[1] Dimensionality reduction techniques to support insider trading detection支持内幕交易检测的降维技术来源:ARXIV_20240304[2] A time stepping deep gradient flow method for option pricing in (rough) diffusion models（粗糙）扩散模型中期权定价的时间步进深梯度流方法来源:ARXIV_20240304[3] Non-Residential Real Estate Prices and Machine Learning: The How and the Why非住宅房地产价格与机器学习：如何以及为什么来源:SSRN_20240304[4] Blockchain Metrics and Indicators in Cryptocurrency Trading加密货币交易中的区块链度量和指标来源:ARXIV_20240305[5] Do Weibo platform experts perform better at predicting stock market 微博平台专家在预测股市方面表现更好吗来源:ARXIV_20240305[6] Regional inflation analysis using social network data利用社会网络数据进行区域通胀分析来源:ARXIV_20240305[7] Detecting Anomalous Events in Object centric Business Processes via Graph Neural Networks用图神经网络检测以对象为中心的业务流程中的异常事件来源:ARXIV_20240305[8] Applying News and Media Sentiment Analysis for Generating Forex Trading Signals应用新闻和媒体情绪分析生成外汇交易信号来源:ARXIV_20240305[9] Advancing Portfolio Construction and Optimization: AI's Role in Boosting Returns, Lowering Risks, and Streamlining Efficiency推进投资组合建设和优化：人工智能在提高回报、降低风险和优化效率方面的作用来源:SSRN_20240305[10] RVRAE红色来源:ARXIV_20240306[11] Transformer for Times Series时代系列变压器来源:ARXIV_20240306
[1] Dimensionality reduction techniques to support insider trading detection
标题:支持内幕交易检测的降维技术作者:Adele Ravagnani, Fabrizio Lillo, Paola Deriu, Piero Mazzarisi, Francesca Medda, Antonio Russo来源:ARXIV_20240304Abstract : Identification of market abuse is an extremely complicated activity that requires the analysis of large and complex datasets. We propose an unsupervised machine learning method for contextual anomaly detection, which allows to support market surveillance aimed at identifying potential insider trading activities. This method lies in the reconstruction based paradigm and employs principal component analysis and autoencoders as dimensionality reduction techniques.......(摘要翻译及全文见知识星球)Keywords :
[2] A time stepping deep gradient flow method for option pricing in (rough) diffusion models
标题:（粗糙）扩散模型中期权定价的时间步进深梯度流方法作者:Antonis Papapantoleon, Jasper Rou来源:ARXIV_20240304
Abstract : We develop a novel deep learning approach for pricing European options in diffusion models, that can efficiently handle high dimensional problems resulting from Markovian approximations of rough volatility models. The option pricing partial differential equation is reformulated as an energy minimization problem, which is approximated in a time stepping fashion by deep artificial neural networks. The proposed scheme respects the asymptotic......(摘要翻译及全文见知识星球)Keywords :
[3] Non-Residential Real Estate Prices and Machine Learning: The How and the Why
标题:非住宅房地产价格与机器学习：如何以及为什么作者:Raffaella Barone来源:SSRN_20240304Abstract : In recent years the real estate sector is moving towards greater social responsibility, promoting the culture of environmental and social sustainability. However, compliance with the rules requires a cultural fabric oriented towards legality. This is mainly relevant in the real estate sector, in which organized crime channels flow of dirty money coming from crimes. We collected data related to non-residential real......(摘要翻译及全文见知识星球)Keywords : Machine Learning, Real estate market, Financial stability, Sustainability, Crimes
[4] Blockchain Metrics and Indicators in Cryptocurrency Trading
标题:加密货币交易中的区块链度量和指标作者:Juan C. King, Roberto Dale, José M. Amigó来源:ARXIV_20240305Abstract : The objective of this paper is the construction of new indicators that can be useful to operate in the cryptocurrency market. These indicators are based on public data obtained from the blockchain network, specifically from the nodes that make up Bitcoin mining. Therefore, our analysis is unique to that network. The results obtained with numerical simulations of algorithmic trading and prediction......(摘要翻译及全文见知识星球)Keywords :
[5] Do Weibo platform experts perform better at predicting stock market
