Optimizes trading frequencies for multi-asset portfolios with small transaction costs.
problem Investment with multiple assets and small transaction costs.
method Optimizes trading frequencies explicitly for multidimensional diffusion setting, compares to alternatives.
result Explicit formulas for optimal trading frequencies and welfare losses.
Study high-frequency trading with fractional Brownian motion, finding optimal strategies and convergence.
problem Maximizing utility in high-frequency trading with fractional Brownian motion.
method Spectral methods for stationary Gaussian sequences, asymptotic growth rate analysis, finite-dimensional distribution convergence.
result Suitably rescaled optimal positions converge to a Gaussian white-noise-type field.
Investigates how rebalancing frequency and transaction costs affect log-optimal portfolios.
problem Impact of rebalancing frequency and transaction costs on log-optimal portfolios.
method Proved equivalence to concave program, derived optimality conditions, tested using intraday and daily data.
result Transaction costs can cause bankruptcy for frequency-dependent log-optimal portfolios, approximating to quadratic concave program.
This paper extends Kelly Criterion to include rebalancing frequency for optimal portfolio selection.
problem Optimizing a portfolio with multiple assets and varying rebalancing frequency.
method Using Kelly Criterion, the paper derives necessary and sufficient conditions for the frequency-based Kelly optimal portfolio.
result Proves the necessity and sufficiency of conditions for the frequency-based Kelly optimal portfolio.
Frequency-based reservoir improves prediction accuracy and optimizes short-term forecasts.
problem Lack of precise explanation and optimization methods for reservoir computing.
method Inspired by brain's oscillatory dynamics, frequency-based reservoir uses an ensemble of independent oscillatory units.
result Frequency-based reservoir performs as well as or better than random reservoirs and can predict complex spatiotemporal dynamics.
Corrects gaps in a method for optimizing high-frequency trading strategies.
problem Optimizing bid and ask limit order strategies in high-frequency trading.
method Uses an approximation method based on Avellaneda and Stoikov's 2008 article, correcting gaps found in it.
result The main answer in Avellaneda and Stoikov's article remains unchanged despite corrections.
Optimal transport improves speech BSS by better aligning spectrogram frequencies.
problem Speech BSS with improved frequency alignment.
method Developed optimal transport NMF for supervised speech BSS.
result Optimal transport NMF leads to better perceptual results than Euclidean NMF.
Game theory model for optimal trading with end-of-day constraints.
problem Optimal trading strategy in a game between slow and fast traders.
method Coupled stochastic control problems, Fredholm integral equation solution.
result Explicit solution to the game with profitable strategies for both players.
Coordinate descent (CD) algorithms have become the method of choice for solving a number of optimization problems in machine learning. They are particularly popular for training linear models, including linear support vector machine classification, LASSO regression, and logistic regression. We consider general CD with …
Optimizes natural frequencies of cellular composites with various microstructures.
problem Designing cellular composites with diverse microstructures for maximizing natural frequencies.
method Data-driven topology optimization with a latent-variable Gaussian process model.
result Cellular designs with multiclass microstructures achieve higher natural frequencies.
New method improves music transcription by treating frequency distributions holistically.
problem Small frequency shifts and variations in sound timbre harm traditional fit measures.
method Optimal transportation and new holistic frequency distribution measure.
result Simplified note templates lead to faster, state-of-the-art performance.
FreSh shifts model's initial frequency spectrum to match target signal, improving neural representation performance.
problem MLPs' low-frequency bias limits capturing high-frequency details accurately.
method FreSh selects embedding hyperparameters to align model's initial output spectrum with target signal's spectrum.
result FreSh improves performance across various neural representation methods and tasks with minimal computational overhead.
FRA-Attack improves adversarial transferability for closed-source MLLMs by aligning visual focus across models.
problem Improving adversarial transferability for closed-source MLLMs, especially with high accuracy.
method Unified frequency-domain regularization approach: high-pass DCT objective for feature alignment and Frequency-domain Gradient Regularization (FGR) for gradient optimization.
result FRA-Attack achieves superior cross-model transferability, especially on GPT-5.4, Claude-Opus-4.6, and Gemini-3-flash.
Study uses multi-kernel Hawkes models to analyze high-frequency price dynamics.
problem Understanding responsive speeds of market participants in high-frequency trading.
method Multi-kernel Hawkes models with conditional Hessian analysis for optimization.
result Existence of multi-kernels (UHF, VHF, HF) in high-frequency price dynamics.
New TVBO algorithm optimizes time-varying functions with varying sampling frequencies.
problem Optimizing time-varying, expensive, noisy functions with constant frequency assumption.
method Formulated practical recommendations and derived upper regret bound for varying sampling frequencies.
result BOLT algorithm outperforms state-of-the-art TVBO algorithms in experiments.
