ML helps select variables for minimum-variance portfolios, reducing risk and improving performance.
problem Optimizing minimum-variance portfolios with relevant predictors.
method Parameterized minimum-variance portfolio weights using a large pool of firm-level characteristics and their transformations.
result ML-selected predictors lead to lower risk and better performance in minimum-variance portfolios.
Investigates the long-only minimum variance portfolio in factor models.
problem Understanding the long-only minimum variance portfolio in factor models.
method Investigates the long-only global minimum variance portfolio in a factor model of returns, providing explicit and geometric descriptions for different factor models.
result Provides rigorous and explicit descriptions of the long-only solution in terms of covariance matrix parameters and geometric descriptions for multiple factors.
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.
Improved portfolio optimization method yields better risk-adjusted returns.
problem Optimizing global minimum variance portfolios with reduced risk.
method k-fold boosted k−BAHC covariance cleaning procedure for correlation matrices. result Our method outperforms other filtering methods in Sharpe ratios, despite higher turnover.
Study introduces AMVP and AMRR for dynamic portfolio optimization in volatile markets.
problem Optimizing portfolios in volatile and nonstationary financial markets.
method Adaptive Minimum-Variance Portfolio (AMVP) framework with ARFIMA-FIGARCH processes and non-Gaussian innovations.
result Demonstrated superior performance in risk reduction and portfolio stability during market breaks.
Develops a neural network for global minimum variance portfolio optimization.
problem Minimizing portfolio variance for large equity covariance matrices.
method Rotation-invariant neural network that learns lag-transformed returns and covariance regularization.
result End-to-end trained model outperforms competitors in realized volatility and Sharpe ratios.
Study long-only minimum variance portfolio in one-factor market with arbitrary sign betas.
problem Characterize the long-only minimum variance portfolio in a one-factor market with mixed-sign betas.
method Explicit solution for long-only minimum variance portfolio, explicit characterization of active set, asymptotic analysis in high-dimensional regime.
result Proportion of active assets in LOMV portfolio converges to F(β∗) in high-dimensional regime, with rate O(F(0)1/3) when F(0)>0. The paper introduces a dynamic MVP model using high-frequency financial data.
problem Capturing the dynamics of minimum variance portfolio weights in financial markets.
method Imposes autoregressive structure on MVP processes and uses CLIME and LASSO for estimation.
result Proposes DR-MVP model with established asymptotic properties.
The study analyzes how covariance estimation errors affect the global minimum-variance portfolio under heavy-tailed distributions.
problem The impact of covariance estimation errors on the global minimum-variance portfolio under heavy-tailed distributions.
method Characterization of covariance-estimation error's effect on GMVP suboptimality, derivation of regret identity and bound, application to heavy-tailed returns.
result The decision geometry of GMVP regret is invariant to a (p-1)-dimensional projection of the error matrix, with invariance to the covariance-scale direction as an exact special case.
This paper introduces a new market-based carbon risk measure for portfolio optimization.
problem The challenge of measuring and managing carbon risk in investment portfolios.
method Develops a market-based carbon risk measure and applies it to minimum variance portfolio construction.
result Market-based carbon risk measures can complement fundamental-based approaches in portfolio optimization.
In this report, we present an unsupervised machine learning method for determining groups of molecular systems according to similarity in their dynamics or structures using Ward's minimum variance objective function. We first apply the minimum variance clustering to a set of simulated tripeptides using the information …
The global minimum-variance portfolio is a typical choice for investors because of its simplicity and broad applicability. Although it requires only one input, namely the covariance matrix of asset returns, estimating the optimal solution remains a challenge. In the presence of high-dimensionality in the data, the samp…
Machine learning factors outperform traditional portfolio optimization methods.
problem Comparing machine learning and traditional portfolio optimization methods.
method Examined machine learning and factor-based portfolio optimization using autoencoder neural networks and dimensionality reduction techniques.
result Minimum-variance portfolios using latent factors derived from autoencoders and sparse methods outperform simpler benchmarks in risk minimization.
Paper uses DFL to optimize portfolio risk and outperforms conventional methods.
problem Optimizing portfolio risk and return under uncertainty.
method Decision-focused learning (DFL) to derive global minimum variance portfolio (GMVP).
result DFL-based methods consistently deliver superior decision performance in portfolio optimization.
