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

MLP, XGBoost, KAN, TDNN, and LSTM-GRU Hybrid RNN with Attention for SPX and NDX European Call Option Pricing 针对 SPX 和 NDX 欧洲看涨期权定价的 MLP、XGBoost、KAN、TDNN 和 LSTM-GRU 混合 RNN 注意事项

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-26 DOI: arxiv-2409.06724

Boris Ter-Avanesov, Homayoon Beigi

{"title":"MLP, XGBoost, KAN, TDNN, and LSTM-GRU Hybrid RNN with Attention for SPX and NDX European Call Option Pricing","authors":"Boris Ter-Avanesov, Homayoon Beigi","doi":"arxiv-2409.06724","DOIUrl":"https://doi.org/arxiv-2409.06724","url":null,"abstract":"We explore the performance of various artificial neural network\u0000architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold\u0000network (KAN), LSTM-GRU hybrid recursive neural network (RNN) models, and a\u0000time-delay neural network (TDNN) for pricing European call options. In this\u0000study, we attempt to leverage the ability of supervised learning methods, such\u0000as ANNs, KANs, and gradient-boosted decision trees, to approximate complex\u0000multivariate functions in order to calibrate option prices based on past market\u0000data. The motivation for using ANNs and KANs is the Universal Approximation\u0000Theorem and Kolmogorov-Arnold Representation Theorem, respectively.\u0000Specifically, we use S&P 500 (SPX) and NASDAQ 100 (NDX) index options traded\u0000during 2015-2023 with times to maturity ranging from 15 days to over 4 years\u0000(OptionMetrics IvyDB US dataset). Black & Scholes's (BS) PDE cite{Black1973}\u0000model's performance in pricing the same options compared to real data is used\u0000as a benchmark. This model relies on strong assumptions, and it has been\u0000observed and discussed in the literature that real data does not match its\u0000predictions. Supervised learning methods are widely used as an alternative for\u0000calibrating option prices due to some of the limitations of this model. In our\u0000experiments, the BS model underperforms compared to all of the others. Also,\u0000the best TDNN model outperforms the best MLP model on all error metrics. We\u0000implement a simple self-attention mechanism to enhance the RNN models,\u0000significantly improving their performance. The best-performing model overall is\u0000the LSTM-GRU hybrid RNN model with attention. Also, the KAN model outperforms\u0000the TDNN and MLP models. We analyze the performance of all models by ticker,\u0000moneyness category, and over/under/correctly-priced percentage.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208130","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

EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods 基于大语言模型和深度学习方法信息融合的欧元兑美元汇率预测

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-23 DOI: arxiv-2408.13214

Hongcheng Ding, Xuanze Zhao, Zixiao Jiang, Shamsul Nahar Abdullah, Deshinta Arrova Dewi

{"title":"EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods","authors":"Hongcheng Ding, Xuanze Zhao, Zixiao Jiang, Shamsul Nahar Abdullah, Deshinta Arrova Dewi","doi":"arxiv-2408.13214","DOIUrl":"https://doi.org/arxiv-2408.13214","url":null,"abstract":"Accurate forecasting of the EUR/USD exchange rate is crucial for investors,\u0000businesses, and policymakers. This paper proposes a novel framework, IUS, that\u0000integrates unstructured textual data from news and analysis with structured\u0000data on exchange rates and financial indicators to enhance exchange rate\u0000prediction. The IUS framework employs large language models for sentiment\u0000polarity scoring and exchange rate movement classification of texts. These\u0000textual features are combined with quantitative features and input into a\u0000Causality-Driven Feature Generator. An Optuna-optimized Bi-LSTM model is then\u0000used to forecast the EUR/USD exchange rate. Experiments demonstrate that the\u0000proposed method outperforms benchmark models, reducing MAE by 10.69% and RMSE\u0000by 9.56% compared to the best performing baseline. Results also show the\u0000benefits of data fusion, with the combination of unstructured and structured\u0000data yielding higher accuracy than structured data alone. Furthermore, feature\u0000selection using the top 12 important quantitative features combined with the\u0000textual features proves most effective. The proposed IUS framework and\u0000Optuna-Bi-LSTM model provide a powerful new approach for exchange rate\u0000forecasting through multi-source data integration.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208126","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

Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications Open-FinLLMs：用于金融应用的开放式多模态大语言模型

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-20 DOI: arxiv-2408.11878

Qianqian Xie, Dong Li, Mengxi Xiao, Zihao Jiang, Ruoyu Xiang, Xiao Zhang, Zhengyu Chen, Yueru He, Weiguang Han, Yuzhe Yang, Shunian Chen, Yifei Zhang, Lihang Shen, Daniel Kim, Zhiwei Liu, Zheheng Luo, Yangyang Yu, Yupeng Cao, Zhiyang Deng, Zhiyuan Yao, Haohang Li, Duanyu Feng, Yongfu Dai, VijayaSai Somasundaram, Peng Lu, Yilun Zhao, Yitao Long, Guojun Xiong, Kaleb Smith, Honghai Yu, Yanzhao Lai, Min Peng, Jianyun Nie, Jordan W. Suchow, Xiao-Yang Liu, Benyou Wang, Alejandro Lopez-Lira, Jimin Huang, Sophia Ananiadou

{"title":"Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications","authors":"Qianqian Xie, Dong Li, Mengxi Xiao, Zihao Jiang, Ruoyu Xiang, Xiao Zhang, Zhengyu Chen, Yueru He, Weiguang Han, Yuzhe Yang, Shunian Chen, Yifei Zhang, Lihang Shen, Daniel Kim, Zhiwei Liu, Zheheng Luo, Yangyang Yu, Yupeng Cao, Zhiyang Deng, Zhiyuan Yao, Haohang Li, Duanyu Feng, Yongfu Dai, VijayaSai Somasundaram, Peng Lu, Yilun Zhao, Yitao Long, Guojun Xiong, Kaleb Smith, Honghai Yu, Yanzhao Lai, Min Peng, Jianyun Nie, Jordan W. Suchow, Xiao-Yang Liu, Benyou Wang, Alejandro Lopez-Lira, Jimin Huang, Sophia Ananiadou","doi":"arxiv-2408.11878","DOIUrl":"https://doi.org/arxiv-2408.11878","url":null,"abstract":"Large language models (LLMs) have advanced financial applications, yet they\u0000often lack sufficient financial knowledge and struggle with tasks involving\u0000multi-modal inputs like tables and time series data. To address these\u0000limitations, we introduce textit{Open-FinLLMs}, a series of Financial LLMs. We\u0000begin with FinLLaMA, pre-trained on a 52 billion token financial corpus,\u0000incorporating text, tables, and time-series data to embed comprehensive\u0000financial knowledge. FinLLaMA is then instruction fine-tuned with 573K\u0000financial instructions, resulting in FinLLaMA-instruct, which enhances task\u0000performance. Finally, we present FinLLaVA, a multimodal LLM trained with 1.43M\u0000image-text instructions to handle complex financial data types. Extensive\u0000evaluations demonstrate FinLLaMA's superior performance over LLaMA3-8B,\u0000LLaMA3.1-8B, and BloombergGPT in both zero-shot and few-shot settings across 19\u0000and 4 datasets, respectively. FinLLaMA-instruct outperforms GPT-4 and other\u0000Financial LLMs on 15 datasets. FinLLaVA excels in understanding tables and\u0000charts across 4 multimodal tasks. Additionally, FinLLaMA achieves impressive\u0000Sharpe Ratios in trading simulations, highlighting its robust financial\u0000application capabilities. We will continually maintain and improve our models\u0000and benchmarks to support ongoing innovation in academia and industry.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208127","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

Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Models Deep-MacroFin：用于连续时间经济模型的知情均衡神经网络

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-19 DOI: arxiv-2408.10368

Yuntao Wu, Jiayuan Guo, Goutham Gopalakrishna, Zisis Poulos

引用次数: 0

Causality-Inspired Models for Financial Time Series Forecasting 金融时间序列预测的因果关系启发模型

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-19 DOI: arxiv-2408.09960

Daniel Cunha Oliveira, Yutong Lu, Xi Lin, Mihai Cucuringu, Andre Fujita

引用次数: 0

Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method 增强风险投资中的初创企业成功预测：GraphRAG 多变量时间序列增强方法

