New kinetic model captures immediate wealth exchanges and their effects.
problem Capturing the dynamics of immediate wealth exchanges in economies.
method Modified dynamics and probabilistic trading criterion introduced.
result Equilibrium wealth distributions are consistent across various trading criteria.
Study finds no statistically significant trading edge in MNQ futures signals from OHLCV data.
problem Testing intraday momentum signals from OHLCV data in MNQ futures under realistic execution constraints.
method 947 trading days of five-minute data, 14 signal families evaluated, strict institutional criteria applied.
result No signal satisfies all criteria simultaneously, gross edge insufficient to overcome costs.
The paper shows that liquidity deficit drives market dynamics and proposes an automated trading machine.
problem Liquidity deficit as the driving force of market dynamics.
method Calculus-like approach based on Radon--Nikodym derivatives to calculate market dynamics. Developed a dynamic equation for future prices.
result The proposed automated trading machine shows promising results but is close to breakeven when fees are considered.
Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short an…
Fairness criteria may harm over time, contrary to conventional wisdom.
problem The impact of fairness criteria on long-term population well-being.
method Study of fairness criteria in a one-step feedback model, analyzing long-term outcomes.
result Static fairness criteria do not necessarily promote improvement over time and may cause harm.
New approach uses causal reasoning to address fairness issues.
problem Fairness criteria based on observational data are limited and unreliable.
method Shifts focus from observational criteria to causal reasoning.
result Formalizes why and when observational criteria fail.
The paper analyzes performance criteria for competing fund managers in Ito-diffusion markets.
problem Analyzing performance of competing fund managers in Ito-diffusion markets.
method Developed forward relative performance criteria and forward Nash equilibrium for passive and competitive cases.
result Extended performance criteria for investment problems in Ito-diffusion markets.
This work explores alternative learning criteria beyond traditional risk.
problem The trade-offs of optimizing for average performance in machine learning.
method Survey and introduction of non-traditional learning criteria.
result Emphasizes the importance of considering the loss distribution for desirable performance.
We propose a Genetic Programming architecture for the generation of foreign exchange trading strategies. The system's principal features are the evolution of free-form strategies which do not rely on any prior models and the utilization of price series from multiple instruments as input data. This latter feature consti…
The paper proposes trading grades in a financial market to address unintended consequences of grading systems.
problem Unintended consequences of grading systems, such as unfair advantages and misaligned incentives.
method A thought experiment in a financial market structure to trade grades, similar to interest rate swaps.
result Grades should be viewed as personal equity, not used for selection criteria.
This paper optimizes stock portfolios considering ESG criteria using Bayesian optimization.
problem Optimizing financial investments while incorporating ESG criteria.
method Bayesian optimization to maximize stock portfolio performance under ESG constraints.
result A scalable approach to optimize stock portfolios that balance financial performance and ESG compliance.
A novel multi-objective optimization framework improves insurance pricing fairness.
problem Exacerbated trade-offs between competing fairness criteria in insurance pricing using machine learning.
method Proposes a novel multi-objective optimization framework using NSGA-II to jointly optimize accuracy and fairness criteria.
result Consistently achieves a balanced compromise between accuracy and fairness, outperforming single-model approaches.
Considering that a trader or a trading algorithm interacting with markets during continuous auctions can be modeled by an iterating procedure adjusting the price at which he posts orders at a given rhythm, this paper proposes a procedure minimizing his costs. We prove the a.s. convergence of the algorithm under assumpt…
Regarding the intraday sequence of high frequency returns of the S&P index as daily realizations of a given stochastic process, we first demonstrate that the scaling properties of the aggregated return distribution can be employed to define a martingale stochastic model which consistently replicates conditioned expecta…
Develops a machine learning system to recommend swaption trades.
problem Derivative traders struggle with analyzing many possible trades daily.
method Pipeline of computing metrics, predicting returns, ranking trades.
result Linear regression with lasso regularization performs well.
Study analyzes investment strategies for fund managers competing in a common market.
problem Optimal investment strategies for fund managers competing in a common market with relative performance criteria.
method Construct explicit constant equilibrium strategies for both finite population games and mean field games.
result Explicit strategies show how competition affects investment behavior in risky assets.
The paper analyzes how investors' wealth can decline collectively under partial information.
problem Investors' wealth can decline collectively under partial information.
method The paper derives a Nash equilibrium for mean-variance portfolio selection under relative performance criteria, considering both full and partial information.
result Relative performance criteria can lead to downward self-reinforcement of investors' wealth, which is more pronounced under partial information.
