[1] Approximating Auction Equilibria with Reinforcement Learning用强化学习近似拍卖均衡来源:ARXIV_20241021[2] Reinforcement Learning in Non Markov Market Making非马尔可夫做市中的强化学习来源:ARXIV_20241021[3] Hierarchical Reinforced Trader (HRT)分层强化交易者（HRT）来源:ARXIV_20241022[4] LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand ForecastingLTPNet集成深度学习和环境决策支持系统用于可再生能源需求预测来源:ARXIV_20241022[5] Conformal Predictive Portfolio Selection一致性预测投资组合选择来源:ARXIV_20241023[6] Inferring Option Movements Through Residual Transactions通过剩余交易推断期权变动来源:ARXIV_20241023[7] Dynamic graph neural networks for enhanced volatility prediction in financial markets用于增强金融市场波动预测的动态图神经网络来源:ARXIV_20241023[8] Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading股票收益预测和投资组合交易中进化RNN的神经进化神经结构搜索来源:ARXIV_20241023[9] Evolution with Opponent Learning Awareness具有对手学习意识的进化来源:ARXIV_20241024[10] Predicting Company Growth by Econophysics informed Machine Learning通过基于经济物理学的机器学习预测公司增长来源:ARXIV_20241024[11] The Impact of Rating Announcements on Stock Returns: A Nonlinear Assessment评级公告对股票回报的影响：一个非线性评估来源:SSRN_20241025
[1] Approximating Auction Equilibria with Reinforcement Learning
标题:用强化学习近似拍卖均衡作者:Pranjal Rawat来源:ARXIV_20241021Abstract : Traditional methods for computing equilibria in auctions become computationally intractable as auction complexity increases, particularly in multi item and dynamic auctions. This paper introduces a self play based reinforcement learning approach that employs advanced algorithms such as Proximal Policy Optimization and Neural Fictitious Self Play to approximate Bayes Nash equilibria. This framework allows for continuous action spaces, high dimensional information states,......(摘要翻译及全文见知识星球)Keywords :
[2] Reinforcement Learning in Non Markov Market Making
标题:非马尔可夫做市中的强化学习作者:Luca Lalor, Anatoliy Swishchuk来源:ARXIV_20241021Abstract : We develop a deep reinforcement learning (RL) framework for an optimal market making (MM) trading problem, specifically focusing on price processes with semi Markov and Hawkes Jump Diffusion dynamics. We begin by discussing the basics of RL and the deep RL framework used, where we deployed the state of the art Soft Actor Critic (SAC) algorithm for the deep learning part.......(摘要翻译及全文见知识星球)Keywords :
[3] Hierarchical Reinforced Trader (HRT)
标题:分层强化交易者（HRT）作者:Zijie Zhao, Roy E. Welsch来源:ARXIV_20241022Abstract : Leveraging Deep Reinforcement Learning (DRL) in automated stock trading has shown promising results, yet its application faces significant challenges, including the curse of dimensionality, inertia in trading actions, and insufficient portfolio diversification. Addressing these challenges, we introduce the Hierarchical Reinforced Trader (HRT), a novel trading strategy employing a bi level Hierarchical Reinforcement Learning framework. The HRT integrates a Proximal Policy Optimization......(摘要翻译及全文见知识星球)Keywords :
[4] LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting
标题:LTPNet集成深度学习和环境决策支持系统用于可再生能源需求预测作者:Te Li, Mengze Zhang, Yan Zhou来源:ARXIV_20241022Abstract : Against the backdrop of increasingly severe global environmental changes, accurately predicting and meeting renewable energy demands has become a key challenge for sustainable business development. Traditional energy demand forecasting methods often struggle with complex data processing and low prediction accuracy. To address these issues, this paper introduces a novel approach that combines deep learning techniques with environmental decision support systems. The......(摘要翻译及全文见知识星球)Keywords :
[5] Conformal Predictive Portfolio Selection
标题:一致性预测投资组合选择作者:Masahiro Kato来源:ARXIV_20241023Abstract : This study explores portfolio selection using predictive models for portfolio returns. Portfolio selection is a fundamental task in finance, and various methods have been developed to achieve this goal. For example, the mean variance approach constructs portfolios by balancing the trade off between the mean and variance of asset returns, while the quantile based approach optimizes portfolios by accounting for tail......(摘要翻译及全文见知识星球)Keywords :
