A multi-agent simulator evaluates trading strategies using Market Replay and Interactive Agent-Based Simulation.
problem Evaluate trading strategies using Market Replay and Interactive Agent-Based Simulation.
method Multi-agent simulator for Market Replay and Interactive Agent-Based Simulation.
result IABS provides a more realistic market environment for evaluating trading strategies.
A new stock selection strategy uses combined machine learning with dynamic weighting methods.
problem Improving stock selection accuracy and performance.
method Combined machine learning algorithms with static and dynamic weighting methods.
result IC-based dynamic weighting outperforms static evaluation metrics in backtested returns and predictive performance.
Strategy evaluation schemes are a crucial factor in any agent-based market model, as they determine the agents' strategy preferences and consequently their behavioral pattern. This study investigates how the strategy evaluation schemes adopted by agents affect their performance in conjunction with the market circumstan…
The paper tackles ICU discharge strategies by evaluating optimal stopping scenarios.
problem Optimal stopping in ICU discharge with complex causal inference challenges.
method Generalized g-formula implementation and application to MIMIC-IV dataset.
result Improved ICU discharge strategies demonstrated with the g-formula approach.
We propose a prediction model based on the minority game in which traders continuously evaluate a complete set of trading strategies with different memory lengths using the strategies' past performance. Based on the chosen trading strategy they determine their prediction of the movement for the following time period of…
Novel method reconstructs liquidity data for CLMMs, optimizing dynamic liquidity strategies.
problem Challenges in evaluating and optimizing CLMMs due to lack of historical liquidity data.
method Reconstructs historical liquidity states from swap transaction data using machine learning.
result Identifies outperformance of dynamic liquidity strategies over uniform allocation benchmarks.
Study evaluates discretized arbitrage strategies in fractional financial markets.
problem Serial correlation in financial markets with fractional Brownian motion.
method Revisit and transfer Shiryaev and Salopek's strategies to a real-world setting, distretizing dynamics and introducing transaction costs.
result Both strategies are promising with respect to terminal portfolio values and loss probabilities.
POAP and pySOT improve surrogate optimization of expensive functions.
problem Optimizing expensive functions with concurrent evaluations.
method Event-driven asynchronous framework for optimization strategies.
result Asynchronous computation offers significant speed-up advantages.
Paper proposes a new evaluation method for NAS search phase.
problem NAS search phase effectiveness not well evaluated.
method Compare NAS solutions with random selection; evaluate weight sharing strategy.
result State-of-the-art NAS algorithms perform similarly to random selection.
Backtests of structured strategies lose much of their predictive power in live trading.
problem Uncertainty in how marketed backtests predict live performance of structured strategies.
method Analysis of 1,726 structured strategies from ten global institutions.
result Raw backtests have limited portability into live trading and deteriorate sharply.
This work evaluates PDA methods without target labels, revealing significant accuracy drops.
problem Evaluating PDA methods without target labels and inconsistent experimental settings.
method Realistic evaluation of 7 PDA methods with 7 model selection strategies on 2 datasets.
result Accuracy drops up to 30 percentage points without target labels, only one method performs well.
LLMs struggle to outperform markets over long periods and diverse stocks.
problem Overstated effectiveness of LLM-based investing strategies due to biases.
method FINSABER framework for systematic backtests over two decades and 100+ symbols.
result Previously reported LLM advantages deteriorate significantly under broader evaluation.
BIS uses bandits to efficiently sample from expensive-to-evaluate densities.
problem Sampling from computationally expensive target densities.
method Sequential selection through multi-armed bandits, optimizing sample set directly.
result BIS achieves accurate sampling with fewer evaluations than adaptive methods.
The paper evaluates dynamic hedging strategies for various financial products.
problem Pricing derivative products with dynamic hedging and issuer-tailored risk.
method Unified constrained discrete stochastic dynamic programming framework with sequential local minimizing strategies.
result Demonstrates flexibility of the unified framework through numerical examples.
Study evaluates hedging strategies for S&P500 index options.
problem Improving returns and risk management in index option portfolios.
method Compared Black-Scholes-Merton and Variance-Gamma models for hedging strategies.
result Systematic option-writing strategies can yield superior returns compared to buy-and-hold benchmarks.
This study evaluates a dynamic pairs trading strategy in cryptocurrencies using cointegration tests.
problem Improving profitability and risk management in cryptocurrency trading.
method Engle-Granger, KSS, Johansen tests; optimal look-back window; mean-reversion speed calibration; microstructure limitations consideration.
result The strategy outperforms naive buy-and-hold in Bitmex exchange with low maximum drawdown.
