Journal of Finance and Data Science最新文献_第6页

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 Epub Date: 2023-11-30 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":"9 ","pages":"Article 100113"},"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

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 Epub Date: 2023-10-14 DOI: 10.1016/j.jfds.2023.100107

Hugo Gobato Souto

引用次数: 2

The cross-section of Chinese corporate bond returns 中国公司债券收益率的横截面

Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-06-20 DOI: 10.1016/j.jfds.2023.100100

Xiaoyan Zhang, Zijian Zhang

引用次数: 1

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 Epub Date: 2023-11-22 DOI: 10.1016/j.jfds.2023.100111

Stephen L. Lenkey, Timothy T. Simin

引用次数: 0

The applications of big data in the insurance industry: A bibliometric and systematic review of relevant literature 大数据在保险业的应用:文献计量学和相关文献的系统回顾

Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-07-20 DOI: 10.1016/j.jfds.2023.100102

Nejla Ellili , Haitham Nobanee , Lama Alsaiari , Hiba Shanti , Bettylucille Hillebrand , Nadeen Hassanain , Leen Elfout

{"title":"The applications of big data in the insurance industry: A bibliometric and systematic review of relevant literature","authors":"Nejla Ellili , Haitham Nobanee , Lama Alsaiari , Hiba Shanti , Bettylucille Hillebrand , Nadeen Hassanain , Leen Elfout","doi":"10.1016/j.jfds.2023.100102","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100102","url":null,"abstract":"<div><p>The insurance industry has changed rapidly over the last few decades. One factor in this change is the continuous growth of massive amounts of data that need to be processed properly to be optimally utilized. This has led to a strong wave of advanced processing technologies that can systematically manage big datasets, such as machine learning and artificial intelligence. This study analyzes the current state of research on big data and insurance. Bibliometric analysis and a systematic review were conducted to analyze the patterns and trends of the subject area, with the main focus on citations as a key measurement unit. This analysis is important to fill the existing gap in the examined area because no other bibliometric analysis has been conducted previously on the same subject; it will also help in establishing a scientific background for future research. The research findings verify that the United States is the most popular and cited country in the research area of big data and insurance at both the single authorship and co-authorship levels. Finally, the major impact of the relationship between big data and the insurance sector was marked by human-related aspects.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100102"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49903011","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

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 Epub Date: 2024-01-09 DOI: 10.1016/j.jfds.2024.100116

Hao Zhou

引用次数: 0

OptionNet: A multiscale residual deep learning model with confidence interval to predict option price OptionNet:基于置信区间的多尺度残差深度学习模型的期权价格预测

Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-09-11 DOI: 10.1016/j.jfds.2023.100105

Luwei Lin , Meiqing Wang , Hang Cheng , Rong Liu , Fei Chen

{"title":"OptionNet: A multiscale residual deep learning model with confidence interval to predict option price","authors":"Luwei Lin , Meiqing Wang , Hang Cheng , Rong Liu , Fei Chen","doi":"10.1016/j.jfds.2023.100105","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100105","url":null,"abstract":"<div><p>Option is an important financial derivative. Accurate option pricing is essential to the development of financial markets. For option pricing, existing time series models and neural networks are difficult to extract multi-scale temporal features from option data, which greatly limits their performance. To solve this problem, we propose a novel deep learning model named as MRC-LSTM-CI. It contains three modules, including Multi-scale Residual CNN module (MRC), Long Short-Term Memory neural network module (LSTM) and confidence interval output module (CI). The proposed model can effectively extract multi-scale features from real market option data, and make interval prediction to provide more information to the decision maker. In addition, the proposed model is further improved using the residual prediction strategy, where the output value is chosen as the residual value between BS theory price and actual market price. Experimental results show that our model has better prediction accuracy than other deep learning models and achieves the state-of-the-art performance.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49874015","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

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 Epub Date: 2023-10-29 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

Investigating the impact financial content structure has on consumer appreciation: An empirical study of Australian statement of advice documents 调查财务内容结构对消费者欣赏的影响:澳大利亚咨询意见声明文件的实证研究

Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-08-25 DOI: 10.1016/j.jfds.2023.100103

Ben Neilson

引用次数: 1

Data-driven estimation of economic indicators with search big data in discontinuous situation 不连续情况下基于搜索大数据的经济指标数据驱动估计

Journal of Finance and Data Science Pub Date : 2023-11-01 Epub Date: 2023-09-12 DOI: 10.1016/j.jfds.2023.100106

Goshi Aoki , Kazuto Ataka , Takero Doi , Kota Tsubouchi

{"title":"Data-driven estimation of economic indicators with search big data in discontinuous situation","authors":"Goshi Aoki , Kazuto Ataka , Takero Doi , Kota Tsubouchi","doi":"10.1016/j.jfds.2023.100106","DOIUrl":"https://doi.org/10.1016/j.jfds.2023.100106","url":null,"abstract":"<div><p>Economic indicators are essential for policymaking and strategic decisions in both the public and private sectors. However, due to delays in the release of government indicators based on macroeconomic factors, there is a high demand for timely estimates or “nowcasting”. Many attempts have been made to overcome this challenge using macro indicators and key variables such as keywords from social networks and search queries, but with a reliance on human selection. We present a fully data-driven methodology using non-prescribed search engine query data (Search Big Data) to approximate economic variables in real time. We evaluate this model by estimating representative Japanese economic indicators and confirm its success in nowcasting prior to official announcements, even during the COVID-19 pandemic, unlike human-selected variable models that struggled. Our model shows consistent performance in nowcasting indices both before and under the pandemic before government announcements, adapting to unexpected circumstances and rapid economic fluctuations. An exhaustive analysis of key queries reveals the pivotal role of libidinal drives and the pursuit of entertainment in influencing economic indicators within the temporal and geographic context examined. This research exemplifies a novel approach to economic forecasting that utilizes contemporary data sources and transcends the limitations of existing methodologies.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"9 ","pages":"Article 100106"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49903012","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