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

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 : 2023-09-12 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":"2023-09-12","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

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 : 2023-09-11 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":"2023-09-11","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

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 : 2023-08-25 DOI: 10.1016/j.jfds.2023.100103

Ben Neilson

引用次数: 1

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 : 2023-07-20 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":"2023-07-20","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

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 : 2023-06-22 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":"2023-06-22","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 : 2023-06-20 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":"2023-06-20","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

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 : 2023-06-20 DOI: 10.1016/j.jfds.2023.100100

Xiaoyan Zhang, Zijian Zhang

引用次数: 1

The great wall of debt: Real estate, political risk, and Chinese local government financing cost 债务长城:房地产、政治风险和中国地方政府融资成本

Journal of Finance and Data Science Pub Date : 2023-04-25 DOI: 10.1016/j.jfds.2023.100098

Andrew Ang , Jennie Bai , Hao Zhou

引用次数: 0

Portfolio optimization using cellwise robust association measures and clustering methods with application to highly volatile markets 使用单元稳健关联度量和聚类方法的投资组合优化，并应用于高度波动的市场

Journal of Finance and Data Science Pub Date : 2023-04-19 DOI: 10.1016/j.jfds.2023.100097

Emmanuel Jordy Menvouta , Sven Serneels , Tim Verdonck

引用次数: 2

What do we learn from stock price reactions to China's first announcement of anti-corruption reforms? 我们从中国首次宣布反腐改革后的股价反应中学到了什么?

Journal of Finance and Data Science Pub Date : 2023-03-04 DOI: 10.1016/j.jfds.2023.100096

Chen Lin , Randall Morck , Bernard Yeung , Xiaofeng Zhao

引用次数: 1