[1] Machine Learning Methods for Pricing Financial Derivatives金融衍生品定价的机器学习方法来源:ARXIV_20240604[2] Distributional Refinement Network分布式精炼网络来源:ARXIV_20240604[3] Statistics Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance数据科学与金融中基于最大熵原理的统计参数化量子电路来源:ARXIV_20240604[4] How Inductive Bias in Machine Learning Aligns with Optimality in Economic Dynamics机器学习中的归纳偏差如何与经济动力学中的最优性相一致来源:ARXIV_20240605[5] Algorithmic Collusion in Dynamic Pricing with Deep Reinforcement Learning深度强化学习动态定价中的算法共谋来源:ARXIV_20240605
[1] Machine Learning Methods for Pricing Financial Derivatives
标题:金融衍生品定价的机器学习方法作者:Lei Fan, Justin Sirignano来源:ARXIV_20240604Abstract : Stochastic differential equation (SDE) models are the foundation for pricing and hedging financial derivatives. The drift and volatility functions in SDE models are typically chosen to be algebraic functions with a small number (less than 5) parameters which can be calibrated to market data. A more flexible approach is to use neural networks to model the drift and volatility functions, which......(摘要翻译及全文见知识星球)Keywords :
[2] Distributional Refinement Network
标题:分布式精炼网络作者:Benjamin Avanzi, Eric Dong, Patrick J. Laub, Bernard Wong来源:ARXIV_20240604Abstract : A key task in actuarial modelling involves modelling the distributional properties of losses. Classic (distributional) regression approaches like Generalized Linear Models (GLMs Nelder and Wedderburn, 1972) are commonly used, but challenges remain in developing models that can (i) allow covariates to flexibly impact different aspects of the conditional distribution, (ii) integrate developments in machine learning and AI to maximise the......(摘要翻译及全文见知识星球)Keywords :
[3] Statistics Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance
标题:数据科学与金融中基于最大熵原理的统计参数化量子电路作者:Xi-Ning Zhuang, Zhao-Yun Chen, Cheng Xue, Xiao-Fan Xu, Chao Wang, Huan-Yu Liu, Tai-Ping Sun, Yun-Jie Wang, Yu-Chun Wu, Guo-Ping Guo来源:ARXIV_20240604Abstract : Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics focused areas such as data science and finance. However, challenges remain in preparing and learning statistical models on a quantum processor due to issues with trainability and interpretability. In this letter, we utilize the maximum entropy principle to design a statistics informed parameterized quantum circuit (SI PQC)......(摘要翻译及全文见知识星球)Keywords :
[4] How Inductive Bias in Machine Learning Aligns with Optimality in Economic Dynamics
标题:机器学习中的归纳偏差如何与经济动力学中的最优性相一致作者:Mahdi Ebrahimi Kahou, James Yu, Jesse Perla, Geoff Pleiss来源:ARXIV_20240605Abstract : This paper examines the alignment of inductive biases in machine learning (ML) with structural models of economic dynamics. Unlike dynamical systems found in physical and life sciences, economics models are often specified by differential equations with a mixture of easy to enforce initial conditions and hard to enforce infinite horizon boundary conditions (e.g. transversality and no ponzi scheme conditions). Traditional methods......(摘要翻译及全文见知识星球)Keywords :
[5] Algorithmic Collusion in Dynamic Pricing with Deep Reinforcement Learning
标题:深度强化学习动态定价中的算法共谋作者:Shidi Deng, Maximilian Schiffer, Martin Bichler来源:ARXIV_20240605Abstract : Nowadays, a significant share of the Business to Consumer sector is based on online platforms like Amazon and Alibaba and uses Artificial Intelligence for pricing strategies. This has sparked debate on whether pricing algorithms may tacitly collude to set supra competitive prices without being explicitly designed to do so. Our study addresses these concerns by examining the risk of collusion when......(摘要翻译及全文见知识星球)Keywords :
