arXiv - QuantFin - Statistical Finance最新文献_第2页

Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market: A Threshold-Based Classification Approach 利用 RNN 和 LSTM 对印度股市进行同步分析：基于阈值的分类方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-27 DOI: arxiv-2409.06728

Sanjay Sathish, Charu C Sharma

引用次数: 0

LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU LSR-IGRU：基于长期短期关系和改进 GRU 的股票走势预测

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-26 DOI: arxiv-2409.08282

Peng Zhu, Yuante Li, Yifan Hu, Qinyuan Liu, Dawei Cheng, Yuqi Liang

{"title":"LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU","authors":"Peng Zhu, Yuante Li, Yifan Hu, Qinyuan Liu, Dawei Cheng, Yuqi Liang","doi":"arxiv-2409.08282","DOIUrl":"https://doi.org/arxiv-2409.08282","url":null,"abstract":"Stock price prediction is a challenging problem in the field of finance and\u0000receives widespread attention. In recent years, with the rapid development of\u0000technologies such as deep learning and graph neural networks, more research\u0000methods have begun to focus on exploring the interrelationships between stocks.\u0000However, existing methods mostly focus on the short-term dynamic relationships\u0000of stocks and directly integrating relationship information with temporal\u0000information. They often overlook the complex nonlinear dynamic characteristics\u0000and potential higher-order interaction relationships among stocks in the stock\u0000market. Therefore, we propose a stock price trend prediction model named\u0000LSR-IGRU in this paper, which is based on long short-term stock relationships\u0000and an improved GRU input. Firstly, we construct a long short-term relationship\u0000matrix between stocks, where secondary industry information is employed for the\u0000first time to capture long-term relationships of stocks, and overnight price\u0000information is utilized to establish short-term relationships. Next, we improve\u0000the inputs of the GRU model at each step, enabling the model to more\u0000effectively integrate temporal information and long short-term relationship\u0000information, thereby significantly improving the accuracy of predicting stock\u0000trend changes. Finally, through extensive experiments on multiple datasets from\u0000stock markets in China and the United States, we validate the superiority of\u0000the proposed LSR-IGRU model over the current state-of-the-art baseline models.\u0000We also apply the proposed model to the algorithmic trading system of a\u0000financial company, achieving significantly higher cumulative portfolio returns\u0000compared to other baseline methods. Our sources are released at\u0000https://github.com/ZP1481616577/Baselines_LSR-IGRU.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250091","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

StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction StockTime：用于股价预测的时间序列专用大型语言模型架构

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-25 DOI: arxiv-2409.08281

Shengkun Wang, Taoran Ji, Linhan Wang, Yanshen Sun, Shang-Ching Liu, Amit Kumar, Chang-Tien Lu

{"title":"StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction","authors":"Shengkun Wang, Taoran Ji, Linhan Wang, Yanshen Sun, Shang-Ching Liu, Amit Kumar, Chang-Tien Lu","doi":"arxiv-2409.08281","DOIUrl":"https://doi.org/arxiv-2409.08281","url":null,"abstract":"The stock price prediction task holds a significant role in the financial\u0000domain and has been studied for a long time. Recently, large language models\u0000(LLMs) have brought new ways to improve these predictions. While recent\u0000financial large language models (FinLLMs) have shown considerable progress in\u0000financial NLP tasks compared to smaller pre-trained language models (PLMs),\u0000challenges persist in stock price forecasting. Firstly, effectively integrating\u0000the modalities of time series data and natural language to fully leverage these\u0000capabilities remains complex. Secondly, FinLLMs focus more on analysis and\u0000interpretability, which can overlook the essential features of time series\u0000data. Moreover, due to the abundance of false and redundant information in\u0000financial markets, models often produce less accurate predictions when faced\u0000with such input data. In this paper, we introduce StockTime, a novel LLM-based\u0000architecture designed specifically for stock price data. Unlike recent FinLLMs,\u0000StockTime is specifically designed for stock price time series data. It\u0000leverages the natural ability of LLMs to predict the next token by treating\u0000stock prices as consecutive tokens, extracting textual information such as\u0000stock correlations, statistical trends and timestamps directly from these stock\u0000prices. StockTime then integrates both textual and time series data into the\u0000embedding space. By fusing this multimodal data, StockTime effectively predicts\u0000stock prices across arbitrary look-back periods. Our experiments demonstrate\u0000that StockTime outperforms recent LLMs, as it gives more accurate predictions\u0000while reducing memory usage and runtime costs.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268344","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

Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning 优化性能：紧凑型模型如何通过微调匹配或超越 GPT 的分类能力

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-22 DOI: arxiv-2409.11408

Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez

{"title":"Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning","authors":"Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez","doi":"arxiv-2409.11408","DOIUrl":"https://doi.org/arxiv-2409.11408","url":null,"abstract":"In this paper, we demonstrate that non-generative, small-sized models such as\u0000FinBERT and FinDRoBERTa, when fine-tuned, can outperform GPT-3.5 and GPT-4\u0000models in zero-shot learning settings in sentiment analysis for financial news.\u0000These fine-tuned models show comparable results to GPT-3.5 when it is\u0000fine-tuned on the task of determining market sentiment from daily financial\u0000news summaries sourced from Bloomberg. To fine-tune and compare these models,\u0000we created a novel database, which assigns a market score to each piece of news\u0000without human interpretation bias, systematically identifying the mentioned\u0000companies and analyzing whether their stocks have gone up, down, or remained\u0000neutral. Furthermore, the paper shows that the assumptions of Condorcet's Jury\u0000Theorem do not hold suggesting that fine-tuned small models are not independent\u0000of the fine-tuned GPT models, indicating behavioural similarities. Lastly, the\u0000resulted fine-tuned models are made publicly available on HuggingFace,\u0000providing a resource for further research in financial sentiment analysis and\u0000text classification.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142250090","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

EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning EX-DRL：利用极端分布强化学习抵御严重损失

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-22 DOI: arxiv-2408.12446

Parvin Malekzadeh, Zissis Poulos, Jacky Chen, Zeyu Wang, Konstantinos N. Plataniotis

{"title":"EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning","authors":"Parvin Malekzadeh, Zissis Poulos, Jacky Chen, Zeyu Wang, Konstantinos N. Plataniotis","doi":"arxiv-2408.12446","DOIUrl":"https://doi.org/arxiv-2408.12446","url":null,"abstract":"Recent advancements in Distributional Reinforcement Learning (DRL) for\u0000modeling loss distributions have shown promise in developing hedging strategies\u0000in derivatives markets. A common approach in DRL involves learning the\u0000quantiles of loss distributions at specified levels using Quantile Regression\u0000(QR). This method is particularly effective in option hedging due to its direct\u0000quantile-based risk assessment, such as Value at Risk (VaR) and Conditional\u0000Value at Risk (CVaR). However, these risk measures depend on the accurate\u0000estimation of extreme quantiles in the loss distribution's tail, which can be\u0000imprecise in QR-based DRL due to the rarity and extremity of tail data, as\u0000highlighted in the literature. To address this issue, we propose EXtreme DRL\u0000(EX-DRL), which enhances extreme quantile prediction by modeling the tail of\u0000the loss distribution with a Generalized Pareto Distribution (GPD). This method\u0000introduces supplementary data to mitigate the scarcity of extreme quantile\u0000observations, thereby improving estimation accuracy through QR. Comprehensive\u0000experiments on gamma hedging options demonstrate that EX-DRL improves existing\u0000QR-based models by providing more precise estimates of extreme quantiles,\u0000thereby improving the computation and reliability of risk metrics for complex\u0000financial risk management.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190917","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

Dynamical analysis of financial stocks network: improving forecasting using network properties 金融股网络动态分析：利用网络特性改进预测

