CryptoRLPM uses on-chain data to improve crypto portfolio management performance.
problem Lack of effective use of on-chain data in RL-based crypto portfolio management.
method Developed CryptoRLPM, an RL-based system that incorporates on-chain data for crypto PM, consisting of five units.
result CryptoRLPM outperforms baselines in ARR, DRR, and SR, especially for Bitcoin.
RL learns to ignore factors in factor investing portfolios.
problem Combining factor investing and reinforcement learning for optimal portfolio allocation.
method RL agent learns through sequential allocations based on firms' characteristics using Dirichlet distributions.
result RL-based portfolios are very close to equally-weighted allocations, indicating agnostic factor learning.
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.
MSPM uses modular agents to manage financial portfolios efficiently.
problem Scalability and reusability issues in RL-based financial portfolio management.
method Modular design with Evolving Agent Module (EAM) and Strategic Agent Module (SAM).
result MSPM improves profit accumulation by at least 186.5% compared to CRP.
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.
MetaTrader combines diverse expert strategies to optimize portfolio performance.
problem Optimizing portfolio performance in changing financial markets.
method Two-stage RL approach: imitation learning followed by a meta-policy.
result MetaTrader significantly outperforms state-of-the-art baselines in balancing profits and risks.
New RL framework simulates financial market dynamics.
problem Complex financial market dynamics under various scenarios.
method Two RL families learn simultaneously, using Deep RL and parametrized reward.
result Agents learn a shared policy for diverse behaviors.
This tutorial reviews RL-based methods for optimizing diffusion models to maximize specific metrics.
problem Optimizing diffusion models to generate samples that maximize specific metrics in practical applications.
method Various RL algorithms including PPO, differentiable optimization, reward-weighted MLE, value-weighted sampling, and path consistency learning.
result Exploration of strengths and limitations of RL-based fine-tuning algorithms and their benefits compared to non-RL-based approaches.
MASA framework uses RL to balance portfolio returns and risks.
problem Managing portfolio risk in turbulent financial markets.
method Multi-agent reinforcement learning with a market observer.
result MASA framework outperforms RL approaches in balancing returns and risks.
Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk. In this paper, we build a multi-agent simulation of a dealer market and demonstrate that it can be used to understand the behavior of a reinforcem…
Systematic review finds reinforcement learning enhances financial tech performance.
problem Improving financial tech performance through reinforcement learning.
method Systematic review using PRISMA technique.
result RL-based strategies outperform other algorithms in financial tech.
SECRM-2D improves RL-based autonomous driving with safety guarantees.
problem Safety and efficiency trade-offs in RL-based autonomous driving.
method RL-based controller with safety constraints for efficient and comfortable driving.
result SECRM-2D avoids crashes and improves efficiency and comfort compared to baselines.
We propose a new neural sequence model training method in which the objective function is defined by α-divergence. We demonstrate that the objective function generalizes the maximum-likelihood (ML)-based and reinforcement learning (RL)-based objective functions as special cases (i.e., ML corresponds to α→0 and R…
This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach for the disentanglement of vulnerabilities caused by representation learning of DRL agents from those that stem from the sensitivity of the DR…
Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research d…
We propose a reinforcement learning (RL) based closed loop power control algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an indoor environment served by small cells. The main contributions of our paper are to 1) use RL to solve performance tuning problems in an indoor cellular network for voic…
Recurrent Neural Networks (RNNs) have been shown to be valuable for constructing Intrusion Detection Systems (IDSs) for network data. They allow determining if a flow is malicious or not already before it is over, making it possible to take action immediately. However, considering the large number of packets that has t…
Paper presents a machine learning-based method for efficiently pricing and hedging autocallable structured notes with multiple underlying assets.
problem Complex pricing and hedging of autocallable notes with multiple underlying assets.
method Machine learning-based pricing method and Distributional Reinforcement Learning (RL) for hedging.
result Significantly improved efficiency in pricing and hedging, with faster computation and better risk management.
