arXiv research

Model shows how price impact and transaction costs affect trading behavior and profits.

SafeMIL learns safer policies by avoiding risky behavior from non-preferred trajectories.

New method disentangles perceptual uncertainty and behavioral costs in partially observable systems.

Neural network approximates Bayesian decision-making parameters.

New method infers human sensorimotor costs from behavior.

Cost-benefit analysis often assumes accurate estimates, but this study finds significant inaccuracies.

Revisits behavioral finance option pricing model to align with rational asset pricing theory.

OSIL learns safe policies from unsafe demonstrations.

Study strategic dynamic pricing for buyers with unknown manipulation costs.

Interactive IL beats BC by state-wise annotation cost.

Introduces a Cost function to measure Legendrian knot obstructions.

Bayesian method infers local rules for collective animal movement.

We consider fractional Black-Scholes market with proportional transaction costs. When transaction costs are present, one trades periodically i.e. we have the discrete trading with equidistance $n^{-1}$ between trading times. We derive a non trivial hedging error for a class of European options with convex payoff in the…

We study the arbitrage opportunities in the presence of transaction costs in a sequence of binary markets approximating the fractional Black-Scholes model. This approximating sequence was constructed by Sottinen and named fractional binary markets. Since, in the frictionless case, these markets admit arbitrage, we aim …

We consider a framework involving behavioral economics and machine learning. Rationally inattentive Bayesian agents make decisions based on their posterior distribution, utility function and information acquisition cost Renyi divergence which generalizes Shannon mutual information). By observing these decisions, how ca…

The paper explores the geometric structure of cost functions in multiple dimensions.

Despite the availability of very detailed data on financial market, agent-based modeling is hindered by the lack of information about real trader behavior. This makes it impossible to validate agent-based models, which are thus reverse-engineering attempts. This work is a contribution to the building of a set of styliz…

Evidence acquisition costs influence disclosure behavior and preference.

Study optimizes product assortment for retailers with repeated exposures and patience costs.

A combination of a priority queueing model and mean field theory shows the emergence of traders' swarm behavior, even when each has a subjective prediction of the market driven by a limit order book. Using a nonlinear Markov model, we analyze the dynamics of traders who select a favorable order price taking into accoun…

We present an expansion for portfolio optimization in the presence of small, instantaneous, quadratic transaction costs. Specifically, the magnitude of transaction costs has a coefficient that is of the order $ε$ small, which leads to the optimization problem having an asymptotically-singular Hamilton-Jacobi-Bellman eq…

IAL uses interactive learning to improve model performance with minimal human feedback.

We derive behavioral finance option pricing formulas consistent with the rational dynamic asset pricing theory. In the existing behavioral finance option pricing formulas, the price process of the representative agent is not a semimartingale, which leads to arbitrage opportunities for the option seller. In the literatu…

Simulation framework assesses ROI of chronic disease adherence and policy timing.

This paper presents a stochastic model for discrete-time trading in financial markets where trading costs are given by convex cost functions and portfolios are constrained by convex sets. The model does not assume the existence of a cash account/numeraire. In addition to classical frictionless markets and markets with …

The paper analyzes transaction fees on blockchains using a priority queue model.

Aims to learn from multiple unpredictable teachers with minimal interaction.

Investigates portfolio selection with transaction costs and stochastic volatility, using deep learning for computation.

This report reviews the Edinburgh tram project's risk management. Projects frequently overrun their cost and timelines and fall short on intended benefits. Cost, schedule, and benefit risk of projects need to be carefully considered to avoid this. The report describes and evaluates risk assessment and management for th…

Machine learning (ML) training algorithms often possess an inherent self-correcting behavior due to their iterative-convergent nature. Recent systems exploit this property to achieve adaptability and efficiency in unreliable computing environments by relaxing the consistency of execution and allowing calculation errors…

The paper teaches robots to navigate by learning costs from expert demonstrations.

Models of spatial firm competition assume that customers are distributed in space and transportation costs are associated with their purchases of products from a small number of firms that are also placed at definite locations. It has been long known that the competition equilibrium is not guaranteed to exist if the mo…

Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. We propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penali…

We study the inverse optimal control problem in social sciences: we aim at learning a user's true cost function from the observed temporal behavior. In contrast to traditional phenomenological works that aim to learn a generative model to fit the behavioral data, we propose a novel variational principle and treat user …

Study optimal execution in a transient price impact model with multiple traders.

Improved risk assessment for UBI using telematics data and AdaBoost.

Bayesian approach detects changepoints with cost-sensitive data fidelity.

We propose an algorithm for exploring the entire regularization path of asymmetric-cost linear support vector machines. Empirical evidence suggests the predictive power of support vector machines depends on the regularization parameters of the training algorithms. The algorithms exploring the entire regularization path…

Optimal trading strategy with predictor and costs, derived equations and shape.

The paper develops a method to learn navigation costs from expert demonstrations in partially observable environments.

Paper analyzes liquidity for everlasting options in DeFi, offering strategies to reduce costs.

The paper analyzes portfolio selection with non-concave utility and transaction costs.

Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfuscation, while behavior-based approaches highly rely on the malware training samples and incur prohi…

Study examines how traders with asymmetric information and adaptive learning strategies affect market efficiency.

We study the power of different types of adaptive (nonoblivious) adversaries in the setting of prediction with expert advice, under both full-information and bandit feedback. We measure the player's performance using a new notion of regret, also known as policy regret, which better captures the adversary's adaptiveness…

Study optimal transport for stationary processes, estimating joinings and costs.

Optimizes sparse mean-reverting portfolios for higher returns.

We consider a novel application of inverse reinforcement learning with behavioral economics constraints to model, learn and predict the commenting behavior of YouTube viewers. Each group of users is modeled as a rationally inattentive Bayesian agent which solves a contextual bandit problem. Our methodology integrates t…