Kyle's equilibrium model stability proven for 1-2 trading times, but not for 3 or more.
problem Stability of Kyle's equilibrium model in a dynamic trading setting.
method Proof of stability for 1-2 trading times, non-stability for 3 or more trading times, independent of input parameters.
result Kyle's equilibrium model is stable for 1-2 trading times but not for 3 or more trading times.
Crowded trades cluster investors, affecting stock price stability.
problem Crowded trades lead to price instability and systemic risk.
method Market clustering measure using granular trading data.
result Market clustering has a causal effect on stock return distribution tails, especially positive tail.
Study stability of trading strategy under market perturbations.
problem Dynamic stability of trading strategy under market changes.
method Established reverse conjugacy characterizations, proved continuity and convergence of indirect utility process.
result Continuity and first-order convergence of indirect utility process under market perturbations.
Generative AI reduces herd behavior in trading, but can also lead to optimal herding.
problem Impact of generative AI on financial stability and herd behavior.
method Laboratory experiments with large language models replicating human trading behavior.
result AI agents make more rational decisions than humans, reducing herd behavior but also potentially leading to optimal herding.
This work explores the trade-offs between stability and accuracy in statistical estimation.
problem Understanding the statistical cost of algorithmic stability.
method Statistical decision-theoretic perspective, focusing on worst-case and average-case stability.
result Optimal stable estimators for mean estimation and regression settings are developed, revealing trade-offs between stability and accuracy.
The overall performance or expected excess risk of an iterative machine learning algorithm can be decomposed into training error and generalization error. While the former is controlled by its convergence analysis, the latter can be tightly handled by algorithmic stability. The machine learning community has a rich his…
In this paper we study the stability and its trade-off with optimization error for stochastic gradient descent (SGD) algorithms in the pairwise learning setting. Pairwise learning refers to a learning task which involves a loss function depending on pairs of instances among which notable examples are bipartite ranking,…
SentARL uses sentiment features to improve trading profits.
problem Improving profit stability in single-asset trading.
method Sentiment-Aware Reinforcement Learning (SentARL) system.
result SentARL consistently outperforms baselines across multiple assets and conditions.
ABM simulates OTC government bond market dynamics, enhancing liquidity and stability.
problem Understanding and ensuring market stability and liquidity in OTC government bond markets.
method Developed a bespoke ABM to simulate market-maker interactions and test hypotheses.
result Greater agent diversity enhances market liquidity and reducing market-making costs improves stability.
Reviews six finance topics, including 'radical complexity'.
problem None explicitly stated, focuses on research directions.
method Informal review and discussion of open questions.
result No specific key result mentioned, focuses on research directions.
We examine the problem of dynamic reserving for risk in multiple currencies under a general coherent risk measure. The reserver requires to hedge risk in a time-consistent manner by trading in baskets of currencies. We show that reserving portfolios in multiple currencies V are time-consistent when (and only…
The stability of money value is an important requisite for a functioning economy, yet it critically depends on the actions of participants in the market themselves. Here we model the value of money as a dynamical variable that results from trading between agents. The basic trading scenario can be recast into an Ising t…
We investigate the role of networks of alliances in preventing (multilateral) interstate wars. We first show that, in the absence of international trade, no network of alliances is peaceful and stable. We then show that international trade induces peaceful and stable networks: trade increases the density of alliances s…
Deep RL strategy improves natural gas trading performance.
problem Improving natural gas trading performance using Deep RL.
method Domain-adapted Deep RL for natural gas futures trading.
result Deep RL strategy outperforms benchmarks and reduces transaction costs.
Dealing with high variance is a significant challenge in model-free reinforcement learning (RL). Existing methods are unreliable, exhibiting high variance in performance from run to run using different initializations/seeds. Focusing on problems arising in continuous control, we propose a functional regularization appr…
New theory shows perishable goods markets are more stable and efficient.
problem Lower stability and efficiency of markets for re-tradable assets compared to perishable goods.
method Reformulation of no-trade and no-arbitrage theorems in neoclassical finance.
result Perishable goods markets exhibit higher stability and efficiency.
New stability analysis improves generalization of multipass SGD.
problem Improper preconditioning affects generalization in multipass SGD.
method Developed on-average stability analysis for multipass SGD.
result Proper preconditioning yields optimal effective dimension dependence.
