Journal of Finance and Data Science最新文献_第8页

Improving insurers’ loss reserve error prediction: Adopting combined unsupervised-supervised machine learning techniques in risk management 改进保险公司损失准备金误差预测:在风险管理中采用联合无监督监督机器学习技术

Journal of Finance and Data Science Pub Date : 2022-11-01 DOI: 10.1016/j.jfds.2022.09.003

In Jung Song , Wookjae Heo

{"title":"Improving insurers’ loss reserve error prediction: Adopting combined unsupervised-supervised machine learning techniques in risk management","authors":"In Jung Song , Wookjae Heo","doi":"10.1016/j.jfds.2022.09.003","DOIUrl":"10.1016/j.jfds.2022.09.003","url":null,"abstract":"<div><p>Emerging literature focuses on insurers' earnings management using estimated liability for unpaid claims, known as loss reserve. An insurance company generally uses the traditional estimation methods with linear estimation to measure loss reserve error, but those methods are often criticized for several statistical shortcomings, such as estimation technique, correlated contributing variables, ignorance of the interactions, and higher-order terms. To overcome such shortcomings, this paper proposes an unsupervised-supervised machine learning approach, hierarchical clustering, and artificial neural network (ANN) by adopting a combined unsupervised-supervised method, cluster analysis (i.e., unsupervised), and various supervised machine learning algorithms such as Boostings, Support Vector Machine (SVM) and RReliefF. We show evidence that each cluster has its own foundation variables to predict and Boosting and ANN estimation provide a more efficient framework to improve insurers' reserve error. Also, the different value and order of RReliefF between Boosting and OLS show the under-or over-estimated predictor, and each year's influential variables are found to be consistent over time, which indicates that the firm's previous year's loss reserve model can predict the future loss reserve error. This paper contributes to the existing literature by suggesting a more robust, consistent, and efficient prediction method (i.e., unsupervised-supervised combination method) to improve insurers' loss reserve error prediction.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"8 ","pages":"Pages 233-254"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918822000137/pdfft?md5=8e507494fba02f659065c034fd48e212&pid=1-s2.0-S2405918822000137-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121438424","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: 2

FinLex: An effective use of word embeddings for financial lexicon generation FinLex:有效地使用词嵌入来生成金融词汇

Journal of Finance and Data Science Pub Date : 2022-11-01 DOI: 10.1016/j.jfds.2021.10.001

Sanjiv R. Das , Michele Donini , Muhammad Bilal Zafar , John He , Krishnaram Kenthapadi

引用次数: 6

CapitalVX: A machine learning model for startup selection and exit prediction CapitalVX:一个用于创业公司选择和退出预测的机器学习模型

Journal of Finance and Data Science Pub Date : 2021-11-01 DOI: 10.1016/j.jfds.2021.04.001

Greg Ross , Sanjiv Das , Daniel Sciro , Hussain Raza

引用次数: 0

Short-term bitcoin market prediction via machine learning 通过机器学习进行短期比特币市场预测

Journal of Finance and Data Science Pub Date : 2021-11-01 DOI: 10.1016/j.jfds.2021.03.001

Patrick Jaquart, David Dann, Christof Weinhardt

引用次数: 64

Inventory effects on the price dynamics of VSTOXX futures quantified via machine learning 通过机器学习量化库存对VSTOXX期货价格动态的影响

Journal of Finance and Data Science Pub Date : 2021-11-01 DOI: 10.1016/j.jfds.2021.06.001

Daniel Guterding

{"title":"Inventory effects on the price dynamics of VSTOXX futures quantified via machine learning","authors":"Daniel Guterding","doi":"10.1016/j.jfds.2021.06.001","DOIUrl":"10.1016/j.jfds.2021.06.001","url":null,"abstract":"<div><p>The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the effect of trader inventory on the price of VSTOXX futures through a combination of stochastic processes and machine learning methods. We formulate a simple and efficient pricing methodology for VSTOXX futures, which assumes a Heston-type stochastic process for the underlying EURO STOXX 50 market. Under these dynamics, approximate analytical formulas for the implied volatility smile and the VSTOXX index have recently been derived. We use the EURO STOXX 50 option implied volatilities and the VSTOXX index value to estimate the parameters of this Heston model. Following the calibration, we calculate theoretical VSTOXX futures prices and compare them to the actual market prices. While theoretical and market prices are usually in line, we also observe time periods, during which the market price does not agree with our Heston model. We collect a variety of market features that could potentially explain the price deviations and calibrate two machine learning models to the price difference: a regularized linear model and a random forest. We find that both models indicate a strong influence of accumulated trader positions on the VSTOXX futures price.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"7 ","pages":"Pages 126-142"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.jfds.2021.06.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91485732","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: 2

