Develops a new method for benchmark portfolios and market outperformance strategies.
problem Creating effective benchmark portfolios for market outperformance.
method Explicit formulaic algorithm and multifactor risk model tailored for long-only portfolios.
result Explicit positive weights for benchmarks without principal components or iterations.
Benchmarking deep time series models for equity portfolios
problem Selecting the best deep time series model for equity portfolios
method Using a CRSP benchmark and multi-criteria acceptability analysis
result No architecture dominates the benchmark, with TransEnc-8 having the highest rank-1 acceptability
Combines absolute and relative wealth in portfolio optimization with power utility functions.
problem Optimizing portfolios with both absolute and relative wealth considerations.
method Integrates power utility functions for absolute and relative wealth, considering multiple benchmarks.
result Obtains an explicit solution for portfolio optimization combining absolute and relative wealth.
The paper introduces a new divergence for portfolio management to outperform a benchmark.
problem Maximizing expected utility of outperformance over a benchmark with constraints.
method Uses α-Bregman-Wasserstein divergence to penalize underperformance more than overperformance. result Proves existence and uniqueness of optimal portfolio strategy and conditions for constraints binding.
Enhances portfolio optimization by considering competitor benchmarks and regret.
problem Optimizing portfolios relative to specified competitors and measuring performance accurately.
method Extends Relative Robust Portfolio Optimisation to include competitor benchmarks and introduces a new way to measure regret.
result Improved portfolio optimization with competitor benchmarks and a new method for measuring regret.
Enhances portfolio optimization under uncertainty using robust multi-objective methods.
problem Uncertainties in real-world portfolio optimization scenarios.
method Robust multi-objective optimization with benchmark comparisons.
result More reliable and adaptable portfolio strategies for market uncertainties.
Unified framework for active and passive portfolio management combining outperformance and tracking.
problem Combining active and passive portfolio management objectives.
method Dynamic asset allocation using stochastic control techniques.
result Explicit closed-form expressions for optimal asset allocation.
In stochastic portfolio theory, a relative arbitrage is an equity portfolio which is guaranteed to outperform a benchmark portfolio over a finite horizon. When the market is diverse and sufficiently volatile, and the benchmark is the market or a buy-and-hold portfolio, functionally generated portfolios introduced by Fe…
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.
Optimal portfolio tracking with dynamic capital injection into a ratcheting benchmark.
problem Optimizing a portfolio's performance by dynamically adding capital to a non-decreasing benchmark.
method Formulated as an unconstrained control problem with a running maximum cost, transformed into an auxiliary problem with a nonlinear HJB equation, solved using probabilistic representation and stochastic flow analysis.
result Established the existence of a unique classical solution to the HJB equation, providing feedback optimal portfolio strategies.
Study benchmarks LLMs in portfolio optimization tasks.
problem Evaluate financial decision-making of LLMs.
method Mathematically explicit portfolio optimization problems with multiple-choice questions.
result Distinct performance patterns among LLMs in different financial tasks.
Study optimizes portfolio to minimize relative drawdown duration, penalizing unfavorable performance states.
problem Minimizing relative drawdown duration in portfolio optimization relative to a benchmark.
method Introduces a benchmark-relative drawdown-duration criterion penalizing unfavorable performance states. Uses a one-dimensional Markovian representation and Hamilton-Jacobi-Bellman equation.
result Derives explicit projection-based characterization of the optimal feedback control and identifies geometric settings for unique strong solutions.
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.
AlphaZeroBeta uses deep reinforcement learning for market-neutral portfolios, outperforming traditional methods.
problem Traditional portfolio management methods often fail during market regime shifts or when assumptions break down.
method Combines a composite reward function and CNN-GRU policy trained end-to-end via Recurrent PPO.
result Achieves higher Sharpe ratios than baselines while maintaining near-zero benchmark correlations.
We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (…
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…
Study finds physical momentum portfolios in Indian stock market yield higher returns than benchmarks.
problem Determining abnormal returns for physical momentum portfolios in the Indian stock market.
method Constructed physical momentum portfolios for daily, weekly, monthly, and yearly timescales, evaluated historical returns and risk profiles.
result Daily time scale physical momentum portfolios showed the strongest reversal with a 16-fold profit.
