Third part of a study on liquidity risk in asset management, focusing on managing the asset-liability liquidity risk.
problem Managing the asset-liability liquidity risk in asset management.
method Develops a methodological and practical framework for liquidity stress testing programs.
result Proposes measurement, management, and monitoring tools for controlling the liquidity gap.
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.
The paper introduces deep learning for ALM, enhancing asset and liability management.
problem Optimizing asset and liability management for treasurers and other applications.
method Deep learning applied to ALM for optimal decision making.
result Enhanced ALM approach for better asset and liability management.
Paper proposes real-time risk metrics for stablecoin protocols.
problem Lack of risk management frameworks for stablecoins.
method Developed two risk metrics: capitalization and liquidity.
result Demonstrated practical benefits of real-time on-chain data.
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.
Portfolio management is the art and science in fiance that concerns continuous reallocation of funds and assets across financial instruments to meet the desired returns to risk profile. Deep reinforcement learning (RL) has gained increasing interest in portfolio management, where RL agents are trained base on financial…
Develops a climate risk model for asset managers.
problem Climate-related risks affecting asset performance and productivity.
method Uses the Vasicek model with downward jumps to represent climate impacts on asset dynamics.
result Expected losses increase over time due to climate-related extreme events.
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.
A fund manager invests both the fund's assets and own private wealth in separate but potentially correlated risky assets, aiming to maximize expected utility from private wealth in the long run. If relative risk aversion and investment opportunities are constant, we find that the fund's portfolio depends only on the fu…
Research proposes a model to estimate transaction costs and assess asset liquidity risk.
problem Lack of standardized models for asset liquidity risk in asset management.
method Develops a market impact model and a two-regime model based on power-law property.
result Defines liquidity measures and applies model to stocks and bonds.
Deep learning enhances financial asset management through new models and data sources.
problem Improving portfolio performance and price forecasting accuracy in financial asset management.
method Systematic review using Scopus database, focusing on deep learning applications in financial asset management from 2018 to 2023.
result Deep learning models show promise in enhancing portfolio performance and price forecasting accuracy.
AAMDRL uses DRL to manage assets in noisy, changing environments.
problem Learning in noisy, self-adapting environments with sequential data.
method Augmented state information, one-period lag, walk forward analysis.
result AAMDRL outperforms traditional methods in asset management.
The paper analyzes competition among fund managers using excess logarithmic returns and constructs games to find optimal allocations.
problem Optimal allocation strategies among fund managers considering excess logarithmic returns.
method Constructs both n-player and mean field games to address the competition problem. result The MFE of the MFG represents the limit of n-player game's equilibrium as n approaches infinity. Shai is a 10B model for asset management tasks, outperforming baselines.
problem Improving performance in asset management tasks.
method Continuous pre-training and fine-tuning on asset management-specific data.
result Shai outperforms baseline models in asset management tasks.
Combines human and AI to optimize fund managers' investment decisions.
problem Improving fund managers' investment practices.
method Combines Inverse Reinforcement Learning and Reinforcement Learning.
result Improves fund managers' investment performance.
A new RL framework tackles asset allocation problems using Monte Carlo simulation.
problem Existing asset allocation methods fail to consider portfolio management and financial market characteristics.
method Proposes a new reinforcement learning framework that considers portfolio state and uses Monte Carlo simulation to prevent overfitting.
result The proposed method outperforms benchmarks in various test intervals.
DRL optimizes asset managers' hedging timing based on market conditions.
problem Optimal timing for hedging strategies given market conditions.
method Deep Reinforcement Learning framework with contextual information, lagged observations, and robust testing.
result Our approach achieves superior returns and lower risk compared to standard methods.
RL agents outperform baselines in asset allocation.
problem Optimizing asset allocation using reinforcement learning.
method Model-free deep RL agents trained on real-world stock prices.
result RL agents significantly outperformed random and uniform allocation.
AI agents manage portfolios, improving on human oversight.
problem Improving strategic asset allocation for institutional investors.
method 50 specialized agents produce capital market assumptions, construct portfolios, critique, and vote on each other's output.
result Meta-agent compares forecasts with realized returns and improves agent performance.
Given a new candidate asset represented as a time series of returns, how should a quantitative investment manager be thinking about assessing its usefulness? This is a key qualitative question inherent to the investment process which we aim to make precise. We argue that the usefulness of an asset can only be determine…
This research proposes methods to model and assess liability liquidity risk in asset management.
problem Lack of standardized models for liability liquidity risk in asset management.
method Statistical models, zero-inflated models, aggregate and individual-based approaches, and factor models.
result Developed mathematical and statistical approaches to estimate and assess redemption shocks.
Study of portfolio management under relative performance concerns using mean field games.
problem Portfolio management problems under relative performance concerns.
method Forward utilities of CARA type, mean field games, best response and equilibrium strategies.
result Solve forward-utility finite player game and mean-field game under asset specialization.
This paper addresses AMMs for expiring assets, ensuring liquidity and risk management.
problem AMMs struggle with assets that expire, leading to liquidity issues and risk exposure.
method Combines AMM and limit-order book features, ensuring liveness and dynamic price adjustment.
result A DEX for expiring assets maintains liquidity and risk management.
