[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119908-en":3,"doc-seo-119908-105":29,"detail-sidebar-cat-0-en-105":89},{"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":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119908,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Private Equity Fund Selection - A Machine Learning Approach","The study trains supervised machine learning models to estimate the probability that a private equity (PE) fund exceeds a public market equivalent (PME) threshold of 1, using only information available to the PE investor at the time of fundraising. Prior research links PE outcomes to factors such as target fund size, management experience, specialization level, industry state, and overall economic conditions. Models are fitted on 1,233 buyout funds and 689 venture capital funds from Preqin.","Conference Name: BuPol London 2024– International Conference on Business, Economics & Policy, 20-21 February  \nConference Dates: 20-21 February 2024  \nConference Venue: The Tomlinson Centre, Queensbridge Road, London, UK  \nAppears in: PEOPLE: International Journal of Social Sciences (ISSN 2454-5899)  \nPublication year: 2024  \nChristopher Rosenqvist, 2024  \nVolume 2024, pp. 191  \nDOI-[https://doi.org/10.20319/icssh.2024.191](https://doi.org/10.20319/icssh.2024.191)  \nThis paper can be cited as: Rosenqvist, C. (2024). Private Equity Fund Selection-A Machine Learning Approach. BuPol London 2024–International Conference on Business, Economics & Policy, 20-21 February, 2024. Proceedings of Social Science and Humanities Research Association (SSHRA), 2024, 191.  \nPRIVATE EQUITY FUND SELECTION-A MACHINE LEARNING  \nAPPROACH  \nChristopher Rosenqvist  \nDepartment of Strategy and Marketing, Stockholm School of Economics, Stockholm, Sweden  \nchristopher.rosenqvist@hhs.se  \nAbstract  \nThe following aims to train a range of supervised machine learning models to predict the probability of a private equity (PE) fund exceeding a public market equivalent (PME) measure of 1 based on the information the PE investor would have at the time offundraising. Past literature has studied a range offactors that appear to drive the performance of PE funds such as targeted fund size, management experience, fund specialization level, state of the industry, and the overall economy. The article investigates the predictive power of these factors. The models are based on a sample of 1,233 Buyout (BO) funds and 689 are Venture Capital (VC) funds sourced from Preqin. The results suggest some degree of predictability in VC funds with the top performing models reaching an out of sample accuracy score of 75% vs a base rate of 70%. For BO funds, the results are less promising with the top models only reaching an out of sample accuracy score of 60%, while failing to surpass the base rate of 61%. Overall, complex machine learning models, such as boosted decision tree-based algorithms and feedforward neural networks, fail to consistently outperform simpler models in both fund categories, which can be attributed to the limited sample size. Many macroeconomic and fundspecific in the variables analysis look to be valuable performance indicators for both the categories offunds. The impact is more profound and statistically significant for VC funds, especially in the case of macroeconomic factors.","cbCaig7QcuBGrXnk","https://ap.wps.com/l/cbCaig7QcuBGrXnk","pdf",545141,1,"English","en",105,"# Abstract\n## Data and Modeling Approach\n## Predictive Results and Accuracy\n## Interpretation of Key Indicators","[{\"question\":\"What is the main goal of the private equity fund selection study?\",\"answer\":\"To train supervised machine learning models that predict the likelihood a private equity fund will outperform a public market equivalent (PME) measure of 1 based on fundraising-time information.\"},{\"question\":\"What dataset is used to train and test the models?\",\"answer\":\"The models use 1,233 buyout funds and 689 venture capital funds sourced from Preqin.\"},{\"question\":\"Do complex machine learning models outperform simpler models?\",\"answer\":\"No. Boosted decision tree algorithms and feedforward neural networks fail to consistently outperform simpler models in both fund categories, which the study links to limited sample size.\"}]","Private Equity Fund Selection - A Machine Learning Approach | PDF",1785726952,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"private-equity-fund-selection-a-machine-learning-approach","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/private-equity-fund-selection-a-machine-learning-approach/119908/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What is the main goal of the private equity fund selection study?","Question",{"text":73,"@type":74},"To train supervised machine learning models that predict the likelihood a private equity fund will outperform a public market equivalent (PME) measure of 1 based on fundraising-time information.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"What dataset is used to train and test the models?",{"text":78,"@type":74},"The models use 1,233 buyout funds and 689 venture capital funds sourced from Preqin.",{"name":80,"@type":71,"acceptedAnswer":81},"Do complex machine learning models outperform simpler models?",{"text":82,"@type":74},"No. Boosted decision tree algorithms and feedforward neural networks fail to consistently outperform simpler models in both fund categories, which the study links to limited sample size.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":90},[91,95,99,103,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]