arXiv - QuantFin - Computational Finance最新文献_第4页

Evaluating Microscopic and Macroscopic Models for Derivative Contracts on Commodity Indices 评估商品指数衍生合约的微观和宏观模型

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-17 DOI: arxiv-2408.00784

Alberto Manzano, Emanuele Nastasi, Andrea Pallavicini, Carlos Vázquez

引用次数: 0

FinDKG: Dynamic Knowledge Graphs with Large Language Models for Detecting Global Trends in Financial Markets FinDKG：利用大型语言模型检测金融市场全球趋势的动态知识图谱

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-15 DOI: arxiv-2407.10909

Xiaohui Victor Li, Francesco Sanna Passino

{"title":"FinDKG: Dynamic Knowledge Graphs with Large Language Models for Detecting Global Trends in Financial Markets","authors":"Xiaohui Victor Li, Francesco Sanna Passino","doi":"arxiv-2407.10909","DOIUrl":"https://doi.org/arxiv-2407.10909","url":null,"abstract":"Dynamic knowledge graphs (DKGs) are popular structures to express different\u0000types of connections between objects over time. They can also serve as an\u0000efficient mathematical tool to represent information extracted from complex\u0000unstructured data sources, such as text or images. Within financial\u0000applications, DKGs could be used to detect trends for strategic thematic\u0000investing, based on information obtained from financial news articles. In this\u0000work, we explore the properties of large language models (LLMs) as dynamic\u0000knowledge graph generators, proposing a novel open-source fine-tuned LLM for\u0000this purpose, called the Integrated Contextual Knowledge Graph Generator\u0000(ICKG). We use ICKG to produce a novel open-source DKG from a corpus of\u0000financial news articles, called FinDKG, and we propose an attention-based GNN\u0000architecture for analysing it, called KGTransformer. We test the performance of\u0000the proposed model on benchmark datasets and FinDKG, demonstrating superior\u0000performance on link prediction tasks. Additionally, we evaluate the performance\u0000of the KGTransformer on FinDKG for thematic investing, showing it can\u0000outperform existing thematic ETFs.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"2012 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719350","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

Machine learning in weekly movement prediction 每周运动预测中的机器学习

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-13 DOI: arxiv-2407.09831

Han Gui

{"title":"Machine learning in weekly movement prediction","authors":"Han Gui","doi":"arxiv-2407.09831","DOIUrl":"https://doi.org/arxiv-2407.09831","url":null,"abstract":"To predict the future movements of stock markets, numerous studies\u0000concentrate on daily data and employ various machine learning (ML) models as\u0000benchmarks that often vary and lack standardization across different research\u0000works. This paper tries to solve the problem from a fresh standpoint by aiming\u0000to predict the weekly movements, and introducing a novel benchmark of random\u0000traders. This benchmark is independent of any ML model, thus making it more\u0000objective and potentially serving as a commonly recognized standard. During\u0000training process, apart from the basic features such as technical indicators,\u0000scaling laws and directional changes are introduced as additional features,\u0000furthermore, the training datasets are also adjusted by assigning varying\u0000weights to different samples, the weighting approach allows the models to\u0000emphasize specific samples. On back-testing, several trained models show good\u0000performance, with the multi-layer perception (MLP) demonstrating stability and\u0000robustness across extensive and comprehensive data that include upward,\u0000downward and cyclic trends. The unique perspective of this work that focuses on\u0000weekly movements, incorporates new features and creates an objective benchmark,\u0000contributes to the existing literature on stock market prediction.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141719351","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

Attribution Methods in Asset Pricing: Do They Account for Risk? 资产定价中的归因方法：它们考虑风险了吗？

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-12 DOI: arxiv-2407.08953

Dangxing Chen, Yuan Gao

引用次数: 0

Unified continuous-time q-learning for mean-field game and mean-field control problems 均场博弈和均场控制问题的统一连续时间 q-learning

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-05 DOI: arxiv-2407.04521

Xiaoli Wei, Xiang Yu, Fengyi Yuan

{"title":"Unified continuous-time q-learning for mean-field game and mean-field control problems","authors":"Xiaoli Wei, Xiang Yu, Fengyi Yuan","doi":"arxiv-2407.04521","DOIUrl":"https://doi.org/arxiv-2407.04521","url":null,"abstract":"This paper studies the continuous-time q-learning in the mean-field\u0000jump-diffusion models from the representative agent's perspective. To overcome\u0000the challenge when the population distribution may not be directly observable,\u0000we introduce the integrated q-function in decoupled form (decoupled\u0000Iq-function) and establish its martingale characterization together with the\u0000value function, which provides a unified policy evaluation rule for both\u0000mean-field game (MFG) and mean-field control (MFC) problems. Moreover,\u0000depending on the task to solve the MFG or MFC problem, we can employ the\u0000decoupled Iq-function by different means to learn the mean-field equilibrium\u0000policy or the mean-field optimal policy respectively. As a result, we devise a\u0000unified q-learning algorithm for both MFG and MFC problems by utilizing all\u0000test policies stemming from the mean-field interactions. For several examples\u0000in the jump-diffusion setting, within and beyond the LQ framework, we can\u0000obtain the exact parameterization of the decoupled Iq-functions and the value\u0000functions, and illustrate our algorithm from the representative agent's\u0000perspective with satisfactory performance.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573213","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

