Journal of Finance and Data Science最新文献_第5页

Machine learning private equity returns 机器学习私募股权投资回报

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-10-18 DOI: 10.1016/j.jfds.2024.100141

Christian Tausch, Marcus Pietz

引用次数: 0

Corrigendum to “Topological tail dependence: evidence from forecasting realized volatility” [The Journal of Finance and Data Science 9 (2023) 100107] 拓扑尾部依赖性：预测实现波动率的证据》[《金融与数据科学杂志》9 (2023) 100107] 更正

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-06-06 DOI: 10.1016/j.jfds.2024.100135

Hugo Gobato Souto

引用次数: 0

Deep unsupervised anomaly detection in high-frequency markets 高频市场中的深度无监督异常检测

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-04-19 DOI: 10.1016/j.jfds.2024.100129

Cédric Poutré , Didier Chételat , Manuel Morales

{"title":"Deep unsupervised anomaly detection in high-frequency markets","authors":"Cédric Poutré , Didier Chételat , Manuel Morales","doi":"10.1016/j.jfds.2024.100129","DOIUrl":"https://doi.org/10.1016/j.jfds.2024.100129","url":null,"abstract":"<div><p>Inspired by recent advances in the deep learning literature, this article introduces a novel hybrid anomaly detection framework specifically designed for limit order book (LOB) data. A modified Transformer autoencoder architecture is proposed to learn rich temporal LOB subsequence representations, which eases the separability of normal and fraudulent time series. A dissimilarity function is then learned in the representation space to characterize normal LOB behavior, enabling the detection of any anomalous subsequences out-of-sample. We also develop a complete trade-based manipulation simulation methodology able to generate a variety of scenarios derived from actual trade–based fraud cases. The complete framework is tested on LOB data of five NASDAQ stocks in which we randomly insert synthetic quote stuffing, layering, and pump-and-dump manipulations. We show that the proposed asset-independent approach achieves new state-of-the-art fraud detection performance, without requiring any prior knowledge of manipulation patterns.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S240591882400014X/pdfft?md5=438a8d321f83d33f31a8f9a74e366e8e&pid=1-s2.0-S240591882400014X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140807851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Overlooked biases from misidentifications of causal structures 因果结构识别错误而产生的被忽视的偏差

Journal of Finance and Data Science Pub Date : 2024-11-01 Epub Date: 2024-02-29 DOI: 10.1016/j.jfds.2024.100127

Simone Cenci

引用次数: 0

Fintech, financial inclusion, digital currency, and CBDC 金融科技、普惠金融、数字货币和 CBDC

Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2024-01-04 DOI: 10.1016/j.jfds.2024.100115

Huining (Henry) Cao, Xiaoyan Zhang, Yi Huang, Yiping Huang, Bernard Yeung

引用次数: 0

Asset allocation using a Markov process of clustered efficient frontier coefficients states 利用聚类有效前沿系数状态的马尔可夫过程进行资产配置

Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-10-30 DOI: 10.1016/j.jfds.2023.100110

Nolan Alexander , William Scherer , Jamey Thompson

{"title":"Asset allocation using a Markov process of clustered efficient frontier coefficients states","authors":"Nolan Alexander , William Scherer , Jamey Thompson","doi":"10.1016/j.jfds.2023.100110","DOIUrl":"10.1016/j.jfds.2023.100110","url":null,"abstract":"<div><p>We propose a novel asset allocation model using a Markov process of states defined by clustered efficient frontier coefficients. While most research in Markov models of the market characterize regimes using return and volatility, we instead propose characterizing these states using efficient frontiers, which provide more information on the interactions of underlying assets that comprise the market. Efficient frontiers can be decomposed to their functional form, a square-root second-order polynomial defined by three coefficients, to provide a dimensionality reduction of the return vector and covariance matrix. Each month, the proposed model hierarchically clusters the monthly coefficients data up to the current month, to characterize the market states, then defines a Markov process on the sequence of states. To incorporate these states into portfolio optimization, for each state, we calculate the tangency portfolio using only return data in that state. We then take the expectation of these weights for each state, weighted by the probability of transitioning from the current state to each state. To empirically validate our proposed model, we employ three sets of assets that span the market, and show that our proposed model significantly outperforms benchmark portfolios.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100110"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918823000260/pdfft?md5=11224414382844d224bb072b3978b6e7&pid=1-s2.0-S2405918823000260-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136152404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

CentralBankRoBERTa: A fine-tuned large language model for central bank communications CentralBankRoBERTa：用于中央银行通信的微调大型语言模型

Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-12-12 DOI: 10.1016/j.jfds.2023.100114

