arXiv - QuantFin - Statistical Finance最新文献_第3页

The Efficient Tail Hypothesis: An Extreme Value Perspective on Market Efficiency 有效尾假设：从极值角度看市场效率

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-13 DOI: arxiv-2408.06661

Junshu Jiang, Jordan Richards, Raphaël Huser, David Bolin

{"title":"The Efficient Tail Hypothesis: An Extreme Value Perspective on Market Efficiency","authors":"Junshu Jiang, Jordan Richards, Raphaël Huser, David Bolin","doi":"arxiv-2408.06661","DOIUrl":"https://doi.org/arxiv-2408.06661","url":null,"abstract":"In econometrics, the Efficient Market Hypothesis posits that asset prices\u0000reflect all available information in the market. Several empirical\u0000investigations show that market efficiency drops when it undergoes extreme\u0000events. Many models for multivariate extremes focus on positive dependence,\u0000making them unsuitable for studying extremal dependence in financial markets\u0000where data often exhibit both positive and negative extremal dependence. To\u0000this end, we construct regular variation models on the entirety of\u0000$mathbb{R}^d$ and develop a bivariate measure for asymmetry in the strength of\u0000extremal dependence between adjacent orthants. Our directional tail dependence\u0000(DTD) measure allows us to define the Efficient Tail Hypothesis (ETH) -- an\u0000analogue of the Efficient Market Hypothesis -- for the extremal behaviour of\u0000the market. Asymptotic results for estimators of DTD are described, and we\u0000discuss testing of the ETH via permutation-based methods and present novel\u0000tools for visualization. Empirical study of China's futures market leads to a\u0000rejection of the ETH and we identify potential profitable investment\u0000opportunities. To promote the research of microstructure in China's derivatives\u0000market, we open-source our high-frequency data, which are being collected\u0000continuously from multiple derivative exchanges.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190936","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

Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach 利用盈利报告进行股票预测：QLoRA 增强型 LLM 方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-13 DOI: arxiv-2408.06634

Haowei Ni, Shuchen Meng, Xupeng Chen, Ziqing Zhao, Andi Chen, Panfeng Li, Shiyao Zhang, Qifu Yin, Yuanqing Wang, Yuxi Chan

{"title":"Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach","authors":"Haowei Ni, Shuchen Meng, Xupeng Chen, Ziqing Zhao, Andi Chen, Panfeng Li, Shiyao Zhang, Qifu Yin, Yuanqing Wang, Yuxi Chan","doi":"arxiv-2408.06634","DOIUrl":"https://doi.org/arxiv-2408.06634","url":null,"abstract":"Accurate stock market predictions following earnings reports are crucial for\u0000investors. Traditional methods, particularly classical machine learning models,\u0000struggle with these predictions because they cannot effectively process and\u0000interpret extensive textual data contained in earnings reports and often\u0000overlook nuances that influence market movements. This paper introduces an\u0000advanced approach by employing Large Language Models (LLMs) instruction\u0000fine-tuned with a novel combination of instruction-based techniques and\u0000quantized low-rank adaptation (QLoRA) compression. Our methodology integrates\u0000'base factors', such as financial metric growth and earnings transcripts, with\u0000'external factors', including recent market indices performances and analyst\u0000grades, to create a rich, supervised dataset. This comprehensive dataset\u0000enables our models to achieve superior predictive performance in terms of\u0000accuracy, weighted F1, and Matthews correlation coefficient (MCC), especially\u0000evident in the comparison with benchmarks such as GPT-4. We specifically\u0000highlight the efficacy of the llama-3-8b-Instruct-4bit model, which showcases\u0000significant improvements over baseline models. The paper also discusses the\u0000potential of expanding the output capabilities to include a 'Hold' option and\u0000extending the prediction horizon, aiming to accommodate various investment\u0000styles and time frames. This study not only demonstrates the power of\u0000integrating cutting-edge AI with fine-tuned financial data but also paves the\u0000way for future research in enhancing AI-driven financial analysis tools.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190937","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

Large Investment Model 大型投资模式

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-12 DOI: arxiv-2408.10255

Jian Guo, Heung-Yeung Shum

引用次数: 0

A GCN-LSTM Approach for ES-mini and VX Futures Forecasting 用于 ES-mini 和 VX 期货预测的 GCN-LSTM 方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-10 DOI: arxiv-2408.05659

Nikolas Michael, Mihai Cucuringu, Sam Howison

引用次数: 0

Comparative analysis of stationarity for Bitcoin and the S&P500 比特币与标准普尔 500 指数的静态比较分析

