This paper explores portfolio management strategies to maximize alpha and minimize beta.
problem Maximizing returns while minimizing risk in investment portfolios.
method Examines asset allocation, diversification, active management, and risk management strategies.
result Combining these strategies optimizes portfolio performance.
Ensemble method for fast portfolio valuation and risk management.
problem Dynamic portfolio valuation and risk management from cash flow data.
method Regression trees for dynamic value process learning.
result Fast and accurate estimator with closed-form solution.
The paper proposes a new model using financial big data to improve portfolio risk analysis.
problem Addressing potential information loss in portfolio risk measurement.
method Uses financial big data to incorporate out-of-target-portfolio information and overcomes the curse of dimensionality.
result The use of financial big data improves small portfolio risk analysis.
The study analyzes ETFs' portfolio optimization and tail-risk management.
problem Analyzing the performance of actively managed ETFs in managing risk and diversification.
method Daily Bloomberg data for 30 funds, evaluating various strategies under long-only and long-short constraints.
result Tangency-type portfolios generally outperform buy-and-hold benchmarks, while minimum-variance and CVaR-minimizing portfolios sacrifice upside for downside control.
Simplified approach to portfolio risk management and hedging in practice.
problem Challenges in applying academic portfolio risk management and hedging in real-world business settings.
method A straightforward approach using convex optimization and quadratic programming.
result Demonstrates how to solve portfolio risk management and hedging problems with CVXOPT.
MILLION framework optimizes portfolio risk and return efficiently.
problem Optimizing risk and return in AI for FinTech portfolio management.
method Two phases: return maximization with auxiliary objectives and risk control with portfolio interpolation and improvement.
result Framework achieves fine-grained risk control and improved return rates.
Limited liability reduces leveraged risk in loan portfolio management models.
problem The impact of limited liability on risk in loan portfolio management models is not well understood.
method Formulated four models to analyze the effect of limited liability on risk and return in loan portfolio management.
result Including limited liability in loan portfolio management models produces better results in minimizing risk and maximizing expected return.
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.
The study assesses carbon risk in investment portfolios and proposes new management strategies.
problem The impact of carbon risk on stock pricing and portfolio construction.
method Developed a BMG risk factor and estimated time-varying carbon beta using a multi-factor model.
result Carbon risk can be incorporated into portfolio construction to reduce unrewarded financial risks.
A machine learning model manages portfolio risk in high dimensions.
problem Managing risk in high-dimensional financial portfolios.
method A supervised learning approach using replicating martingales and polynomial/neural network bases.
result The model outperforms naive Monte Carlo and least-squares Monte Carlo methods.
Proposes a bond portfolio solution for managing interest rate risk.
problem Managing long-term assets and liabilities under interest rate risk.
method Proposes a bond portfolio solution based on ambiguity-averse preferences, accommodating various constraints and interest rate perturbations.
result Optimal portfolio can be computed as a simple generalized least squares problem, enhancing out-of-sample performance.
Proposes a new framework to manage venture capital portfolio risk by focusing on deal-level correlations.
problem Managing venture capital portfolio risk, especially extreme outcomes.
method Gaussian-copula-based framework that learns deal-level dependence from observed joint success frequencies.
result Correlation amplifies extreme upside outcomes, shifting portfolio distribution toward heavier right tails.
Myopic optimization outperforms reinforcement learning in portfolio management, leading to lower returns and higher risks.
problem Reinforcement learning strategies in portfolio management yield lower or negative returns and higher risks compared to myopic optimization.
method Modeling execution/liquidation frictions with mark-to-market accounting, using Malliavin calculus to derive policy gradients and risk shadow price, and quantifying phantom profit.
result Myopic optimization outperforms reinforcement learning in portfolio management, leading to better returns and lower risks.
New formula for portfolio risk management using conditional PDEs.
problem Optimal diversification and risk management of portfolios.
method Closed-form formula for conditional probability, Gaussian copulas, conditional risk-neutral PDE.
result Dynamic monitoring of portfolio volatilities and weights from PDEs.
This paper compares modern portfolio theories and applies them to real-world portfolio selection.
problem Balancing risk and return in financial investments.
method Introduction of Markowitz's MPT and Fernholz's SPT, application of four models (Markowitz, Constant Correlation, Single Index, Multi-Factor), and use of Portfolio Algorithm and time series models for prediction.
result Comparison and evaluation of portfolio performance and risk management strategies.