Enhanced options trading strategies using advanced portfolio optimization.
problem Generating consistent positive returns in high-frequency options trading.
method Advanced portfolio optimization techniques applied to SPY options data.
result Sophisticated strategies incorporating advanced Greeks show potential in high-frequency trading.
A new approach to reinforcement learning improves policy performance by adjusting control frequency.
problem Improving reinforcement learning performance by optimizing control frequency.
method Introducing action persistence and a novel algorithm, PFQI, to learn optimal value function at a given persistence.
result PFQI effectively learns optimal value function with action persistence, improving reinforcement learning performance.
Improves Bayesian optimization efficiency for mixed variable spaces.
problem Boosting sample efficiency in Bayesian optimization for mixed variable spaces.
method Proposes frequency modulated (FM) kernels to model complex dependencies across different types of variables.
result BO-FM outperforms competitors in various optimization problems.
Optimizes high-frequency trading strategies in limit order books.
problem Impact of recent orders on future order submission rates.
method Discrete Markov chain model for LOB dynamics, Markov decision process for optimal order placement.
result Optimal policy using limit, cancellations, and market orders to maximize execution price.
The paper explores how rebalancing frequency affects stock portfolio performance using a control-theoretic approach.
problem The impact of high-frequency trading on portfolio performance in Kelly-optimal stock portfolios.
method The problem is set in a control-theoretic framework, and the main question is addressed through simulations of real stock prices.
result If an asset is dominant, optimal portfolios consist of this asset alone, rendering rebalancing frequency moot.
Researchers reveal how neural networks implicitly favor low-frequency functions.
problem Why deep neural networks generalize well despite having more parameters than samples.
method Proposed a linear F-Principle dynamics to predict and explain the learning of two-layer ReLU NNs.
result Explicitly penalizing higher frequencies in the optimization process improves generalization.
A predictor improves power grid frequency forecasts up to one hour.
problem Improving frequency forecast for better power system stability.
method Developed a weighted-nearest-neighbor (WNN) predictor.
result Forecasts for up to one hour are more precise than averaged daily profiles.
Neural nets optimize dynamic hedging strategies with transaction costs.
problem Optimal hedging strategy in presence of transaction costs and discrete time.
method Convolutional neural network trained to infer optimal hedging frequencies.
result Dynamic multiscale hedging strategy reduces risk and maximizes profit.
Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in…
Study detects spoofing in high-frequency trading using micro-structural analysis.
problem Challenges in detecting spoofing due to complex electronic platforms and high-frequency trading.
method Micro-structural study in a simplified setting, optimization of spoofing strategy, monitoring with Wasserstein distance.
result Optimal spoofing strategy and its impact on market imbalance quantified.
SNNs enhance high-frequency price spike forecasting in HFT environments.
problem Conventional financial models fail to capture fine temporal structure in high-frequency price spikes.
method Application of Spiking Neural Networks (SNNs) with hyperparameter tuning via Bayesian Optimization (BO).
result SNN models optimized with PSA achieve significantly higher cumulative returns in backtesting.
DNNs initially capture low-frequency components before high-frequency ones, a phenomenon called F-Principle.
problem Understanding why DNNs generalize well despite overfitting.
method Empirical study on real and synthetic datasets, focusing on frequency components captured by DNNs.
result DNNs capture dominant low-frequency components first, then high-frequency ones, a phenomenon called F-Principle.
Improved DOA estimation with distributed sensors across multiple frequencies.
problem Sensor gain uncertainties and directional perturbations in multi-frequency scenarios.
method Distributed optimization with local coherence models and iterative exchange of information.
result Advantages in statistical and computational efficiency through parallel iterative technique.
This work analyzes how often to update the target network in Q-learning.
problem Understanding the optimal frequency of target network updates in Q-learning.
method Formulated target updates as a nested optimization scheme, derived finite-time convergence analysis.
result Optimal target update frequency increases geometrically over time.
Transforms game optimization dynamics into frequency domain for precise hyperparameter analysis.
problem Analyzing convergence of hyperparameters in game optimization.
method Frequency-domain framework using High-Resolution Differential Equations (HRDEs) and Laplace transforms.
result Derives precise convergence criteria for the Lookahead algorithm.
High-frequency traders manage inventories to exploit price information, leading to mean-reverting inventories and excess trading.
problem Managing inventories for high-frequency traders in imperfect competition.
method Analyzes Nash equilibria for inventory-averse HFTs using nonlinear equations and asymptotic analysis.
result Optimal inventories become mean-reverting and vanish in the continuous-time limit, while HFTs' profits converge to risk-neutral counterparts.
