arXiv - QuantFin - Statistical Finance最新文献_第10页

Dimensionality reduction techniques to support insider trading detection 支持内幕交易检测的降维技术

arXiv - QuantFin - Statistical Finance Pub Date : 2024-03-01 DOI: arxiv-2403.00707

Adele Ravagnani, Fabrizio Lillo, Paola Deriu, Piero Mazzarisi, Francesca Medda, Antonio Russo

引用次数: 0

MambaStock: Selective state space model for stock prediction MambaStock：用于股票预测的选择性状态空间模型

arXiv - QuantFin - Statistical Finance Pub Date : 2024-02-29 DOI: arxiv-2402.18959

Zhuangwei Shi

{"title":"MambaStock: Selective state space model for stock prediction","authors":"Zhuangwei Shi","doi":"arxiv-2402.18959","DOIUrl":"https://doi.org/arxiv-2402.18959","url":null,"abstract":"The stock market plays a pivotal role in economic development, yet its\u0000intricate volatility poses challenges for investors. Consequently, research and\u0000accurate predictions of stock price movements are crucial for mitigating risks.\u0000Traditional time series models fall short in capturing nonlinearity, leading to\u0000unsatisfactory stock predictions. This limitation has spurred the widespread\u0000adoption of neural networks for stock prediction, owing to their robust\u0000nonlinear generalization capabilities. Recently, Mamba, a structured state\u0000space sequence model with a selection mechanism and scan module (S6), has\u0000emerged as a powerful tool in sequence modeling tasks. Leveraging this\u0000framework, this paper proposes a novel Mamba-based model for stock price\u0000prediction, named MambaStock. The proposed MambaStock model effectively mines\u0000historical stock market data to predict future stock prices without handcrafted\u0000features or extensive preprocessing procedures. Empirical studies on several\u0000stocks indicate that the MambaStock model outperforms previous methods,\u0000delivering highly accurate predictions. This enhanced accuracy can assist\u0000investors and institutions in making informed decisions, aiming to maximize\u0000returns while minimizing risks. This work underscores the value of Mamba in\u0000time-series forecasting. Source code is available at\u0000https://github.com/zshicode/MambaStock.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140010546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Navigating Complexity: Constrained Portfolio Analysis in High Dimensions with Tracking Error and Weight Constraints 驾驭复杂性：具有跟踪误差和权重约束的高维度受限投资组合分析

arXiv - QuantFin - Statistical Finance Pub Date : 2024-02-27 DOI: arxiv-2402.17523

Mehmet Caner, Qingliang Fan, Yingying Li

{"title":"Navigating Complexity: Constrained Portfolio Analysis in High Dimensions with Tracking Error and Weight Constraints","authors":"Mehmet Caner, Qingliang Fan, Yingying Li","doi":"arxiv-2402.17523","DOIUrl":"https://doi.org/arxiv-2402.17523","url":null,"abstract":"This paper analyzes the statistical properties of constrained portfolio\u0000formation in a high dimensional portfolio with a large number of assets.\u0000Namely, we consider portfolios with tracking error constraints, portfolios with\u0000tracking error jointly with weight (equality or inequality) restrictions, and\u0000portfolios with only weight restrictions. Tracking error is the portfolio's\u0000performance measured against a benchmark (an index usually), {color{black}{and\u0000weight constraints refers to specific allocation of assets within the\u0000portfolio, which often come in the form of regulatory requirement or fund\u0000prospectus.}} We show how these portfolios can be estimated consistently in\u0000large dimensions, even when the number of assets is larger than the time span\u0000of the portfolio. We also provide rate of convergence results for weights of\u0000the constrained portfolio, risk of the constrained portfolio and the Sharpe\u0000Ratio of the constrained portfolio. To achieve those results we use a new\u0000machine learning technique that merges factor models with nodewise regression\u0000in statistics. Simulation results and empirics show very good performance of\u0000our method.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140011231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks CaT-GNN：通过因果时序图神经网络加强信用卡欺诈检测

arXiv - QuantFin - Statistical Finance Pub Date : 2024-02-22 DOI: arxiv-2402.14708