Portfolio managers are typically constrained by turnover limits, minimum and maximum stock positions, cardinality, a target market capitalization and sometimes the need to hew to a style (such as growth or value). In addition, portfolio managers often use multifactor stock models to choose stocks based upon their respe…
STRAPSim measures ETF portfolio similarity better than existing methods.
problem Measuring portfolio similarity for ETFs and portfolios.
method Semantic, two-level, residual-aware portfolio similarity computation.
result STRAPSim outperforms existing methods in predictive accuracy and ranking alignment.
Paper introduces benchmark-neutral pricing for long-term contracts.
problem High prices of long-term contracts under risk-neutral pricing.
method Uses growth optimal portfolio as numeraire and new pricing measure.
result Identifies minimal possible prices for contingent claims.
Investors optimize their portfolios within a Wasserstein ball to match a benchmark's risk profile.
problem Optimizing portfolio performance while maintaining risk proximity to a benchmark.
method Optimal dynamic strategy selection based on minimizing distortion risk measures within a Wasserstein ball.
result An optimal dynamic strategy exists and can be calculated through isotonic projections.
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.
Paper solves a max-min game for complex performance benchmarks.
problem Max-min portfolio game with complex performance benchmarks.
method Solves a max-min game for complex performance benchmarks using the axiom of choice.
result Exact maximin strategy found for arbitrary performance benchmarks.
The paper shows that benchmark-neutral pricing minimizes option prices.
problem Pricing extreme-maturity European put options on diversified indices.
method Benchmark-neutral pricing applied to a drifted time-transformed squared Bessel process.
result Benchmark-neutral price is the minimal possible price, risk-neutral price is more expensive.
PortBench benchmarks LLMs for PM, revealing their weaknesses in diversification and robustness.
problem Lack of benchmarks for LLM-driven portfolio management, especially in diversification and robustness.
method Developed a comprehensive benchmark with a static QA dataset and a dynamic allocation pipeline, introducing metrics to evaluate correlation and robustness.
result 90% of LLMs fail to outperform a basic equal-weight allocation, highlighting their limitations in diversification and robustness.
Investigates optimal investment strategies under CPT with risk-free and risky assets over multiple periods.
problem Optimal portfolio selection under CPT with constraints and stochastic benchmark.
method Numerical analysis of optimal CPT-investment strategies sensitivity to model parameters.
result Investment strategies under CPT are sensitive to model parameters.
The paper presents a framework for optimizing crypto-currency portfolios using generative models.
problem Optimizing crypto-currency portfolios using generative models.
method The approach involves evaluating diverse pairings of generative model forecasts and objective functions, using simulations and blending strategies.
result Eclectic blended portfolios outperform individual generative model-based portfolios.
Deep RL model uses multimodal data for better stock portfolio optimization.
problem Optimizing trading strategies for SP100 stocks using complex data sources.
method Multimodal deep reinforcement learning with state tensors, CNNs, and RNNs.
result Agent outperforms standard benchmarks in portfolio performance.
A simple, yet reasonably accurate, analytical technique is proposed for multi-factor structural credit portfolio models. The accuracy of the technique is demonstrated by benchmarking against Monte Carlo simulations. The approach presented here may be of high interest to practitioners looking for transparent, intuitive,…
We develop a statistical framework to benchmark and select large language models based on their risks.
problem Benchmarking and selecting large language models based on their associated risks.
method A distributional framework using first and second order stochastic dominance, linked to mean-risk models in finance.
result Formalizes a risk-aware approach for model selection, balancing risk and utility.
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…
Adaptive robust strategy improves online portfolio selection by managing market trends and costs.
problem Optimizing sequential investment decisions in volatile markets.
method Robust optimization with adaptive parameter adjustment.
result Adaptive scheme outperforms existing strategies in cumulative returns and Sharpe ratios.
Improved portfolio optimization reduces sensitivity to neural network initialization.
problem High sensitivity to neural network initialization in portfolio optimization.
method Robust end-to-end framework for risk budgeting portfolios.
result Enhanced stability in portfolio optimization without compromising performance.
