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

Research on credit card default repayment prediction model 信用卡违约还款预测模型研究

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-07-11 DOI: 10.1016/j.jfds.2024.100136

Junhong Li, Jijia Kang, Jie Wu, Hongpin Wang, Xiaoguang Yang

{"title":"Research on credit card default repayment prediction model","authors":"Junhong Li, Jijia Kang, Jie Wu, Hongpin Wang, Xiaoguang Yang","doi":"10.1016/j.jfds.2024.100136","DOIUrl":"10.1016/j.jfds.2024.100136","url":null,"abstract":"<div><p>This study compares the predictive ability of various machine learning models for credit card default repayment within different prediction frameworks, using data from a commercial bank in China. Firstly, utilizing different tree models, we explore the impact on post-default repayment of different factors. Next, a split-sample time series prediction is carried out with two neural network algorithms, BPNN and ELM. The outcomes indicate that, ELM yields a significantly superior prediction performance compared to the BPNN model. Thirdly, the predictive performances of ten machine learning models are compared using full-sample data. The findings demonstrate that XGBoost and ELM models have superior predictive performances in full-sample analyses. Fourthly, this study employs the EMD data decomposition technique to examine the predictive ability of the XGBoost and ELM models in various frequency data. The results indicate that the predictive efficacy may differ depending on the frequency and repayment period after default. The findings are valuable for commercial banks in developing a framework and selecting a methodology to address the challenge of predicting credit card default payments.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918824000217/pdfft?md5=21435a376ab3e2fef9741931c14d8cf4&pid=1-s2.0-S2405918824000217-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141638741","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

What drives liquidity in the Chinese credit bond markets? 是什么推动了中国信用债券市场的流动性？

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-10-09 DOI: 10.1016/j.jfds.2024.100139

Jingyuan Mo , Marti G. Subrahmanyam

引用次数: 0

CPC-SAX: Data mining of financial chart patterns with symbolic aggregate approXimation and instance-based multilabel classification 利用深度学习和统计学习对金融数据进行集合预测

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-06-03 DOI: 10.1016/j.jfds.2024.100132

Konstantinos Nikolaou

{"title":"CPC-SAX: Data mining of financial chart patterns with symbolic aggregate approXimation and instance-based multilabel classification","authors":"Konstantinos Nikolaou","doi":"10.1016/j.jfds.2024.100132","DOIUrl":"10.1016/j.jfds.2024.100132","url":null,"abstract":"<div><p>In order to be able to classify financial chart patterns through machine learning, we introduced and applied a novel classification algorithm on time series data of different financial assets through SAX (Symbolic Aggregate approXimation), a transformation algorithm. After applying a linear regression model on the features of a dataset to reduce the number of parameters needed, converting real valued data to strings of characters through Piecewise Aggregate Approximation (PAA) and labelling each level increasingly with Latin alphabets characters, the new algorithm called CPC-SAX (Chart Pattern Classification) compares vectors describing the ASCII value changes along the string and classifies them using already labelled SAX-transformed data. The results show satisfying accuracy scores on data of different time windows and types of assets. We also obtain information on the appearance of said patterns. By reaching our goal of properly classifying chart patterns as they appear, we can have a better indication of the future price trend, allowing the investor/trader to make better informed decisions.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100132"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918824000175/pdfft?md5=231f2d62031e05b4e39adbf2530d03c2&pid=1-s2.0-S2405918824000175-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141281391","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

China's GDP at risk: The role of housing prices 中国的 GDP 面临风险：房价的作用

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-10-10 DOI: 10.1016/j.jfds.2024.100140

Peipei Li , Yuan Wang , Licheng Zhang , Xueying Zhang

引用次数: 0

Revising data collection methodology - evidence from the Australian financial sector 修订数据收集方法--来自澳大利亚金融业的证据

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-05-21 DOI: 10.1016/j.jfds.2024.100131

Ben Neilson , Tom Marty , Nat Daley

{"title":"Revising data collection methodology - evidence from the Australian financial sector","authors":"Ben Neilson , Tom Marty , Nat Daley","doi":"10.1016/j.jfds.2024.100131","DOIUrl":"10.1016/j.jfds.2024.100131","url":null,"abstract":"<div><p>Time requirements of data collection account for a significant portion of the total time required to provide financial advice. This research applies data collection software to the financial planning process seeking to identify benefits that may assist to reduce rising barriers of accessing financial advice. Experimental two-phase study seeks qualitative input surrounding problematic themes before quantitative input records impacts of data collection software use. The research seeks to evidence beneficial impacts that software use may have on the data collection requirements by way of comparison between traditional and software methodologies in Australian professional practice. Respondents were asked to complete data collection inputs using both traditional and digital methods with metrics recorded throughout the process. Input from 112 consumers and 71 practising advisers were recorded. Results suggest the use of software may decrease time taken to complete task and often results in higher levels of data accuracy. Traditional methods were affiliated with extended time periods and lower levels of data accuracy. Results aim to evolve methods of traditional practise within the financial sector. The research provides original contributions to financial planning literature by examining the potential impact data collection methodologies may have on reducing barriers to accessing financial services in Australia.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100131"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918824000163/pdfft?md5=e29161b9f336f45f98910a8ba06bb187&pid=1-s2.0-S2405918824000163-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141132579","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 relationship between processes and profit: A work-based assessment of process used in Australian financial planning firms 调查流程与利润之间的关系：对澳大利亚财务规划公司使用的流程进行基于工作的评估