Enhanced PD-implied ratings by targeting the credit rating migration matrix 通过针对信用评级迁移矩阵增强pd隐含评级

Journal of Finance and Data Science Pub Date : 2021-11-01 DOI: 10.1016/j.jfds.2021.05.001

Jin-Chuan Duan , Shuping Li

引用次数: 4

Negative conversion premium 负转换溢价

Journal of Finance and Data Science Pub Date : 2021-11-01 DOI: 10.1016/j.jfds.2020.11.001

Zhijian (James) Huang , Li Xu

{"title":"Negative conversion premium","authors":"Zhijian (James) Huang , Li Xu","doi":"10.1016/j.jfds.2020.11.001","DOIUrl":"https://doi.org/10.1016/j.jfds.2020.11.001","url":null,"abstract":"<div><p>We document frequent occurrences of negative conversion premium (NCP) events in the Chinese convertible bond market, when the bond is convertible and the underlying stock can be freely sold. This implies that when an NCP event occurs, existing stock holders can earn a riskless profit through a long-short strategy which sells the underlying stock and buys the convertible bond at the same time, then converts the bond into stocks. Facing short sale constraints, traders not holding any position in the underlying stock can still profit from an overnight trading strategy which buys the convertible bond at the NCP event day <em>t</em>, then sells the converted stock on day <em>t</em> + 1. We also find that the next-day opening prices following NCP events are significantly lower, which is evidence for the stock selling from the overnight trading strategy. Overall, our findings show that investors in China are aware of the NCP events and they earn abnormal returns through active trading. However, it remains as a puzzle why existing stock holders such as institutional investors do not trade away the negative conversion premium through the riskless long-short strategy.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"7 ","pages":"Pages 1-21"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.jfds.2020.11.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91709764","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: 2

Pairwise acquisition prediction with SHAP value interpretation 用SHAP值解释两两采集预测

Journal of Finance and Data Science Pub Date : 2021-11-01 DOI: 10.1016/j.jfds.2021.02.001

Katsuya Futagami , Yusuke Fukazawa , Nakul Kapoor , Tomomi Kito

{"title":"Pairwise acquisition prediction with SHAP value interpretation","authors":"Katsuya Futagami , Yusuke Fukazawa , Nakul Kapoor , Tomomi Kito","doi":"10.1016/j.jfds.2021.02.001","DOIUrl":"https://doi.org/10.1016/j.jfds.2021.02.001","url":null,"abstract":"<div><p>Predicting future pairs of the acquirer and acquiree companies is important for acquisition or investment strategy. This prediction is a challenging problem due to the following requirements: to incorporate various non-financial factors and to address the lack of negative samples. Concerning the former, we proposed including a network feature that represented the importance of an acquirer and an acquiree in the investment and category networks, as well as a company relation feature associated with their similarity and closeness. Considering the latter requirement, as negative examples, we set the pairs of acquirers and acquirees with the features that were similar to those of positive examples. This allowed learning minor differences between the companies selected for acquisition and the candidate ones. We evaluated our proposed prediction model using 2000–2018 acquisition logs collected from CrunchBase. Based on the analysis of the high SHapley additive explanation (SHAP) value features, we found that the newly considered network and company relation features had high significance (10 out of 22 top key features). We also clarified how these novel features contributed to the prediction of acquisition occurrence by interpreting the SHAP value.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"7 ","pages":"Pages 22-44"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.jfds.2021.02.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91747220","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

How does the creditor conflict affect bond IPO underpricing? 债权人冲突如何影响债券IPO抑价?

Journal of Finance and Data Science Pub Date : 2021-11-01 DOI: 10.1016/j.jfds.2021.03.002

Susheng Wang , Xinjie Wang , Yuan Wang , Xueying Zhang

引用次数: 1

Deep deterministic portfolio optimization 深度确定性投资组合优化

Journal of Finance and Data Science Pub Date : 2020-11-01 DOI: 10.1016/j.jfds.2020.06.002

Ayman Chaouki , Stephen Hardiman , Christian Schmidt , Emmanuel Sérié , Joachim de Lataillade

引用次数: 12