We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, , and at each time step invest a particular fraction, , of their budget. The return on investment (RoI), , is characterized by a periodic function with different types and leve…
On-device research index
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.
169,051 papers · 148 categories
Trend · papers per month
48 results for “risk-seeking strategies”
The paper studies risk-sharing allocations for risk-seeking agents using a common distortion risk measure.
problem Characterizing Pareto-optimal risk-sharing allocations for risk-seeking agents.
method Modeling preferences with a common distortion risk measure and analyzing three settings: risk-averse, risk-seeking, and inverse S-shaped distortion.
result Pareto-optimal allocations for risk-seeking agents are counter-monotonic, not comonotonic.
Examines optimal risk sharing with realistic risk attitudes, finding risk seeking in certain subdomains.
problem Optimal risk sharing with empirically realistic risk attitudes.
method Allows for risk-seeking agents, generalizes expected utility, and uses counter-monotonic improvement theorem.
result First empirical results on optimal risk sharing with realistic risk attitudes.
Cryptocurrency traders increase stock risk-seeking behavior.
problem Understanding the motivations behind cryptocurrency trading.
method Individual-level brokerage data analysis of stock trading behavior.
result Cryptocurrency traders increase risk-seeking behavior in stocks when engaging in cryptocurrency trading.
Optimizes portfolios with costs, showing existence of optimal strategies.
problem Risk-sensitive portfolio optimization with transaction costs.
method Log-return i.i.d. framework, Bellman equation analysis.
result Existence of optimal strategies for risk-averse and risk-seeking cases.
This paper improves multi-agent reinforcement learning by distinguishing non-stationary samples based on likelihood.
problem Non-stationarity in decentralized multi-agent reinforcement learning environments.
method Likelihood Quantile Networks (LQN) that distinguish non-stationary samples based on likelihood of returns.
result LQN leads to more stable, sample-efficient, and convergent joint optimal policies compared to previous methods.
RiskMiner discovers formulaic alphas using MCTS for better performance.
problem Mining formulaic alphas without considering structural information and alpha correlations.
method Formulates alpha mining as an MDP and solves it with a risk-seeking MCTS.
result Our method outperforms state-of-the-art benchmarks and achieves the most profitable results.
Robust Risk-Aware Option Hedgingq-fin.CP
Study uses RL to hedge financial derivatives, showing robust strategies outperform non-robust ones.
problem Risk mitigation and gain-seeking in hedging path-dependent financial derivatives.
method Robust risk-aware reinforcement learning (RL) with policy gradient approach.
result Robust hedging strategies outperform non-robust ones under varying data generating processes.
Optimal risk sharing found for heterogeneous risk attitudes using distortion risk measures.
problem Risk sharing in economies with diverse risk attitudes.
method Modeling preferences with distortion risk measures, using comonotonic and counter-monotonic principles.
result Optimal risk sharing strategies identified based on risk attitudes, reducing the -agent problem to a two-agent formulation.
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.
Deep learning finds mathematical equations from data.
problem Discovering underlying mathematical expressions from datasets.
method Uses a recurrent neural network to search for mathematical expressions and optimizes using a risk-seeking policy gradient.
result Outperforms existing methods in recovering exact symbolic expressions.
Study risk sharing among agents with varying risk preferences.
problem Risk sharing among agents with heterogeneous risk measures.
method Derive explicit solutions for inf-convolution and counter-monotonic inf-convolution under varying risk seeking.
result Explicit solutions for inf-convolution and counter-monotonic inf-convolution can be represented by a generalization of distortion risk measures.
We introduce an equilibrium asset pricing model, which we build on the relationship between a novel risk measure, the Expected Downside Risk (EDR) and the expected return. On the one hand, our proposed risk measure uses a nonparametric approach that allows us to get rid of any assumption on the distribution of returns.…
Optimal trading strategy in Proof-of-Stake blockchain using continuous-time control.
problem Finding the optimal balance between stake utility and consumption utility in Proof-of-Stake blockchain.
method Continuous-time control approach, dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations.
result Close-form solutions for linear and convex utility functions, optimal strategies identified.
Agent optimizes risky asset trading times based on Prospect Theory.
problem Optimizing speculative trading times with transaction costs.
method Formulated as a sequential optimal stopping problem, characterized the solution.
result Trading patterns influenced by preference and market friction.
A Unified Formula of the Optimal Portfolio for Piecewise Hyperbolic Absolute Risk Aversion Utilitiesq-fin.MF
Unified formula for optimal portfolio under piecewise hyperbolic risk aversion.
problem Optimizing portfolios with piecewise hyperbolic risk aversion utilities.
method Derive a unified closed-form formula for the optimal portfolio.
result Unified formula reflects risk aversion behaviors and risk-taking behaviors.