This paper surveys RL methods for quantitative trading.
problem Challenges in sequential decision making for financial markets.
method Taxonomy of RL-based QT models and state of the art summary.
result RL can solve complex QT tasks.
Survey of deep RL in intelligent transportation systems.
problem Optimizing traffic signals and autonomous driving using deep RL.
method Comprehensive review of deep RL applications in traffic control and autonomous driving.
result Summarizes existing works in deep RL-based transportation applications.
Hybrid framework optimizes reinsurance using generative models and reinforcement learning.
problem Traditional reinsurance optimization relies on restrictive assumptions and static designs.
method Combines VAEs for joint distribution learning and PPO for dynamic treaty parameter adaptation.
result Hybrid method produces more resilient outcomes with higher surpluses and lower tail risk.
Survey of RL methods for optimizing power grid topologies.
problem Optimizing power grid operation with adaptive control strategies.
method Reinforcement Learning (RL) for dynamic and uncertain environments.
result Comprehensive evaluation of RL-based methods for power grid topology optimization.
Language systems have been of great interest to the research community and have recently reached the mass market through various assistant platforms on the web. Reinforcement Learning methods that optimize dialogue policies have seen successes in past years and have recently been extended into methods that personalize …
A new method improves ridesharing efficiency using QMIX.
problem Improving ridesharing dispatch efficiency with complex environments.
method QMIX for centralized training with decentralized execution.
result QMIX outperforms IDQN in various scenarios.
End-to-end automatic speech recognition (ASR) models are increasingly large and complex to achieve the best possible accuracy. In this paper, we build an AutoML system that uses reinforcement learning (RL) to optimize the per-layer compression ratios when applied to a state-of-the-art attention based end-to-end ASR mod…
Neural architecture search (NAS) aims to discover network architectures with desired properties such as high accuracy or low latency. Recently, differentiable NAS (DNAS) has demonstrated promising results while maintaining a search cost orders of magnitude lower than reinforcement learning (RL) based NAS. However, DNAS…
A RL-based method adds conditional controls to pre-trained diffusion models.
problem Precise control over generated samples in diffusion models.
method Formulates the task as an RL problem, using a classifier and KL divergence as reward functions.
result Produces soft-optimal policies that maximize reward functions, enabling conditional sampling.
This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is presented and its challenges are investigated. The problem is then tackled by the RL based on DC-OPF (Optimal P…
Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models have been proposed. In this paper, we formulate the online attack/anomaly detection…
We reduce the computational cost of Neural AutoML with transfer learning. AutoML relieves human effort by automating the design of ML algorithms. Neural AutoML has become popular for the design of deep learning architectures, however, this method has a high computation cost. To address this we propose Transfer Neural A…
IMM uses imitation learning and predictive representation learning to improve market making strategies.
problem Challenges in training RL agents for multi-price level market making strategies.
method IMM combines RL and imitation learning, introducing effective state and action representations and a representation learning unit.
result IMM outperforms existing RL-based market making strategies in financial criteria.
Safe RL-based vibration control using LQR guidance.
problem Training risks in RL-based vibration control.
method Hybrid control framework combining LQR and RL.
result LQR controller outperforms uncontrolled scenario.
Fundamental portfolio beats market portfolio under certain conditions.
problem Empirical evidence of fundamental portfolio outperformance.
method Theoretical foundation based on stock price reversion to fundamental values.
result Fundamental portfolio outperforms market portfolio under strong reversion conditions.
Paper uses RL for better credit scoring and underwriting.
problem Traditional underwriting methods are ungeneralizable in complex scenarios.
method Adapts RL principles for credit scoring, incorporating action space renewal and multi-choice actions.
result RL-based algorithms outperform traditional methods in aligned data scenarios.
