CPPS selects portfolios using conformal prediction for better returns.
problem Optimizing portfolio returns with predictive models and uncertainty.
method Conformal prediction framework for portfolio selection.
result CPPS outperforms simpler strategies in delivering superior returns.
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.
Grover search for optimal portfolios based on Sharpe ratio.
problem Finding optimal portfolios with specific risk-return characteristics.
method Grover's algorithm applied to portfolio selection with oracles.
result Quantum algorithms can efficiently find optimal portfolios.
New heuristic selects fewer assets for efficient portfolios, reducing costs.
problem High transaction costs and fees from including many assets in portfolios.
method Surrogate formulation to select assets, re-optimizes portfolio with fewer assets.
result Effective in constructing portfolios with fewer assets, reducing costs.
SCS identifies a range of plausible equally weighted portfolios, quantifying selection uncertainty.
problem Uncertainty in selecting the best equally weighted portfolio subset.
method Introduces Selection Confidence Set (SCS) for EWPs, covering plausible portfolios with high probability.
result SCS quantifies selection uncertainty and covers the unknown optimal selection with high probability.
We compare three network portfolio selection methods; hierarchical clustering trees, minimum spanning trees and neighbor-Nets, with random and industry group selection methods on twelve years of data from the 30 Dow Jones Industrial Average stocks from 2001 to 2013 for very small private investor sized portfolios. We f…
Paper presents a new framework for optimal asset and signal combination.
problem Optimal asset and signal combination problem.
method Two-stage approach: reformulate dynamic portfolio selection problem, then use Canonical Correlation Analysis.
result Improved performance of proposed method over natural benchmarks.
Paper solves a complex portfolio selection problem with time-inconsistent preferences.
problem Time-inconsistent preferences in portfolio selection.
method Unified framework with minimal assumptions, proving existence and uniqueness of solution.
result Existence and uniqueness of square-integrable solution for the integral equation.
Study optimal portfolio selection using average and current profitability of risky assets.
problem Continuous-time mean-variance portfolio selection in time-varying financial markets.
method Introduced AP and CP indexes; estimated AP and CP using second-order variation of an auxiliary wealth process.
result Estimations of AP and CP are more accurate than traditional MLE.
Paper explores two methods for optimal portfolio selection in financial markets.
problem Optimal portfolio selection for financial markets with jumps.
method Maximum principle and dynamic programming approach.
result Relationship between two methods and their adjoint processes.
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.
Enhanced portfolio selection using sentiment data and LSTM.
problem Improving portfolio selection through sentiment analysis and price prediction.
method Semantic Attention Model for sentiment prediction, LSTM for price prediction, mean-variance strategy for portfolio optimization.
result Sentiment-aware portfolio strategies outperform non-sentiment aware models on average.
ChatGPT selects stocks for investment portfolios, but optimization models improve results.
problem Using AI for investment advice due to model inaccuracies.
method Used ChatGPT to generate a stock universe, then compared various portfolio optimization strategies.
result Combining AI-generated stock selection with advanced optimization models yields better investment outcomes.
The problem of portfolio optimization is one of the most important issues in asset management. This paper proposes a new dynamic portfolio strategy based on the time-varying structures of MST networks in Chinese stock markets, where the market condition is further considered when using the optimal portfolios for invest…
Comprehensive review of robust portfolio selection models.
problem Addressing uncertainty in financial portfolio optimization.
method Classification and analysis of various models and approaches.
result Identification of open research questions.
Paper optimizes portfolio selection with ICX order constraints.
problem Minimizing portfolio variance with ICX order constraints.
method Optimal and efficient portfolios are derived in closed form.
result Closed-form solutions for optimal and efficient portfolios.
Paper uses RL to optimize multi-asset portfolios in fluctuating markets.
problem Optimizing multi-asset portfolios in time-varying financial markets.
method Soft Actor-Critic (SAC) algorithm for policy learning, policy iteration process.
result SAC algorithm outperforms in various criteria in simulated and real financial markets.