Yifan Duan, Guibin Zhang, Shilong Wang, Xiaojiang Peng, Wang Ziqi, Junyuan Mao, Hao Wu, Xinke Jiang, Kun Wang

{"title":"CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks","authors":"Yifan Duan, Guibin Zhang, Shilong Wang, Xiaojiang Peng, Wang Ziqi, Junyuan Mao, Hao Wu, Xinke Jiang, Kun Wang","doi":"arxiv-2402.14708","DOIUrl":"https://doi.org/arxiv-2402.14708","url":null,"abstract":"Credit card fraud poses a significant threat to the economy. While Graph\u0000Neural Network (GNN)-based fraud detection methods perform well, they often\u0000overlook the causal effect of a node's local structure on predictions. This\u0000paper introduces a novel method for credit card fraud detection, the\u0000textbf{underline{Ca}}usal textbf{underline{T}}emporal\u0000textbf{underline{G}}raph textbf{underline{N}}eural textbf{N}etwork\u0000(CaT-GNN), which leverages causal invariant learning to reveal inherent\u0000correlations within transaction data. By decomposing the problem into discovery\u0000and intervention phases, CaT-GNN identifies causal nodes within the transaction\u0000graph and applies a causal mixup strategy to enhance the model's robustness and\u0000interpretability. CaT-GNN consists of two key components: Causal-Inspector and\u0000Causal-Intervener. The Causal-Inspector utilizes attention weights in the\u0000temporal attention mechanism to identify causal and environment nodes without\u0000introducing additional parameters. Subsequently, the Causal-Intervener performs\u0000a causal mixup enhancement on environment nodes based on the set of nodes.\u0000Evaluated on three datasets, including a private financial dataset and two\u0000public datasets, CaT-GNN demonstrates superior performance over existing\u0000state-of-the-art methods. Our findings highlight the potential of integrating\u0000causal reasoning with graph neural networks to improve fraud detection\u0000capabilities in financial transactions.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139949644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantifying neural network uncertainty under volatility clustering 量化波动聚类下的神经网络不确定性

arXiv - QuantFin - Statistical Finance Pub Date : 2024-02-22 DOI: arxiv-2402.14476

Steven Y. K. Wong, Jennifer S. K. Chan, Lamiae Azizi

引用次数: 0

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

arXiv - QuantFin - Statistical Finance Pub Date : 2024-02-19 DOI: arxiv-2403.00785

Oluwafemi F Olaiyapo

引用次数: 0

Ploutos: Towards interpretable stock movement prediction with financial large language model Ploutos：利用金融大型语言模型实现可解释的股票走势预测

arXiv - QuantFin - Statistical Finance Pub Date : 2024-02-18 DOI: arxiv-2403.00782

Hanshuang Tong, Jun Li, Ning Wu, Ming Gong, Dongmei Zhang, Qi Zhang

{"title":"Ploutos: Towards interpretable stock movement prediction with financial large language model","authors":"Hanshuang Tong, Jun Li, Ning Wu, Ming Gong, Dongmei Zhang, Qi Zhang","doi":"arxiv-2403.00782","DOIUrl":"https://doi.org/arxiv-2403.00782","url":null,"abstract":"Recent advancements in large language models (LLMs) have opened new pathways\u0000for many domains. However, the full potential of LLMs in financial investments\u0000remains largely untapped. There are two main challenges for typical deep\u0000learning-based methods for quantitative finance. First, they struggle to fuse\u0000textual and numerical information flexibly for stock movement prediction.\u0000Second, traditional methods lack clarity and interpretability, which impedes\u0000their application in scenarios where the justification for predictions is\u0000essential. To solve the above challenges, we propose Ploutos, a novel financial\u0000LLM framework that consists of PloutosGen and PloutosGPT. The PloutosGen\u0000contains multiple primary experts that can analyze different modal data, such\u0000as text and numbers, and provide quantitative strategies from different\u0000perspectives. Then PloutosGPT combines their insights and predictions and\u0000generates interpretable rationales. To generate accurate and faithful\u0000rationales, the training strategy of PloutosGPT leverage rearview-mirror\u0000prompting mechanism to guide GPT-4 to generate rationales, and a dynamic token\u0000weighting mechanism to finetune LLM by increasing key tokens weight. Extensive\u0000experiments show our framework outperforms the state-of-the-art methods on both\u0000prediction accuracy and interpretability.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction RAGIC：股票区间构建的风险意识生成对抗模型

arXiv - QuantFin - Statistical Finance Pub Date : 2024-02-16 DOI: arxiv-2402.10760

