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We develop a general framework for applying the Kelly criterion to stock markets. By supplying an arbitrary probability distribution modeling the future price movement of a set of stocks, the Kelly fraction for investing each stock can be…

Portfolio Management · Quantitative Finance 2018-08-21 Tim Byrnes , Tristan Barnett

The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an…

Portfolio Management · Quantitative Finance 2024-06-18 Adam Korniejczuk , Robert Ślepaczuk

In this paper we extend the stability results of [4]}. Our utility maximization problem is defined as an essential supremum of conditional expectations of the terminal values of wealth processes, conditioned on the filtration at the…

Portfolio Management · Quantitative Finance 2011-03-28 Erhan Bayraktar , Ross Kravitz

We study the finite mutually exclusive outcome version of risk-constrained Kelly optimization with explicit state prices. The market has outcome probabilities $p_i>0$, state prices $q_i>0$, terminal wealths $W_i=c+x_i/q_i$, and a…

Optimization and Control · Mathematics 2026-04-14 Christopher D. Long

Value iteration is a popular algorithm for finding near optimal policies for POMDPs. It is inefficient due to the need to account for the entire belief space, which necessitates the solution of large numbers of linear programs. In this…

Artificial Intelligence · Computer Science 2011-07-04 N. L. Zhang , W. Zhang

We propose a logical framework combining a game-theoretic study of abilities of agents to achieve quantitative objectives in multi-player games by optimizing payoffs or preferences on outcomes with a logical analysis of the abilities of…

Logic in Computer Science · Computer Science 2013-03-05 Nils Bulling , Valentin Goranko

We consider the problem of helping agents improve by setting short-term goals. Given a set of target skill levels, we assume each agent will try to improve from their initial skill level to the closest target level within reach or do…

Computer Science and Game Theory · Computer Science 2022-03-02 Saba Ahmadi , Hedyeh Beyhaghi , Avrim Blum , Keziah Naggita

The accumulation of individual fitness or wealth is modelled as a population game in which pairs of individuals are recurrently and randomly matched to play a game over a resource. In addition, all individuals have random access to a…

Physics and Society · Physics 2021-08-13 Sylvain Gibaud , Jorgen W. Weibull

Ergodicity economics is a new branch of economic theory that notes the conceptual difference between time averages and expectation values, which coincide only for ergodic observables. It postulates that individual agents maximise the time…

Economics · Quantitative Finance 2021-03-02 Ole Peters , Alexander Adamou

In a financial market model, we consider variations of the problem of minimizing the expected time to upcross a certain wealth level. For exponential Levy markets, we show the asymptotic optimality of the growth-optimal portfolio for the…

Portfolio Management · Quantitative Finance 2009-04-14 Constantinos Kardaras , Eckhard Platen

This paper studies a two-person trading game in continuous time that generalizes Garivaltis (2018) to allow for stock prices that both jump and diffuse. Analogous to Bell and Cover (1988) in discrete time, the players start by choosing fair…

General Economics · Economics 2022-10-24 Alex Garivaltis

The paper discusses capital allocation using the Euler formula and focuses on the risk measures Value-at-Risk (VaR) and Expected shortfall (ES). Some new results connected to this capital allocation is known. Two examples illustrate that…

Risk Management · Quantitative Finance 2024-05-02 Lars Holden

In this work, a machine learning approach is developed for predicting the outcomes of football matches. The novelty of this research lies in the utilisation of the Kelly Index to first classify matches into categories where each one denotes…

Machine Learning · Computer Science 2022-11-30 Yiming Ren , Teo Susnjak

Performance analysis of all kinds of randomised search heuristics is a rapidly growing and developing field. Run time and solution quality are two popular measures of the performance of these algorithms. The focus of this paper is on the…

Neural and Evolutionary Computing · Computer Science 2019-11-11 Jun He , Thomas Jansen , Christine Zarges

We consider a multi-agent resource allocation setting that models the assignment of papers to reviewers. A recurring issue in allocation problems is the compatibility of welfare/efficiency and fairness. Given an oracle to find a…

Computer Science and Game Theory · Computer Science 2019-08-02 Haris Aziz , Xin Huang , Nicholas Mattei , Erel Segal-Halevi

We introduce the problem of ranking with slot constraints, which can be used to model a wide range of application problems -- from college admission with limited slots for different majors, to composing a stratified cohort of eligible…

Information Retrieval · Computer Science 2023-10-30 Wentao Guo , Andrew Wang , Bradon Thymes , Thorsten Joachims

Many high-stakes AI deployments proceed only if every stakeholder deems the system acceptable relative to their own minimum standard. With randomization over a finite menu of options, this becomes a feasibility question: does there exist a…

Computer Science and Game Theory · Computer Science 2026-04-21 Davin Choo , Paul W. Goldberg , Nicholas Teh

Commercial entries, such as hotels, are ranked according to score by a search engine or recommendation system, and the score of each can be improved upon by making a targeted investment, e.g., advertising. We study the problem of how a…

Computer Science and Game Theory · Computer Science 2022-03-29 Amir Ban , Moshe Tennenholtz

When testing a statistical hypothesis, is it legitimate to deliberate on the basis of initial data about whether and how to collect further data? Game-theoretic probability's fundamental principle for testing by betting says yes, provided…

Methodology · Statistics 2023-08-30 Glenn Shafer

We study time-inconsistent recursive stochastic control problems, i.e., for which the Bellman principle of optimality does not hold. For this class of problems classical optimal controls may fail to exist, or to be relevant in practice, and…

Optimization and Control · Mathematics 2024-03-14 Elisa Mastrogiacomo , Marco Tarsia