[6] Inferring Option Movements Through Residual Transactions
标题:通过剩余交易推断期权变动作者:Carl von Havighorst, Vincil Bishop III来源:ARXIV_20241023Abstract : This research presents a novel approach to predicting option movements by analyzing residual transactions, which are trades that deviate from standard hedging activities. Unlike traditional methods that primarily focus on open interest and trading volume, this study argues that residuals can reveal nuanced insights into institutional sentiment and strategic positioning. By examining these deviations, the model identifies early indicators of market......(摘要翻译及全文见知识星球)Keywords :
[7] Dynamic graph neural networks for enhanced volatility prediction in financial markets
标题:用于增强金融市场波动预测的动态图神经网络作者:Pulikandala Nithish Kumar, Nneka Umeorah, Alex Alochukwu来源:ARXIV_20241023Abstract : Volatility forecasting is essential for risk management and decision making in financial markets. Traditional models like Generalized Autoregressive Conditional Heteroskedasticity (GARCH) effectively capture volatility clustering but often fail to model complex, non linear interdependencies between multiple indices. This paper proposes a novel approach using Graph Neural Networks (GNNs) to represent global financial markets as dynamic graphs. The Temporal Graph Attention Network......(摘要翻译及全文见知识星球)Keywords :
[8] Neuroevolution Neural Architecture Search for Evolving RNNs in Stock Return Prediction and Portfolio Trading
标题:股票收益预测和投资组合交易中进化RNN的神经进化神经结构搜索作者:Zimeng Lyu, Amulya Saxena, Rohaan Nadeem, Hao Zhang, Travis Desell来源:ARXIV_20241023Abstract : Stock return forecasting is a major component of numerous finance applications. Predicted stock returns can be incorporated into portfolio trading algorithms to make informed buy or sell decisions which can optimize returns. In such portfolio trading applications, the predictive performance of a time series forecasting model is crucial. In this work, we propose the use of the Evolutionary eXploration of Augmenting......(摘要翻译及全文见知识星球)Keywords :
[9] Evolution with Opponent Learning Awareness
标题:具有对手学习意识的进化作者:Yann Bouteiller, Karthik Soma, Giovanni Beltrame来源:ARXIV_20241024Abstract : The universe involves many independent co learning agents as an ever evolving part of our observed environment. Yet, in practice, Multi Agent Reinforcement Learning (MARL) applications are usually constrained to small, homogeneous populations and remain computationally intensive. In this paper, we study how large heterogeneous populations of learning agents evolve in normal form games. We show how, under assumptions commonly made......(摘要翻译及全文见知识星球)Keywords :
[10] Predicting Company Growth by Econophysics informed Machine Learning
标题:通过基于经济物理学的机器学习预测公司增长作者:Ruyi Tao, Kaiwei Liu, Xu Jing, Jiang Zhang来源:ARXIV_20241024Abstract : Predicting company growth is crucial for strategic adjustment, operational decision making, risk assessment, and loan eligibility reviews. Traditional models for company growth often focus too much on theory, overlooking practical forecasting, or they rely solely on time series forecasting techniques, ignoring interpretability and the inherent mechanisms of company growth. In this paper, we propose a machine learning based prediction framework that......(摘要翻译及全文见知识星球)Keywords :
[11] The Impact of Rating Announcements on Stock Returns: A Nonlinear Assessment
标题:评级公告对股票回报的影响：一个非线性评估作者:Marco Corazza,Giacomo di Tollo,Gianni Filograsso来源:SSRN_20241025Abstract : This study proposes a novel machine learning-based approach to assess the potentially nonlinear impact of credit announcements. In the first step a statistical test is performed to evaluate whether the mean returns before and after the announcement are different, then in the second step we predict whether an actual variation of mean returns takes place. Based on an international dataset of credit......(摘要翻译及全文见知识星球)Keywords : Abnormal returns, Event study, machine learning, DAX, FTSE100, NIKKEI225.