Performance of investment managers are evaluated in comparison with benchmarks, such as financial indices. Due to the operational constraint that most professional databases do not track the change of constitution of benchmark portfolios, standard tests of performance suffer from the "look-ahead benchmark bias," when t…
A new method tracks market performance without active management.
problem Active portfolio management does not outperform benchmarks.
method Developed a hybrid PCA-based tracking portfolio strategy.
result The hybrid PCA strategy outperforms optimization-based approaches.
Paper proposes an efficient MM method for optimizing mean-reverting portfolios in finance.
problem Optimizing mean-reverting portfolios in financial markets considering mean-reversion strength, variance, and investment constraints.
method Majorization-Minimization (MM) method.
result The proposed method significantly outperforms other methods in financial market simulations.
A DRL framework optimizes portfolios using a LFSS module for feature extraction.
problem Optimizing dynamic portfolios in financial markets.
method Deep Reinforcement Learning with a Latent Feature State Space module.
result The proposed DRL framework outperforms benchmarks in portfolio optimization.
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.
Algorithm finds near-optimal VaR portfolios using MILP, improving risk management.
problem Computing optimal VaR portfolios is hard due to non-convexity and combinatorial nature.
method Formulates VaR portfolio problem as MILP, uses alternate formulations for guarantees.
result Near-optimal VaR portfolios with near-optimality guarantees.
Signed network models reduce portfolio risk by considering negative edges in financial markets.
problem Tackles portfolio optimization in financial markets by exploiting negative edges in network representations.
method Proposes a discrete optimization scheme to reduce asset selection, building time series of signed networks from asset returns.
result Empirical results show that signed network portfolios perform similarly to classical mean-variance optimization and equally weighted benchmarks.
Lower bound on portfolio underperformance risk over time.
problem Minimizing risk of a portfolio underperforming a benchmark over long periods.
method Modelled prices of securities as geometric Brownian motions with nonlinear coefficients and economic factor modeled by Ito equation. Obtained a tight lower bound on underperformance probability.
result Lower bound on decay rate of underperformance probability is tight and can be achieved with epsilon-optimal portfolios under certain conditions.
Enhances portfolio performance using deep reinforcement learning and future rewards.
problem Improving existing high-performing portfolio strategies through dynamic rebalancing.
method Proximal Policy Optimization (PPO) and Oracle agents for dynamic rebalancing; Regret-based Sharpe reward function; Transaction cost scheduler; Future-looking reward function; Circular block bootstrap training.
result Significantly enhanced portfolio performance compared to traditional strategies and baselines.
Paper applies machine learning to financial asset allocation using SPT.
problem Investment strategies based on company sizes outperform benchmarks.
method Gaussian processes (GPs) applied to SPT framework for optimal investment strategies.
result Machine learning approach outperforms existing SPT strategies.
Onflow optimizes portfolio allocation with gradient flows, robust to transaction fees.
problem Optimizing portfolio allocation with transaction costs.
method Gradient flow reinforcement learning method for dynamic asset allocation.
result Onflow outperforms benchmarks in high transaction cost regimes.
Introduces new performance measures using scaled utility functions.
problem Performance measurement in financial contexts.
method Certainty equivalents defined via scaled utility functions, well-posed portfolio optimization problem under generic conditions.
result Link between portfolio dynamics, benchmark process, and utility function choice in the long-run setting.
Deep RL for portfolio management shows poor robustness.
problem Robustness of Deep RL algorithms in online portfolio management.
method Proposed a training and evaluation process for assessing DRL algorithms.
result Most Deep RL algorithms are not robust, generalizing poorly and degrading quickly.
The paper designs mean-reverting portfolios with budget constraints.
problem Designing mean-reverting portfolios with a budget constraint.
method General problem formulation, optimization of mean-reversion criterion, consideration of portfolio variance, and investment budget constraint. Proposed specific problems and efficient algorithms.
result Our methods generate consistent profits and outperform traditional and benchmark methods.
A new method uses GATs to optimise portfolios of mid-cap firms, outperforming traditional methods.
problem Optimising portfolios of mid-cap firms considering interdependencies and firms at risk of default.
method Graph Attention Networks (GATs) applied to large-scale financial data.
result The GAT-based portfolio outperforms traditional benchmarks over a long period.