arXiv research

Examines optimal risk sharing with realistic risk attitudes, finding risk seeking in certain subdomains.

The paper studies risk-sharing allocations for risk-seeking agents using a common distortion risk measure.

RiskMiner discovers formulaic alphas using MCTS for better performance.

Optimal risk sharing found for heterogeneous risk attitudes using distortion risk measures.

Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of $\textit{symbolic regression}$. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. …

Are cryptocurrency traders driven by a desire to invest in a new asset class to diversify their portfolio or are they merely seeking to increase their levels of risk? To answer this question, we use individual-level brokerage data and study their behavior in stock trading around the time they engage in their first cryp…

We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, $x(t)$, and at each time step invest a particular fraction, $q(t)$, of their budget. The return on investment (RoI), $r(t)$, is characterized by a periodic function with different types and leve…

Study risk sharing among agents with varying risk preferences.

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.…

Unified formula for optimal portfolio under piecewise hyperbolic risk aversion.

Study risk-sensitive reinforcement learning with entropic risk measures and generative models.

The paper addresses human-like decision-making in multi-agent systems using bounded risk-sensitive Markov Games.

Optimizes portfolios with costs, showing existence of optimal strategies.

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.

This study measures price risk aversion using indirect utility functions in a lab experiment.

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…

Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items. In practical settings, a ranked list of top-$k$ items from the estimated preference order is recommended to the end user in the decreasing …

A speculative agent with Prospect Theory preference chooses the optimal time to purchase and then to sell an indivisible risky asset to maximize the expected utility of the round-trip profit net of transaction costs. The optimization problem is formulated as a sequential optimal stopping problem and we provide a comple…

We show that coherent risk measures are ineffective in curbing the behaviour of investors with limited liability or excessive tail-risk seeking behaviour if the market admits statistical arbitrage opportunities which we term $ρ$-arbitrage for a risk measure $ρ$. We show how to determine analytically whether such $ρ$-ar…

The paper proposes a dynamic risk measure approach for evaluating defined-contribution pension funds.

Study uses RL to hedge financial derivatives, showing robust strategies outperform non-robust ones.

In reinforcement learning the Q-values summarize the expected future rewards that the agent will attain. However, they cannot capture the epistemic uncertainty about those rewards. In this work we derive a new Bellman operator with associated fixed point we call the `knowledge values'. These K-values compress both the …

Dynamic risk constraints help limit risky behavior in financial portfolios.

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.

When multiple agents learn in a decentralized manner, the environment appears non-stationary from the perspective of an individual agent due to the exploration and learning of the other agents. Recently proposed deep multi-agent reinforcement learning methods have tried to mitigate this non-stationarity by attempting t…

Reinforcement learning (RL) has achieved remarkable performance in numerous sequential decision making and control tasks. However, a common problem is that learned nearly optimal policy always overfits to the training environment and may not be extended to situations never encountered during training. For practical app…

The study examines insurance demand under rough volatility and path-dependent shocks.

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…

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…

Optimal trading strategy in Proof-of-Stake blockchain using continuous-time control.

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…

Study optimal control strategy for hedge funds managers with PSAHARA utility family.

We solve an optimal consumption problem with habit formation constraints.

This work builds a hedging mechanism for experimental risk.

This paper proposes a new clustering method based on Stochastic Dominance for asset allocation.

New algorithms optimize risk in reinforcement learning with exponential utility.