Jingyi Gu, Wenlu Du, Guiling Wang

{"title":"RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction","authors":"Jingyi Gu, Wenlu Du, Guiling Wang","doi":"arxiv-2402.10760","DOIUrl":"https://doi.org/arxiv-2402.10760","url":null,"abstract":"Efforts to predict stock market outcomes have yielded limited success due to\u0000the inherently stochastic nature of the market, influenced by numerous\u0000unpredictable factors. Many existing prediction approaches focus on\u0000single-point predictions, lacking the depth needed for effective\u0000decision-making and often overlooking market risk. To bridge this gap, we\u0000propose a novel model, RAGIC, which introduces sequence generation for stock\u0000interval prediction to quantify uncertainty more effectively. Our approach\u0000leverages a Generative Adversarial Network (GAN) to produce future price\u0000sequences infused with randomness inherent in financial markets. RAGIC's\u0000generator includes a risk module, capturing the risk perception of informed\u0000investors, and a temporal module, accounting for historical price trends and\u0000seasonality. This multi-faceted generator informs the creation of\u0000risk-sensitive intervals through statistical inference, incorporating\u0000horizon-wise insights. The interval's width is carefully adjusted to reflect\u0000market volatility. Importantly, our approach relies solely on publicly\u0000available data and incurs only low computational overhead. RAGIC's evaluation\u0000across globally recognized broad-based indices demonstrates its balanced\u0000performance, offering both accuracy and informativeness. Achieving a consistent\u000095% coverage, RAGIC maintains a narrow interval width. This promising outcome\u0000suggests that our approach effectively addresses the challenges of stock market\u0000prediction while incorporating vital risk considerations.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"23 6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139902514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Financially Inclusive Credit Products Through Financial Time Series Clustering 通过金融时间序列聚类开发普惠金融信贷产品

arXiv - QuantFin - Statistical Finance Pub Date : 2024-02-16 DOI: arxiv-2402.11066

Tristan Bester, Benjamin Rosman

{"title":"Towards Financially Inclusive Credit Products Through Financial Time Series Clustering","authors":"Tristan Bester, Benjamin Rosman","doi":"arxiv-2402.11066","DOIUrl":"https://doi.org/arxiv-2402.11066","url":null,"abstract":"Financial inclusion ensures that individuals have access to financial\u0000products and services that meet their needs. As a key contributing factor to\u0000economic growth and investment opportunity, financial inclusion increases\u0000consumer spending and consequently business development. It has been shown that\u0000institutions are more profitable when they provide marginalised social groups\u0000access to financial services. Customer segmentation based on consumer\u0000transaction data is a well-known strategy used to promote financial inclusion.\u0000While the required data is available to modern institutions, the challenge\u0000remains that segment annotations are usually difficult and/or expensive to\u0000obtain. This prevents the usage of time series classification models for\u0000customer segmentation based on domain expert knowledge. As a result, clustering\u0000is an attractive alternative to partition customers into homogeneous groups\u0000based on the spending behaviour encoded within their transaction data. In this\u0000paper, we present a solution to one of the key challenges preventing modern\u0000financial institutions from providing financially inclusive credit, savings and\u0000insurance products: the inability to understand consumer financial behaviour,\u0000and hence risk, without the introduction of restrictive conventional credit\u0000scoring techniques. We present a novel time series clustering algorithm that\u0000allows institutions to understand the financial behaviour of their customers.\u0000This enables unique product offerings to be provided based on the needs of the\u0000customer, without reliance on restrictive credit practices.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"140 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Emoji Driven Crypto Assets Market Reactions 表情符号驱动的加密资产市场反应

arXiv - QuantFin - Statistical Finance Pub Date : 2024-02-16 DOI: arxiv-2402.10481

Xiaorui Zuo, Yao-Tsung Chen, Wolfgang Karl Härdle

引用次数: 0