This study presents an ANWSER model (asset network systemic risk model) to quantify the risk of financial contagion which manifests itself in a financial crisis. The transmission of financial distress is governed by a heterogeneous bank credit network and an investment portfolio of banks. Bankruptcy reproductive ratio …
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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 infers risk preferences from portfolio choices and measures portfolio efficiency.
problem Measuring the efficiency of household investment portfolios based on risk preferences.
method Statistical analysis of portfolio choices and demographic information over six years.
result Implied risk aversion increases with wealth and financial literacy, impacting portfolio efficiency.
Financial markets are exposed to systemic risk, the risk that a substantial fraction of the system ceases to function and collapses. Systemic risk can propagate through different mechanisms and channels of contagion. One important form of financial contagion arises from indirect interconnections between financial insti…
Study uses vine copulas to optimize financial portfolios during and after the financial crisis.
problem Optimizing financial portfolios during and after the financial crisis.
method Modeling dependency structures using vine copulas, testing different portfolio strategies, analyzing various copulas.
result Vine copulas reduce portfolio risk better than simple copulas, especially during the financial crisis.
Financial instability transition under heterogeneous investments and portfolio diversificationq-fin.RM
Investment diversification affects financial stability, depending on network connectivity.
problem Analyzing stability of financial networks with diversified portfolios.
method Random matrix dynamical model with portfolio rebalancing, considering heterogeneity and diversification effects.
result Stability/instability transition depends on the largest eigenvalue of the random matrix.
Study uses MTD model to optimize portfolios by capturing complex financial asset relationships.
problem Capturing nonlinear and directional relationships in financial markets.
method Directed and weighted financial networks using Mixture Transition Distribution (MTD) model.
result Portfolio optimization with network-based assortativity measures outperforms classical methods.
Survey examines types of systemic risk in financial networks.
problem Understanding systemic risk in financial networks.
method Taxonomy of systemic risk types and regulatory measures.
result Different types of systemic risk identified.
Diffolio uses a diffusion model for multivariate financial forecasting and portfolio construction.
problem Probabilistic forecasting of multivariate financial time-series with complex cross-sectional dependencies.
method Diffolio employs a denoising network with hierarchical attention architecture, incorporating asset-level and market-level layers and a correlation-guided regularizer.
result Diffolio outperforms various probabilistic forecasting baselines in multivariate forecasting accuracy and portfolio performance.
Develops BPDS for better financial portfolio decisions.
problem Model uncertainty in financial time series forecasting.
method Bayesian dynamic modelling and predictive decision synthesis.
result Improved predictive and decision outcomes compared to traditional Bayesian analysis.
Deep Generative Models for Synthetic Financial Data: Applications to Portfolio and Risk Modelingq-fin.ST
This paper uses deep generative models to create synthetic financial data for portfolio and risk modeling.
problem Challenges in empirical research due to privacy, accessibility, and reproducibility issues in financial data.
method Investigates the use of Time-series Generative Adversarial Networks (TimeGAN) and Variational Autoencoders (VAEs) to generate synthetic financial return series.
result Synthetic data from TimeGAN closely mimics real financial data in distributional shapes, volatility, and autocorrelation.
L2GMOM learns financial networks and optimizes momentum strategies.
problem Expensive databases and financial expertise limit network construction accessibility.
method End-to-end machine learning framework (L2GMOM) that learns networks and optimizes trading signals.
result Significant improvement in portfolio profitability and risk control with Sharpe ratio of 1.74.
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.
Sentiment analysis from LLMs improves financial trading performance.
problem Improving dynamic strategy optimization in financial markets.
method Integration of sentiment analysis from LLMs into RL frameworks.
result Sentiment-enhanced RL models outperform traditional RL models in net worth and cumulative profit.
New portfolio optimization method considers both asset-specific and systemic risks for financial networks.
problem Optimizing portfolios with both idiosyncratic and systemic risks in financial networks.
method Developed a multi-objective optimization model that incorporates idiosyncratic variance and network clustering coefficient.
result Optimal portfolios outperform in terms of return measures and have less drawdown compared to traditional strategies.
