The Journal of Financial Data Science最新文献_第4页

An Integrated Framework on Human-in-the-Loop Risk Analytics 人在循环风险分析的集成框架

The Journal of Financial Data Science Pub Date : 2022-12-15 DOI: 10.3905/jfds.2022.1.116

Peng Liu

{"title":"An Integrated Framework on Human-in-the-Loop Risk Analytics","authors":"Peng Liu","doi":"10.3905/jfds.2022.1.116","DOIUrl":"https://doi.org/10.3905/jfds.2022.1.116","url":null,"abstract":"Risk analytics is an integral component in the overall assessment of the risk profile for potential and existing obligors. For example, credit worthiness is often assessed via the use of scorecards, which are regulatory credit risk models developed based on historical data and domain expertise in banks and financial institutions. A pure statistical model, however, often fails to entertain regulatory requirements on both predictiveness and interpretability at the same time. Instead, practical risk models are developed by incorporating expert opinions within the development process, such as forcing the direction of travel for certain financial factors. In this article, the author proposes a unified framework, termed constrained and partially regularized logistic regression (CPR-LR) model, on how human inputs could be embedded in the statistical estimation procedure when developing credit risk models. By expressing such inputs as model constraints at different levels, the proposed approach serves as an effective solution to developing intuitive, easy-to-interpret, and statistically robust credit risk models, as demonstrated in the author’s experiments. This work also contributes to the growing field of human-in-the-loop model development, in which the author shows that domain expertise can be formulated as model constraints, thus biasing the resulting statistical model to be more interpretable and regulation compliant.","PeriodicalId":199045,"journal":{"name":"The Journal of Financial Data Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123003377","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

Ensemble Meta-Labeling 合奏Meta-Labeling

The Journal of Financial Data Science Pub Date : 2022-12-14 DOI: 10.3905/jfds.2022.1.114

Dennis Thumm, P. Barucca, J. Joubert

引用次数: 0

ESG Text Classification: An Application of the Prompt-Based Learning Approach ESG文本分类:基于提示学习方法的应用

The Journal of Financial Data Science Pub Date : 2022-12-14 DOI: 10.3905/jfds.2022.1.115

Zhengzheng Yang, Le Zhang, Xiaoyu Wang, Yubo Mai

引用次数: 1

Point-in-Time Language Model for Geopolitical Risk Events 地缘政治风险事件的时间点语言模型

The Journal of Financial Data Science Pub Date : 2022-12-14 DOI: 10.3905/jfds.2022.1.113

Matthias Apel, A. Betzer, B. Scherer

引用次数: 0

Relevance-Based Prediction: A Transparent and Adaptive Alternative to Machine Learning 基于相关性的预测:机器学习的透明和自适应替代方案

The Journal of Financial Data Science Pub Date : 2022-12-01 DOI: 10.3905/jfds.2022.1.110

M. Czasonis, M. Kritzman, D. Turkington

引用次数: 1

Visualizing Structures in Financial Time-Series Datasets through Affinity-Based Diffusion Transition Embedding 基于亲和性扩散转移嵌入的金融时间序列数据结构可视化

The Journal of Financial Data Science Pub Date : 2022-12-01 DOI: 10.3905/jfds.2022.1.111

Rui Ding

{"title":"Visualizing Structures in Financial Time-Series Datasets through Affinity-Based Diffusion Transition Embedding","authors":"Rui Ding","doi":"10.3905/jfds.2022.1.111","DOIUrl":"https://doi.org/10.3905/jfds.2022.1.111","url":null,"abstract":"In this work, the author proposes a modified version of PHATE, a diffusion map-based embedding algorithm that is tuned for working on financial time-series data primarily. The new algorithm, financial affinity-based diffusion transition embedding (FATE), takes in user-specified distance metrics that make sense for time-series data and uses symmetrized f-divergences applied to the diffusion probabilities as the final embedding distance before passing them into a metric multidimensional scaling step. The proposed visualization method reveals both local and global structures of the input time-series dataset. Performance of this visualization algorithm is first demonstrated through numerical experiments with Dow Jones 30 stock returns and S&P 100 stock returns. The author compares FATE visualization results using correlation-type distances with t-stochastic neighbor embedding and PHATE embeddings, among others, to demonstrate the advantages and new perspectives of FATE both qualitatively and quantitatively. On the other hand, experiments on synthetic ARMA time series with fine control of the structure of the underlying model parameters are provided. The results demonstrate the ability of transfer function information distance and time-lagged Hellinger distance to identify structures within the generating time-series models from their time-series realizations alone, which cannot be identified by correlation-type distances or Euclidean distances. The author concludes that the choice of distance metrics has an important role in the kind of structure one can uncover from time-series datasets.","PeriodicalId":199045,"journal":{"name":"The Journal of Financial Data Science","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115169079","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: 2