Markowitz (1952, 1959) laid down the ground-breaking work on the mean-variance analysis. Under his framework, the theoretical optimal allocation vector can be very different from the estimated one for large portfolios due to the intrinsic difficulty of estimating a vast covariance matrix and return vector. This can res…
The paper uses TDA to select stocks for a sparse portfolio, improving performance across market scenarios.
problem Sparse portfolio selection in financial markets.
method Topological data analysis (TDA) for clustering stock price movements.
result The TDA-based clustering strategy significantly enhances sparse portfolio performance.
New algorithm for online portfolio selection with reduced runtime.
problem Maximizing total return in online portfolio selection.
method Minimizes current logarithmic loss regularized by log-determinant of Hessian.
result Achieves regret guarantee similar to Universal Portfolios with reduced runtime.
Study solves optimal portfolio selection using HJB equation.
problem Optimal portfolio selection problem.
method Maximal monotone operator method, Banach fixed-point theorem, Fourier transform, monotone operators technique.
result Existence and uniqueness of solution to HJB equation.
Given a set of assets and an investment capital, the classical portfolio selection problem consists in determining the amount of capital to be invested in each asset in order to build the most profitable portfolio. The portfolio optimization problem is naturally modeled as a mean-risk bi-criteria optimization problem w…
The paper solves portfolio selection using Rényi divergence and optimization.
problem Single-period portfolio selection under CRRA utility.
method Information-theoretic lens, Rényi divergence, Rényi entropy, Blahut-Arimoto-style alternating optimization.
result CRRA portfolio selection is equivalent to a Rényi information-projection problem.
New methods for equity fund selection and portfolio construction using mutual fund top holdings.
problem Classic equity fund selection and portfolio construction problems.
method Propose an easy-to-implement framework to produce a long-short portfolio from mutual fund top holdings.
result Generate impressive results and show statistical evidence.
BPASGM uses sparse graphical models to optimize portfolio selection.
problem Portfolio optimization in high-dimensional settings with estimation error.
method BPASGM extends BPA to a sparse graphical model, screening assets for diversification.
result BPASGM portfolios outperform standard mean-variance portfolios in risk-adjusted performance.
Study optimal portfolio selection with Recovery Average Value at Risk, showing better control over liabilities.
problem Optimizing portfolios with a new risk measure under known or uncertain distributions.
method Existence results for mean-risk optimal portfolios under different distributional assumptions.
result Portfolio selection under Recovery Average Value at Risk provides better control over liabilities.
We report the results of fifteen sets of portfolio selection simulations using stocks in the ASX200 index for the period May 2000 to December 2013. We investigated five portfolio selection methods, randomly and from within industrial groups, and three based on neighbor-Net phylogenetic networks. We report that using ra…
TDA improves stock portfolio selection by analyzing data structure.
problem Traditional portfolio selection methods fail to handle stock market data complexities.
method Two-stage method involving time series generation and clustering with TDA features.
result TDA-based portfolio outperforms other methods consistently over different time frames.
A new perspective on portfolio selection using realized returns.
problem Choosing between two investments with the same expected return.
method Modeling realized returns as random variables and applying the CAPM formula.
result The CAPM formula applies to realized returns, not just their expectations.
This paper considers portfolio construction in a dynamic setting. We specify a loss function comprised of utility and complexity components with an unknown tradeoff parameter. We develop a novel regret-based criterion for selecting the tradeoff parameter to construct optimal sparse portfolios over time.
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.
PS^2 selects assets then weights for high-dimensional investing.
problem High-dimensional mean--variance investing challenges.
method Two-step framework: Lasso screening followed by standard portfolio estimation.
result FPS^2 with defactored returns improves performance.
Study quantifies model risk in dynamic portfolio selection using KL divergence.
problem Model risk in financial portfolio selection under uncertainty.
method Defined model risk as KL divergence loss, solved nonlinear equations for optimal robust strategy.
result Optimal robust strategy can be obtained semi-analytically in worst case scenario.
New model considers wealth and time affecting risk aversion in portfolio selection.
problem Optimal investment strategy and consumption process depend on wealth and future income balance.
method Proposed a new mean-variance-utility framework with time and state-dependent risk aversion, solved using game theory.
result Equilibrium investment and consumption policies derived, aligning with investor behavior.