New method reduces uncertainty in Q-values for better reinforcement learning.
problem Uncertainty in expected future rewards in reinforcement learning.
method Introduces knowledge values that combine expected rewards and uncertainty.
result Achieves Bayes regret bound of O(L √SA T).
Dynamic risk constraints help limit risky behavior in financial portfolios.
problem Static risk measures fail to control tail-risk-seeking traders.
method Introduces dynamic risk constraints applied throughout the trading horizon.
result Dynamic risk constraints can effectively limit risky behavior in portfolios.
Kyle (1985) builds a pioneering and influential model, in which an insider with long-lived private information submits an optimal order in each period given the market maker's pricing rule. An inconsistency exists to some extent in the sense that the ``constant pricing rule " actually assumes an adaptive expected price…
Study risk-sensitive reinforcement learning with entropic risk measures and generative models.
problem Risk-sensitive reinforcement learning in discounted MDPs with recursive entropic risk measures.
method Introduced Model-Based ERM -Value Iteration (MB-RS-QVI) and derived PAC bounds on sample complexity for value and policy learning.
result PAC bounds show exponential dependence on , with tight bounds in and .
Study optimal control strategy for hedge funds managers with PSAHARA utility family.
problem Optimizing risk and reward in incomplete markets with non-monotone risk aversion and convex compensation.
method Introduced PSAHARA utility family to model non-monotone risk aversion and convex compensation. Proved concavification techniques for non-concave utility functions. Derived explicit optimal control strategy.
result PSAHARA utility induces risk-taking behavior even with convex compensation, leading to high returns and volatility.
The paper addresses human-like decision-making in multi-agent systems using bounded risk-sensitive Markov Games.
problem Modeling human-like decision-making in multi-agent systems with risk-seeking and loss-aversion behaviors.
method Forward policy design and inverse reward learning with iterative reasoning and cumulative prospect theory.
result The proposed algorithms demonstrate both risk-averse and risk-seeking behaviors in multi-agent systems.
The study examines insurance demand under rough volatility and path-dependent shocks.
problem Optimal insurance and investment strategies under rough volatility and path-dependent shocks.
method Rough volatility model and Hawkes process with power kernel, Functional Ito formula extension.
result Individuals demand more catastrophe insurance when path-dependent effects are considered.
Prospect theory is widely viewed as the best available descriptive model of how people evaluate risk in experimental settings. According to prospect theory, people are risk-averse with respect to gains and risk-seeking with respect to losses, a phenomenon called "loss aversion". Despite of the fact that prospect theory…
The vast majority of works on option pricing operate on the assumption of risk neutral valuation, and consequently focus on the expected value of option returns, and do not consider risk parameters, such as variance. We show that it is possible to give explicit formulae for the variance of European option returns (vani…
Study preferences over uncertain time payments, finds growth-optimality better than expected utility theory.
problem Understanding how people make decisions with uncertain timing of payments.
method Normative model of growth-optimality, revisiting experimental evidence on time lotteries.
result Growth-optimality better explains experimental data on time lotteries than expected discounted utility theory.
This study measures price risk aversion using indirect utility functions in a lab experiment.
problem Measuring risk aversion with uncertain prices in experimental economics.
method Using indirect utility functions and a multiple price list method in a lab experiment.
result Price risk aversion is statistically greater than payoff risk aversion.
Risk aversion in economic transactionscond-mat.stat-mech
Most people are risk-averse (risk-seeking) when they expect to gain (lose). Based on a generalization of ``expected utility theory'' which takes this into account, we introduce an automaton mimicking the dynamics of economic operations. Each operator is characterized by a parameter q which gauges people's attitude unde…
A new algorithm improves top- recommendation accuracy by considering item payoffs uncertainty.
problem Suboptimal performance in top- recommendation rankings due to varying item payoffs.
method Proposes a risk-seeking utility function for ranking items based on estimated preference scores.
result Risk-seeking ranking yields the best performance in top- recommendations.
Ineffective risk measures fail to control risky investor behavior in markets with arbitrage opportunities.
problem Ineffectiveness of coherent risk measures in managing risky investor behavior in markets with arbitrage opportunities.
method Analytical determination of -arbitrage portfolios and consideration of realistic numerical examples of incomplete markets.
result Expected shortfall constraints can be ineffective in realistic markets, but reasonable expected utility constraints are effective.
We introduce a representation theory for risk operations on locally compact groups in a partition of unity on a topological manifold for Markowitz-Tversky-Kahneman (MTK) reference points. We identify (1) risk torsion induced by the flip rate for risk averse and risk seeking behaviour, and (2) a structure constant or co…
The paper studies risk-sensitive learning schemes and provides learning bounds for empirical OCE minimizers.
problem Risk-sensitive learning aims to minimize risk-averse measures of loss.
method Proposes learning bounds for empirical OCE minimizers based on Rademacher average and variance.
result Provides two learning bounds on the performance of empirical OCE minimizers.