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-21 DOI: arxiv-2408.11759

Ixandra Achitouv

引用次数: 0

Combining supervised and unsupervised learning methods to predict financial market movements 结合监督和非监督学习方法预测金融市场动向

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-19 DOI: arxiv-2409.03762

Gabriel Rodrigues Palma, Mariusz Skoczeń, Phil Maguire

{"title":"Combining supervised and unsupervised learning methods to predict financial market movements","authors":"Gabriel Rodrigues Palma, Mariusz Skoczeń, Phil Maguire","doi":"arxiv-2409.03762","DOIUrl":"https://doi.org/arxiv-2409.03762","url":null,"abstract":"The decisions traders make to buy or sell an asset depend on various\u0000analyses, with expertise required to identify patterns that can be exploited\u0000for profit. In this paper we identify novel features extracted from emergent\u0000and well-established financial markets using linear models and Gaussian Mixture\u0000Models (GMM) with the aim of finding profitable opportunities. We used\u0000approximately six months of data consisting of minute candles from the Bitcoin,\u0000Pepecoin, and Nasdaq markets to derive and compare the proposed novel features\u0000with commonly used ones. These features were extracted based on the previous 59\u0000minutes for each market and used to identify predictions for the hour ahead. We\u0000explored the performance of various machine learning strategies, such as Random\u0000Forests (RF) and K-Nearest Neighbours (KNN) to classify market movements. A\u0000naive random approach to selecting trading decisions was used as a benchmark,\u0000with outcomes assumed to be equally likely. We used a temporal cross-validation\u0000approach using test sets of 40%, 30% and 20% of total hours to evaluate the\u0000learning algorithms' performances. Our results showed that filtering the time\u0000series facilitates algorithms' generalisation. The GMM filtering approach\u0000revealed that the KNN and RF algorithms produced higher average returns than\u0000the random algorithm.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190940","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

A new measure of risk using Fourier analysis 利用傅立叶分析法衡量风险的新方法

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-18 DOI: arxiv-2408.10279

Michael Grabinski, Galiya Klinkova

引用次数: 0

Stylized facts in Web3 Web3 中的风格化事实

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-14 DOI: arxiv-2408.07653

A. Christian Silva, Shen-Ning Tung, Wwi-Ru Chen

{"title":"Stylized facts in Web3","authors":"A. Christian Silva, Shen-Ning Tung, Wwi-Ru Chen","doi":"arxiv-2408.07653","DOIUrl":"https://doi.org/arxiv-2408.07653","url":null,"abstract":"This paper presents a comprehensive statistical analysis of the Web3\u0000ecosystem, comparing various Web3 tokens with traditional financial assets\u0000across multiple time scales. We examine probability distributions, tail\u0000behaviors, and other key stylized facts of the returns for a diverse range of\u0000tokens, including decentralized exchanges, liquidity pools, and centralized\u0000exchanges. Despite functional differences, most tokens exhibit well-established\u0000empirical facts, including unconditional probability density of returns with\u0000heavy tails gradually becoming Gaussian and volatility clustering. Furthermore,\u0000we compare assets traded on centralized (CEX) and decentralized (DEX)\u0000exchanges, finding that DEXs exhibit similar stylized facts despite different\u0000trading mechanisms and often divergent long-term performance. We propose that\u0000this similarity is attributable to arbitrageurs striving to maintain similar\u0000centralized and decentralized prices. Our study contributes to a better\u0000understanding of the dynamics of Web3 tokens and the relationship between CEX\u0000and DEX markets, with important implications for risk management, pricing\u0000models, and portfolio construction in the rapidly evolving DeFi landscape.\u0000These results add to the growing body of literature on cryptocurrency markets\u0000and provide insights that can guide the development of more accurate models for\u0000DeFi markets.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142190935","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

Model-based and empirical analyses of stochastic fluctuations in economy and finance 基于模型和经验的经济和金融随机波动分析

arXiv - QuantFin - Statistical Finance Pub Date : 2024-08-14 DOI: arxiv-2408.16010

Rubina Zadourian

引用次数: 0