LoCoV reduces portfolio optimization errors from sample covariance matrices.
problem Large errors in sample covariance matrix for optimal portfolio weights.
method LoCoV (low dimension covariance voting) algorithm to reduce these errors.
result LoCoV outperforms classical methods in portfolio optimization experiments.
A scalable gradient-based framework for sparse portfolio selection.
problem Sparse minimum-variance portfolio selection with cardinality constraint.
method Gradient-based optimization with Boolean relaxation and tunable parameter.
result Matches commercial solvers in most instances, differing by a few assets with negligible error in portfolio variance.
Optimal asset allocation is a key topic in modern finance theory. To realize the optimal asset allocation on investor's risk aversion, various portfolio construction methods have been proposed. Recently, the applications of machine learning are rapidly growing in the area of finance. In this article, we propose the Stu…
Neural-SDE models improve option hedging with lower errors and robustness.
problem Improving option hedging strategies using machine learning.
method Derive sensitivity-based and minimum-variance-based hedging strategies using neural-SDE market models.
result Neural-SDE models achieve lower hedging errors and are more robust than traditional models.
New shrinkage estimator for GMV portfolio reduces risk in high-dimensional asset settings.
problem Estimating the global minimum variance portfolio in high-dimensional settings with limited data.
method Dynamic shrinkage of the GMV portfolio using previous data as a target.
result The new estimator outperforms traditional methods in high-dimensional asset settings.
Improved portfolio optimization method reduces risk and improves performance.
problem Minimizing risk in large portfolios with limited data.
method Combines Tikhonov regularization and direct shrinkage of portfolio weights.
result Significantly reduces out-of-sample variance and Sharpe ratio compared to existing methods.
Paper proposes a generalized precision matrix for t-Student distributions to improve portfolio optimization.
problem Limitations of inverse covariance matrix in non-Gaussian settings.
method Exploits local dependence function to define generalized precision matrix (GPM) for multivariate t-Student distribution.
result GPM leads to statistically significant lower out-of-sample variances in minimum-variance portfolios.
Project predicts stock prices for robust portfolio design in Indian sectors.
problem Precise stock price prediction for robust portfolio design.
method Minimum variance and optimal risk portfolio optimization using past stock prices.
result Backtesting shows improved performance of optimized portfolios over equal weight portfolio.
A new estimator for asynchronous tick data shows better correlation estimates.
problem Estimating correlation from asynchronous tick data.
method Derive a minimum-variance estimator and a fast linear-time estimator.
result The fast tickwise estimator has smaller estimation errors than the usual method.
In this study, we construct two tests for the weights of the global minimum variance portfolio (GMVP) in a high-dimensional setting, namely, when the number of assets p depends on the sample size n such that np→c∈(0,1) as n tends to infinity. In the case of a singular covariance matrix with rank…
Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the extreme risk index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses. With more than 400 stocks to choose from, ou…
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.
This study compares Markowitz and Single-Index models for Malaysian stocks.
problem Optimizing portfolio selection for Malaysian stocks using different models.
method Applied Markowitz and Single-Index models to 10-year historical data of 10 stocks and a risk-free asset.
result Comparison of minimum variance and maximum Sharpe portfolios for both models under various constraints.
Paper finds efficient OPE estimator for multiple logging policies with minimum variance.
problem Finding optimal importance sampling weights for multiple logging policies with varying variances.
method Established efficiency bound under stratified sampling and proposed an estimator achieving this bound.
result Proposed estimator achieves minimum variance for any instance.
In this short report, we discuss how coordinate-wise descent algorithms can be used to solve minimum variance portfolio (MVP) problems in which the portfolio weights are constrained by lq norms, where 1≤q≤2. A portfolio which weights are regularised by such norms is called a sparse portfolio (Brodie et …
In this paper Portfolio Optimization techniques were used to determine the most favorable investment portfolio. In particular, stock indices of three companies, namely Microsoft Corporation, Christian Dior Fashion House and Shevron Corporation were evaluated. Using this data the amounts invested in each asset when a po…
Paper develops a robust hedging framework to reduce market risk and uncertainty.
problem Managing uncertainty and risk exposure in portfolio management.
method Combines high-frequency realized variance, covariance measures, and autoregressive models for multi-step volatility forecasting. Uses a box-uncertainty robust optimization scheme to derive a closed-form solution for the robust hedge ratio.
result Robust hedge ratios are more stable and entail lower turnover than standard dynamic hedges, improving downside protection and risk-adjusted performance.