This paper compares three portfolio designs for Indian stocks.
problem Designing an optimum portfolio that balances return and risk.
method Three approaches: minimum risk, optimum risk, and Eigen portfolios.
result Optimum risk portfolios and Eigen portfolios identified for each sector.
Project predicts stock prices for robust portfolio design in Indian sectors.
problem Precise stock price prediction for robust portfolio design.
method Minimum variance and optimal risk portfolio optimization using past stock prices.
result Backtesting shows improved performance of optimized portfolios over equal weight portfolio.
A new factor analysis method using ICA reduces portfolio concentration and diversifies excess kurtosis.
problem Standard factor analysis suffers from issues with pairwise correlations of asset returns.
method Identifies factors based on non-Gaussianity instead of variance, using ICA.
result Fat-tailed portfolios significantly reduce portfolio concentration and winner-takes-all problem.
Robotic navigation improves with RL and ultrasound images.
problem Improving accuracy in robotic navigation for ultrasound-guided procedures.
method Combines deep Q-networks with ultrasound images and binary classifier.
result Significantly improved navigation accuracy compared to pure RL and SL.
Paper uses neural networks to compress large portfolios of options, reducing risk and capital requirements.
problem Managing risk and capital requirements for large portfolios of financial options.
method Artificial neural network framework for portfolio compression, static hedging, and risk management.
result The compressed portfolio's risk profiles align closely with the target portfolio's, reducing capital requirements.
Consider a family of portfolio strategies with the aim of achieving the asymptotic growth rate of the best one. The idea behind Cover's universal portfolio is to build a wealth-weighted average which can be viewed as a buy-and-hold portfolio of portfolios. When an optimal portfolio exists, the wealth-weighted average c…
In this paper Portfolio Optimization techniques were used to determine the most favorable investment portfolio. In particular, stock indices of three companies, namely Microsoft Corporation, Christian Dior Fashion House and Shevron Corporation were evaluated. Using this data the amounts invested in each asset when a po…
This study compares three portfolio design approaches for stock selection.
problem Designing a profitable portfolio with precise stock returns and risks.
method Three portfolio design approaches: mean-variance portfolio, hierarchical risk parity, and autoencoder-based portfolio.
result Autoencoder portfolios outperform MVP on annual returns, but MVP is best on risk-adjusted returns.
A market portfolio is a portfolio in which each asset is held at a weight proportional to its market value. Functionally generated portfolios are portfolios for which the logarithmic return relative to the market portfolio can be decomposed into a function of the market weights and a process of locally finite variation…
New portfolios outperform traditional methods by using factor weights.
problem Improving portfolio allocation in markets driven by factors.
method Factor-weighted Dirichlet portfolios outperform uniform Dirichlet portfolios.
result Factor-weighted portfolios outperform uniformly sampled portfolios in market returns.
This study compares two portfolio optimization methods on Indian stocks.
problem Designing an optimal portfolio considering stock returns and risks.
method Hierarchical Risk Parity and Eigen Portfolio approaches on NIFTY 50 sectors.
result Hierarchical Risk Parity portfolio outperforms Eigen portfolio in most sectors tested.
Signature portfolios approximate optimal wealth in non-Markovian markets.
problem Approximating optimal wealth in non-Markovian markets.
method Linear path-functional portfolios based on signatures of market weights.
result Signature portfolios can uniformly approximate any continuous portfolio function.
The effect of proportional transaction costs on systematically generated portfolios is studied empirically. The performance of several portfolios (the index tracking portfolio, the equally-weighted portfolio, the entropy-weighted portfolio, and the diversity-weighted portfolio) in the presence of dividends and transact…
The paper introduces eigen-portfolios using PCA to improve portfolio construction in finance.
problem Overfitting and poor generalization in selecting a single eigen-portfolio.
method Principal Component Analysis (PCA) to derive eigen-portfolios from asset return correlation matrices.
result An ensemble strategy combining multiple top-performing eigen-portfolios significantly improves out-of-sample performance.