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Criticcs.LG
Improves RL generalization by minimizing adversarial risk.
problem Overfitting to training environments and poor generalization to unseen scenarios.
method Introduces minimax formulation and distributional framework to RL.
result Trained policy shows improved generalization to different environments.
We consider market players with tail-risk-seeking behaviour as exemplified by the S-shaped utility introduced by Kahneman and Tversky. We argue that risk measures such as value at risk (VaR) and expected shortfall (ES) are ineffective in constraining such players. We show that, in many standard market models, product d…
We solve an optimal consumption problem with habit formation constraints.
problem Maximizing utility with habit formation constraints.
method Formulated and solved a deterministic optimal consumption problem.
result Optimal consumption policies derived explicitly.
This work builds a hedging mechanism for experimental risk.
problem Risk of financial and statistical bankruptcy in experimentation.
method Game-theoretic statistics framework, capitalization of test martingale wealth process, Markowitz portfolio theory, hedging instrument.
result Investigator can hedge against the null hypothesis and avoid ruin.
This paper proposes a new clustering method based on Stochastic Dominance for asset allocation.
problem Traditional clustering methods fail to capture risk dominance relationships among assets.
method Integrates Stochastic Dominance theory with machine learning algorithms to construct a Stochastic Dominance Coefficient Matrix and modify clustering algorithms.
result The proposed method effectively facilitates customized asset allocation for investors.
New algorithms optimize risk in reinforcement learning with exponential utility.
problem Optimizing rewards under risk in reinforcement learning with unknown transition kernels.
method Two model-free algorithms: Risk-Sensitive Value Iteration (RSVI) and Risk-Sensitive Q-learning (RSQ).
result Proved near-optimal regret bounds for RSVI and RSQ.
This study analyzes mutual influence on investment strategies of financial market agents.
problem Mutual influence among agents in financial markets and its impact on investment strategies.
method Formulated optimal investment differential game problem, derived analytical solutions, proposed fast algorithm, and theoretically analyzed mutual influence.
result Agents' optimal strategies converge to the asymptotic strategy when mutual influence is strong and approaches infinity.
Investment strategies ensure wealth bounded away from zero in a competitive market.
problem Ensuring wealth bounded away from zero in a competitive investment market.
method Stochastic game-theoretic model with survival strategies.
result Survival strategies are asymptotically equivalent and allow faster wealth accumulation.
Paper proposes a new framework for combining investment strategies without market-specific assumptions.
problem Lack of a distribution-free and consistent preference framework for decision-making in combining investment strategies.
method Introduces a novel framework for decision-making in combining strategies, free from market conditions and statistical assumptions.
result Proposed strategies outperform individual component strategies in long-term wealth accumulation, with small tradeoffs in Sharpe ratios.
Study improves StarCraft bot's strategy selection with partial observations.
problem Selecting effective strategies in real-time strategy games with limited information.
method Utilized full game state information during training to predict opponent strategies.
result Substantial win rate improvements over a fixed-strategy baseline.
This paper introduces strategies to maximize arbitrage profits in decentralized exchanges.
problem Maximizing profits from arbitrage loops in decentralized exchanges.
method Three strategies: MaxPrice, MaxMax, and Convex Optimization.
result The Convex Optimization strategy yields the highest monetized arbitrage profit in theory and practice.
In this paper we propose an investing strategy based on neural network models combined with ideas from game-theoretic probability of Shafer and Vovk. Our proposed strategy uses parameter values of a neural network with the best performance until the previous round (trading day) for deciding the investment in the curren…
Automated strategies improve model adaptation efficiency.
problem Manual adaptation strategies are time-consuming and costly.
method Flexible adaptive mechanism deployment for automated adaptation strategies.
result Automated strategies achieve better or comparable performance.
New method selects best exploration strategies in uncertain environments.
problem Selecting optimal strategies in unknown, multi-strategy environments.
method Formulates Multi-Armed Bandits problem with diversity of effects as reward signal.
result Method outperforms fixed mixtures of strategies in diverse, challenging conditions.
Stratify unifies and improves multi-step forecasting strategies.
problem Lack of unified frameworks for multi-step forecasting strategies.
method Proposes Stratify, a parameterized framework for multi-step forecasting.
result Novel strategies in Stratify outperform existing ones in over 84% of experiments.
New trading strategies yield gains on average in various market scenarios.
problem Developing trading strategies that consistently yield positive gains in different market conditions.
method Introducing generalized statistical arbitrage concepts and profitable strategies based on information systems.
result Constructed profitable generalized strategies with good performance on simulated and real market data.