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Related papers: Non-Stationary Dividend-Price Ratios

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Discrimination between non-stationarity and long-range dependency is a difficult and long-standing issue in modelling financial time series. This paper uses an adaptive spectral technique which jointly models the non-stationarity and…

Statistical Finance · Quantitative Finance 2019-02-12 Nick James , Roman Marchant , Richard Gerlach , Sally Cripps

Sample average approximation--based stochastic dynamic programming (SDP) and model predictive control (MPC) are two different methods for approaching multistage stochastic optimization. In this paper we investigate the conditions under…

Optimization and Control · Mathematics 2026-02-10 Dominic S. T. Keehan , Andrew B. Philpott , Edward J. Anderson

Non-stationary environments are challenging for reinforcement learning algorithms. If the state transition and/or reward functions change based on latent factors, the agent is effectively tasked with optimizing a behavior that maximizes…

Machine Learning · Computer Science 2021-05-21 Lucas N. Alegre , Ana L. C. Bazzan , Bruno C. da Silva

We study episodic reinforcement learning in non-stationary linear (a.k.a. low-rank) Markov Decision Processes (MDPs), i.e, both the reward and transition kernel are linear with respect to a given feature map and are allowed to evolve either…

Machine Learning · Computer Science 2021-12-28 Ahmed Touati , Pascal Vincent

Linear mixed models (LMMs) are used as an important tool in the data analysis of repeated measures and longitudinal studies. The most common form of LMMs utilize a normal distribution to model the random effects. Such assumptions can often…

Methodology · Statistics 2016-02-16 Hien D. Nguyen , Geoffrey J. McLachlan

We generalize the classic Shiller cyclically adjusted price-earnings ratio (CAPE) used for prediction of future total returns of the stock market. We treat earnings growth as exogenous. The difference between log wealth and log earnings is…

Statistical Finance · Quantitative Finance 2025-03-13 Andrey Sarantsev

This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing…

Computational Finance · Quantitative Finance 2018-10-19 Ryan Ferguson , Andrew Green

We study the set of marginal utility-based prices of a financial derivative in the case where the investor has a non-replicable random endowment. We provide an example showing that even in the simplest of settings - such as Samuelson's…

Mathematical Finance · Quantitative Finance 2018-08-17 Kasper Larsen , Halil Mete Soner , Gordan Žitković

Reinforcement learning (RL) for exponential-utility optimization in discounted Markov decision processes (MDPs) lacks principled value-based algorithms. We address this gap in the fixed risk-aversion setting. Building on the Bellman-type…

Machine Learning · Computer Science 2026-05-11 Gugan Thoppe , L. A. Prashanth , Ankur Naskar , Sanjay Bhat

We consider the robust utility maximization using a static holding in derivatives and a dynamic holding in the stock. There is no fixed model for the price of the stock but we consider a set of probability measures (models) which are not…

Probability · Mathematics 2013-07-19 Erhan Bayraktar , Zhou Zhou

We study the risk performance of distributed learning for the regularization empirical risk minimization with fast convergence rate, substantially improving the error analysis of the existing divide-and-conquer based distributed learning.…

Machine Learning · Computer Science 2019-01-21 Yong Liu , Jian Li , Weiping Wang

This paper introduced key aspects of applying Machine Learning (ML) models, improved trading strategies, and the Quasi-Reversibility Method (QRM) to optimize stock option forecasting and trading results. It presented the findings of the…

Computational Finance · Quantitative Finance 2022-11-30 Zheng Cao , Raymond Guo , Wenyu Du , Jiayi Gao , Kirill V. Golubnichiy

Adopting a probabilistic approach we determine the optimal dividend payout policy of a firm whose surplus process follows a controlled arithmetic Brownian motion and whose cash-flows are discounted at a stochastic dynamic rate. Dividends…

Optimization and Control · Mathematics 2021-06-22 Elena Bandini , Tiziano De Angelis , Giorgio Ferrari , Fausto Gozzi

This study proposes a portfolio optimization framework that integrates advanced deep learning architectures with traditional financial models to enhance risk-adjusted performance. Using historical data from 2015-2023 across equities, ETFs,…

Computational Engineering, Finance, and Science · Computer Science 2026-04-28 Samuel Ozechi , Banjo Francis , Wisdom Yakanu , Joe Wayne Byers

This paper considers the optimal dividend payment problem in piecewise-deterministic compound Poisson risk models. The objective is to maximize the expected discounted dividend payout up to the time of ruin. We provide a comparative study…

Optimization and Control · Mathematics 2016-08-02 Runhuan Feng , Hans Volkmer , Shuaiqi Zhang , Chao Zhu

Statistical inference for high dimensional parameters (HDPs) can be based on their intrinsic correlation; that is, parameters that are close spatially or temporally tend to have more similar values. This is why nonlinear mixed-effects…

Methodology · Statistics 2024-01-30 Nan Zheng , Noel Cadigan

We consider off-policy policy evaluation with function approximation (FA) in average-reward MDPs, where the goal is to estimate both the reward rate and the differential value function. For this problem, bootstrapping is necessary and,…

Machine Learning · Computer Science 2022-10-19 Shangtong Zhang , Yi Wan , Richard S. Sutton , Shimon Whiteson

This paper provides an empirical study explores the application of deep learning algorithms-Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-in constructing long-short stock…

Statistical Finance · Quantitative Finance 2024-11-26 Junjie Guo

We study continuous-time portfolio selection under monotone mean-variance (MMV) preferences in a jump-diffusion model, presenting an explicit solution different from that under classical mean-variance (MV) preferences in dynamic settings…

Mathematical Finance · Quantitative Finance 2024-05-14 Yuchen Li , Zongxia Liang , Shunzhi Pang

We analyze the \textit{Large Deviation Probability (LDP)} of linear factor models generated from non-identically distributed components with \textit{regularly-varying} tails, a large subclass of heavy tailed distributions. An efficient…

Statistics Theory · Mathematics 2019-12-10 Farzad Pourbabaee , Omid Shams Solari