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.
Study examines how risk tolerance impacts long-term investment returns.
Model shows how heterogeneity in strategies and risk tolerance affects financial market stability.
ADGAN improves risk tolerance prediction by aligning cross-domain data.
We analyze a nonlinear equation proposed by F. Black (1968) for the optimal portfolio function in a log-normal model. We cast it in terms of the risk tolerance function and provide, for general utility functions, existence, uniqueness and regularity results, and we also examine various monotonicity, concavity/convexity…
We study a portfolio optimization problem for competitive agents with CRRA utilities and a common finite time horizon. The utility of an agent depends not only on her absolute wealth and consumption but also on her relative wealth and consumption when compared to the averages among the other agents. We derive a closed …
In complex systems like financial market, risk tolerance of individuals is crucial for system resilience.The single-security price limit, designed as risk tolerance to protect investors by avoiding sharp price fluctuation, is blamed for feeding market panic in times of crash.The relationship between the critical market…
We present turnpike-type results for the risk tolerance function in an incomplete market setting under time-monotone forward performance criteria. We show that, contrary to the classical case, the temporal and spatial limits do not coincide. We also show that they depend directly on the left- and right-end of the suppo…
Simplified NFT games discussed with methods for extracting value.
We maximize the expected utility from terminal wealth for an HARA investor when the market price of risk is an unobservable random variable. We compute the optimal portfolio explicitly and explore the effects of learning by comparing it with the corresponding myopic policy. In particular, we show that, for a market pri…
Algorithms optimize fair portfolios for diverse risk-tolerant consumers.
In the general framework of a semimartingale financial model and a utility function defined on the positive real line, we compute the first-order expansion of marginal utility-based prices with respect to a ``small'' number of random endowments. We show that this linear approximation has some important qualitative …
Study uses AI agents to improve equity portfolio management.
The study analyzes how large language models form and express investor risk profiles.
Study -player and mean-field games in Itô-diffusion markets with competitive or homophilous interactions.
Bayesian Robust Optimization for Imitation Learning (BROIL) balances risk and reward.
Proposes methods for online conformal prediction with nested prediction sets across multiple confidence levels.
We analyze a family of portfolio management problems under relative performance criteria, for fund managers having CARA or CRRA utilities and trading in a common investment horizon in log-normal markets. We construct explicit constant equilibrium strategies for both the finite population games and the corresponding mea…
We introduce a general decision tree framework to value an option to invest/divest in a project, focusing on the model risk inherent in the assumptions made by standard real option valuation methods. We examine how real option values depend on the dynamics of project value and investment costs, the frequency of exercis…
A new reinforcement learning framework separates users into risk-tolerant and risk-averse groups for better performance.
We study the sensitivity of the expected utility maximization problem in a continuous semi-martingale market with respect to small changes in the market price of risk. Assuming that the preferences of a rational economic agent are modeled with a general utility function, we obtain a second-order expansion of the value …
We consider thin incomplete financial markets, where traders with heterogeneous preferences and risk exposures have motive to behave strategically regarding the demand schedules they submit, thereby impacting prices and allocations. We argue that traders relatively more exposed to market risk tend to submit more elasti…
We consider an investor who seeks to maximize her expected utility derived from her terminal wealth relative to the maximum performance achieved over a fixed time horizon, and under a portfolio drawdown constraint, in a market with local stochastic volatility (LSV). In the absence of closed-form formulas for the value …
This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment. In this paper, the stock future prices (technical features) are predicted using Support Vector Regression. Thereafter, the predicted prices are used to recommend which portions of the budget an investor shou…
The multi-armed bandit (MAB) problem is a classical learning task that exemplifies the exploration-exploitation tradeoff. However, standard formulations do not take into account {\em risk}. In online decision making systems, risk is a primary concern. In this regard, the mean-variance risk measure is one of the most co…
Enhances traditional MV model for socially responsible investors.
The paper analyzes risk measures and optimal reserve allocation strategies.
Research funding agencies routinely use a proportion of their total revenues to support internal administration and marketing costs. The ratio of administration to total costs, referred to as the administration ratio, is highly variable and within any single fund depends on many factors including the number and average…
The large majority of risk-sharing transactions involve few agents, each of whom can heavily influence the structure and the prices of securities. This paper proposes a game where agents' strategic sets consist of all possible sharing securities and pricing kernels that are consistent with Arrow-Debreu sharing rules. F…
In recent years, the evaluation of the minimal investment risk of the quenched disordered system of a portfolio optimization problem and the investment concentration of the optimal portfolio has been actively investigated using the analysis methods of statistical mechanical informatics. However, the work to date has no…
Investors use various asset allocation strategies to meet financial goals.
Paper introduces Lambda EVaR, a new risk measure.
This paper studies the portfolio optimization problem when the investor's utility is general and the return and volatility of the risky asset are fast mean-reverting, which are important to capture the fast-time scale in the modeling of stock price volatility. Motivated by the heuristic derivation in [J.-P. Fouque, R. …
Study improves portfolio optimization for Indonesian banks using robust methods.
Adapts safe policies for exploration in high-risk settings.
Study optimal investment decisions for diverse risk-tolerant agents.
Financial advisors use KYC info but not client behaviours to guide investments.
The paper tackles risk-averse multi-armed bandit with linear payoffs.
End-to-end portfolio system accounts for model risk.
The paper optimizes reinsurance under uncertain dependence among insurers.
The paper optimizes risk-sensitive RL with CVaR, achieving near-minimax-optimal results.
Optimizes portfolio growth rate for a behavioral investor considering terminal relative growth rate.
Optimal reinsurance contracts designed for a continuum of risk types.
Recent research has documented a significant rise in the volatility (e.g., expected squared change) of individual incomes in the U.S. since the 1970s. Existing measures of this trend abstract from individual heterogeneity, effectively estimating an increase in average volatility. We decompose this increase in average v…
Paper analyzes how latency affects optimal order execution in markets.
Study risk sharing with Lambda VaR under diverse beliefs.
Proposes a method to choose thresholds for LLM evaluation metrics.
A framework identifies worst-case decision points in safety-critical scenarios, improving risk assessment by 10 hours.
Enhances robo-advisors with client investment preference inference.