Research evaluates three risk models for portfolio construction during market downturns.
problem Challenges in constructing quantitative portfolios using statistical risk models.
method Three statistical risk models tested on 1,000 stocks across four periods.
result Models consistently outperform market returns in various crises.
Paper introduces Market-adaptive Ratio for better portfolio management.
problem Traditional risk-adjusted ratios fail to account for bull and bear markets.
method Integrates ρ parameter and uses reinforcement learning to adjust portfolio allocations dynamically. result Market-adaptive Ratio outperforms traditional ratios in bull and bear markets.
We study the problem of portfolio insurance from the point of view of a fund manager, who guarantees to the investor that the portfolio value at maturity will be above a fixed threshold. If, at maturity, the portfolio value is below the guaranteed level, a third party will refund the investor up to the guarantee. In ex…
Risk diversification is one of the dominant concerns for portfolio managers. Various portfolio constructions have been proposed to minimize the risk of the portfolio under some constrains including expected returns. We propose a portfolio construction method that incorporates the complex valued principal component anal…
Hybrid model combines risk measures for better portfolio allocation.
problem Optimizing portfolios with various risk measures.
method Mean-variance hybrid model combining spectral risk measure and quantile optimization.
result Hybrid model outperforms classical mean-variance model in risk allocation.
DeltaHedge uses AI to optimize portfolio options trading.
problem Balancing risk and return in volatile markets.
method Multi-agent framework integrating reinforcement learning and options hedging.
result Outperforms traditional and standalone models.
Combines RL and BF for risk-managed portfolio optimization.
problem Risk management in RL-based portfolio optimization under high volatility.
method Integrates reinforcement learning with barrier functions for dynamic risk control.
result Demonstrates superior performance in real-world data compared to RL-only approaches.
This paper proposes a new portfolio allocation method using LLMs to outperform traditional strategies.
problem Persistent tradeoff between risk and return in portfolio management.
method Follow-the-leader approach with sentiment-based trade filtering and LLM-driven hedging.
result Empirical results show a 69% increase in annualized returns and 119% in Sharpe ratio compared to SPY buy-and-hold.
The paper uses clustering and integer programming to optimize stock selection for investment funds.
problem Maximizing profits and minimizing risk in stock markets.
method Data-oriented analysis and clustering techniques with integer programming.
result Reconstructed NASDAQ 100 index fund example demonstrates effectiveness.
Unified framework combines views and optimization for better portfolio management.
problem Optimizing portfolio weights with dynamic adjustment based on volatility.
method Dynamic sliding window adjusting horizon, factor estimates, BL posterior returns, and weights over time.
result Outperforms dynamic mean-variance optimization without BL views, providing stronger downside risk control.
Community detection improves stock market portfolio optimization.
problem Improving portfolio optimization in financial markets.
method Community detection in correlation-based networks of worldwide stock markets.
result Portfolios constructed using community detection outperform traditional methods.
The downside risk of a portfolio of (equity)assets is generally substantially higher than the downside risk of its components. In particular in times of crises when assets tend to have high correlation, the understanding of this difference can be crucial in managing systemic risk of a portfolio. In this paper we genera…
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.
Paper proposes a CNN model for improved multi-asset portfolio risk prediction.
problem Challenges in risk management of multi-asset portfolios due to limited correlation capture.
method Uses CNN and image processing to convert financial data into images for enhanced feature extraction.
result CNN model significantly outperforms traditional methods in risk prediction accuracy.
Optimizes trading portfolios considering risk and profit.
problem Balancing risk and profit in trading portfolios.
method Risk-Aware Trading Swarm (RATS) algorithm.
result RATS improves portfolio performance and risk management.
The 20/60/20 rule improves risk management and portfolio optimization in finance.
problem Understanding and managing financial data with heavy tails.
method Application of the 20/60/20 rule to stock market data, development of new measures for tail heaviness, and integration into portfolio optimization.
result The 20/60/20 rule enhances robustness and performance in portfolio optimization.
Deep neural networks reduce portfolio tail-risk by 99% in crisis-era simulations.
problem Managing tail risk in financial portfolios.
method Parameterizing convex-risk minimization with deep neural networks.
result Significant reduction in one-day 99% CVaR.