Study analyzes stock performance before, during, and after the pandemic.
problem Impact of the pandemic on stock performance and risk.
method Daily data of most traded companies in Colombia from 2015 to 2023, using minimum variance approach.
result Portfolio returns and risks varied significantly during the pandemic.
Proposes a robust portfolio method for large asset universes.
problem Outliers in return data affect traditional portfolio optimizations.
method Robust PCA, shrinkage estimation, and adaptive portfolio weights.
result Superior portfolio performance in numerical and empirical tests.
We estimate the global minimum variance (GMV) portfolio in the high-dimensional case using results from random matrix theory. This approach leads to a shrinkage-type estimator which is distribution-free and it is optimal in the sense of minimizing the out-of-sample variance. Its asymptotic properties are investigated a…
The discrete-time mean-variance portfolio selection formulation, a representative of general dynamic mean-risk portfolio selection problems, does not satisfy time consistency in efficiency (TCIE) in general, i.e., a truncated pre-committed efficient policy may become inefficient when considering the corresponding trunc…
The only input to attain the portfolio weights of global minimum variance portfolio (GMVP) is the covariance matrix of returns of assets being considered for investment. Since the population covariance matrix is not known, investors use historical data to estimate it. Even though sample covariance matrix is an unbiased…
This paper describes an empirical study of shortfall optimization with Barra Extreme Risk. We compare minimum shortfall to minimum variance portfolios in the US, UK, and Japanese equity markets using Barra Style Factors (Value, Growth, Momentum, etc.). We show that minimizing shortfall generally improves performance ov…
Study shows the corrected Akaike criterion is inadmissible for estimating Kullback-Leibler discrepancy.
problem Inadmissibility of the corrected Akaike information criterion for estimating Kullback-Leibler discrepancy.
method Loss estimation framework to demonstrate inadmissibility and provide improved estimators.
result Improved estimators of Kullback-Leibler discrepancy are provided and perform well in reduced-rank situations.
In this paper we consider the worst-case model risk approach described in Glasserman and Xu (2014). Portfolio selection with model risk can be a challenging operational research problem. In particular, it presents an additional optimisation compared to the classical one. We find the analytical solution for the optimal …
We present an online approach to portfolio selection. The motivation is within the context of algorithmic trading, which demands fast and recursive updates of portfolio allocations, as new data arrives. In particular, we look at two online algorithms: Robust-Exponentially Weighted Least Squares (R-EWRLS) and a regulari…
We address the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through latent time-varying stochastic factors. The use of a global-local shrinkage prior for the elements of the factor loadings matrix pulls loadings on superfluous factors…
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. FEAT estimates free energy using adaptive transports.
problem Estimating free energy across scientific domains.
method Uses learned transports and stochastic interpolants.
result Provides consistent, minimum-variance estimators.
Improved covariance matrix forecasting for S&P 500 using factor models and shrinkage.
problem Forecasting large covariance matrices of returns in finance.
method Decompose covariance matrix into firm-level factors and sectoral restrictions. Estimate using VHAR models with LASSO.
result Significantly improved forecasting precision compared to benchmarks.
The paper models financial markets using information theory to minimize information.
problem Understanding the dynamics of financial markets.
method Modeling financial market dynamics with independent stationary scalar diffusions, interpreting the market as a communication system, and minimizing information-theoretical joint information.
result Financial market dynamics are represented by squared radial Ornstein-Uhlenbeck processes with additivity and self-similarity properties.
Humans are able to accelerate their learning by selecting training materials that are the most informative and at the appropriate level of difficulty. We propose a framework for distributing deep learning in which one set of workers search for the most informative examples in parallel while a single worker updates the …
Method cleans covariance matrices for better statistical inference.
problem Reducing estimation noise in covariance matrices for better statistical inference.
method Robust yet flexible hierarchical ansatz with bootstrap procedure.
result Lower realized risk in global minimum variance portfolios.
In this work we explore the use of metric index structures, which accelerate nearest neighbor queries, in the scenario where we need to interleave insertions and queries during deployment. This use-case is inspired by a real-life need in malware analysis triage, and is surprisingly understudied. Existing literature ten…