arXiv - QuantFin - Computational Finance最新文献

A deep primal-dual BSDE method for optimal stopping problems 优化停止问题的深度原始双 BSDE 方法

arXiv - QuantFin - Computational Finance Pub Date : 2024-09-11 DOI: arxiv-2409.06937

Jiefei Yang, Guanglian Li

引用次数: 0

Robust financial calibration: a Bayesian approach for neural SDEs 稳健的财务校准：神经 SDE 的贝叶斯方法

arXiv - QuantFin - Computational Finance Pub Date : 2024-09-10 DOI: arxiv-2409.06551

Christa Cuchiero, Eva Flonner, Kevin Kurt

引用次数: 0

MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction MANA-Net：利用新闻加权减轻聚合情感同质化，增强市场预测能力

arXiv - QuantFin - Computational Finance Pub Date : 2024-09-09 DOI: arxiv-2409.05698

Mengyu Wang, Tiejun Ma

{"title":"MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction","authors":"Mengyu Wang, Tiejun Ma","doi":"arxiv-2409.05698","DOIUrl":"https://doi.org/arxiv-2409.05698","url":null,"abstract":"It is widely acknowledged that extracting market sentiments from news data\u0000benefits market predictions. However, existing methods of using financial\u0000sentiments remain simplistic, relying on equal-weight and static aggregation to\u0000manage sentiments from multiple news items. This leads to a critical issue\u0000termed ``Aggregated Sentiment Homogenization'', which has been explored through\u0000our analysis of a large financial news dataset from industry practice. This\u0000phenomenon occurs when aggregating numerous sentiments, causing representations\u0000to converge towards the mean values of sentiment distributions and thereby\u0000smoothing out unique and important information. Consequently, the aggregated\u0000sentiment representations lose much predictive value of news data. To address\u0000this problem, we introduce the Market Attention-weighted News Aggregation\u0000Network (MANA-Net), a novel method that leverages a dynamic market-news\u0000attention mechanism to aggregate news sentiments for market prediction.\u0000MANA-Net learns the relevance of news sentiments to price changes and assigns\u0000varying weights to individual news items. By integrating the news aggregation\u0000step into the networks for market prediction, MANA-Net allows for trainable\u0000sentiment representations that are optimized directly for prediction. We\u0000evaluate MANA-Net using the S&P 500 and NASDAQ 100 indices, along with\u0000financial news spanning from 2003 to 2018. Experimental results demonstrate\u0000that MANA-Net outperforms various recent market prediction methods, enhancing\u0000Profit & Loss by 1.1% and the daily Sharpe ratio by 0.252.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226610","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

QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCE QuantFactor REINFORCE：利用有方差限制的 REINFORCE 挖掘稳定的公式化阿尔法因子

arXiv - QuantFin - Computational Finance Pub Date : 2024-09-08 DOI: arxiv-2409.05144

Junjie Zhao, Chengxi Zhang, Min Qin, Peng Yang

{"title":"QuantFactor REINFORCE: Mining Steady Formulaic Alpha Factors with Variance-bounded REINFORCE","authors":"Junjie Zhao, Chengxi Zhang, Min Qin, Peng Yang","doi":"arxiv-2409.05144","DOIUrl":"https://doi.org/arxiv-2409.05144","url":null,"abstract":"The goal of alpha factor mining is to discover indicative signals of\u0000investment opportunities from the historical financial market data of assets.\u0000Deep learning based alpha factor mining methods have shown to be powerful,\u0000which, however, lack of the interpretability, making them unacceptable in the\u0000risk-sensitive real markets. Alpha factors in formulaic forms are more\u0000interpretable and therefore favored by market participants, while the search\u0000space is complex and powerful explorative methods are urged. Recently, a\u0000promising framework is proposed for generating formulaic alpha factors using\u0000deep reinforcement learning, and quickly gained research focuses from both\u0000academia and industries. This paper first argues that the originally employed\u0000policy training method, i.e., Proximal Policy Optimization (PPO), faces several\u0000important issues in the context of alpha factors mining, making it ineffective\u0000to explore the search space of the formula. Herein, a novel reinforcement\u0000learning based on the well-known REINFORCE algorithm is proposed. Given that\u0000the underlying state transition function adheres to the Dirac distribution, the\u0000Markov Decision Process within this framework exhibit minimal environmental\u0000variability, making REINFORCE algorithm more appropriate than PPO. A new\u0000dedicated baseline is designed to theoretically reduce the commonly suffered\u0000high variance of REINFORCE. Moreover, the information ratio is introduced as a\u0000reward shaping mechanism to encourage the generation of steady alpha factors\u0000that can better adapt to changes in market volatility. Experimental evaluations\u0000on various real assets data show that the proposed algorithm can increase the\u0000correlation with asset returns by 3.83%, and a stronger ability to obtain\u0000excess returns compared to the latest alpha factors mining methods, which meets\u0000the theoretical results well.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208120","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

