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.
Optimal option portfolios under Sharpe Ratio maximization with skew-elliptical t-distributed returns
Curriculum learning has been successfully used in reinforcement learning to accelerate the learning process, through knowledge transfer between tasks of increasing complexity. Critical tasks, in which suboptimal exploratory actions must be minimized, can benefit from curriculum learning, and its ability to shape explor…
The paper proposes a new approach to portfolio selection that maximizes diversification and return.
Batch Reinforcement Learning (RL) algorithms attempt to choose a policy from a designer-provided class of policies given a fixed set of training data. Choosing the policy which maximizes an estimate of return often leads to over-fitting when only limited data is available, due to the size of the policy class in relatio…
Maximizes stock portfolio predictability using machine learning.
MILLION framework optimizes portfolio risk and return efficiently.
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…
This paper explores portfolio management strategies to maximize alpha and minimize beta.
When trading incurs proportional costs, leverage can scale an asset's return only up to a maximum multiple, which is sensitive to its volatility and liquidity. In a model with one safe and one risky asset, with constant investment opportunities and proportional costs, we find strategies that maximize long term returns …
CV outperforms mean-variance for stock returns, minimizing risk and maximizing growth.
The signal-noise ratio of a portfolio of p assets, its expected return divided by its risk, is couched as an estimation problem on the sphere. When the portfolio is built using noisy data, the expected value of the signal-noise ratio is bounded from above via a Cramer-Rao bound, for the case of Gaussian returns. The bo…
We consider an investor, whose portfolio consists of a single risky asset and a risk free asset, who wants to maximize his expected utility of the portfolio subject to the Value at Risk assuming a heavy tail distribution of the stock prices return. We use Markov Decision Process and dynamic programming principle to get…
We provide an economic interpretation of the practice consisting in incorporating risk measures as constraints in a classic expected return maximization problem. For what we call the infimum of expectations class of risk measures, we show that if the decision maker (DM) maximizes the expectation of a random return unde…
The thesis models financial returns using mixtures of generalized normal distributions.
MACE optimizes stock portfolios for maximal predictability.
In modern portfolio theory, the balancing of expected returns on investments against uncertainties in those returns is aided by the use of utility functions. The Kelly criterion offers another approach, rooted in information theory, that always implies logarithmic utility. The two approaches seem incompatible, too loos…
We investigate the planar maximally filtered graphs of the portfolio of the 300 most capitalized stocks traded at the New York Stock Exchange during the time period 2001-2003. Topological properties such as the average length of shortest paths, the betweenness and the degree are computed on different planar maximally f…
GA-MSSR optimizes forex trading rules for higher returns and reduced risk.
We study Spectral Measures of Risk from the perspective of portfolio optimization. We derive exact results which extend to general Spectral Measures M_phi the Pflug--Rockafellar--Uryasev methodology for the minimization of alpha--Expected Shortfall. The minimization problem of a spectral measure is shown to be equivale…
Limited liability reduces leveraged risk in loan portfolio management models.
In the present paper, the primal-dual problem consisting of the investment risk minimization problem and the expected return maximization problem in the mean-variance model is discussed using replica analysis. As a natural extension of the investment risk minimization problem under only a budget constraint that we anal…
New RL formulation for maximizing maximum reward in molecule generation.
The study finds that maximizing median returns is the only viable strategy in portfolio selection.
The paper proposes an asset allocation strategy using the Sortino ratio for better performance.
The paper tackles budget allocation for multiple campaigns using a novel combinatorial bandit approach.
The paper optimizes portfolios in a market with hidden drift and random expert opinions.
We consider a general discrete-time financial market with proportional transaction costs as in [Kabanov, Stricker and Rásonyi Finance and Stochastics 7 (2003) 403--411] and [Schachermayer Math. Finance 14 (2004) 19--48]. In addition to the usual investment in financial assets, we assume that the agents can invest part …
Optimizes portfolios with GM returns using convex optimization.
Framework trains safe agents avoiding deceptive behavior.
Extends Kelly Criterion to more complex betting scenarios.
We consider a popular model of microeconomics with countably many assets: the Arbitrage Pricing Model. We study the problem of optimal investment under an expected utility criterion and look for conditions ensuring the existence of optimal strategies. Previous results required a certain restrictive hypothesis on the ta…
Paper proposes a method to solve log-optimal portfolios under ambiguous return distributions.
Being able to predict the occurrence of extreme returns is important in financial risk management. Using the distribution of recurrence intervals---the waiting time between consecutive extremes---we show that these extreme returns are predictable on the short term. Examining a range of different types of returns and th…
Algorithm improves recommendation subset selection in the presence of biases.
The Kelly rule fails to maximize growth in a time-changed return setting.
Proposes a new model to maximize out-of-sample Sharpe ratios by forecasting tangency portfolios.
Develops a model for analyzing cryptocurrency returns focusing on extreme values.
The paper solves a portfolio selection problem in incomplete markets by balancing utility and risk.
Study optimal trading strategies with expert signals in a hidden Gaussian drift market.
The paper studies optimal investment using acceptability indices to maximize portfolio performance.
Closed-form optimal portfolios for exponential utility in small/large markets.
Paper uses Bayesian optimization to find best Supertrend indicator settings.
Metaheuristics optimize portfolios with pre-assignment and margin trading for better risk-adjusted returns.
Aims to create safe reinforcement learning policies by considering individual harm.
We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph which describes a set of subtasks and their dependencies that are unknown to the agent. The agent needs to quickly adapt to the task over few episodes during adaptation phase to maximize the return in the test phase. In…
In this paper, we study a certain class of online optimization problems, where the goal is to maximize a function that is not necessarily concave and satisfies the Diminishing Returns (DR) property under budget constraints. We analyze a primal-dual algorithm, called the Generalized Sequential algorithm, and we obtain t…
Dynamic trading strategies, in the spirit of trend-following or mean-reversion, represent an only partly understood but lucrative and pervasive area of modern finance. Assuming Gaussian returns and Gaussian dynamic weights or signals, (e.g., linear filters of past returns, such as simple moving averages, exponential we…
Noisy Max and Sparse Vector are selection algorithms for differential privacy and serve as building blocks for more complex algorithms. In this paper we show that both algorithms can release additional information for free (i.e., at no additional privacy cost). Noisy Max is used to return the approximate maximizer amon…