This paper evaluates six strategies for mitigating imbalanced data: oversampling, undersampling, ensemble methods, specialized algorithms, class weight adjustments, and a no-mitigation approach referred to as the baseline. These strategies were tested on 58 real-life binary imbalanced datasets with imbalance rates rang…
Meta-model framework improves efficiency in parameter estimation of dynamical systems.
problem Efficiently estimating parameters of complex dynamical systems with computationally expensive objective functions.
method Dynamic adaptation of surrogate model and substitution strategy using a meta-model framework.
result Significant improvement in optimization efficiency, reducing evaluations by up to 77%.
New model improves graph attention for relational data.
problem Improving graph attention models for relational data.
method Relational Graph Attention Networks (R-GAT) extending non-relational graph attention to relational data.
result R-GAT performs worse than expected, but some configurations marginally improve molecular property modeling.
A large class of trading strategies focus on opportunities offered by the yield curve. In particular, a set of yield curve trading strategies are based on the view that the yield curve mean-reverts. Based on these strategies' positive performance, a multiple pairs trading strategy on major currency pairs was implemente…
The paper proposes a dynamic risk measure approach for evaluating defined-contribution pension funds.
problem Periodic evaluation of defined-contribution pension funds to manage risk and improve projections.
method Dynamic risk measure criterion, model-free reinforcement learning, Lee-Carter mortality model.
result Periodic evaluations lead to more risk-averse strategies, while mortality improvements encourage risk-seeking behaviors.
Paper proposes a self-learning framework for reject inference in credit scoring.
problem Sample bias in credit scoring models due to training on accepted cases only.
method Develops a self-learning framework considering distinct training regimes for iterative labeling and model training, introduces a new evaluation measure.
result Demonstrates the superiority of the adjusted self-learning framework over regular self-learning and previous reject inference strategies.
We introduce a new general framework for constructing the best trading strategy for a given historical indicator. We construct the unique trading strategy with the highest expected return. This optimal strategy may be implemented directly, or its expected return may be used as a benchmark to evaluate how far away from …
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…
Generative model improves intraday electricity price forecasting.
problem Intraday electricity price forecasting for improved trading strategies.
method Generative neural network model for probabilistic path forecasts.
result Generative model leads to higher profit gains than benchmark methods.
Research evaluates data poisoning attacks on regression learning and introduces a new defense strategy.
problem Data poisoning attacks on regression learning threaten model integrity in critical systems.
method Realistic scenarios, novel black-box attack, and evaluation on 26 datasets.
result Mean squared error (MSE) increases to 150% with only 2% poisoned samples.
This review explores how multiple agents learn to communicate in complex environments.
problem Learning effective communication strategies among multiple agents in partially observable environments.
method Review of recent algorithms and models for improving communication between agents, including Deep Recurrent Q-Networks.
result Introduction of a novel entropy-based evaluation metric for communication strategies.
We first estimate the average growth of a company's annual income and its variance by using both real company data and a numerical model which we already introduced a couple of years ago. Investment strategies expecting for income growth is evaluated based on the numerical model. Our numerical simulation suggests the p…
Presents SPEED, an algorithm for optimal policy evaluation in linear bandits with heteroscedastic noise.
problem Optimal data collection for policy evaluation in linear bandits with heteroscedastic reward noise.
method Formulated an optimal design for weighted least squares estimates, derived the optimal sample allocation, introduced SPEED algorithm, and derived regret bounds.
result SPEED leads to policy evaluation with MSE comparable to oracle strategy and significantly lower than random policy execution.
Study evaluates initialization strategies for infinite hidden Markov models.
problem Limited attention to initialization in infinite hidden Markov models.
method Systematically evaluated distance-based clustering, model-based, and uniform initializations.
result Distance-based clustering initializations consistently outperform other methods.
Dynamic sentiment analysis improves stock trading strategies.
problem Improving stock trading performance through dynamic agent switching.
method Combining sentiment analysis with deep reinforcement learning and dynamic agent switching.
result Dynamic sentiment-based trading strategy outperforms traditional methods.
Study optimal portfolio strategies with periodic evaluation under short-selling prohibition.
problem Optimal portfolio strategies with periodic evaluation under short-selling prohibition.
method Reformulate the original problem into an auxiliary one-period optimization problem and introduce dual control problem.
result Derive and verify the value function and optimal constrained portfolio for the original problem.