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-06 DOI: arxiv-2408.02973

Yaoyue Tang, Karina Arias-Calluari, Michael S. Harré

{"title":"Comparative analysis of stationarity for Bitcoin and the S&P500","authors":"Yaoyue Tang, Karina Arias-Calluari, Michael S. Harré","doi":"arxiv-2408.02973","DOIUrl":"https://doi.org/arxiv-2408.02973","url":null,"abstract":"This paper compares and contrasts stationarity between the conventional stock\u0000market and cryptocurrency. The dataset used for the analysis is the intraday\u0000price indices of the S&P500 from 1996 to 2023 and the intraday Bitcoin indices\u0000from 2019 to 2023, both in USD. We adopt the definition of `wide sense\u0000stationary', which constrains the time independence of the first and second\u0000moments of a time series. The testing method used in this paper follows the\u0000Wiener-Khinchin Theorem, i.e., that for a wide sense stationary process, the\u0000power spectral density and the autocorrelation are a Fourier transform pair. We\u0000demonstrate that localized stationarity can be achieved by truncating the time\u0000series into segments, and for each segment, detrending and normalizing the\u0000price return are required. These results show that the S&P500 price return can\u0000achieve stationarity for the full 28-year period with a detrending window of 12\u0000months and a constrained normalization window of 10 minutes. With truncated\u0000segments, a larger normalization window can be used to establish stationarity,\u0000indicating that within the segment the data is more homogeneous. For Bitcoin\u0000price return, the segment with higher volatility presents stationarity with a\u0000normalization window of 60 minutes, whereas stationarity cannot be established\u0000in other segments.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933529","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

NeuralFactors: A Novel Factor Learning Approach to Generative Modeling of Equities 神经因子：股票生成模型的新型因子学习方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-02 DOI: arxiv-2408.01499

Achintya Gopal

{"title":"NeuralFactors: A Novel Factor Learning Approach to Generative Modeling of Equities","authors":"Achintya Gopal","doi":"arxiv-2408.01499","DOIUrl":"https://doi.org/arxiv-2408.01499","url":null,"abstract":"The use of machine learning for statistical modeling (and thus, generative\u0000modeling) has grown in popularity with the proliferation of time series models,\u0000text-to-image models, and especially large language models. Fundamentally, the\u0000goal of classical factor modeling is statistical modeling of stock returns, and\u0000in this work, we explore using deep generative modeling to enhance classical\u0000factor models. Prior work has explored the use of deep generative models in\u0000order to model hundreds of stocks, leading to accurate risk forecasting and\u0000alpha portfolio construction; however, that specific model does not allow for\u0000easy factor modeling interpretation in that the factor exposures cannot be\u0000deduced. In this work, we introduce NeuralFactors, a novel machine-learning\u0000based approach to factor analysis where a neural network outputs factor\u0000exposures and factor returns, trained using the same methodology as variational\u0000autoencoders. We show that this model outperforms prior approaches both in\u0000terms of log-likelihood performance and computational efficiency. Further, we\u0000show that this method is competitive to prior work in generating realistic\u0000synthetic data, covariance estimation, risk analysis (e.g., value at risk, or\u0000VaR, of portfolios), and portfolio optimization. Finally, due to the connection\u0000to classical factor analysis, we analyze how the factors our model learns\u0000cluster together and show that the factor exposures could be used for embedding\u0000stocks.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"193 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933528","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

NeuralBeta: Estimating Beta Using Deep Learning NeuralBeta：使用深度学习估算贝塔值

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-02 DOI: arxiv-2408.01387

Yuxin Liu, Jimin Lin, Achintya Gopal

引用次数: 0

Inferring financial stock returns correlation from complex network analysis 从复杂网络分析中推断金融股回报相关性

arXiv - QuantFin - Statistical Finance Pub Date : 2024-07-29 DOI: arxiv-2407.20380

Ixandra Achitouv

引用次数: 0

Contrastive Learning of Asset Embeddings from Financial Time Series 从金融时间序列对比学习资产嵌入

arXiv - QuantFin - Statistical Finance Pub Date : 2024-07-26 DOI: arxiv-2407.18645

Rian Dolphin, Barry Smyth, Ruihai Dong

{"title":"Contrastive Learning of Asset Embeddings from Financial Time Series","authors":"Rian Dolphin, Barry Smyth, Ruihai Dong","doi":"arxiv-2407.18645","DOIUrl":"https://doi.org/arxiv-2407.18645","url":null,"abstract":"Representation learning has emerged as a powerful paradigm for extracting\u0000valuable latent features from complex, high-dimensional data. In financial\u0000domains, learning informative representations for assets can be used for tasks\u0000like sector classification, and risk management. However, the complex and\u0000stochastic nature of financial markets poses unique challenges. We propose a\u0000novel contrastive learning framework to generate asset embeddings from\u0000financial time series data. Our approach leverages the similarity of asset\u0000returns over many subwindows to generate informative positive and negative\u0000samples, using a statistical sampling strategy based on hypothesis testing to\u0000address the noisy nature of financial data. We explore various contrastive loss\u0000functions that capture the relationships between assets in different ways to\u0000learn a discriminative representation space. Experiments on real-world datasets\u0000demonstrate the effectiveness of the learned asset embeddings on benchmark\u0000industry classification and portfolio optimization tasks. In each case our\u0000novel approaches significantly outperform existing baselines highlighting the\u0000potential for contrastive learning to capture meaningful and actionable\u0000relationships in financial data.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866155","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

Financial Statement Analysis with Large Language Models 利用大型语言模型分析财务报表

arXiv - QuantFin - Statistical Finance Pub Date : 2024-07-25 DOI: arxiv-2407.17866

Alex Kim, Maximilian Muhn, Valeri Nikolaev

引用次数: 0