Deep RL learns optimal trading strategies.
problem Optimizing trading strategies using deep reinforcement learning.
method Deep deterministic policy gradient algorithm applied to simple trading environments.
result Deep RL can recover optimal trading strategies and achieve close-to-optimal rewards.
Investigates optimal portfolio strategies in markets with latent side information.
problem Investment problem in markets with latent dependence structure and side information.
method Dynamic and constant portfolio strategies, analyzing log-optimal portfolio as benchmark.
result Optimal dynamic strategy growth rate asymptotically converges to constant strategy in stationary markets.
Deep learning improves portfolio optimization in volatile markets.
problem Challenges in long-only, multi-asset strategies across market cycles.
method Training DL models with limited regime data using pre-training techniques and transformer architectures.
result Models show resilience and improved predictive accuracy in volatile markets.
The article proposes optimal learning strategies for machine learning-based reliability analysis.
problem Improving computational efficiency and accuracy in machine learning-based reliability analysis.
method Theorems and mathematical proofs for optimal learning strategies considering and neglecting correlations among design samples.
result The optimal learning strategy considering Kriging correlation outperforms other methods in terms of reduced evaluations of performance functions.
Paper uses DDPG to learn optimal execution strategies in dynamic markets.
problem Learning non-Markovian optimal execution strategies in dynamic financial markets.
method Introduces a novel actor-critic algorithm based on DDPG for transient price impact modeling.
result Successfully approximates optimal execution strategy through numerical experiments.
Optimizes investment and reinsurance strategies with unknown parameters.
problem Optimal investment and reinsurance with partially unknown model parameters.
method Robust approach using generalized HJB equation and Clarke gradients.
result Explicit optimal investment strategy and bounds for optimal reinsurance.
New method learns optimal prediction strategies in adversarial games.
problem Learning optimal prediction procedures in uncertain data environments.
method Adversarial Monte Carlo approach with neural network architecture.
result Optimal strategy is equivariant and invariant to various transformations.
Novel evolutionary strategy solves stochastic constrained optimization problems.
problem Optimizing objective functions with stochastic constraints in reinforcement learning.
method Design of a novel optimization algorithm with a sufficient decrease mechanism for stochastic constrained problems.
result Demonstrated convergence of the algorithm on control tasks and constrained optimization problems.
Study uses reinforcement learning to optimize trading strategies.
problem Developing an optimal execution strategy for traders.
method Reinforcement learning model using ABIDES simulator.
result Reinforcement learning model outperforms standard strategies.
CoNES optimizes blackbox functions using convex optimization and information geometry.
problem Optimizing high-dimensional blackbox functions efficiently.
method Formulated as a convex program that adapts evolutionary strategies gradient estimates.
result Vastly outperforms conventional blackbox optimization methods on benchmarks and MuJoCo tasks.
Optimizes credit index option hedging with reinforcement learning.
problem Finding the best strategy for hedging credit index options.
method Applied reinforcement learning with TRVO algorithm in a realistic setting.
result The derived hedging strategy outperforms traditional methods.
Deep learning optimizes VWAP strategy for lower transaction costs.
problem Designing an efficient VWAP strategy for dynamic markets.
method Hierarchical deep reinforcement learning (Macro-Meta-Micro Trader).
result Our approach achieves an average cost saving of 1.16 base points.
Optimizes trading returns using Hurst exponent and Q-learning.
problem Maximizing returns from momentum and mean reversion strategies.
method Classifies assets using Hurst exponent and uses Q-learning to improve trading algorithms.
result Trading with Hurst exponent can achieve higher returns but at higher risk.
We proposed a new Portfolio Management method termed as Robust Log-Optimal Strategy (RLOS), which ameliorates the General Log-Optimal Strategy (GLOS) by approximating the traditional objective function with quadratic Taylor expansion. It avoids GLOS's complex CDF estimation process,hence resists the "Butterfly Effect" …
Article proposes a profitable intraday trading strategy for Chinese stocks.
problem Intraday trading opportunities in Chinese stock market.
method Markowitz optimization and Multilayer Perceptron (MLP) for stock price prediction.
result Validation of Markowitz portfolio optimization and MLP for intraday stock price prediction.
