[1] Application of Natural Language Processing in Financial Risk Detection自然语言处理在金融风险检测中的应用来源:ARXIV_20240617[2] NewswireNewswire来源:ARXIV_20240617[3] Statistical arbitrage in multi pair trading strategy based on graph clustering algorithms in US equities market基于图聚类算法的美国股市多对交易策略中的统计套利来源:ARXIV_20240618[4] Operator Deep Smoothing for Implied Volatility隐含波动性的算子深度平滑来源:ARXIV_20240618[5] Trading Devil交易恶魔来源:ARXIV_20240618[6] A Survey of Large Language Models for Financial Applications金融应用程序的大型语言模型综述来源:ARXIV_20240619[7] Financial Assets Dependency Prediction Utilizing Spatiotemporal Patterns利用时空模式进行金融资产依赖性预测来源:ARXIV_20240619
[1] Application of Natural Language Processing in Financial Risk Detection
标题:自然语言处理在金融风险检测中的应用作者:Liyang Wang, Yu Cheng, Ao Xiang, Jingyu Zhang, Haowei Yang来源:ARXIV_20240617Abstract : This paper explores the application of Natural Language Processing (NLP) in financial risk detection. By constructing an NLP based financial risk detection model, this study aims to identify and predict potential risks in financial documents and communications. First, the fundamental concepts of NLP and its theoretical foundation, including text mining methods, NLP model design principles, and machine learning algorithms, are introduced.......(摘要翻译及全文见知识星球)Keywords :
[2] Newswire
标题:Newswire作者:Emily Silcock, Abhishek Arora, Luca D'Amico-Wong, Melissa Dell来源:ARXIV_20240617Abstract : In the U.S. historically, local newspapers drew their content largely from newswires like the Associated Press. Historians argue that newswires played a pivotal role in creating a national identity and shared understanding of the world, but there is no comprehensive archive of the content sent over newswires. We reconstruct such an archive by applying a customized deep learning pipeline to hundreds......(摘要翻译及全文见知识星球)Keywords :
[3] Statistical arbitrage in multi pair trading strategy based on graph clustering algorithms in US equities market
标题:基于图聚类算法的美国股市多对交易策略中的统计套利作者:Adam Korniejczuk, Robert Ślepaczuk来源:ARXIV_20240618Abstract : The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an ensemble of machine learning classifiers have been used to improve risk adjusted returns and increase immunity to transaction costs over existing approaches. The study seeks to provide......(摘要翻译及全文见知识星球)Keywords :
[4] Operator Deep Smoothing for Implied Volatility
标题:隐含波动性的算子深度平滑作者:Lukas Gonon, Antoine Jacquier, Ruben Wiedemann来源:ARXIV_20240618Abstract : We devise a novel method for implied volatility smoothing based on neural operators. The goal of implied volatility smoothing is to construct a smooth surface that links the collection of prices observed at a specific instant on a given option market. Such price data arises highly dynamically in ever changing spatial configurations, which poses a major limitation to foundational machine learning......(摘要翻译及全文见知识星球)Keywords :
[5] Trading Devil
标题:交易恶魔作者:Orson Mengara来源:ARXIV_20240618Abstract : With the growing use of voice activated systems and speech recognition technologies, the danger of backdoor attacks on audio data has grown significantly. This research looks at a specific type of attack, known as a Stochastic investment based backdoor attack (MarketBack), in which adversaries strategically manipulate the stylistic properties of audio to fool speech recognition systems. The security and integrity of......(摘要翻译及全文见知识星球)Keywords :
[6] A Survey of Large Language Models for Financial Applications
标题:金融应用程序的大型语言模型综述作者:Yuqi Nie, Yaxuan Kong, Xiaowen Dong, John M. Mulvey, H. Vincent Poor, Qingsong Wen, Stefan Zohren来源:ARXIV_20240619Abstract : 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 data, and generating human preferred contents. In this survey, we explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive......(摘要翻译及全文见知识星球)Keywords :
标题:利用时空模式进行金融资产依赖性预测作者:Haoren Zhu, Pengfei Zhao, Wilfred Siu Hung NG, Dik Lun Lee来源:ARXIV_20240619Abstract : Financial assets exhibit complex dependency structures, which are crucial for investors to create diversified portfolios to mitigate risk in volatile financial markets. To explore the financial asset dependencies dynamics, we propose a novel approach that models the dependencies of assets as an Asset Dependency Matrix (ADM) and treats the ADM sequences as image sequences. This allows us to leverage deep learning......(摘要翻译及全文见知识星球)Keywords :