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.
Adaptive strategies reduce pension fund costs and risks.
problem Managing longevity and volatility risks in pension funds.
method Modular simulation framework with customizable metrics.
result Substantial reduction in pension plan costs and default risk.
Study finds stock selection ability of Chinese mutual funds is better than asset allocation ability.
problem Evaluating the performance of actively managed mutual funds in China.
method Developed performance measures for asset allocation and selection using holding-based models and compared them with Fama-French and Treynor-Mazuy models.
result Stock selection ability from holding-based models is positively correlated with Fama-French model, while industry allocation is positively correlated with Treynor-Mazuy model.
Paper uses AI to predict market trends better than traditional methods.
problem Traditional trend following and momentum investing are limited.
method Uses deep learning and AI techniques for market trend prediction.
result Improves asset manager performance by increasing returns and reducing drawdowns.
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.
This paper systematizes knowledge on synthetic assets in crypto.
problem Disparate academic literature on synthetic assets in crypto.
method Broad perspective, general framework, data-driven analyses.
result Highlights risks and areas of research interest in synthetic assets.
Method detects and visualizes changes in financial markets' asset relationships.
problem Detecting and explaining changes in financial markets' asset relationships.
method Construct co-occurrence networks, calculate Graph-Based Entropy, apply Differential Network.
result Visualization of changes in financial markets with high interpretability.
Develops a theory linking managers' disclosures to market pricing.
problem Linking managers' earnings guidance to market pricing.
method Mathematical theory of managerial disclosure in asset pricing.
result Foundational approach for understanding disclosure impacts.
The kind of realized mission inflows the sensitivity to risk. Among other factors, the risk results from decision about liquid assets investment level and liquid assets financing. The higher the risk exposure, the higher the level of liquid assets. If the specific risk exposure is smaller, the more aggressive could be …
We numerically study an Asset Liability Management problem linked to the decommissioning of French nuclear power plants. We link the risk aversion of practitioners to an optimization problem. Using different price models we show that the optimal solution is linked to a de-risking management strategy similar to a concav…
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.
Active management is a term that has many meanings and we have found the defining characteristics needed for success as an "active manager" elusive within the literature. In this paper we offer a set of criteria that defines an active manager and his success. In order to facilitate this, we introduce several definition…
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…
AI advances impact asset management, offering new decision-making capabilities.
problem AI's impact on asset management and potential disruption.
method Analyzing how AI capabilities (reading, reasoning, decision-making) can be applied to asset management.
result AI can revolutionize asset management, but risks vary by fund type.
WaveCorr uses deep reinforcement learning to manage portfolios more effectively.
problem Dynamic portfolio rebalancing with multiple factors.
method Introduces WaveCorr, a DRL network with permutation invariant correlation processing.
result WaveCorr outperforms existing methods with up to 25% improvement in Sharpe ratio.
Optimizes fund manager's wealth with partial information on market risk.
problem Maximizing wealth with incomplete information about market risk.
method Formulated as optimization under partial information, solved via martingale method and concavification.
result Shows how learning about market risk affects optimal investment strategy.
This paper compares different DRO formulations for pension fund management.
problem Navigating uncertainty in asset liability management for pension funds.
method Three DRO formulations: mixture, box, and Wasserstein ambiguity sets.
result Wasserstein and box ambiguity sets outperform traditional approaches in fund performance.
In this article we extend earlier work on the jump-diffusion risk-sensitive asset management problem [SIAM J. Fin. Math. (2011) 22-54] by allowing jumps in both the factor process and the asset prices, as well as stochastic volatility and investment constraints. In this case, the HJB equation is a partial integro-diffe…
The basic financial purpose of a firm is to maximize its value. An inventory management system should also contribute to realization of this basic aim. Many current asset management models currently found in financial management literature were constructed with the assumption of book profit maximization as basic aim. H…
Paper uses news data to model asset correlations without market data.
problem Traditional risk models rely on market data; this paper offers an alternative.
method Uses encoder-only language models to embed news data, then calculates asset return distributions and covariance through Energy Distance.
result Established connections between distributional differences and excess returns co-movements using Energy Distance.
Paper proposes a RL approach for ALM with superior performance.
problem Dynamic asset-liability management in financial markets.
method Continuous-time RL with LQ formulation, policy gradient, adaptive and scheduled exploration.
result Method outperforms traditional and state-of-the-art RL algorithms in ALM.
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.
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.
The paper refutes standard asset pricing models and introduces new theories.
problem Inaccuracies in standard asset pricing models.
method Introduces new theories and empirical tests to explain asset pricing anomalies.
result New theories explain why standard models are inaccurate and provide insights.
Develops a framework for optimal investment in assets with different liquidity constraints.
problem Optimal investment-consumption problem for a utility-maximizing investor with lower-bound constraints.
method Generalized martingale approach and decomposition of the problem into subproblems.
result Explicit formulas for optimal strategies derived for power-utility functions.