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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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275480107 · May 202619922001200920172026
48 results for exogenous signals

Optimizes PnL using linear signals in quantitative finance.

problem Maximizing profit and loss in financial trading.
method Unsupervised machine learning approach that maximizes Sharpe Ratio through linear relationships and parameter optimization.
result Empirical validation and effectiveness of the model on U.S. Treasury ETF.

The paper tackles reinforcement learning with exogenous variables and rewards.

problem Exogenous state variables and rewards slow reinforcement learning by introducing uncontrolled variation.
method Formalizes exogenous state variables and rewards, decomposes MDP into exogenous and endogenous components, and introduces algorithms to discover these components.
result Optimal policies for the endogenous MDP are also optimal for the original MDP, but the endogenous MDP is easier to solve due to reduced variance.

A new method for portfolio optimization using signature signatures to incorporate path-dependencies.

problem Traditional portfolio optimization models struggle with path-dependencies and exogenous signals.
method Signature Trading framework using rough path signatures to represent trading strategies.
result Efficient incorporation of exogenous signals and drawdown control in optimal strategies.

This paper improves parameter estimation for autonomous systems with unmodeled dynamics.

problem Accurate parameter estimation for risk-aware autonomous systems with unmodeled dynamics.
method Spectral lines-based approach for estimating parameters of dynamic models, allowing deterministic unmodeled dynamics.
result The proposed method leads to non-asymptotic bounds on parameter estimation error, robust to unmodeled dynamics, and matches existing literature in ideal conditions.

The study uses Random Matrix Theory to identify structural changes in stock markets during shocks.

problem Understanding structural changes in stock markets during exogenous shocks.
method Random Matrix Theory and complexity gap analysis.
result The complexity gap collapses during shocks, indicating strong synchronization, and widens before shocks, signaling a rich structure.

LSTMs improve bond yield forecasting with unique signals.

problem Improving bond yield forecasting accuracy.
method Long short-term memory (LSTM) networks with sequence-to-sequence architectures and LSTM-LagLasso methodology.
result Univariate LSTM models with additional memory can achieve similar results as multivariate MLP models using exogenous information.

This paper relaxes the common prior assumption in the public and private information game of Morris and Shin (2000, 2004). For the generalized game, where the agent's prior expectations are heterogenous, it derives a sharp condition for the emergence of unique/multiple equilibria. This condition indicates that unique e…

2013-12-30abs ↗pdf ↗

A growing part of the behavioral finance literature has addressed some of the stylized facts of financial time series as macroscopic patterns emerging from herding interactions among groups of agents with heterogeneous trading strategies and a limited rationality. We extend a stochastic herding formalism introduced for…

2013-02-26abs ↗pdf ↗

Signals coming from multivariate higher order conditional moments as well as the information contained in exogenous covariates, can be effectively exploited by rational investors to allocate their wealth among different risky investment opportunities. This paper proposes a new flexible dynamic copula model being able t…

2016-01-20abs ↗pdf ↗

Study on markets with insiders receiving private signals affecting asset prices and information flow.

problem Understanding markets with heterogeneous information flows and private signals.
method Proves existence of a partial communication equilibrium with jumps in information and prices.
result The public information flow and asset prices jump at each private signal time, creating incomplete markets between jumps.

Study finds non-monotonic Value of Information in dynamic multi-market monopoly.

problem Investigates non-monotonicity in Value of Information for a price-setting monopolist.
method Uses a Bayesian inverse problem with Kalman-Bucy-Stratonovich filter in a dynamic discrete model.
result Non-monotonic relationship between signal variance and Value of Information.

This paper extends a Kyle model to include price-responsive traders, revealing new dynamics and equilibria.

problem Real-world market dynamics involve price-responsive traders, affecting market equilibrium and insider profits.
method Developed a continuous-time Kyle model with two types of price-responsive traders (momentum and contrarian), leading to a forward-backward Riccati system for equilibrium.
result The model shows that feedback effects can lead to multiple equilibria and amplify price informativeness.

We focus on the influence of external sources of information upon financial markets. In particular, we develop a stochastic agent-based market model characterized by a certain herding behavior as well as allowing traders to be influenced by an external dynamic signal of information. This signal can be interpreted as a …

2015-06-11abs ↗pdf ↗

Study classifies stock price jumps as exogenous or endogenous using news data.

problem Differentiating between exogenous and endogenous price jumps.
method Synchronized news data with order book data to analyze stock price movements.
result Exogenous jumps are abrupt and follow a decaying power-law, while endogenous jumps are progressively accelerating.

New method learns exogenous variable distributions for better causal optimization.

problem Maximizing target variables in structural causal models.
method Learn exogenous variable distributions to improve surrogate models' fidelity.
result Improves approximation of structural causal models and broader application scenarios.