This study compares VaR-based portfolio insurance with CPPI in a regime-switching market.
problem Designing dynamic portfolio insurance strategies in a market with multiple regimes.
method Extends VaR-based portfolio insurance to a Markov-modulated regime-switching market, comparing it to CPPI.
result CPPI strategy generally offers better risk-return tradeoff and stability.
Study examines AutoML adaptation to evolving data.
problem Understanding and improving AutoML performance with concept drift.
method 6 concept drift adaptation strategies evaluated on various AutoML approaches.
result Robust AutoML techniques can be developed to handle concept drift.
Adaptive replication improves stochastic function optimization.
problem Challenges in accurately estimating functions with high variance.
method Trust-region-based Bayesian optimization with adaptive replication.
result Adaptive replication substantially improves solution accuracy and efficiency.
The paper ranks experts based on task performance with noisy evaluations.
problem Ranking experts based on their performance across multiple tasks with noisy evaluations.
method Develops adaptive strategies for ranking experts with a bound on the number of queries.
result Proves strategies allowing to recover the correct ranking of experts with high probability.
This paper evaluates deferring systems using causal inference.
problem Evaluating the impact of deferring systems on model accuracy is challenging.
method The paper uses a causal inference framework to evaluate deferring systems, distinguishing between scenarios with and without access to human predictions.
result The approach allows identifying causal effects of deferring strategies on predictive accuracy.
Informer model with GMADL loss outperforms benchmarks in high frequency Bitcoin trading.
problem Developing automated trading strategies for high frequency Bitcoin data.
method Informer architecture with RMSE, GMADL, and Quantile loss functions.
result Informer model with GMADL loss function outperforms benchmarks in trading outcomes.
A new method for evaluating and selecting policies in contextual bandits improves confidence intervals and policy quality.
problem Evaluating and selecting policies in contextual bandits with logged data.
method Self-normalized Importance Weighting (SN) estimator with Efron-Stein tail inequality and multiplicative bias control.
result The method provides tighter confidence intervals and better policy selection compared to competitors.
Paper proposes a new framework to compare trading strategies by accounting for market conditions.
problem Lack of information on how trading strategy performance varies with market conditions.
method Uses a GAMLSS/ZAGA framework to model the Adjusted Information Ratio (IR∗) for a SVMP and BH strategy across 146 folds of the S&P 500. result Dominance of SVMP over BH is conditional on market regime, as shown by differences in expected IR∗ and its variance. Evaluates deep learning models in histopathology for robustness and classification strategies.
problem Lack of comprehensive evaluation of histopathology models beyond accuracy.
method Developed a new methodology to evaluate models on five histopathology datasets, including vision transformers and CNNs.
result Identified insights into cancer classification strategies and robustness against stain variations.
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 paper introduces a framework to select efficient datasets for preserving model rankings.
problem Efficient evaluation of machine learning models on small, representative datasets.
method Bootstrap aggregation, clustering, design criteria, random baselines, and greedy farthest-first (FAFI).
result Several selection strategies improve rank preservation compared to random subsets, especially in time series classification.
This study compares and evaluates categorical kernels for Gaussian process regression.
problem Challenges in designing effective categorical kernels for Gaussian process regression.
method Reproducible comparative study of existing kernels, new evaluation metrics, and clustering-based nested kernels.
result Nested kernels outperform other methods, especially when group structure is unknown or unknown.
This study optimizes trading strategy parameters using walk-forward techniques and finds robust performance.
problem Optimizing trading strategy performance through parameter optimization.
method Walk-forward optimization with varying window lengths, tested on Bitcoin, Binance Coin, and Ethereum.
result The strategy outperforms Buy-and-Hold with lower drawdown and higher Information Ratio.
The VIX is used to enhance quantitative trading strategies.
problem Improving Sharpe ratio and reducing trading risks in quantitative strategies.
method Postprocessing quantitative strategies with VIX signals.
result Increased Sharpe ratio and reduced trading risks.
Numeracy is the ability to understand and work with numbers. It is a necessary skill for composing and understanding documents in clinical, scientific, and other technical domains. In this paper, we explore different strategies for modelling numerals with language models, such as memorisation and digit-by-digit composi…
AlphaForgeBench evaluates LLMs as quantitative researchers, not trading agents, to address instability in financial decision-making.
problem Behavioral instability of LLMs in sequential decision-making under financial uncertainty.
method Proposes AlphaForgeBench, a framework that requires LLMs to generate executable alpha factors and compose factor-based trading strategies.
result Eliminates execution-induced instability and provides a rigorous benchmark for evaluating financial reasoning.