{"title":"Using machine learning to predict investors’ switching behaviour","authors":"Paul Nixon , Evan Gilbert","doi":"10.1016/j.jbef.2024.100992","DOIUrl":null,"url":null,"abstract":"<div><p>Individual investors’ decisions to switch investments very often lead to significantly lower investment returns so having an effective predictive model of these switches would be of value to clients, advisors and investment managers. A random forest algorithm was applied to a new dataset of over 20 million observations relating to 95,685 clients on Momentum Investments’ platform between 2018 and 2024. It identified a combination of investor characteristics (number of holdings, past switching behaviour, total assets) and external features (past returns, macroeconomic variables) as the key features of investor switch behaviour. This model exceeds commercially accepted standards in respect of the AUC and Gini metrics showcasing the model’s strength in its ranking capability. It can thus provide a useful basis for client segmentation and engagement by financial advisors.</p></div>","PeriodicalId":47026,"journal":{"name":"Journal of Behavioral and Experimental Finance","volume":"44 ","pages":"Article 100992"},"PeriodicalIF":4.3000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Behavioral and Experimental Finance","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214635024001072","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
Individual investors’ decisions to switch investments very often lead to significantly lower investment returns so having an effective predictive model of these switches would be of value to clients, advisors and investment managers. A random forest algorithm was applied to a new dataset of over 20 million observations relating to 95,685 clients on Momentum Investments’ platform between 2018 and 2024. It identified a combination of investor characteristics (number of holdings, past switching behaviour, total assets) and external features (past returns, macroeconomic variables) as the key features of investor switch behaviour. This model exceeds commercially accepted standards in respect of the AUC and Gini metrics showcasing the model’s strength in its ranking capability. It can thus provide a useful basis for client segmentation and engagement by financial advisors.
期刊介绍：
Behavioral and Experimental Finance represent lenses and approaches through which we can view financial decision-making. The aim of the journal is to publish high quality research in all fields of finance, where such research is carried out with a behavioral perspective and / or is carried out via experimental methods. It is open to but not limited to papers which cover investigations of biases, the role of various neurological markers in financial decision making, national and organizational culture as it impacts financial decision making, sentiment and asset pricing, the design and implementation of experiments to investigate financial decision making and trading, methodological experiments, and natural experiments.
Journal of Behavioral and Experimental Finance welcomes full-length and short letter papers in the area of behavioral finance and experimental finance. The focus is on rapid dissemination of high-impact research in these areas.
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