arXiv - QuantFin - Trading and Market Microstructure最新文献

Optimal position-building strategies in Competition 竞争中的最佳阵地建设战略

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-09-05 DOI: arxiv-2409.03586

Neil A. Chriss

引用次数: 0

MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model MarS：由生成式基础模型支持的金融市场模拟引擎

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-09-04 DOI: arxiv-2409.07486

Junjie Li, Yang Liu, Weiqing Liu, Shikai Fang, Lewen Wang, Chang Xu, Jiang Bian

{"title":"MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model","authors":"Junjie Li, Yang Liu, Weiqing Liu, Shikai Fang, Lewen Wang, Chang Xu, Jiang Bian","doi":"arxiv-2409.07486","DOIUrl":"https://doi.org/arxiv-2409.07486","url":null,"abstract":"Generative models aim to simulate realistic effects of various actions across\u0000different contexts, from text generation to visual effects. Despite efforts to\u0000build real-world simulators, leveraging generative models for virtual worlds,\u0000like financial markets, remains underexplored. In financial markets, generative\u0000models can simulate market effects of various behaviors, enabling interaction\u0000with market scenes and players, and training strategies without financial risk.\u0000This simulation relies on the finest structured data in financial market like\u0000orders thus building the finest realistic simulation. We propose Large Market\u0000Model (LMM), an order-level generative foundation model, for financial market\u0000simulation, akin to language modeling in the digital world. Our financial\u0000Market Simulation engine (MarS), powered by LMM, addresses the need for\u0000realistic, interactive and controllable order generation. Key objectives of\u0000this paper include evaluating LMM's scaling law in financial markets, assessing\u0000MarS's realism, balancing controlled generation with market impact, and\u0000demonstrating MarS's potential applications. We showcase MarS as a forecast\u0000tool, detection system, analysis platform, and agent training environment. Our\u0000contributions include pioneering a generative model for financial markets,\u0000designing MarS to meet domain-specific needs, and demonstrating MarS-based\u0000applications' industry potential.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194500","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

Logarithmic regret in the ergodic Avellaneda-Stoikov market making model 阿韦拉内达-斯托伊科夫做市商遍历模型中的对数遗憾

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-09-03 DOI: arxiv-2409.02025

Jialun Cao, David Šiška, Lukasz Szpruch, Tanut Treetanthiploet

引用次数: 0

A Financial Time Series Denoiser Based on Diffusion Model 基于扩散模型的金融时间序列去噪器

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-09-02 DOI: arxiv-2409.02138

Zhuohan Wang, Carmine Ventre

{"title":"A Financial Time Series Denoiser Based on Diffusion Model","authors":"Zhuohan Wang, Carmine Ventre","doi":"arxiv-2409.02138","DOIUrl":"https://doi.org/arxiv-2409.02138","url":null,"abstract":"Financial time series often exhibit low signal-to-noise ratio, posing\u0000significant challenges for accurate data interpretation and prediction and\u0000ultimately decision making. Generative models have gained attention as powerful\u0000tools for simulating and predicting intricate data patterns, with the diffusion\u0000model emerging as a particularly effective method. This paper introduces a\u0000novel approach utilizing the diffusion model as a denoiser for financial time\u0000series in order to improve data predictability and trading performance. By\u0000leveraging the forward and reverse processes of the conditional diffusion model\u0000to add and remove noise progressively, we reconstruct original data from noisy\u0000inputs. Our extensive experiments demonstrate that diffusion model-based\u0000denoised time series significantly enhance the performance on downstream future\u0000return classification tasks. Moreover, trading signals derived from the\u0000denoised data yield more profitable trades with fewer transactions, thereby\u0000minimizing transaction costs and increasing overall trading efficiency.\u0000Finally, we show that by using classifiers trained on denoised time series, we\u0000can recognize the noising state of the market and obtain excess return.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"162 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194496","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

Simulation of Social Media-Driven Bubble Formation in Financial Markets using an Agent-Based Model with Hierarchical Influence Network 利用基于代理的分层影响网络模型模拟社交媒体驱动的金融市场泡沫形成

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-09-01 DOI: arxiv-2409.00742

Gonzalo Bohorquez, John Cartlidge

引用次数: 0

Bitcoin ETF: Opportunities and risk 比特币 ETF：机遇与风险

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-08-30 DOI: arxiv-2409.00270

