[1] Enhancement of price trend trading strategies via image induced importance weights通过图像诱导的重要性权重增强价格趋势交易策略来源:ARXIV_20240819[2] High Frequency Trading Liquidity Analysis Application of Machine Learning Classification机器学习分类在高频交易流动性分析中的应用来源:ARXIV_20240820[3] Learning to Optimally Stop a Diffusion Process学习如何最佳地阻止扩散过程来源:ARXIV_20240820[4] Exploratory Optimal Stopping探索性最佳停车来源:ARXIV_20240820[5] How Small is Big Enough Open Labeled Datasets and the Development of Deep Learning足够大的数据集和深度学习的发展来源:ARXIV_20240821[6] Hedging in Jump Diffusion Model with Transaction Costs具有交易成本的跳跃扩散模型中的套期保值来源:ARXIV_20240821[7] Can an unsupervised clustering algorithm reproduce a categorization system 无监督聚类算法能否再现分类系统来源:ARXIV_20240821[8] Deep MacroFinDeep MacroFin来源:ARXIV_20240821[9] Tax Credits and Household Behavior税收抵免与家庭行为来源:ARXIV_20240821
[1] Enhancement of price trend trading strategies via image induced importance weights
标题:通过图像诱导的重要性权重增强价格趋势交易策略作者:Zhoufan Zhu, Ke Zhu来源:ARXIV_20240819Abstract : We open up the black box to identify the predictive general price patterns in price chart images via the deep learning image analysis techniques. Our identified price patterns lead to the construction of image induced importance (triple I) weights, which are applied to weighted moving average the existing price trend trading signals according to their level of importance in......(摘要翻译及全文见知识星球)Keywords :
[2] High Frequency Trading Liquidity Analysis Application of Machine Learning Classification
标题:机器学习分类在高频交易流动性分析中的应用作者:Sid Bhatia, Sidharth Peri, Sam Friedman, Michelle Malen来源:ARXIV_20240820Abstract : This research presents a comprehensive framework for analyzing liquidity in financial markets, particularly in the context of high frequency trading. By leveraging advanced machine learning classification techniques, including Logistic Regression, Support Vector Machine, and Random Forest, the study aims to predict minute level price movements using an extensive set of liquidity metrics derived from the Trade and Quote (TAQ) data. The......(摘要翻译及全文见知识星球)Keywords :
[3] Learning to Optimally Stop a Diffusion Process
标题:学习如何最佳地阻止扩散过程作者:Min Dai, Yu Sun, Zuo Quan Xu, Xun Yu Zhou来源:ARXIV_20240820Abstract : We study optimal stopping for a diffusion process with unknown model primitives within the continuous time reinforcement learning (RL) framework developed by Wang et al. (2020). By penalizing its variational inequality, we transform the stopping problem into a stochastic optimal control problem with two actions. We then randomize control into Bernoulli distributions and add an entropy regularizer to encourage exploration. We......(摘要翻译及全文见知识星球)Keywords :
[4] Exploratory Optimal Stopping
标题:探索性最佳停车作者:Jodi Dianetti, Giorgio Ferrari, Renyuan Xu来源:ARXIV_20240820Abstract : This paper explores continuous time and state space optimal stopping problems from a reinforcement learning perspective. We begin by formulating the stopping problem using randomized stopping times, where the decision maker s control is represented by the probability of stopping within a given time specifically, a bounded, non decreasing, c dl g control process. To encourage exploration and facilitate learning,......(摘要翻译及全文见知识星球)Keywords :
[5] How Small is Big Enough Open Labeled Datasets and the Development of Deep Learning
标题:足够大的数据集和深度学习的发展作者:Daniel Souza, Aldo Geuna, Jeff Rodríguez来源:ARXIV_20240821Abstract : We investigate the emergence of Deep Learning as a technoscientific field, emphasizing the role of open labeled datasets. Through qualitative and quantitative analyses, we evaluate the role of datasets like CIFAR 10 in advancing computer vision and object recognition, which are central to the Deep Learning revolution. Our findings highlight CIFAR 10 s crucial role and enduring influence on the field,......(摘要翻译及全文见知识星球)Keywords :
[6] Hedging in Jump Diffusion Model with Transaction Costs
标题:具有交易成本的跳跃扩散模型中的套期保值作者:Hamidreza Maleki Almani, Foad Shokrollahi, Tommi Sottinen来源:ARXIV_20240821Abstract : We consider the jump diffusion risky asset model and study its conditional prediction laws. Next, we explain the conditional least square hedging strategy and calculate its closed form for the jump diffusion model, considering the Black Scholes framework with interpretations related to investor priorities and transaction costs. We investigate the explicit form of this result for the particular case of the......(摘要翻译及全文见知识星球)Keywords :
[7] Can an unsupervised clustering algorithm reproduce a categorization system
标题:无监督聚类算法能否再现分类系统作者:Nathalia Castellanos, Dhruv Desai, Sebastian Frank, Stefano Pasquali, Dhagash Mehta来源:ARXIV_20240821Abstract : Peer analysis is a critical component of investment management, often relying on expert provided categorization systems. These systems consistency is questioned when they do not align with cohorts from unsupervised clustering algorithms optimized for various metrics. We investigate whether unsupervised clustering can reproduce ground truth classes in a labeled dataset, showing that success depends on feature selection and the chosen......(摘要翻译及全文见知识星球)Keywords :
[8] Deep MacroFin
标题:Deep MacroFin作者:Yuntao Wu, Jiayuan Guo, Goutham Gopalakrishna, Zisis Poulos来源:ARXIV_20240821Abstract : In this paper, we present Deep MacroFin, a comprehensive framework designed to solve partial differential equations, with a particular focus on models in continuous time economics. This framework leverages deep learning methodologies, including conventional Multi Layer Perceptrons and the newly developed Kolmogorov Arnold Networks. It is optimized using economic information encapsulated by Hamilton Jacobi Bellman equations and coupled algebraic equations. The......(摘要翻译及全文见知识星球)Keywords :
[9] Tax Credits and Household Behavior
标题:税收抵免与家庭行为作者:Jialin Dong, Kshama Dwarakanath, Svitlana Vyetrenko来源:ARXIV_20240821Abstract : There has been a growing interest in multi agent simulators in the domain of economic modeling. However, contemporary research often involves developing reinforcement learning (RL) based models that focus solely on a single type of agents, such as households, firms, or the government. Such an approach overlooks the adaptation of interacting agents thereby failing to capture the complexity of real world......(摘要翻译及全文见知识星球)Keywords :