Replicates and improves a deep learning framework for financial portfolio management.
problem Financial portfolio optimization problem
method Deep Reinforcement Learning Framework with EIIE topology, PVM, OSBL, and reward function
result Framework performs well in cryptocurrency market but less so in stock market
This paper uses MIS to identify key financial institutions with minimal risk contagion.
problem Mitigating systemic risk during extreme financial events.
method Applying extreme value theory and MIS from graph theory to identify diversified portfolios.
result Identified a subset of institutions with minimal extremal dependence for diversified portfolios.
Graphical models improve portfolio optimization for financial time series.
problem Optimizing portfolios with time-varying covariance patterns.
method Various graphical models (PCA-KMeans, autoencoders, dynamic clustering, structural learning) to capture covariance matrix patterns.
result Graphical models outperform baseline methods in generating steady returns with low risk.
High-dimensional covariance matrix estimators on simulated portfolios with complex structuresq-fin.CP
Study high-dimensional covariance matrix estimators for complex portfolios, improving financial metrics.
problem Estimating covariance matrices in high-dimensional portfolios with nested and one-factor structures.
method Combining random matrix theory, free probability, deterministic equivalents, and two-step covariance estimators.
result Two-step estimators improve financial metrics in complex and one-factor covariance models.
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.
New covariance estimator for financial portfolios.
problem Estimating large financial covariances in non-stationary environments.
method Exponentially weighted averages and cross-validation for nonlinearly shrinking sample eigenvalues.
result Our estimator performs well in large dimensions compared to existing estimators.
Deep Bellman Hedgingq-fin.CP
Deep Bellman Hedging uses reinforcement learning to optimize financial portfolio hedging.
problem Optimizing financial portfolio hedging with derivatives and trading frictions.
method Actor-critic reinforcement learning algorithm with continuous state and action spaces.
result Trained model provides optimal hedge for any initial portfolio and market state.
Closed-form optimal portfolios for exponential utility in small/large markets.
problem Optimal portfolios maximizing exponential utility in small/large financial markets.
method Closed-form expressions for optimal portfolios in small markets, convergence to large market optimal utility, numerical procedure for general utility functions.
result Optimal utility in large markets converges to optimal utility in small markets, requiring infinite diversification.
New vine copula method forecasts portfolio risk measures robust to market downturns.
problem Inaccurate risk measure estimation for financial portfolios due to lack of cross-dependency capture.
method Combines vine copulas with ARMA-GARCH models for marginal risk estimation.
result Portfolio is robust to American market downturns but not European market.
Study on diversifying equity portfolios during financial crises and stability.
problem Determining the effectiveness of diversification strategies during financial crises and stability.
method Analysis of 20 years of US stock price data, including GFC and COVID-19 crashes, using eigenvalues, graph-theoretic diagnostics, and hierarchical clustering.
result During financial crises, diversification via sector-based portfolios is ineffective, while during stability, 30-40 stocks provide sufficient diversification.
Reinforcement Learning for Portfolio Optimization with a Financial Goal and Defined Time Horizonsq-fin.PM
Enhanced portfolio optimization for a financial goal with G-Learning and GIRL algorithms.
problem Maximize portfolio value while minimizing periodic contributions by a target date in volatile markets.
method Combines G-Learning and GIRL algorithms for dynamic portfolio optimization.
result Improved Sharpe Ratio from 0.42 to 0.483, demonstrating robust optimization in volatile markets.
Paper introduces TVaRD, a new topological risk measure for financial portfolios.
problem Traditional risk measures like VaR and CVaR are insufficient for complex market conditions.
method Topological data analysis (TDA) using cohomology groups on financial time series data.
result TVaRD reveals significant changes in financial time series during stress conditions.
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.
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.
Study compares different integrals for optimal portfolio optimization with insider information.
problem Optimizing portfolios in a financial market with insider information.
method Anticipating stochastic calculus and various integrals (Russo-Vallois forward, Ayed-Kuo, Hitsuda-Skorokhod).
result The Hitsuda-Skorokhod and Ayed-Kuo integrals do not provide a financially meaningful investment strategy.
SNNs optimize cross-market portfolios with neuromorphic computing, reducing computational overhead and improving returns.
problem Complex cross-market portfolio optimization with high-frequency, multi-dimensional datasets.
method Leaky Integrate-and-Fire neuron dynamics, adaptive thresholding, spike-timing-dependent plasticity, lateral inhibition, hierarchical clustering, population-based spike encoding, multiple decoding strategies.
result SNNs deliver superior risk-adjusted returns and reduced volatility compared to ANN benchmarks, with improved computational efficiency.