Paper optimizes energy trading on DA markets using RL.
problem Volatility and randomness in renewable energy sources.
method Markov Decision Process, reinforcement learning, evolutionary algorithm.
result RL-based strategy generates highest market profits.
Deep learning improves optimal trading strategies in complex markets.
problem Finding optimal trading strategies in markets with multiple time scales.
method Deep differentiable reinforcement learning applied to optimal trading.
result Deep learning leads to more accurate and stable optimal trading strategies.
Balances the regret of different algorithms in bandit and RL problems.
problem Model selection in bandit and reinforcement learning.
method Estimates and balances the empirical regrets of algorithms.
result Achieves near-optimal regret compared to the optimal base algorithm.
GP-MRO discovers robust mixed strategies for unknown objectives.
problem Optimizing unknown objectives against worst-case uncertain parameters.
method Sequential learning from noisy point evaluations, combining online learning and Gaussian processes.
result GP-MRO finds robust mixed strategies that significantly improve performance over deterministic strategies.
A new trading strategy using reinforcement learning for statistical arbitrage.
problem Traditional statistical arbitrage models rely on model assumptions and price deviations from a long-term mean.
method Empirical reversion time metric, reinforcement learning framework, and state space optimization.
result Optimal mean reversion strategy identified through reinforcement learning.
Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as ou…
Study optimizes investment strategies in volatile markets using machine learning and Bayesian techniques.
problem Enhancing portfolio management in volatile markets.
method Market segmentation into ten volatility-based states, real-time asset allocation adjustments using Bayesian Markov switching model.
result Dynamic portfolio achieves significantly higher risk-adjusted returns and total returns.
Paper optimizes stock option forecasting using ML models and improved trading strategies.
problem Improving accuracy of stock option predictions and trading decisions.
method Application of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Quasi-Reversibility Method (QRM).
result Optimized stock option investment results through improved trading strategies and model combination.
Study uses RL to optimize dynamic portfolios, addressing non-stationarity and constraints.
problem Non-stationarity and investment constraints in dynamic portfolio optimization.
method Reinforcement learning with regime change variables and practical constraints integration.
result Enhanced prediction accuracy through incorporation of regime change variables.
Evolutionary Strategies optimize hyper-parameters for off-policy learning.
problem Hyper-parameter sensitivity in off-policy learning.
method Application of Evolutionary Strategies for online hyper-parameter tuning.
result Our method outperforms state-of-the-art baselines.
Optimal asset allocation strategy outperforms stochastic benchmark.
problem Achieving higher terminal wealth than a stochastic benchmark.
method Data-driven Neural Network optimization framework for dynamic asset allocation.
result Optimal adaptive strategy outperforms benchmark with higher median and right-skewed terminal wealth.
Deep Hedging learns optimal strategies for various risk levels.
problem Finding optimal hedging policies for diverse risk aversions.
method Continuous Reinforcement Learning with actor-critic algorithm.
result Demonstrated effectiveness in a stochastic volatility model.
No-regret learning fails to converge to Nash equilibria in mixed strategies.
problem Limiting behavior of mixed strategies in repeated games.
method Study of optimal no-regret learning algorithms for 2x2 competitive games.
result Limiting mixed strategies cannot converge to Nash equilibria under mean-based and monotonic updates.
Model trains agents to optimize saving and investment strategies for diverse retirement needs.
problem Optimal saving and investment strategies for individuals in varied employment and income profiles.
method Deep reinforcement learning to train intelligent agents with heterogeneous profiles.
result Flexible methodology estimates lifetime consumption and investment choices for different profiles.