Paper forecasts financial trading durations using a new point process model.
problem Forecasting limit order book durations in high-frequency financial data.
method Self-exciting flexible residual point process incorporating empirical distributional features.
result The model achieves strong predictive performance compared to alternative approaches.
This work's purpose is to understand the dynamics of limit order books in order-driven markets. We try to illustrate a dynamical trading mechanism attached to the microstructure of limit order markets. We capture the iterative nature of trading processes, which is critical in the dynamics of bid-ask pairs and the switc…
The paper explores a Multi-Objective RL approach for trading that generalizes reward functions.
problem Improving performance in single-asset trading through adaptive reward functions.
method Developed a Multi-Objective Deep Reinforcement Learning algorithm to generalize reward functions and discount factors.
result The Multi-Objective algorithm demonstrates increased predictive stability and better performance in sparse reward scenarios.
The paper analyzes the trade-off between smoothness and sparsity in GCN using lp-regularized learning.
problem Quantifying the trade-off between smoothness and sparsity in GCN.
method Proposes a novel SGD proximal algorithm for GCNs with an inexact operator to analyze the stability of the ℓp-regularized stochastic learning. result Establishes an explicit theoretical understanding of GCN with ℓp-regularized stochastic learning. Algorithmic stablecoins optimize monetary policy to balance price stability.
problem Persistent inflation from centralized monetary policy.
method Propose and study a rule-based monetary policy model for algorithmic stablecoins.
result Optimal trade-off between price stability and supply stability.
This paper introduces Libra to analyze and optimize generalization in Federated Learning.
problem Inconsistent local optima in Federated Learning lead to poor generalization performance.
method Introduces Libra, a generalization dynamics analysis framework for algorithm-dependent excess risk minimization.
result Libra highlights the trade-offs between model stability and gradient norms in Federated Learning.
Ecological systems have a high level of complexity combined with stability and rich biodiversity. Recently, the analysis of their properties and evolution has been pushed forward on a basis of concept of mutualistic networks that provides a detailed understanding of their features being linked to a high nestedness of t…
Study on market instability in multi-agent trading with price impact and transaction costs.
problem Analyzing market instability in multi-agent trading with price impact and transaction costs.
method Analytical and numerical methods to study Nash equilibria and stability conditions.
result Conditions on model parameters determine market stability, including scaling of market impact and transaction cost.
Stabilizing black-box algorithms through task-oriented randomization
problem Ensuring stability of black-box models
method Task-oriented randomization
result Established rigorous theoretical foundations and demonstrated effectiveness through simulations and real-world applications
MOSS optimizes decision rules for accuracy and stability.
problem Constructing stable sets of decision rules.
method Multi-objective optimization framework incorporating sparsity, accuracy, and stability.
result MOSS outperforms state-of-the-art rule ensembles in predictive performance and stability.
This paper analyzes a time-dependent CFMM called RMM-01, focusing on its pricing and stability.
problem Analyzing the pricing and stability of a time-dependent CFMM called RMM-01.
method Introducing the general framework for CFMMs, analyzing pricing properties, and examining time-varying price stability.
result Determining parameter bounds for RMM-01 to achieve a more stable price than Uniswap.
New approach uses RS levels to improve stock trading RL model efficiency and stability.
problem Stability and adaptivity challenges in current financial RL applications.
method Leverages RS levels as regularization terms in MBRL for stock trading optimization.
result Enhanced performance and better profit gain with less riskiness compared to pure MBRL models.
This paper builds a model of high-frequency equity returns by separately modeling the dynamics of trade-time returns and trade arrivals. Our main contributions are threefold. First, we characterize the distributional behavior of high-frequency asset returns both in ordinary clock time and in trade time. We show that wh…
Paper analyzes stability and generalization of SCO algorithms.
problem Understanding how SCO algorithms perform on unseen data.
method Algorithmic stability analysis in statistical learning theory.
result Derives dimension-independent excess risk bounds for SCGD and SCSC.