GraphCNNpred: A stock market indices prediction using a Graph based deep learning system GraphCNNpred：使用基于图形的深度学习系统预测股市指数

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-04 DOI: arxiv-2407.03760

Yuhui Jin

引用次数: 0

The Structure of Financial Equity Research Reports -- Identification of the Most Frequently Asked Questions in Financial Analyst Reports to Automate Equity Research Using Llama 3 and GPT-4 金融股票研究报告的结构 -- 利用 Llama 3 和 GPT-4 识别金融分析师报告中最常见的问题，实现股票研究自动化

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-04 DOI: arxiv-2407.18327

Adria Pop, Jan Spörer, Siegfried Handschuh

{"title":"The Structure of Financial Equity Research Reports -- Identification of the Most Frequently Asked Questions in Financial Analyst Reports to Automate Equity Research Using Llama 3 and GPT-4","authors":"Adria Pop, Jan Spörer, Siegfried Handschuh","doi":"arxiv-2407.18327","DOIUrl":"https://doi.org/arxiv-2407.18327","url":null,"abstract":"This research dissects financial equity research reports (ERRs) by mapping\u0000their content into categories. There is insufficient empirical analysis of the questions answered in ERRs.\u0000In particular, it is not understood how frequently certain information appears,\u0000what information is considered essential, and what information requires human\u0000judgment to distill into an ERR. The study analyzes 72 ERRs sentence-by-sentence, classifying their 4940\u0000sentences into 169 unique question archetypes. We did not predefine the\u0000questions but derived them solely from the statements in the ERRs. This\u0000approach provides an unbiased view of the content of the observed ERRs.\u0000Subsequently, we used public corporate reports to classify the questions'\u0000potential for automation. Answers were labeled \"text-extractable\" if the\u0000answers to the question were accessible in corporate reports. 78.7% of the questions in ERRs can be automated. Those automatable question\u0000consist of 48.2% text-extractable (suited to processing by large language\u0000models, LLMs) and 30.5% database-extractable questions. Only 21.3% of questions\u0000require human judgment to answer. We empirically validate using Llama-3-70B and GPT-4-turbo-2024-04-09 that\u0000recent advances in language generation and information extraction enable the\u0000automation of approximately 80% of the statements in ERRs. Surprisingly, the\u0000models complement each other's strengths and weaknesses well. The research confirms that the current writing process of ERRs can likely\u0000benefit from additional automation, improving quality and efficiency. The\u0000research thus allows us to quantify the potential impacts of introducing large\u0000language models in the ERR writing process. The full question list, including the archetypes and their frequency, will be\u0000made available online after peer review.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141865953","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

CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications CatMemo 参加 FinLLM 挑战任务：利用金融应用中的数据融合微调大型语言模型

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-02 DOI: arxiv-2407.01953

Yupeng Cao, Zhiyuan Yao, Zhi Chen, Zhiyang Deng

引用次数: 0

Predicting public market behavior from private equity deals 从私募股权交易预测公共市场行为

arXiv - QuantFin - Computational Finance Pub Date : 2024-07-01 DOI: arxiv-2407.01818

Paolo Barucca, Flaviano Morone

引用次数: 0

AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors AlphaForge：挖掘并动态组合公式化阿尔法因子的框架

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-26 DOI: arxiv-2406.18394

Hao Shi, Cuicui Luo, Weili Song, Xinting Zhang, Xiang Ao

{"title":"AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors","authors":"Hao Shi, Cuicui Luo, Weili Song, Xinting Zhang, Xiang Ao","doi":"arxiv-2406.18394","DOIUrl":"https://doi.org/arxiv-2406.18394","url":null,"abstract":"The variability and low signal-to-noise ratio in financial data, combined\u0000with the necessity for interpretability, make the alpha factor mining workflow\u0000a crucial component of quantitative investment. Transitioning from early manual\u0000extraction to genetic programming, the most advanced approach in this domain\u0000currently employs reinforcement learning to mine a set of combination factors\u0000with fixed weights. However, the performance of resultant alpha factors\u0000exhibits inconsistency, and the inflexibility of fixed factor weights proves\u0000insufficient in adapting to the dynamic nature of financial markets. To address\u0000this issue, this paper proposes a two-stage formulaic alpha generating\u0000framework AlphaForge, for alpha factor mining and factor combination. This\u0000framework employs a generative-predictive neural network to generate factors,\u0000leveraging the robust spatial exploration capabilities inherent in deep\u0000learning while concurrently preserving diversity. The combination model within\u0000the framework incorporates the temporal performance of factors for selection\u0000and dynamically adjusts the weights assigned to each component alpha factor.\u0000Experiments conducted on real-world datasets demonstrate that our proposed\u0000model outperforms contemporary benchmarks in formulaic alpha factor mining.\u0000Furthermore, our model exhibits a notable enhancement in portfolio returns\u0000within the realm of quantitative investment.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501637","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