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-03-05 DOI: 10.1016/j.jfds.2024.100128

Ben Neilson

{"title":"Investigating the relationship between processes and profit: A work-based assessment of process used in Australian financial planning firms","authors":"Ben Neilson","doi":"10.1016/j.jfds.2024.100128","DOIUrl":"https://doi.org/10.1016/j.jfds.2024.100128","url":null,"abstract":"<div><p>The research explores relationship dynamics between process and profit in Australian professional practise. We analyse data collected from 134 financial planning firms located in Southeast Queensland as a sample size. The research introduces a complete financial planning process framework designed to measure the impact that process may have on the relationship with firm profit. Quantitative profit data was recorded using Dovetail software to capture results and evidence regression between groups. The research found that firms’ processes are positively associated with profit, and both process and profit contribute to the decreasing influence of firm agency theory. The research suggests that process could be leveraged as an asset to develop commercial advantages. The research may help identify new measures of standard practise, develop the perception of Australian financial firms and assist to reduce barriers of accessing financial services.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918824000138/pdfft?md5=91f4bca2d9982e90a70cdc2817adf108&pid=1-s2.0-S2405918824000138-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140349945","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

Reinforcement prompting for financial synthetic data generation 金融合成数据生成的强化提示

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-08-03 DOI: 10.1016/j.jfds.2024.100137

Xiangwu Zuo , Anxiao (Andrew) Jiang , Kaixiong Zhou

{"title":"Reinforcement prompting for financial synthetic data generation","authors":"Xiangwu Zuo , Anxiao (Andrew) Jiang , Kaixiong Zhou","doi":"10.1016/j.jfds.2024.100137","DOIUrl":"10.1016/j.jfds.2024.100137","url":null,"abstract":"<div><p>The emergence of Large Language Models (LLMs) has unlocked unprecedented potential for comprehending and generating human-like text, fueling advances in the finance domain – a tool that can shape investment strategies and market predictions. Nevertheless, challenges stemming from the necessity for extensive labeled data and the imperative for data privacy remain. The generation of high-quality synthetic data emerges as a promising avenue to circumvent these issues. In this paper, we introduce a novel methodology, named “Reinforcement Prompting”, to address these challenges. Our strategy employs a policy network as a Selector to generate prompts, and an LLM as an Executor to produce financial synthetic data. This synthetic data generation process preserves data privacy and mitigates the dependency on real-world labeled datasets. We validate the effectiveness of our approach through experimental evaluations. Our results indicate that models trained on synthetic data generated via our approach exhibit competitive performance when compared to those trained on actual financial data, thereby bridging the performance gap. This research provides a novel solution to the challenges of data privacy and labeled data scarcity in financial sentiment analysis, offering considerable advancement in the field of financial machine learning.</p></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405918824000229/pdfft?md5=00bc590d50782ff3979a1146c9c7d2aa&pid=1-s2.0-S2405918824000229-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992690","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

Liquidity risk analysis via drawdown-based measures 以缩减为基础的流动性风险分析

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-09-24 DOI: 10.1016/j.jfds.2024.100138

Guglielmo D'Amico, Bice Di Basilio, Filippo Petroni

引用次数: 0

Machine learning private equity returns 机器学习私募股权投资回报

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-10-18 DOI: 10.1016/j.jfds.2024.100141

Christian Tausch, Marcus Pietz

引用次数: 0

NFT price and sales characteristics prediction by transfer learning of visual attributes 基于视觉属性迁移学习的NFT价格与销售特征预测

Journal of Finance and Data Science Pub Date : 2024-12-01 Epub Date: 2024-11-16 DOI: 10.1016/j.jfds.2024.100148

Mustafa Pala, Emre Sefer

{"title":"NFT price and sales characteristics prediction by transfer learning of visual attributes","authors":"Mustafa Pala, Emre Sefer","doi":"10.1016/j.jfds.2024.100148","DOIUrl":"10.1016/j.jfds.2024.100148","url":null,"abstract":"<div><div>Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent surge of interest in trading them which makes them another type of alternative investment. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework.</div><div>Here, we focus on three related prediction problems over NFTs: Predicting NFTs sale price, inferring whether a given NFT will participate in a secondary sale, and predicting NFT's sale price change over time. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, seller's and buyer's centralities in the trading network, and collection's resale probability. We categorize input NFTs into six categories based on their characteristics. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, and EfficientNet seems to perform the best. In general, CNN and XGBoost consistently outperformed the rest of them across all categories. We also publish our novel NFT dataset with temporal price knowledge, which is the first dataset to have NFT prices over time rather than at a single time point. Our code and NFT datasets are publicly available at <span><span>https://github.com/seferlab/deep_nft</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":36340,"journal":{"name":"Journal of Finance and Data Science","volume":"10 ","pages":"Article 100148"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175785","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