Study proposes DRL for investor-specific portfolio optimization considering asset volatility.
problem Dynamic allocation of funds balancing risk and return under market conditions.
method Volatility-guided Deep Reinforcement Learning (DRL) framework.
result Proposed DRL portfolios outperform baseline strategies.
This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.
problem Optimizing portfolio allocation with a penalty for deviation from a reference portfolio.
method Formulated as a McKean-Vlasov control problem, provides explicit solutions and asymptotic expansions.
result The penalized portfolio strategy outperforms standard mean-variance and reference portfolios in most cases.
Investor selects portfolios based on news attention in a hidden Markov model.
problem Mean-variance portfolio selection in a dynamic attention context.
method Closed-loop equilibrium strategies via extended HJB equation and Markov chain approximation.
result Equilibrium strategies found through iterative algorithm and numerical examples.
Study on portfolio selection and risk arbitrage in financial markets.
problem Analyzing optimal portfolios and risk arbitrage in financial markets with coherent risk measures.
method Characterization of optimal portfolios, dual representation, and interplay between EMMs and absolutely continuous measures.
result The absence of ρ-arbitrage is linked to the interplay between EMMs and absolutely continuous measures. Generative AI models enhance sector-based investment portfolios, but performance varies by market conditions.
problem Improving investment performance through better stock selection in volatile markets.
method Applied LLMs from OpenAI, Google, Anthropic, DeepSeek, and xAI to select and weight stocks within S&P 500 sectors.
result LLM-weighted portfolios outperform sector indices in stable markets but underperform in volatile ones.
We present a method for constructing the log-optimal portfolio using the well-calibrated forecasts of market values. Dawid's notion of calibration and the Blackwell approachability theorem are used for computing well-calibrated forecasts. We select a portfolio using this "artificial" probability distribution of market …
We empirically test predictability on asset price by using stock selection rules based on maximum drawdown and its consecutive recovery. In various equity markets, monthly momentum- and weekly contrarian-style portfolios constructed from these alternative selection criteria are superior not only in forecasting directio…
The paper proposes a new approach to portfolio selection that maximizes diversification and return.
problem Maximizing diversification and return in portfolio selection.
method A bi-objective model that maximizes a diversification measure and portfolio expected return.
result The return-diversification approach outperforms strategies based on diversification or classical risk-return approaches.
A deep reinforcement learning method for cost-sensitive portfolio selection.
problem Non-stationary price series and complex asset correlations make feature learning hard, and practical cost constraints are not considered.
method A two-stream portfolio policy network and a cost-sensitive reward function are developed using deep reinforcement learning.
result The method achieves superior performance in profitability, cost-sensitivity, and representation abilities.
The classical dynamic programming-based optimal stochastic control methods fail to cope with nonseparable dynamic optimization problems as the principle of optimality no longer applies in such situations. Among these notorious nonseparable problems, the dynamic mean-variance portfolio selection formulation had posted a…
New EI strategies using OWA and SSD for excess return.
problem Selecting EI portfolios that stochastically dominate a benchmark.
method Proposes a new OWA-based EI model and introduces a new SSD criterion.
result OWA-based EI portfolios stochastically dominate a benchmark and generate excess return.
In this paper, we introduce a matrix-valued time series model for foreign exchange market. We then formulate trading matrices, foreign exchange options and return options (matrices), as well as on-line portfolio strategies. Moreover, we attempt to predict returns of portfolios by developing a cross rate method. This le…
New model improves portfolio selection by analyzing tensor data.
problem Improving portfolio selection through better analysis of style returns.
method Introducing a tensor dynamic conditional correlation (TDCC) model with trace-normalization and dimension-normalization.
result The TDCC model enhances portfolio selection across multiple markets.
New model uses Half-Full/Half-Empty approach for better portfolio selection.
problem Improving portfolio selection through behavioral finance.
method Generalized Half-Full/Half-Empty approach to positive/negative lotteries, developing nonconvex optimization and mixed-integer linear programming models.
result The Half-Full/Half-Empty model outperforms other methods in risk and profitability.