Managing Editor’s Letter 总编辑的信

The Journal of Financial Data Science Pub Date : 2022-10-31 DOI: 10.3905/jfds.2022.4.4.001

F. Fabozzi

引用次数: 0

Using Machine Learning to Model Advised-Investor Behavior 使用机器学习建模建议投资者行为

The Journal of Financial Data Science Pub Date : 2022-10-31 DOI: 10.3905/jfds.2022.4.4.025

Han-Tai Shiao, Cynthia A. Pagliaro, D. Mehta

{"title":"Using Machine Learning to Model Advised-Investor Behavior","authors":"Han-Tai Shiao, Cynthia A. Pagliaro, D. Mehta","doi":"10.3905/jfds.2022.4.4.025","DOIUrl":"https://doi.org/10.3905/jfds.2022.4.4.025","url":null,"abstract":"During periods of extreme market volatility, such as that experienced during the COVID-19 pandemic, advised investors may consider impulsive and inappropriate investment decisions like moving all assets to cash. Financial advisors, through proactive behavioral coaching, can help their clients avoid such decisions. But which clients need the most help? A predictive model that better identifies the clients most likely to react to market volatility can be an invaluable tool for financial advisors. Such a model requires insight into the investors’ mindset. In previous work, the authors focused on the perspective of the financial advisor and used natural language processing to explore advisors’ summary notes to extract such investor insights. They then used this novel data source as input for a machine-learning model to predict the investors most in need of intervention during volatile market periods. In this article, the authors further expand the model to include a unique dataset of investors’ digital activity, including investor-initiated contacts (via web, email, and phone) and web activity (page view and browsing history), to better reveal investor intention. Using machine-learning techniques, the authors build a model using this novel dataset as well as advisor notes, transaction activity, and a market volatility index to identify advised investors most in need of proactive intervention. The authors further describe the implication such work has for both traditional and robo-advisory service models.","PeriodicalId":199045,"journal":{"name":"The Journal of Financial Data Science","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128218491","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

Tackling the Exponential Scaling of Signature-Based Generative Adversarial Networks for High-Dimensional Financial Time-Series Generation 高维金融时间序列生成中基于签名的生成对抗网络的指数尺度问题

The Journal of Financial Data Science Pub Date : 2022-09-24 DOI: 10.3905/jfds.2022.1.109

Fernando de Meer Pardo, Peter Schwendner, Marcus Wunsch

{"title":"Tackling the Exponential Scaling of Signature-Based Generative Adversarial Networks for High-Dimensional Financial Time-Series Generation","authors":"Fernando de Meer Pardo, Peter Schwendner, Marcus Wunsch","doi":"10.3905/jfds.2022.1.109","DOIUrl":"https://doi.org/10.3905/jfds.2022.1.109","url":null,"abstract":"Generative adversarial networks (GANs) have been shown to be able to generate samples of complex financial time series, particularly by employing the concept of path signatures, a universal description of the geometric properties of a data stream whose expected value uniquely characterizes the time series. Specifically, the SigCWGAN model (Ni et al. 2020) can generate time series of arbitrary length; however, the parameters of the neural network employed grow exponentially with the dimension of the underlying time series, which makes the model intractable when seeking to generate large financial market scenarios. To overcome this problem of dimensionality, the authors propose an iterative generation procedure relying on the concept of hierarchies in financial markets. The authors construct an ensemble of GANs that they call the Hierarchical-SigCWGAN, which is based on hierarchical clustering that approximates signatures in the spirit of the original model. The Hierarchical-SigCWGAN can scale to higher dimensions and generate large-dimensional scenarios in which the joint behavior of all the assets in the market is replicated. The model is validated by comparing its performance on a series of similarity metrics with respect to the original SigCWGAN on a dataset in which it is still tractable and by showing its scalability on a larger dataset.","PeriodicalId":199045,"journal":{"name":"The Journal of Financial Data Science","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115049795","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

Meta-Labeling Architecture Meta-Labeling架构

The Journal of Financial Data Science Pub Date : 2022-09-16 DOI: 10.3905/jfds.2022.1.108

M. Meyer, J. Joubert, Mesias Alfeus

引用次数: 0