Proposes a network-based strategy to manage financial market risks.
problem Managing extreme events in volatile financial markets.
method Extreme value theory, network model, maximum independent set, value at risk, expected shortfall.
result Developed portfolio strategies improve risk diversification.
Enhanced Transformer models predict ETF portfolio performance by optimizing covariance and semi-covariance matrices.
problem Static covariance estimates fail to capture dynamic market fluctuations and non-linear correlations.
method Transformer-based models for real-time covariance and semi-covariance predictions.
result Portfolios optimized with semi-covariance matrix outperform those with standard covariance matrix, especially in volatile conditions.
Study uses RL to optimize crypto portfolios with two-sided transactions and lending.
problem Managing downside risk and capital optimization in high-risk crypto markets.
method Integrates RL with a new environmental formulation and PnL-based reward function, using SAC agent with CNN-MHA.
result Significantly outperforms benchmarks, especially in high-volatility scenarios.
Hybrid approach combines Markowitz's theory with reinforcement learning for optimal portfolio management.
problem Optimizing investment portfolios while balancing returns and risks.
method Knowledge distillation for training reinforcement learning agents.
result Achieves highest yield and Sharpe ratio of 2.03, ensuring top profitability with low risk.
Optimizes Iron Condor portfolios for better risk and profit management.
problem Transient value process of Iron Condor portfolios not well studied.
method Formulated as a stochastic optimal control problem, using bounded martingale assumption.
result Optimal stopping time aligns with expiration for submartingale value process.
Developed a new risk measure, CRI, for evaluating concentrated portfolios.
problem Current risk assessment methods fail to adequately evaluate concentrated portfolios.
method Modified Herfindahl-Hirschman index to create CRI.
result CRI provides a single numeric score for evaluating portfolio risks.
Paper presents a neural network method for efficient xVA computation and risk management.
problem High-dimensional counterparty credit risk valuation and management.
method Neural network-based BSDE solver for coupled system of BSDEs for xVA.
result Efficient computation of xVA for high-dimensional portfolios.
Blockchain funds balance risk and return for various investors.
problem Creating diversified portfolios with risk parity for different risk appetites.
method Developed three funds (Alpha, Beta, Gamma) with distinct risk and return profiles, setting weights inversely proportional to risk.
result Blockchain enables investors to select their preferred risk-return combination and allocate wealth accordingly.
The basic financial purpose of an enterprise is maximization of its value. Trade credit management should also contribute to realization of this fundamental aim. Many of the current asset management models that are found in financial management literature assume book profit maximization as the basic financial purpose. …
Paper uses inverse optimization to measure risk preference from investment portfolios.
problem Measuring subjective risk preference in investment portfolios.
method Inverse optimization on mean-variance framework.
result Quantified risk preference parameters validated with existing measures.
Investigates how ESG mandates affect portfolio efficiency and risk premia.
problem The inefficiency of portfolios under ESG mandates and the associated risk premia.
method Analyzes equilibrium conditions with ESG constraints and mean-variance investors.
result Negative ESG premium arises due to ESG constraint, not risk factor.
Deep learning improves portfolio management by optimizing asset weights.
problem Traditional portfolio managers are outperformed by deep learning models in trading.
method Proposes a deep reinforcement learning portfolio manager that allocates weights to assets.
result The proposed portfolio manager outperforms conventional managers in risk-adjusted returns.
Managing a portfolio to a risk model can tilt the portfolio toward weaknesses of the model. As a result, the optimized portfolio acquires downside exposure to uncertainty in the model itself, what we call "second order risk." We propose a risk measure that accounts for this bias. Studies of real portfolios, in asset-by…
A new DQN algorithm improves portfolio management and risk assessment in digital assets.
problem Singular prediction mode and limited data source in deep learning models for asset management.
method Introduced DQN algorithm into asset management portfolios, considering market risk.
result Performance exceeds benchmark, proving DRL algorithm's effectiveness in portfolio management.
Paper introduces a new method for risk-sensitive investment management using RL.
problem Risk-sensitive portfolio management with unknown model parameters.
method Combines RL and risk-sensitive stochastic control with Gaussian perturbations for exploration.
result Endogenous relative-entropy regularization and optimal investment strategy derived.
We derive simple return models for several classes of bond portfolios. With only one or two risk factors our models are able to explain most of the return variations in portfolios of fixed rate government bonds, inflation linked government bonds and investment grade corporate bonds. The underlying risk factors have nat…