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-18 DOI: arxiv-2408.09420

Gao Zitian, Xiao Yihao

引用次数: 0

Gradient Reduction Convolutional Neural Network Policy for Financial Deep Reinforcement Learning 用于金融深度强化学习的梯度降低卷积神经网络策略

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-16 DOI: arxiv-2408.11859

Sina Montazeri, Haseebullah Jumakhan, Sonia Abrasiabian, Amir Mirzaeinia

{"title":"Gradient Reduction Convolutional Neural Network Policy for Financial Deep Reinforcement Learning","authors":"Sina Montazeri, Haseebullah Jumakhan, Sonia Abrasiabian, Amir Mirzaeinia","doi":"arxiv-2408.11859","DOIUrl":"https://doi.org/arxiv-2408.11859","url":null,"abstract":"Building on our prior explorations of convolutional neural networks (CNNs)\u0000for financial data processing, this paper introduces two significant\u0000enhancements to refine our CNN model's predictive performance and robustness\u0000for financial tabular data. Firstly, we integrate a normalization layer at the\u0000input stage to ensure consistent feature scaling, addressing the issue of\u0000disparate feature magnitudes that can skew the learning process. This\u0000modification is hypothesized to aid in stabilizing the training dynamics and\u0000improving the model's generalization across diverse financial datasets.\u0000Secondly, we employ a Gradient Reduction Architecture, where earlier layers are\u0000wider and subsequent layers are progressively narrower. This enhancement is\u0000designed to enable the model to capture more complex and subtle patterns within\u0000the data, a crucial factor in accurately predicting financial outcomes. These\u0000advancements directly respond to the limitations identified in previous\u0000studies, where simpler models struggled with the complexity and variability\u0000inherent in financial applications. Initial tests confirm that these changes\u0000improve accuracy and model stability, suggesting that deeper and more nuanced\u0000network architectures can significantly benefit financial predictive tasks.\u0000This paper details the implementation of these enhancements and evaluates their\u0000impact on the model's performance in a controlled experimental setting.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208131","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

Stochastic Calculus for Option Pricing with Convex Duality, Logistic Model, and Numerical Examination 期权定价的随机微积分与凸对偶、逻辑模型和数值检验

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-11 DOI: arxiv-2408.05672

Zheng Cao

引用次数: 0

Why Groups Matter: Necessity of Group Structures in Attributions 群体为何重要？群体结构在归因中的必要性

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-11 DOI: arxiv-2408.05701

Dangxing Chen, Jingfeng Chen, Weicheng Ye

引用次数: 0

Consumer Transactions Simulation through Generative Adversarial Networks 通过生成式对抗网络模拟消费者交易

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-07 DOI: arxiv-2408.03655

Sergiy Tkachuk, Szymon Łukasik, Anna Wróblewska

{"title":"Consumer Transactions Simulation through Generative Adversarial Networks","authors":"Sergiy Tkachuk, Szymon Łukasik, Anna Wróblewska","doi":"arxiv-2408.03655","DOIUrl":"https://doi.org/arxiv-2408.03655","url":null,"abstract":"In the rapidly evolving domain of large-scale retail data systems,\u0000envisioning and simulating future consumer transactions has become a crucial\u0000area of interest. It offers significant potential to fortify demand forecasting\u0000and fine-tune inventory management. This paper presents an innovative\u0000application of Generative Adversarial Networks (GANs) to generate synthetic\u0000retail transaction data, specifically focusing on a novel system architecture\u0000that combines consumer behavior modeling with stock-keeping unit (SKU)\u0000availability constraints to address real-world assortment optimization\u0000challenges. We diverge from conventional methodologies by integrating SKU data\u0000into our GAN architecture and using more sophisticated embedding methods (e.g.,\u0000hyper-graphs). This design choice enables our system to generate not only\u0000simulated consumer purchase behaviors but also reflects the dynamic interplay\u0000between consumer behavior and SKU availability -- an aspect often overlooked,\u0000among others, because of data scarcity in legacy retail simulation models. Our\u0000GAN model generates transactions under stock constraints, pioneering a\u0000resourceful experimental system with practical implications for real-world\u0000retail operation and strategy. Preliminary results demonstrate enhanced realism\u0000in simulated transactions measured by comparing generated items with real ones\u0000using methods employed earlier in related studies. This underscores the\u0000potential for more accurate predictive modeling.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"183 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968706","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