The World Trade Web (WTW) is a weighted network whose nodes correspond to countries with edge weights reflecting the value of imports and/or exports between countries. In this paper we introduce to this macroeconomic system the notion of extinction analysis, a technique often used in the analysis of ecosystems, for the…
Investigates stability of Epstein-Zin problem under market distortions.
problem Stability of Epstein-Zin problem in incomplete markets.
method Analyzes perturbations in returns and volatility, and interest rate; proves convergence of optimal solutions.
result Proves convergence of optimal consumption streams and value functions in the limit of model perturbations.
DeltaHedge uses AI to optimize portfolio options trading.
problem Balancing risk and return in volatile markets.
method Multi-agent framework integrating reinforcement learning and options hedging.
result Outperforms traditional and standalone models.
New analysis improves understanding of bilevel optimization stability and generalization.
problem Understanding how well bilevel optimization algorithms generalize.
method Algorithmic stability arguments and generalization bounds for three bilevel minimax solvers.
result Precise trade-off between algorithmic stability, generalization gaps, and practical settings.
Trading affects grid frequency fluctuations, making them more extreme.
problem Impact of trading on grid frequency stability.
method Analysis of frequency time series from 2011 and 2017.
result Trading modifies frequency fluctuation statistics, making large deviations more likely.
The paper improves decision tree stability for health care applications.
problem Stability of decision trees in health care applications.
method Introducing a new distance metric to determine tree stability and proposing a novel training methodology.
result On average, a 4.6% decrease in predictive power yields a 38% increase in model stability.
We stabilize the Kumaraswamy distribution for efficient sampling and differentiation.
problem Numerical instabilities in the Kumaraswamy distribution's inverse CDF and log-pdf.
method Identified and resolved numerical issues, introduced a stabilized KS distribution.
result Stabilized Kumaraswamy distribution supports efficient sampling and differentiation.
New algorithms help machines forget old data efficiently.
problem Machine learning models can retain old data, hindering new learning.
method Developed TV-stable algorithms based on noisy SGD for convex and non-convex functions.
result Achieved efficient unlearning with upper and lower bounds on risk.
Optimal trading strategy derived for nonlinear price impact models.
problem Optimal trading with nonlinear price impact induced by alpha signals.
method Variational approach, nonlinear Fredholm equation, iterative scheme.
result Existence and uniqueness of optimal trading strategy under monotonicity condition.
We propose a heterogeneous agent market model (HAM) in continuous time. The market is populated by fundamental traders and chartists, who both use simple linear trading rules. Most of the related literature explores stability, price dynamics and profitability either within deterministic models or by simulation. Our nov…
Improved stability analysis of neural network systems using Zames-Falb multipliers.
problem Analyzing stability of linear systems with neural network nonlinearities.
method Using integral quadratic constraints, sector-bounded and slope-restricted structure, and acausal Zames-Falb multipliers.
result Flexible and versatile framework for stability analysis with improved computational efficiency.
We consider the dynamics of a smart grid system characterized by widespread distributed generation and storage devices. We assume that agents are free to trade electric energy over the network and we focus on the emerging market dynamics. We consider three different models for the market dynamics for which we present a…
We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers. These classifiers are trained only on globally annotated images (bags), but often can provide fine-grained annotations for image pixels or patches (instances). This is interesting for computer aided diagnosis (CAD…
Analyzes how many people can receive stable income in a pooled annuity fund.
problem Quantifying the trade-off between income stability and the number of members in a pooled annuity fund.
method Investment returns held constant, systematic longevity risk omitted. Derived an analytical expression for income stability.
result The number of fund members who receive stable income is independent of the mortality model.
New risk measures improve portfolio diversification and stability.
problem Concentration risk in traditional portfolio optimization methods.
method Equal-correlation portfolio strategy with mathematical optimization.
result Improved risk diversification and stable returns.
Study confirms eurozone interbank market stability but finds higher collateral reuse.
problem Analyzing eurozone interbank market behavior and stability.
method Examined secured transactions data from ECB, tested stylized facts, measured network properties.
result Observed higher collateral reuse and network symmetry compared to unsecured markets.
Algorithm of multicurrency trading at the market of Forex is realized on the basis of nonlinear stochastic wavelets. The distinctive feature of the algorithm is the possibility of weakly- and strongly connected horizontal self-assemblies, as well as use of nested structures. On-line trading with eight currency couples …