Signature of maturity in cryptocurrency volatility 加密货币波动的成熟标志

arXiv - QuantFin - Computational Finance Pub Date : 2024-09-05 DOI: arxiv-2409.03676

Asim Ghosh, Soumyajyoti Biswas, Bikas K. Chakrabarti

引用次数: 0

Pricing American Options using Machine Learning Algorithms 使用机器学习算法为美式期权定价

arXiv - QuantFin - Computational Finance Pub Date : 2024-09-05 DOI: arxiv-2409.03204

Prudence Djagba, Callixte Ndizihiwe

{"title":"Pricing American Options using Machine Learning Algorithms","authors":"Prudence Djagba, Callixte Ndizihiwe","doi":"arxiv-2409.03204","DOIUrl":"https://doi.org/arxiv-2409.03204","url":null,"abstract":"This study investigates the application of machine learning algorithms,\u0000particularly in the context of pricing American options using Monte Carlo\u0000simulations. Traditional models, such as the Black-Scholes-Merton framework,\u0000often fail to adequately address the complexities of American options, which\u0000include the ability for early exercise and non-linear payoff structures. By\u0000leveraging Monte Carlo methods in conjunction Least Square Method machine\u0000learning was used. This research aims to improve the accuracy and efficiency of\u0000option pricing. The study evaluates several machine learning models, including\u0000neural networks and decision trees, highlighting their potential to outperform\u0000traditional approaches. The results from applying machine learning algorithm in\u0000LSM indicate that integrating machine learning with Monte Carlo simulations can\u0000enhance pricing accuracy and provide more robust predictions, offering\u0000significant insights into quantitative finance by merging classical financial\u0000theories with modern computational techniques. The dataset was split into\u0000features and the target variable representing bid prices, with an 80-20\u0000train-validation split. LSTM and GRU models were constructed using TensorFlow's\u0000Keras API, each with four hidden layers of 200 neurons and an output layer for\u0000bid price prediction, optimized with the Adam optimizer and MSE loss function.\u0000The GRU model outperformed the LSTM model across all evaluated metrics,\u0000demonstrating lower mean absolute error, mean squared error, and root mean\u0000squared error, along with greater stability and efficiency in training.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208122","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

MoA is All You Need: Building LLM Research Team using Mixture of Agents MoA 是你所需要的一切：利用混合代理建立法学硕士研究团队

arXiv - QuantFin - Computational Finance Pub Date : 2024-09-04 DOI: arxiv-2409.07487

Sandy Chen, Leqi Zeng, Abhinav Raghunathan, Flora Huang, Terrence C. Kim

{"title":"MoA is All You Need: Building LLM Research Team using Mixture of Agents","authors":"Sandy Chen, Leqi Zeng, Abhinav Raghunathan, Flora Huang, Terrence C. Kim","doi":"arxiv-2409.07487","DOIUrl":"https://doi.org/arxiv-2409.07487","url":null,"abstract":"Large Language Models (LLMs) research in the financial domain is particularly\u0000complex due to the sheer number of approaches proposed in literature.\u0000Retrieval-Augmented Generation (RAG) has emerged as one of the leading methods\u0000in the sector due to its inherent groundedness and data source variability. In\u0000this work, we introduce a RAG framework called Mixture of Agents (MoA) and\u0000demonstrate its viability as a practical, customizable, and highly effective\u0000approach for scaling RAG applications. MoA is essentially a layered network of\u0000individually customized small language models (Hoffmann et al., 2022)\u0000collaborating to answer questions and extract information. While there are many\u0000theoretical propositions for such an architecture and even a few libraries for\u0000generally applying the structure in practice, there are limited documented\u0000studies evaluating the potential of this framework considering real business\u0000constraints such as cost and speed. We find that the MoA framework, consisting\u0000of small language models (Hoffmann et al., 2022), produces higher quality and\u0000more grounded responses across various financial domains that are core to\u0000Vanguard's business while simultaneously maintaining low costs.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208125","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

Attention-Based Reading, Highlighting, and Forecasting of the Limit Order Book 基于注意力的限价订单簿阅读、突出显示和预测

arXiv - QuantFin - Computational Finance Pub Date : 2024-09-03 DOI: arxiv-2409.02277

Jiwon Jung, Kiseop Lee

引用次数: 0

Advancing Financial Forecasting: A Comparative Analysis of Neural Forecasting Models N-HiTS and N-BEATS 推进金融预测：神经预测模型 N-HiTS 和 N-BEATS 的比较分析

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-31 DOI: arxiv-2409.00480

Mohit Apte, Yashodhara Haribhakta

引用次数: 0

Trading with Time Series Causal Discovery: An Empirical Study 利用时间序列因果发现进行交易：实证研究

arXiv - QuantFin - Computational Finance Pub Date : 2024-08-28 DOI: arxiv-2408.15846

Ruijie Tang

引用次数: 0