Journal of Finance and Data Science最新文献_第4页

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 These outdated bag-of-words methods often ignore context and cannot distinguish who these sentiments are addressing.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":"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

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":"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

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":"9 ","pages":"Article 100109"},"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

Expert aggregation for financial forecasting 专家汇总财务预测

Journal of Finance and Data Science Pub Date : 2023-11-01 DOI: 10.1016/j.jfds.2023.100108

Carl Remlinger , Clémence Alasseur , Marie Brière , Joseph Mikael

{"title":"Expert aggregation for financial forecasting","authors":"Carl Remlinger , Clémence Alasseur , Marie Brière , Joseph Mikael","doi":"10.1016/j.jfds.2023.100108","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100108","url":null,"abstract":"<div><p>Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest. But choosing between several algorithms can be challenging, as their estimation accuracy may be unstable over time. Online aggregation of experts combine the forecasts of a finite set of models in a single approach without making any assumption about the models. In this paper, a Bernstein Online Aggregation (BOA) procedure is applied to the construction of long-short strategies built from individual stock return forecasts coming from different machine learning models. The online mixture of experts leads to attractive portfolio performances even in non-stationary environments. The inclusion of neural networks experts in the aggregation contributes to a better average return, while Ordinary Least Squares with Huber Loss experts contribute to lower risk. The aggregation outperforms individual algorithms, offering a higher portfolio Sharpe ratio, lower shortfall, with a similar turnover. Extensions to expert and aggregation specialisations are also proposed to improve the overall mixture on a family of portfolio evaluation metrics.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918823000247/pdfft?md5=65d3b507a8c88e3a7a8f29685f31fdb9&pid=1-s2.0-S2405918823000247-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139099853","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

Stock pledged loans and market crash risk: Evidence from China 股票质押贷款和市场崩溃风险:来自中国的证据

Journal of Finance and Data Science Pub Date : 2023-11-01 DOI: 10.1016/j.jfds.2023.100104

Feng Li , Jun Qian , Haofei Wang , Julie Lei Zhu

{"title":"Stock pledged loans and market crash risk: Evidence from China","authors":"Feng Li , Jun Qian , Haofei Wang , Julie Lei Zhu","doi":"10.1016/j.jfds.2023.100104","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100104","url":null,"abstract":"<div><p>Stock pledged loans have become prevalent among large shareholders of listed firms in China. The <em>largest</em> shareholder pledges a greater fraction of her holdings as collateral for credit when the firm is in growth industries, less profitable, <em>not</em> state owned, and has higher leverage. Stock performance of highly pledged firms is indistinguishable from that of firms with low pledge ratios in 2017. During 2018, however, highly pledged firms have worse stock returns and operating performance, and experienced ‘contagion’ – the crash risk of one highly pledged stock spreading to others. Using a regulatory reform in 2013 that allowed securities companies to provide stock pledged loans, we find that obtaining these personal loans had <em>no</em> adverse effects on the firms when the pledge ratio was low. Overall, forced sales of pledged stocks and worsened agency conflict are responsible for the poor performance of highly pledged firms during the 2018 bear market.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S240591882300020X/pdfft?md5=be8ce95c83d9d2d0e69e97a4f0bfc370&pid=1-s2.0-S240591882300020X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92046209","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