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.
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.
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.
A neural network method improves CVA computations for complex financial portfolios.
problem Improving accuracy of CVA computations for large, diverse portfolios of financial derivatives.
method Proposes a neural network-based approach to adjust exercise strategies for counterparty default risk.
result Shows significant overestimation of CVA by standard methods, especially for non-extreme cases.
Quantum optimization aids in financial crash prediction and portfolio management.
problem Hard financial optimization problems.
method Quantum algorithms for financial crashes and portfolio optimization.
result Quantum strategies improve financial prediction and portfolio management.
End-to-end portfolio optimization using quantum annealing for financial decision problems.
problem Optimizing financial portfolios with quantum computing constraints.
method Hybrid pipeline combining quantum and classical optimization.
result Quantum-assisted portfolio optimization can achieve competitive returns.
This tutorial introduces quantum computing for financial portfolio optimization.
problem Combinatorial portfolio optimization in financial markets.
method Application of Quantum Approximate Optimization Algorithm (QAOA) to portfolio optimization.
result Quality of combinatorial portfolio optimization solutions using QAOA on quantum simulator.
Paper introduces lexical ratio to measure portfolio diversification.
problem Traditional diversification metrics overlook non-numerical relationships.
method Uses textual data to capture diversification dimensions through entropy-based insights.
result Lexical ratio (LR) outperforms traditional metrics in optimizing portfolio returns.
This paper explores deep learning for financial trading, integrating sentiment analysis.
problem Maximizing profit and minimizing loss in financial trading.
method Supervised and reinforcement learning schemes, integrating sentiment analysis.
result Demonstrates the effectiveness of deep learning methods in financial trading.
Portfolio Optimization under Dynamic Rebalancing via Topological Data Analysis and News Sentimentsq-fin.PM
This paper optimizes portfolios using TDA and financial news sentiment.
problem Effective portfolio diversification through understanding asset similarity.
method Integrates TDA with FinBERT sentiment scores for dynamic rebalancing.
result Outperforms traditional methods in returns and risk-adjusted performance.
Empirical study shows Randomized Signature Methods improve portfolio optimization in financial markets.
problem Drift estimation in non-linear, non-parametric financial markets is challenging.
method Applied Randomized Signature Methods for non-linear, non-parametric drift estimation in multi-variate financial markets.
result Randomized Signature Methods provide features on the same scale and improve portfolio optimization in real-world settings.
The paper optimizes portfolios by selecting financial ratios via PCA for better value investment.
problem Embedding value investment in portfolio optimization models.
method Principal Component Analysis (PCA) to filter out dominant financial ratios, then applying portfolio optimization model with second-order stochastic dominance criteria.
result PCA-SPO(B) strategy outperforms other models in terms of downside deviation, CVaR, VaR, Sortino, Rachev, and STARR ratios.
Develops pathwise analysis for log-optimal portfolios using rough paths theory.
problem Analyzing stability and approximation of log-optimal portfolios.
method Pathwise approach based on càdlàg rough paths theory.
result Establishes pathwise stability and error estimates for log-optimal portfolios.
Systemic risk arises as a multi-layer network phenomenon. Layers represent direct financial exposures of various types, including interbank liabilities, derivative- or foreign exchange exposures. Another network layer of systemic risk emerges through common asset holdings of financial institutions. Strongly overlapping…
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.
Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocketq-fin.CP
DeepPocket uses graph convolutional reinforcement learning for better financial portfolio management.
problem Maximizing return on investment while managing risk in correlated financial assets.
method Graph convolutional reinforcement learning framework with feature extraction, local information collection, and actor-critic reinforcement learning.
result DeepPocket outperformed market indexes on five real-life datasets over three investment periods, including during the Covid-19 crisis.
Portfolio Optimization for Index Tracking with Constraints on Downside Risk and Carbon Footprintq-fin.RM
This paper optimizes decarbonized indices for financial tracking, balancing risk and environmental impact.
problem Balancing financial performance with environmental responsibilities in the context of climate risks.
method Develops decarbonized indices using mean-VaR and mean-ES optimization methods.
result Optimized indices reduce financial risk and carbon footprint, providing a balanced investment option.