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.
Paper presents a deep reinforcement learning algorithm for online trading without offline training.
problem Developing a fully online trading algorithm without offline training.
method Double Deep Q-learning with Fast Learning Networks, defining terminal states for money conservation. result The algorithm outperforms random action trading and captures different market trends.
Deep RL algorithm trades high-dimensional stock portfolios.
problem Trading high-dimensional stock portfolios with data gaps and non-unique history lengths.
method Deep Q-learning algorithm, sequentially setting up environments, rewarding based on asset returns and cash reservation.
result Algorithm outperforms all passive and active benchmarks by a large margin.
Develops fair clinical risk prediction models using counterfactual reasoning.
problem Addressing biases in clinical risk prediction models for underrepresented groups.
method Augmented counterfactual fairness criteria applied to electronic health records data.
result Demonstrates the feasibility of fair clinical risk prediction models using counterfactual inference.
The paper validates a classifier for identifying intraday regime shifts in MNQ futures.
problem Developing reliable trading signals from intraday regime shifts in MNQ futures.
method Constructed a composite day-classification system using three observable conditions.
result Classifier-positive days exhibit distinct intraday behavior but fail to generate profitable trading signals.
Hybrid framework improves machine learning interpretability for decision making.
problem Trade-off between model performance and interpretability in machine learning.
method Neural Network-based Multiple Criteria Decision Aiding (NN-MCDA) combining additive value model and MLP.
result Enhanced interpretability of machine learning models with good performance.
Proposes a new framework for predicting stock market movements using sparse neural architectures.
problem Challenging problem of predicting stock market movements using technical indicators.
method Multi-criteria optimization approach to evolve sparse neural architectures.
result Evolved parsimonious networks with better generalization capabilities.
This paper uses SARIMA models to forecast Nifty 50 index.
problem Forecasting the movement of Nifty 50 index.
method Applied Seasonal Auto Regressive Integrated Moving Average (SARIMA) models.
result The most appropriate SARIMA model was selected based on AIC criteria.
Detects crypto pump-and-dump schemes with a thresholding-based model.
problem Detecting genuine anomalies from minor trading fluctuations.
method Combining threshold-based criteria with EWMA and volatility measures.
result Balances high true-positive detection with minimal noise.
Hour-Aware Adaptive Risk Management for Autonomous Memecoin Trading
problem Autonomous memecoin trading system performance
method Hour-of-day effects, filter precision, fragility
result 40.5% win rate, mean per-trade return of +0.62%, cumulative +117.7%
New metrics using Laplace approximation improve Gaussian process model selection.
problem Finding a balance between model accuracy, interpretability, and simplicity.
method Introducing multiple metrics based on the Laplace approximation to evaluate Gaussian process models.
result Our metrics provide comparable performance to dynamic nested sampling but are significantly faster.
Paper designs a penalty for model order selection using information criteria.
problem Selecting the correct model order from a set of candidate models.
method Designs a penalty for the generalized information criterion (GIC) to minimize underestimation.
result Optimal penalty minimizes underestimation while keeping overestimation below a specified level.
Study examines how traders optimize in a market with differing beliefs about price formation.
problem Optimizing trading actions in a market with heterogeneous beliefs about price formation.
method Analysis of mean-field game limit of a stochastic game with non-standard forward-backward SDEs.
result Nash equilibrium found through a non-standard vector-valued forward-backward SDE, with solutions constructed using expectations of filtered states.
DeepScalper uses RL to capture intraday trading opportunities, balancing risk and profit.
problem Capturing fleeting intraday trading opportunities in high-frequency markets.
method Dueling Q-network, reward function with hindsight bonus, encoder-decoder architecture, risk-aware auxiliary task.
result Significantly outperforms state-of-the-art baselines in financial criteria.
Benchmarking deep time series models for equity portfolios
problem Selecting the best deep time series model for equity portfolios
method Using a CRSP benchmark and multi-criteria acceptability analysis
result No architecture dominates the benchmark, with TransEnc-8 having the highest rank-1 acceptability
We solve a version of the optimal trade execution problem when the mid asset price follows a displaced diffusion. Optimal strategies in the adapted class under various risk criteria, namely value-at-risk, expected shortfall and a new criterion called "squared asset expectation" (SAE), related to a version of the cost v…
Investment managers assess new assets against a reference universe, identifying four criteria for usefulness.
problem Determining the usefulness of a new asset in an investment portfolio.
method Identifying four criteria for asset usefulness, quantifying each criterion with scalable algorithms.
result New assets must provide incremental diversification and predictability to be useful.
