[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117096-en":3,"doc-seo-117096-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117096,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","How to use machine learning in finance - Applied techniques and R package guidance","The paper addresses the growing adoption of machine learning models in banking and insurance, focusing on practical techniques for finance-related research. It introduces core supervised and unsupervised learning paradigms and then surveys commonly used machine learning approaches, offering guidance on method selection for financial applications. The study emphasizes implementation support through R packages and builds a taxonomy of current and future ML use cases in finance. It also discusses limitations and perspectives to inform responsible deployment.","Munich Personal RePEc Archive  \nHow to use machine learning in finance  \nMestiri, Sami  \nUniversité de Monastir  \nOctober 2023  \nOnline at [https://mpra. ub. uni-muenchen. de/120045/](https://mpra. ub. uni-muenchen. de/120045/)  \n[MPRA Paper No. 120045](MPRA Paper No. 120045) , [posted 05 Feb 2024 08:16 UTC](posted 05 Feb 2024 08:16 UTC)  \nHow to use machine learning in finance  \nSami Mestiri 1  \nApplied Economics and Simulation  \nFaculty of Management and Economic Sciences of Mahdia, University of Monastir, Tunisia. Rue Ibn Sina Hiboun, Mahdia Tunisia  \nAbstract: In the last years, the financial sector has seen an increase in the use of machine learning models in banking and insurance contexts. Advanced analytic teams in the financial community are implementing these models regularly. In this paper, i present the different Machine Learning techniques used, and provide some suggestions on the choice of methods in financial applications. We refer the reader to the R packages that can be used to compute the Machine learning methods  \nJEL codes: C45, G00  \nKeywords : Financial applications; Machine learning ; R software.  \n1 Introduction  \nMachine learning (ML) is an application of Artificial Intelligence (AI) that allows systems to learn and improve from experience without being explicitly programmed. In effect, it is about developing predictive models that can access data and use it to learn on their own. There are several types of learning, we distinguish:  \nSupervised learning: is done using a truth, that is, we have prior knowledge of what the output values for our samples should be. Therefore, the goal of this type of learning is to learn a function that given a sample of data and the desired results, in order to best approximate the relationship between observable inputs and outputs. There are two types of supervised learning. Classification algorithms which seek to predict a class/category and Regression algorithms which seek to predict a continuous value.  \nUnsupervised learning: aims to data structure inference. The two most common subcategories in unsupervised learning are clustering and dimensionality reduction. In clustering observations are grouped in such a method as to produce high intra-group similarity and low inter-group similarity. The different types of clustering methods that have been proposed are entropy-based, density-based and distribution-based methods. Reduction of dimensionality aims to increase the information density of the data by reducing their dimensionality while retaining most of the inherent information. There are different techniques based on principal component analysis (PCA) which derive linear combinations of the original variables to cover as much of the variance in the data as possible. Second, neural network-based methods reduce dimensionality with particular architectures.  \nAI is increasingly entering our daily lives with impressive applications. This article discusses the use of ML to solve problems in finance research. The contribution of this  \n1 [https://orcid.org/0000-0002-2060-3242](https://orcid.org/0000-0002-2060-3242)  \narticle is threefold. First, we provide an introduction to Machine Learning. Next we pay particular attention to the different R package implemented (see Mestiri.S (2019)  \n[23]) . We build a taxonomy of current and future ML applications in finance. Finally, we study the prospects of ML applications in finance. The research paper is organized as follows: Section 2 presents the different Machine Learning techniques used. In section 3, we present a taxonomy of existing ML applications. The fourth section is devoted to limitation and perspective. Finally, we conclude in section 5 .  \n2 Machine learning techniques  \n2.1 Linear Discriminant Analysis (LDA)  \nRonald Fisher (1933)[10] pioneered work on discriminant analysis. In his work, he developed a statistical technique for defaults prediction, by developing a linear combination of quantitative predictor variables. This linear","cbCaimCdIO0qTims","https://ap.wps.com/l/cbCaimCdIO0qTims","pdf",394819,1,11,"English","en",105,"# Introduction\n# Machine learning techniques\n## Linear Discriminant Analysis (LDA)\n## Logistic Regression (LR)\n## Decision Trees (DT)","[{\"question\":\"What does machine learning mean in the context of finance?\",\"answer\":\"Machine learning is an AI application that learns and improves from data without explicit programming, enabling predictive models used to extract patterns from financial datasets.\"},{\"question\":\"What are the main types of learning discussed?\",\"answer\":\"The paper distinguishes supervised learning (classification and regression) and unsupervised learning (such as clustering and dimensionality reduction).\"},{\"question\":\"Which R resources are mentioned for implementing methods?\",\"answer\":\"It refers to R packages and functions such as lda from the MASS library and glm/logit from the stats library to estimate and run the presented models.\"}]","How to use machine learning in finance - 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