopt optimization-based portfolio construction methods.
method Unifies Hierarchical Risk Parity and Minimum Variance approaches.
result Schur complementary allocation reveals the connection between HRP and Minimum Variance.
MPM uses machine learning to switch between two portfolio strategies for better risk management.
problem Adaptive portfolio strategy selection for improved risk management.
method XGBoost learns to switch between HRP and NRP strategies.
result MPM outperforms both HRP and NRP in risk-reward profile and interpretability.
This paper optimizes portfolios using HRP and CLA algorithms on NIFTY 50 stocks.
problem Designing an optimal stock portfolio with accurate forecasting of future returns and risks.
method Uses hierarchical risk parity and critical line algorithms on NIFTY 50 stocks.
result Hierarchical risk parity algorithm outperformed the critical line algorithm on test data.
Study compares three portfolio optimization methods on Indian stocks.
problem Optimizing portfolios for the Indian stock market.
method Three portfolio optimization methods (MVP, HRP, HERC) applied to 15 sectors.
result Identified portfolios with highest cumulative return, lowest volatility, and best Sharpe Ratio.
Hybrid QAOA approach optimizes portfolios with strict constraints, outperforming classical methods.
problem Combinatorial optimization under strict cardinality constraints in portfolio management.
method Constraint-preserving QAOA with XY-mixers and Trotterized initialization.
result QAOA achieves a Sharpe Ratio of 1.81, significantly outperforming classical methods.
A new portfolio method uses NMF for risk budgeting, outperforming classical methods.
problem Portfolio diversification and risk management in crypto and traditional assets.
method Risk factor budgeting using convex Non-negative Matrix Factorization (NMF).
result Our method outperforms classical portfolio allocations in diversification and risk profile.
RPS uses graph-based representation learning for better portfolio optimization.
problem Improving portfolio optimization with better returns and lower risks.
method RPS redefines the distance matrix of financial assets using Representation Learning and Clustering algorithms.
result RPS proposes a heuristic to select closer to the optimal subset of assets.
This study compares two portfolio optimization methods on Indian stocks.
problem Designing an optimal portfolio considering stock returns and risks.
method Hierarchical Risk Parity and Eigen Portfolio approaches on NIFTY 50 sectors.
result Hierarchical Risk Parity portfolio outperforms Eigen portfolio in most sectors tested.
This study compares three portfolio optimization methods on Indian stocks.
problem Comparing portfolio optimization methods on Indian stocks.
method Mean-Variance, Hierarchical Risk Parity, and Reinforcement Learning approaches.
result Reinforcement Learning outperformed other methods in terms of Sharpe ratio.
This study compares three portfolio design approaches for stock selection.
problem Designing a profitable portfolio with precise stock returns and risks.
method Three portfolio design approaches: mean-variance portfolio, hierarchical risk parity, and autoencoder-based portfolio.
result Autoencoder portfolios outperform MVP on annual returns, but MVP is best on risk-adjusted returns.
A deep probabilistic model analyzes DNA-encoded library data for efficient screening.
problem Complex data from DNA-encoded library experiments mask underlying signals.
method Compositional deep probabilistic model of DEL data, modeling latent reactions between synthons.
result DEL-Compose model demonstrates strong performance and valuable insights.
End-to-end framework optimizes financial metrics using neural networks.
problem Difficult portfolio optimization in financial markets due to non-stationarity and high costs.
method Directly optimizes differentiable financial metrics via neural networks, incorporating realistic costs and rebalancing.
result Best model achieves +7.86% total return, outperforming S&P 500 by 12.38 percentage points.
Paper presents a new method for better financial market forecasting.
problem Traditional investment strategies fail to capture market nuances and risks.
method Combines deep learning, factor integration, and correlated stock analysis.
result Enhanced diversification and performance capture in financial markets.
TRP uses tree-based approach for market-neutral portfolios.
problem Creating non-binary, market-neutral portfolios with signed signals.
method Tree-based portfolio construction with minimum-spanning-tree and sector-anchored variants.
result TRP outperforms HRP in preserving signal direction and managing exposures.