Moritz Pfeifer , Vincent P. Marohl

{"title":"CentralBankRoBERTa: A fine-tuned large language model for central bank communications","authors":"Moritz Pfeifer , Vincent P. Marohl","doi":"10.1016/j.jfds.2023.100114","DOIUrl":"10.1016/j.jfds.2023.100114","url":null,"abstract":"<div><p>Central bank communications are an important tool for guiding the economy and fulfilling monetary policy goals. Natural language processing (NLP) algorithms have been used to analyze central bank communications. These outdated bag-of-words methods often ignore context and cannot distinguish who these sentiments are addressing. Recent research has introduced deep-learning-based NLP algorithms, also known as large language models (LLMs), which take context into account. This study applies LLMs to central bank communications and constructs CentralBankRoBERTa, a state-of-the-art economic agent classifier that distinguishes five basic macroeconomic agents and binary sentiment classifier that identifies the emotional content of sentences in central bank communications. The absence of large-language models in the central bank communications literature may be attributed to a lack of appropriately labeled datasets. To address this gap, we introduce our model, CentralBankRoBERTa, offering an easy-to-use and standardized tool for scholars of central bank communications.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100114"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918823000302/pdfft?md5=f9ee367fba22f338380c708e9da5b5a4&pid=1-s2.0-S2405918823000302-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139012960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hedging using reinforcement learning: Contextual k-armed bandit versus Q-learning 使用强化学习的套期保值:语境k-武装强盗与q -学习

Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-06-22 DOI: 10.1016/j.jfds.2023.100101

Loris Cannelli , Giuseppe Nuti , Marzio Sala , Oleg Szehr

{"title":"Hedging using reinforcement learning: Contextual k-armed bandit versus Q-learning","authors":"Loris Cannelli , Giuseppe Nuti , Marzio Sala , Oleg Szehr","doi":"10.1016/j.jfds.2023.100101","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100101","url":null,"abstract":"<div><p>The construction of replication strategies for contingent claims in the presence of risk and market friction is a key problem of financial engineering. In real markets, continuous replication, such as in the model of Black, Scholes and Merton (BSM), is not only unrealistic but is also undesirable due to high transaction costs. A variety of methods have been proposed to balance between effective replication and losses in the incomplete market setting. With the rise of Artificial Intelligence (AI), AI-based hedgers have attracted considerable interest, where particular attention is given to Recurrent Neural Network systems and variations of the <em>Q</em>-learning algorithm. From a practical point of view, sufficient samples for training such an AI can only be obtained from a simulator of the market environment. Yet if an agent is trained solely on simulated data, the run-time performance will primarily reflect the accuracy of the simulation, which leads to the classical problem of model choice and calibration. In this article, the hedging problem is viewed as an instance of a risk-averse contextual <em>k</em>-armed bandit problem, which is motivated by the simplicity and sample-efficiency of the architecture, which allows for realistic online model updates from real-world data. We find that the <em>k</em>-armed bandit model naturally fits to the Profit and Loss formulation of hedging, providing for a more accurate and sample efficient approach than <em>Q</em>-learning and reducing to the Black-Scholes model in the absence of transaction costs and risks.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100101"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49903014","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: 6

Making it into a successful series a funding: An analysis of Crunchbase and LinkedIn data 成功的a轮融资:对Crunchbase和LinkedIn数据的分析

Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-06-20 DOI: 10.1016/j.jfds.2023.100099

Yiea-Funk Te , Michèle Wieland , Martin Frey , Asya Pyatigorskaya , Penny Schiffer , Helmut Grabner

{"title":"Making it into a successful series a funding: An analysis of Crunchbase and LinkedIn data","authors":"Yiea-Funk Te , Michèle Wieland , Martin Frey , Asya Pyatigorskaya , Penny Schiffer , Helmut Grabner","doi":"10.1016/j.jfds.2023.100099","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100099","url":null,"abstract":"<div><p>Startups are a key force driving economic development, and the success of these high-risk ventures can bring huge profits to venture capital firms. The ability to predict the success of startups is a major advantage for investors to outperform their competitors. In this study, we explore the potential of using publicly available LinkedIn profiles as an alternative and complementary data source to Crunchbase for predicting startup success. We provide a comprehensive review of the existing literature on the factors that influence startup success to create a large set of features for predictive modeling. We train two models for predicting startup success employing light gradient boosting that use LinkedIn data as a standalone and as a complementary data source, and compare them to baseline models based on Crunchbase data. We show that using LinkedIn as a complementary data source yields the best result with a mean area under the curve (AUC) value of 84%. We also provide a thorough analysis of what types of information contribute most to modeling startup success using the Shapley value method. Our models and analysis can be used to develop a decision support system to facilitate startup screening and the due diligence process for venture capital firms.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100099"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49903015","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: 1

An analysis of conditional mean-variance portfolio performance using hierarchical clustering 利用分层聚类分析条件均值-方差投资组合绩效

Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-11-13 DOI: 10.1016/j.jfds.2023.100112

Stephen R. Owen

引用次数: 0