Zobrazeno 1 - 10
of 900
pro vyhledávání: '"MULVEY, JOHN"'
Autor:
Shu, Yizhan, Mulvey, John M.
This article explores dynamic factor allocation by analyzing the cyclical performance of factors through regime analysis. The authors focus on a U.S. equity investment universe comprising seven long-only indices representing the market and six style
Externí odkaz:
http://arxiv.org/abs/2410.14841
Autor:
Nie, Yuqi, Kong, Yaxuan, Dong, Xiaowen, Mulvey, John M., Poor, H. Vincent, Wen, Qingsong, Zohren, Stefan
Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. These models have demonstrated remarkable capabilities in understanding context, processing vast amounts of d
Externí odkaz:
http://arxiv.org/abs/2406.11903
This article introduces a novel hybrid regime identification-forecasting framework designed to enhance multi-asset portfolio construction by integrating asset-specific regime forecasts. Unlike traditional approaches that focus on broad economic regim
Externí odkaz:
http://arxiv.org/abs/2406.09578
This article investigates a regime-switching investment strategy aimed at mitigating downside risk by reducing market exposure during anticipated unfavorable market regimes. We highlight the statistical jump model (JM) for market regime identificatio
Externí odkaz:
http://arxiv.org/abs/2402.05272
The criticality of prompt and precise traffic forecasting in optimizing traffic flow management in Intelligent Transportation Systems (ITS) has drawn substantial scholarly focus. Spatio-Temporal Graph Neural Networks (STGNNs) have been lauded for the
Externí odkaz:
http://arxiv.org/abs/2308.07496
Autor:
Li, Xiaoyue, Mulvey, John M.
Optimal execution of a portfolio have been a challenging problem for institutional investors. Traders face the trade-off between average trading price and uncertainty, and traditional methods suffer from the curse of dimensionality. Here, we propose
Externí odkaz:
http://arxiv.org/abs/2306.08809
This paper introduces the MCTS algorithm to the financial world and focuses on solving significant multi-period financial planning models by combining a Monte Carlo Tree Search algorithm with a deep neural network. The MCTS provides an advanced start
Externí odkaz:
http://arxiv.org/abs/2202.07734
Portfolio optimization has been a central problem in finance, often approached with two steps: calibrating the parameters and then solving an optimization problem. Yet, the two-step procedure sometimes encounter the "error maximization" problem where
Externí odkaz:
http://arxiv.org/abs/2107.04636
We propose a novel reinforcement learning based framework PoBRL for solving multi-document summarization. PoBRL jointly optimizes over the following three objectives necessary for a high-quality summary: importance, relevance, and length. Our strateg
Externí odkaz:
http://arxiv.org/abs/2105.08244
We employ model predictive control for a multi-period portfolio optimization problem. In addition to the mean-variance objective, we construct a portfolio whose allocation is given by model predictive control with a risk-parity objective, and provide
Externí odkaz:
http://arxiv.org/abs/2103.10813