We study Exo-MDPs to reduce sample complexity in reinforcement learning.

problem Reducing sample complexity in reinforcement learning for structured MDPs.
method Introducing Exo-MDPs and proving structural equivalence to linear mixture MDPs, establishing regret bounds.
result Proved O(H3/2dK)O(H^{3/2}d\sqrt{K}) regret bound for Exo-MDPs, matching lower bounds.

This paper uses NARX neural networks for macroeconomic forecasting and goal setting.

problem Improving accuracy in macroeconomic forecasting and goal setting.
method Literature review and construction of specific NARX neural networks for macroeconomic indicators.
result NARX neural networks can be trained to make accurate predictions for macroeconomic indicators and national goals.

New approach uses 'forward-looking' counterfactuals for treatment choice.

problem Using traditional 'retrospective' counterfactuals in treatment choice leads to counterintuitive results.
method Introduces 'counterfactual treatment choice' for forward-looking counterfactuals.
result Mismatches between interventional and forward-looking counterfactuals can lead to counterintuitive results.

The paper analyzes binary option markets with exogenous information and price sensitivity.

problem Analyzing binary option markets with exogenous information and price sensitivity.
method Derive and analyze a continuous model of binary option markets with exogenous information, using Filippov surfaces and general assumptions on purchasing rules.
result Price always converges when exogenous information is constant, and price sensitivity affects price lag vs. information.

Enhances random forest performance with exogenous randomness.

problem Improving random forest performance through exogenous randomness.
method Developed non-asymptotic MSE expansions for individual trees and forests, identified two types of randomness, and conducted simulations.
result Exogenous randomness, particularly feature subsampling, reduces both bias and variance of random forests.

HL algorithms improve resource allocation in cloud environments.

problem Sequential decision-making under uncertainty with exogenous variables.
method HL algorithms leverage exogenous variable samples to infer counterfactual consequences.
result HL algorithms outperform classic methods and reinforcement learning in resource allocation.

Systems with long-range persistence and memory are shown to exhibit different precursory as well as recovery patterns in response to shocks of exogeneous versus endogeneous origins. By endogeneous, we envision either fluctuations resulting from an underlying chaotic dynamics or from a stochastic forcing origin which ma…

2002-06-05abs ↗pdf ↗

We consider the problem of identifying the causal direction between two discrete random variables using observational data. Unlike previous work, we keep the most general functional model but make an assumption on the unobserved exogenous variable: Inspired by Occam's razor, we assume that the exogenous variable is sim…

2016-11-12abs ↗pdf ↗

Study finds macroeconomic indicators predict health workforce and infrastructure measures.

problem Evaluating the predictive value of macroeconomic indicators for public health targets.
method Examined multiple forecasting approaches including neural networks, generalized additive models, random forests, and time series models with exogenous indicators.
result Macroeconomic indicators provide consistent and reproducible predictive signals for health workforce and infrastructure measures, but less so for other targets.

Study liquidity provision with exogenous competition using a reference market maker.

problem Liquidity provision in the presence of exogenous competition.
method Model a reference market maker who optimizes posted depths, competing market makers using a rule of thumb.
result Model admits approximate closed-form solution for a linear-quadratic goal functional.

Paper simplifies complex causal identifiability problems with exogenous isomorphism.

problem Achieving consistent answers to causal questions in Structural Causal Models.
method Introducing exogenous isomorphism and proposing EI\sim_{\mathrm{EI}}-identifiability.
result Unified and generalized theories for practical applications in counterfactual reasoning.

We reverse engineer dynamics of financial contagion to find the scenario of smallest exogenous shock that, should it occur, would lead to a given final systemic loss. This reverse stress test can be used to identify the potential triggers of systemic events, and it removes the arbitrariness in the selection of shock sc…

2017-02-28abs ↗pdf ↗

Proposes a new test for validating multivariate dynamic regression models.

problem Inadequate exogeneity conditions for conventional model specification tests in dynamic systems.
method Develops a generalized Durbin estimator for multiple-equation systems with dynamic dependencies, and constructs Wald tests.
result Bootstrap-based Wald tests improve finite-sample size control and validate the null hypothesis in multifactor models.

Proposes Exogenous Matching for efficient counterfactual estimation.

problem Efficient estimation of counterfactual expressions in general settings.
method Transforms variance minimization into conditional distribution learning.
result Outperforms other importance sampling methods in counterfactual estimation.

Paper proposes a new model to assess risks in energy storage systems considering both exogenous and endogenous uncertainties.

problem Current risk assessment ignores the stochastic nature of energy storage availability.
method Data-driven unified model with exogenous and endogenous uncertainty description for four types of generic energy storage.
result Comparative results show more severe risks for endogenous uncertainty, suggesting new strategies for system operators.

Study on limits of LLM-based multi-agent planning reliability.

problem Reliability limits of LLM-based multi-agent planning.
method Modeling LLM-based multi-agent architecture as a decision network, showing dominance by centralized Bayes decision maker.
result Optimizing multi-agent directed acyclic graphs under communication budget is equivalent to choosing a constrained experiment.