标题:微博平台专家在预测股市方面表现更好吗作者:Ziyuan Ma, Conor Ryan, Jim Buckley, Muslim Chochlov来源:ARXIV_20240305Abstract : Sentiment analysis can be used for stock market prediction. However, existing research has not studied the impact of a user s financial background on sentiment based forecasting of the stock market using artificial neural networks. In this work, a novel combination of neural networks is used for the assessment of sentiment based stock market prediction, based on the financial background of......(摘要翻译及全文见知识星球)Keywords :
[6] Regional inflation analysis using social network data
标题:利用社会网络数据进行区域通胀分析作者:Vasilii Chsherbakov Ilia Karpov来源:ARXIV_20240305Abstract : Inflation is one of the most important macroeconomic indicators that have a great impact on the population of any country and region. Inflation is influenced by range of factors, one of which is inflation expectations. Many central banks take this factor into consideration while implementing monetary policy within the inflation targeting regime. Nowadays, a lot of people are active users of......(摘要翻译及全文见知识星球)Keywords :
[7] Detecting Anomalous Events in Object centric Business Processes via Graph Neural Networks
标题:用图神经网络检测以对象为中心的业务流程中的异常事件作者:Alessandro Niro, Michael Werner来源:ARXIV_20240305Abstract : Detecting anomalies is important for identifying inefficiencies, errors, or fraud in business processes. Traditional process mining approaches focus on analyzing flattened , sequential, event logs based on a single case notion. However, many real world process executions exhibit a graph like structure, where events can be associated with multiple cases. Flattening event logs requires selecting a single case identifier which......(摘要翻译及全文见知识星球)Keywords :
[8] Applying News and Media Sentiment Analysis for Generating Forex Trading Signals
标题:应用新闻和媒体情绪分析生成外汇交易信号作者:Oluwafemi F Olaiyapo来源:ARXIV_20240305Abstract : The objective of this research is to examine how sentiment analysis can be employed to generate trading signals for the Foreign Exchange (Forex) market. The author assessed sentiment in social media posts and news articles pertaining to the United States Dollar (USD) using a combination of methods lexicon based analysis and the Naive Bayes machine learning algorithm. The findings indicate......(摘要翻译及全文见知识星球)Keywords :
[9] Advancing Portfolio Construction and Optimization: AI's Role in Boosting Returns, Lowering Risks, and Streamlining Efficiency
标题:推进投资组合建设和优化：人工智能在提高回报、降低风险和优化效率方面的作用作者:Michael Schopf来源:SSRN_20240305Abstract : This paper is a practical guide on how Artificial Intelligence (AI) and Machine Learning (ML) can support professional investors in portfolio construction and optimisation and identifies three methods for seamlessly integrating ML-based portfolio construction into an existing investment process. It provides a compelling comparative analysis of traditional techniques and modern ML-based approaches to portfolio construction and optimisation. The paper illustrates how,......(摘要翻译及全文见知识星球)Keywords : Portfolio Construction, Portfolio Optimization, Investment, Asset Management, Portfolio Management, Artificial Intelligence, Finance
[10] RVRAE
标题:红色作者:Yilun Wang, Shengjie Guo来源:ARXIV_20240306Abstract : In recent years, the dynamic factor model has emerged as a dominant tool in economics and finance, particularly for investment strategies. This model offers improved handling of complex, nonlinear, and noisy market conditions compared to traditional static factor models. The advancement of machine learning, especially in dealing with nonlinear data, has further enhanced asset pricing methodologies. This paper introduces a groundbreaking......(摘要翻译及全文见知识星球)Keywords :
[11] Transformer for Times Series
标题:时代系列变压器作者:Pierre Brugiere, Gabriel Turinici来源:ARXIV_20240306Abstract : The transformer models have been extensively used with good results in a wide area of machine learning applications including Large Language Models and image generation. Here, we inquire on the applicability of this approach to financial time series. We first describe the dataset construction for two prototypical situations a mean reverting synthetic Ornstein Uhlenbeck process on one hand and real......(摘要翻译及全文见知识星球)Keywords :
