Journal of Finance and Data Science最新文献_第2页

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 DOI: 10.1016/j.jfds.2024.100115

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

引用次数: 0

A general framework for portfolio construction based on generative models of asset returns 基于资产收益生成模型的投资组合构建总体框架

Journal of Finance and Data Science Pub Date : 2023-11-01 DOI: 10.1016/j.jfds.2023.100113

Tuoyuan Cheng , Kan Chen

{"title":"A general framework for portfolio construction based on generative models of asset returns","authors":"Tuoyuan Cheng , Kan Chen","doi":"10.1016/j.jfds.2023.100113","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100113","url":null,"abstract":"<div><p>In this paper, we present an integrated approach to portfolio construction and optimization, leveraging high-performance computing capabilities. We first explore diverse pairings of generative model forecasts and objective functions used for portfolio optimization, which are evaluated using performance-attribution models based on least absolute shrinkage and selection operator (LASSO). We illustrate our approach using extensive simulations of crypto-currency portfolios, and we show that the portfolios constructed using the vine-copula generative model and the Sharpe-ratio objective function consistently outperform. To accommodate a wide array of investment strategies, we further investigate portfolio blending and propose a general framework for evaluating and combining investment strategies. We employ an extension of the multi-armed bandit framework and use value models and policy models to construct eclectic blended portfolios based on past performance. We consider similarity and optimality measures for value models and employ probability-matching (“blending”) and a greedy algorithm (“switching”) for policy models. The eclectic portfolios are also evaluated using LASSO models. We show that the value model utilizing cosine similarity and logit optimality consistently delivers robust superior performances. The extent of outperformance by eclectic portfolios over their benchmarks significantly surpasses that achieved by individual generative model-based portfolios over their respective benchmarks.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918823000296/pdfft?md5=d79808b84c71271aae85d6e7e66cc3dd&pid=1-s2.0-S2405918823000296-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138839770","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

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 DOI: 10.1016/j.jfds.2023.100112

Stephen R. Owen

引用次数: 0

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 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":null,"pages":null},"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

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 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":null,"pages":null},"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

Topological tail dependence: Evidence from forecasting realized volatility 拓扑尾依赖:来自预测已实现波动的证据

Journal of Finance and Data Science Pub Date : 2023-11-01 DOI: 10.1016/j.jfds.2023.100107

Hugo Gobato Souto

引用次数: 2

A dynamic partial equilibrium model of capital gains taxation 资本利得税的动态局部均衡模型

Journal of Finance and Data Science Pub Date : 2023-11-01 DOI: 10.1016/j.jfds.2023.100111

Stephen L. Lenkey, Timothy T. Simin

引用次数: 0

Research frontiers of the Chinese financial markets 中国金融市场研究前沿

Journal of Finance and Data Science Pub Date : 2023-11-01 DOI: 10.1016/j.jfds.2024.100116

Hao Zhou

引用次数: 0

Machine learning in classifying bitcoin addresses 分类比特币地址的机器学习

Journal of Finance and Data Science Pub Date : 2023-11-01 DOI: 10.1016/j.jfds.2023.100109

Leonid Garin , Vladimir Gisin

{"title":"Machine learning in classifying bitcoin addresses","authors":"Leonid Garin , Vladimir Gisin","doi":"10.1016/j.jfds.2023.100109","DOIUrl":"10.1016/j.jfds.2023.100109","url":null,"abstract":"<div><p>The emergence of the Bitcoin cryptocurrency marked a new era of illegal transactions. Cryptocurrency provides some level of anonymity allowing its users to create an unlimited number of wallets with alias addresses, which makes it challenging to identify the actual user. This is used by criminals for the purpose of making illegal transactions. At the same time, Bitcoin stores and provides information about all committed transactions, which opens up opportunities for identifying suspicious behavior patterns in this network using data mining. The problem of detecting suspicious activity in the Bitcoin network can be solved with sufficiently high accuracy using machine learning methods. The paper provides a comparative study of various machine learning methods to solve the mentioned problem: logistic regression, decision tree, random forest, gradient boosting. Selecting hyper parameters, rebalancing the dataset, and active learning are particularly important. The most important hyperparameters of the algorithms are described. Metrics show that the gradient boosting looks the most promising. In total 38 features of bitcoin addresses were identified. The top features are presented in the paper.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918823000259/pdfft?md5=dcd018899c4df905d0f9dda9a9ff1a7a&pid=1-s2.0-S2405918823000259-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136127683","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

The inaugural Journal of Finance and Data Science Conference was held successfully in Beijing 首届《金融与数据科学杂志》大会在北京成功举办

Journal of Finance and Data Science Pub Date : 2023-11-01 DOI: 10.1016/j.jfds.2024.100119

引用次数: 0