Di Wu

{"title":"Bitcoin ETF: Opportunities and risk","authors":"Di Wu","doi":"arxiv-2409.00270","DOIUrl":"https://doi.org/arxiv-2409.00270","url":null,"abstract":"The year 2024 witnessed a major development in the cryptocurrency industry\u0000with the long-awaited approval of spot Bitcoin exchange-traded funds (ETFs).\u0000This innovation provides investors with a new, regulated path to gain exposure\u0000to Bitcoin through a familiar investment vehicle (Kumar et al., 2024). However,\u0000unlike traditional ETFs that directly hold underlying assets, Bitcoin ETFs rely\u0000on a creation and redemption process managed by authorized participants (APs).\u0000This unique structure introduces distinct characteristics in terms of\u0000premium/discount behavior compared to traditional ETFs. This paper investigates\u0000the premium and discount patterns observed in Bitcoin ETFs during first\u0000four-month period (January 11th, 2024, to May 17th, 2024). Our analysis reveals\u0000that these patterns differ significantly from those observed in traditional\u0000index ETFs, potentially exposing investors to additional risk factors. By\u0000identifying and analyzing these risk factors associated with Bitcoin ETF\u0000premiums/discounts, this paper aims to achieve two key objectives: Enhance\u0000market understanding: Equip and market and investors with a deeper\u0000comprehension of the unique liquidity risks inherent in Bitcoin ETFs. Provide a\u0000clearer risk management frameworks: Offer a clearer perspective on the\u0000risk-return profile of digital asset ETFs, specifically focusing on Bitcoin\u0000ETFs. Through a thorough analysis of premium/discount behavior and the\u0000underlying factors contributing to it, this paper strives to contribute\u0000valuable insights for investors navigating the evolving landscape of digital\u0000asset investments","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194497","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

Controllable Financial Market Generation with Diffusion Guided Meta Agent 利用扩散引导元代理生成可控金融市场

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-08-23 DOI: arxiv-2408.12991

Yu-Hao Huang, Chang Xu, Yang Liu, Weiqing Liu, Wu-Jun Li, Jiang Bian

引用次数: 0

MEV Capture and Decentralization in Execution Tickets 执行票中的 MEV 捕获和权力下放

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-08-21 DOI: arxiv-2408.11255

Jonah Burian, Davide Crapis, Fahad Saleh

引用次数: 0

Less is more: AI Decision-Making using Dynamic Deep Neural Networks for Short-Term Stock Index Prediction 少即是多：利用动态深度神经网络进行短期股指预测的人工智能决策

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-08-21 DOI: arxiv-2408.11740

CJ Finnegan, James F. McCann, Salissou Moutari

{"title":"Less is more: AI Decision-Making using Dynamic Deep Neural Networks for Short-Term Stock Index Prediction","authors":"CJ Finnegan, James F. McCann, Salissou Moutari","doi":"arxiv-2408.11740","DOIUrl":"https://doi.org/arxiv-2408.11740","url":null,"abstract":"In this paper we introduce a multi-agent deep-learning method which trades in\u0000the Futures markets based on the US S&P 500 index. The method (referred to as\u0000Model A) is an innovation founded on existing well-established machine-learning\u0000models which sample market prices and associated derivatives in order to decide\u0000whether the investment should be long/short or closed (zero exposure), on a\u0000day-to-day decision. We compare the predictions with some conventional\u0000machine-learning methods namely, Long Short-Term Memory, Random Forest and\u0000Gradient-Boosted-Trees. Results are benchmarked against a passive model in\u0000which the Futures contracts are held (long) continuously with the same exposure\u0000(level of investment). Historical tests are based on daily daytime trading\u0000carried out over a period of 6 calendar years (2018-23). We find that Model A\u0000outperforms the passive investment in key performance metrics, placing it\u0000within the top quartile performance of US Large Cap active fund managers. Model\u0000A also outperforms the three machine-learning classification comparators over\u0000this period. We observe that Model A is extremely efficient (doing less and\u0000getting more) with an exposure to the market of only 41.95% compared to the\u0000100% market exposure of the passive investment, and thus provides increased\u0000profitability with reduced risk.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194503","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

High-Frequency Trading Liquidity Analysis | Application of Machine Learning Classification 高频交易流动性分析｜机器学习分类的应用

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-08-19 DOI: arxiv-2408.10016

Sid Bhatia, Sidharth Peri, Sam Friedman, Michelle Malen

{"title":"High-Frequency Trading Liquidity Analysis | Application of Machine Learning Classification","authors":"Sid Bhatia, Sidharth Peri, Sam Friedman, Michelle Malen","doi":"arxiv-2408.10016","DOIUrl":"https://doi.org/arxiv-2408.10016","url":null,"abstract":"This research presents a comprehensive framework for analyzing liquidity in\u0000financial markets, particularly in the context of high-frequency trading. By\u0000leveraging advanced machine learning classification techniques, including\u0000Logistic Regression, Support Vector Machine, and Random Forest, the study aims\u0000to predict minute-level price movements using an extensive set of liquidity\u0000metrics derived from the Trade and Quote (TAQ) data. The findings reveal that\u0000employing a broad spectrum of liquidity measures yields higher predictive\u0000accuracy compared to models utilizing a reduced subset of features. Key\u0000liquidity metrics, such as Liquidity Ratio, Flow Ratio, and Turnover,\u0000consistently emerged as significant predictors across all models, with the\u0000Random Forest algorithm demonstrating superior accuracy. This study not only\u0000underscores the critical role of liquidity in market stability and transaction\u0000costs but also highlights the complexities involved in short-interval market\u0000predictions. The research suggests that a comprehensive set of liquidity\u0000measures is essential for accurate prediction, and proposes future work to\u0000validate these findings across different stock datasets to assess their\u0000generalizability.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194504","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