Robo-advisors use MPC to create dynamic investment strategies.
problem Static allocation methods limit robo-advisors' effectiveness.
method Combines MPC with Hidden Markov Model and Black-Litterman for dynamic asset allocation.
result MPC-based strategies outperform static approaches in dynamic and risk-budgeting criteria.
GT-Score reduces overfitting in trading strategies by integrating multiple criteria.
problem Overfitting in data-driven financial models leads to unreliable out-of-sample performance.
method Integrates performance, statistical significance, consistency, and downside risk into a composite objective function.
result Improves generalization ratio by 98% compared to baseline objective functions in walk-forward validation.
This work forecasts electricity prices using Bayesian regime detection and conditional neural processes.
problem Forecasting electricity prices with optimal operational outcomes.
method Bayesian regime detection with conditional neural processes, integrating multi-criteria decision support.
result R-NP model outperformed other models in comprehensive operational utility assessments.
EarnHFT tackles HFT challenges with hierarchical RL, significantly outperforming existing methods.
problem Challenges in applying RL to HFT due to long trajectories and market volatility.
method Three-stage hierarchical RL framework: Q-teacher, diverse RL agents, and minute-level router.
result Significantly outperforms 6 state-of-the-art baselines in profitability.
DeepFair improves fairness in recommender systems without sacrificing accuracy.
problem Lack of bias management in recommender systems leads to unfair recommendations for minority groups.
method Deep Learning based Collaborative Filtering algorithm that balances fairness and accuracy.
result It is possible to make fair recommendations without losing significant accuracy.
FairVIC improves fairness in neural networks without sacrificing accuracy.
problem Mitigating bias in automated decision-making systems, particularly in deep learning models.
method Integrates variance, invariance, and covariance terms into the loss function during training to abstract fairness concepts.
result Significant improvements in fairness across all tested metrics without compromising accuracy.
The paper introduces effort-centric fairness to address masked inequality in lending decisions.
problem Masked inequality in lending decisions where rejected applicants face unequal burdens in reaching future approval.
method Developed an effort-centric framework measuring effort as minimum weighted cost of feasible changes, distinguishing feature-independent actions from structural shifts. Defined parity by comparing average minimum effort across protected groups and embedded in an in-processing fairness objective.
result Effort parity complements predictive fairness by revealing and mitigating hidden barriers to future credit access while making operational trade-offs explicit.
A new framework AlphaMix combines multiple trading experts to improve stock investment decisions.
problem Inconsistent financial predictions and lack of model uncertainty in investment decisions.
method Reformulate quantitative investment as a multi-task learning problem, and propose AlphaMix framework.
result AlphaMix significantly outperforms state-of-the-art baselines in financial criteria.
Paper proposes CFAθ for continuous fairness in algorithms.
problem Algorithmic fairness in machine learning.
method Optimal transport theory for continuous interpolation between fairness definitions.
result Continuous interpolation between individual and group fairness.
The paper shows cross-validation fails in learning Gaussian graphical model structures.
problem Cross-validation's failure in learning Gaussian graphical model structures.
method Finite-sample bounds on misidentification probability of Lasso estimator.
result Cross-validation is inconsistent for learning Gaussian graphical model structures.
Investment strategy without fixed horizon in ambiguous market conditions.
problem Dynamic portfolio choice in an ambiguous market.
method Formulated as a robust forward performance process reflecting dynamic investor preference.
result Market risk premium and utility risk premium determine trading direction and worst-case scenarios.
Criteria for homeomorphisms in grid generation.
problem Grid generation problems in topology and geometry.
method General and specific criteria for homeomorphisms and diffeomorphisms.
result Criteria applied to grid generation problems.
Paper proposes PMformer for better cryptocurrency price forecasting.
problem Huge volatility and trade-off between univariate and multivariate models.
method Partial-multivariate approach using PMformer.
result PMformer achieves significant statistical accuracy in forecasting.
Algorithm identifies interpretable subgroups with elevated treatment effects.
problem Estimating high-dimensional, uninterpretable CATE results.
method Rule sets summarizing CATE estimates, optimizing subgroup size and effect size.
result Frontier of Pareto optimal rule sets for subgroup identification.