In this paper, we study the adversarial robustness of subspace learning problems. Different from the assumptions made in existing work on robust subspace learning where data samples are contaminated by gross sparse outliers or small dense noises, we consider a more powerful adversary who can first observe the data matr…
Given the return series for a set of instruments, a \emph{trading strategy} is a switching function that transfers wealth from one instrument to another at specified times. We present efficient algorithms for constructing (ex-post) trading strategies that are optimal with respect to the total return, the Sterling ratio…
Personalized recommendation systems (RS) are extensively used in many services. Many of these are based on learning algorithms where the RS uses the recommendation history and the user response to learn an optimal strategy. Further, these algorithms are based on the assumption that the user interests are rigid. Specifi…
Optimizes trading in CFMMs and exchanges using deep learning.
problem Optimizing trading strategies in CFMMs and exchanges.
method Develops a model accounting for interaction between CFMMs and exchanges, employs deep Galerkin method to solve dynamic programming equation.
result Optimal strategy outperforms naïve strategies and is not prone to price slippage.
Investment strategy in ambiguous financial markets with learning
problem Continuous time investment problem in multi-asset Black-Scholes market with model ambiguity
method Optimal dynamic investment strategy within the class of all adapted strategies which allow for learning
result Ambiguity averse investors invest less in risky assets
We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction strategy with Jeffreys prior and sequential normalized maximum likelihood (SNML) coincide and are optimal if and only if the latter is exchangeable, and if and only if the opti…
We propose a general-purpose approach to discovering active learning (AL) strategies from data. These strategies are transferable from one domain to another and can be used in conjunction with many machine learning models. To this end, we formalize the annotation process as a Markov decision process, design universal s…
Paper uses DRL to optimize portfolios, balancing risk and return.
problem Optimizing portfolios under market uncertainty and risk constraints.
method Integrates Sharpe ratio-based reward with risk control mechanisms, uses PPO for adaptive asset allocation.
result DRL agent stabilizes volatility but sacrifices risk-adjusted returns.
In online portfolio optimization the investor makes decisions based on new, continuously incoming information on financial assets (typically their prices). In our study we consider a learning algorithm, namely the Kiefer--Wolfowitz version of the Stochastic Gradient method, that converges to the log-optimal solution in…
PES method reduces bias in gradient estimation for unrolled graphs.
problem High variance and bias in gradient estimation for unrolled computation graphs.
method Divide graph into unrolls, apply ES update, accumulate correction terms.
result PES provides unbiased, low-variance gradient estimates.
New approach minimizes tail risk in option hedging.
problem Minimizing tail risk in option hedging strategies.
method Risk-sensitive reinforcement learning without parametric models.
result Significantly lower tail risk and higher mean P&L than delta hedging.
Study proposes DRL for investor-specific portfolio optimization considering asset volatility.
problem Dynamic allocation of funds balancing risk and return under market conditions.
method Volatility-guided Deep Reinforcement Learning (DRL) framework.
result Proposed DRL portfolios outperform baseline strategies.
E-learning systems are capable of providing more adaptive and efficient learning experiences for students than the traditional classroom setting. A key component of such systems is the learning strategy, the algorithm that designs the learning paths for students based on information such as the students' current progre…
Membership inference determines, given a sample and trained parameters of a machine learning model, whether the sample was part of the training set. In this paper, we derive the optimal strategy for membership inference with a few assumptions on the distribution of the parameters. We show that optimal attacks only depe…
A new algorithm learns optimal source placement in large networks.
problem Optimizing source placement in large scale networks with unknown processes.
method Graph-Kernel Multi-Armed Bandit (Grab-UCB) algorithm with adaptive graph dictionary model.
result Online learning algorithm outperforms offline methods in terms of cumulative regret, sample efficiency, and computational complexity.
Paper solves investment strategy optimization with deep learning.
problem Maximizing investor utility with optimal asset allocation.
method Solves PDEs with Deep Galerkin method.
result Deep learning algorithm outperforms finite difference method.
FlowHFT learns adaptive trading strategies from multiple models for diverse market conditions.
problem Traditional HFT models are limited by specific market conditions and cannot adapt to dynamic markets.
method FlowHFT uses flow matching policy to learn from multiple expert models and adapt to various market scenarios.
result FlowHFT consistently outperforms individual expert models in multiple market conditions.
Paper introduces MADL loss function for better AIS model optimization.
problem Optimizing machine learning models for AIS construction.
method Proposes Mean Absolute Directional Loss (MADL) function.
result MADL function improves hyperparameter selection and investment strategy efficiency.
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.