Method improves clarity in forecasting spatio-temporal data.
problem Forecasting spatio-temporal data with clarity and interpretability.
method Supervised semi-nonnegative matrix factorization with frequency regularization.
result Method offers clearer interpretability in forecasting spatio-temporal data.
Optimizing betting frequency in dynamic games with Kelly criterion.
problem Finding the optimal betting frequency in a dynamic game setting.
method Using Kelly's expected logarithmic growth criterion, the study analyzes the performance of high-frequency and low-frequency bettors.
result The optimal performance gn* changes with n, and the high-frequency case does not always lead to the best performance.
Improved LSTM cell for high-frequency trading forecasts.
problem Precise stock price forecasting with minimal lags.
method Revised long short-term memory (LSTM) cell with optimal gate/state selection.
result Lower forecasting error compared to other recurrent neural networks.
Bayesian method estimates line frequencies with uncertainty.
problem Bayesian estimation of continuous frequencies.
method Variational Bayesian inference with von Mises mixtures.
result Significantly improved performance over point estimates.
Estimates Ornstein-Uhlenbeck process from high-frequency data, reducing noise bias.
problem Parameter estimation bias in high-frequency stock data.
method Maximum likelihood estimator robust to market microstructure noise.
result Noise-robust estimator leads to improved pairs trading strategy profitability.
A new model forecasts optimal portfolio weights from high-frequency data.
problem Forecasting optimal portfolio weights from high-frequency data.
method Dynamic Conditional Weights (DCW) model for portfolio weights dynamics.
result DCW model outperforms other models in portfolio allocations and measures.
A genetic algorithm-based method extracts features for epilepsy EEG classification.
problem Classifying epileptic EEG signals for accurate diagnosis.
method GAFDS method using genetic algorithm for frequency-domain feature search and optimization.
result GAFDS features improve classification accuracy compared to nonlinear features.
Lazy, perfectly informed investors trade infrequently due to costs.
problem The paradox of an omniscient yet lazy investor trading infrequently.
method Formalized the paradox using geometric and fractional Brownian motion models, derived closed-form profit functions, and proved existence and uniqueness of the optimal trading frequency.
result The optimal trading frequency can be interpreted through the fractal dimension of the price path.
The paper explores how to measure and optimize ad reach while maintaining user privacy.
problem Measuring ad reach while preserving user privacy in online advertising.
method Introduces k-anonymity and probabilistic discounting for frequency capping. result Privacy introduces a significant performance drop but with manageable costs.
The paper analyzes RL in high-frequency market making with theoretical and practical implications.
problem Applying RL to high-frequency market making with theoretical rigor.
method Theoretical analysis bridging RL and financial economics, focusing on sampling frequency effects.
result An interesting tradeoff between error and complexity in RL algorithms as sampling frequency decreases.
DRL agents learn to trade Intel stock with stable positive returns.
problem Active high frequency trading in the stock market.
method End-to-end DRL framework using Proximal Policy Optimization, Sequential Model Based Optimization, and LOB-based meta-features.
result DRL agents create dynamic trading strategies with stable positive returns.
Novel LSTM network predicts pulsar timing residuals with few-shot data.
problem Predicting pulsar timing residuals with limited data.
method Long Short-Term Memory (LSTM) network optimized with model-agnostic meta-learning and particle swarm optimization.
result Robust generalization and accurate predictions across high-frequency test domains with minimal data.
Study optimal liquidation strategies under partial information in high-frequency trading.
problem Optimal liquidation strategies in high-frequency trading with incomplete information.
method Modeling price formation through Hawkes processes, incorporating liquidity as a hidden Markov process, and formulating as an impulse control problem.
result Development of an algorithm to approximate optimal liquidation strategies.
This work proves convergence of adaptive resampling for random Fourier features.
problem Sampling Fourier frequencies well for high-dimensional data.
method Data adaptive resampling of Fourier frequencies, asymptotically optimal.
result Proves convergence of adaptive resampling method for regression and classification problems.
Deep neural network learns optimal trading controls for high-frequency finance.
problem Optimal trading on high-frequency data with market impact and limited data.
method Deep neural network, Monte-Carlo initialization, transfer learning, explainable controls.
result Neural network learns optimal controls for trader preferences.
We propose a framework to study optimal trading policies in a one-tick pro-rata limit order book, as typically arises in short-term interest rate futures contracts. The high-frequency trader has the choice to trade via market orders or limit orders, which are represented respectively by impulse controls and regular con…
New algorithm uses machine learning to predict high-frequency trading returns.
problem Improving prediction accuracy in high-frequency trading.
method Iterative optimization and activation functions in deep learning, combined with VPIN, GARCH, and SVM. result The model significantly improved prediction of market liquidity and trading returns.