[1] Portfolio Management using Deep Reinforcement Learning使用深度强化学习的投资组合管理来源:ARXIV_20240506[2] Explainable Risk Classification in Financial Reports财务报告中可解释的风险分类来源:ARXIV_20240506[3] Simulating the economic impact of rationality through reinforcement learning and agent based modelling通过强化学习和基于代理的建模模拟理性的经济影响来源:ARXIV_20240506[4] Modelling Opaque Bilateral Market Dynamics in Financial Trading金融交易中不透明的双边市场动态建模来源:ARXIV_20240507[5] Policy Gradient for Online Pricing在线定价的政策梯度来源:ARXIV_20240507
[1] Portfolio Management using Deep Reinforcement Learning
标题:使用深度强化学习的投资组合管理作者:Ashish Anil Pawar, Vishnureddy Prashant Muskawar, Ritesh Tiku来源:ARXIV_20240506Abstract : Algorithmic trading or Financial robots have been conquering the stock markets with their ability to fathom complex statistical trading strategies. But with the recent development of deep learning technologies, these strategies are becoming impotent. The DQN and A2C models have previously outperformed eminent humans in game playing and robotics. In our work, we propose a reinforced portfolio manager offering assistance in......(摘要翻译及全文见知识星球)Keywords :
[2] Explainable Risk Classification in Financial Reports
标题:财务报告中可解释的风险分类作者:Xue Wen Tan, Stanley Kok来源:ARXIV_20240506Abstract : Every publicly traded company in the US is required to file an annual 10 K financial report, which contains a wealth of information about the company. In this paper, we propose an explainable deep learning model, called FinBERT XRC, that takes a 10 K report as input, and automatically assesses the post event return volatility risk of its associated company. In......(摘要翻译及全文见知识星球)Keywords :
[3] Simulating the economic impact of rationality through reinforcement learning and agent based modelling
标题:通过强化学习和基于代理的建模模拟理性的经济影响作者:Simone Brusatin, Tommaso Padoan, Andrea Coletta, Domenico Delli Gatti, Aldo Glielmo来源:ARXIV_20240506Abstract : Agent based models (ABMs) are simulation models used in economics to overcome some of the limitations of traditional frameworks based on general equilibrium assumptions. However, agents within an ABM follow predetermined, not fully rational, behavioural rules which can be cumbersome to design and difficult to justify. Here we leverage multi agent reinforcement learning (RL) to expand the capabilities of ABMs with......(摘要翻译及全文见知识星球)Keywords :
[4] Modelling Opaque Bilateral Market Dynamics in Financial Trading
标题:金融交易中不透明的双边市场动态建模作者:Alicia Vidler, Toby Walsh来源:ARXIV_20240507Abstract : Exploring complex adaptive financial trading environments through multi agent based simulation methods presents an innovative approach within the realm of quantitative finance. Despite the dominance of multi agent reinforcement learning approaches in financial markets with observable data, there exists a set of systematically significant financial markets that pose challenges due to their partial or obscured data availability. We, therefore, devise a......(摘要翻译及全文见知识星球)Keywords :
[5] Policy Gradient for Online Pricing
标题:在线定价的政策梯度作者:Lukasz Szpruch, Tanut Treetanthiploet, Yufei Zhang来源:ARXIV_20240507Abstract : Combining model based and model free reinforcement learning approaches, this paper proposes and analyzes an epsilon policy gradient algorithm for the online pricing learning task. The algorithm extends epsilon greedy algorithm by replacing greedy exploitation with gradient descent step and facilitates learning via model inference. We optimize the regret of the proposed algorithm by quantifying......(摘要翻译及全文见知识星球)Keywords :
