[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123726-en":3,"doc-seo-123726-105":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},123726,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR IPO UNDERPERFORMANCE PREDICTION - Research paper","Initial Public Offerings (IPOs) are a widely used route for companies to raise capital, yet many IPOs underperform after listing and disappoint investors. This research investigates multiple machine learning approaches—AdaBoost, Random Forest, Logistic Regression, ANN, and SVM—to predict IPO underperformance. The study compiles and pre-processes a multi-year IPO dataset, trains models, and evaluates predictive performance. Results indicate that the Artificial Neural Network (ANN) model performs best, with 68.11% accuracy. Feature-related analysis highlights factors linked to underperformance, supporting more informed decisions for investors and financial analysts.","JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH-ISSN(O): 2454-3225  \nPravinkumar M Sonsare et al, JOAASR-Vol-5-6-Novmber-2023:1-12 1  \nA COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR IPO UNDERPERFORMANCE PREDICTION  \nPravinkumar M Sonsare 1 *†, Ashtavinayak Pande 1 , Sudhanshu Kumar 1 , Akshay Kurve 1 , Chinmay Shanbhag 1  \n1 Shri Ramdeobaba College of Engineering and Management, Nagpur  \nAbstract  \nInitial Public Offerings (IPOs) are a popular way for companies to raise capital and enter the public markets. However, many IPOs underperform and fail to meet the expectations of investors. In this research paper, we explore the use of different machine learning models, namely AdaBoost, Random Forest, Logistic Regression, ANN and SVM for predicting IPO underperformance. We collect and pre-process a dataset of IPOs from the past few years and use it to train. We evaluate the performance of each model. Our results show that Artificial Neural Network model is better suited for predicting IPO underperformance. Additionally, our analysis provides insights into the factors that contribute to underperformance and highlights the importance of certain features in predicting IPO performance. Our research provides valuable information for investors and financial analysts interested in predicting the performance of IPOs and mitigating the risks associated with IPO investments. We have tested machine learning models, namely AdaBoost, Random Forest, Logistic Regression, ANNand SVM. After Comparing the accuracy of all the models, we arrived at the conclusion that ANN model performed the best with an accuracy of 68.11% .  \nKeywords: Initial Public Offerings (IPOs), Machine learning, AdaBoost, Logistic regression, Support Vector Machines (SVM), Financial analysis, Supervised learning Classification  \n1 INTRODUCTION  \nInitial Public Offerings (IPOs) are an important way for companies to raise capital by offering their shares to the public. However, the success of IPOs is not guaranteed, as many newly issued stocks experience underperformance shortly after going public. This underperformance can  \n* Corresponding author.  \n†E-mail: [sonsarep@rknec.edu](sonsarep@rknec.edu)*  \nhave significant implications for investors, underwriters, and the broader economy.Traditional methods of analysing IPOs rely heavily on financial and accounting metrics. However, in recent years, machine learning algorithms have shown promise in predicting stock performance. By leveraging the power of these algorithms, it may be possible to develop more accurate models for predicting IPO underperformance.  \nOur work aims to investigate the use of all machine learning algorithms for predicting the performance of IPO. We will explore the potential of various machine learning techniques such as regression models, decision trees and neural networks in predicting IPO underperformance. We will also examine the role of different variables in predicting underperformance such as financial metrics, market trends, and company-specific factors. The prediction of IPO underperformance has largely relied on traditional financial and accounting metrics such as p/e (price to earning) ratio, earnings per share and market capitalization [4] [5] . These metrics can provide valuable insights. They may not capture all of the factors that influence IPO performance.  \nThis article will begin with a review of relevant literature on IPO underperformance and machine learning in finance. We will then describe the data used in our analysis, including financial and market data for a sample of IPOs. We will then apply various machine learning algorithms to this data, evaluating their performance in predicting underperformance.  \nOverall, the findings of this research could have important implications for investors, underwriters and regulators. By developing more accurate models for predicting IPO underperformance. We may be able to improve the efficiency and stability of the IPO market.  \n2 DATA PR","cbCaisFoGEpf5wY0","https://ap.wps.com/l/cbCaisFoGEpf5wY0","pdf",399233,1,12,"English","en",105,"# Abstract\n# Introduction\n# Data Preparation\n# Proposed Methodology","[{\"question\":\"Which machine learning models are evaluated for predicting IPO underperformance?\",\"answer\":\"The study evaluates AdaBoost, Random Forest, Logistic Regression, ANN, and SVM to predict IPO underperformance.\"},{\"question\":\"What model performs best in the reported results?\",\"answer\":\"The Artificial Neural Network (ANN) model shows the best performance, achieving an accuracy of 68.11%.\"},{\"question\":\"How is the dataset prepared before training the models?\",\"answer\":\"The approach handles missing values (mean for numerical and mode for categorical), removes outliers using the IQR rule, and replaces outliers with the nearest non-outlier values.\"}]","A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR IPO UNDERPERFORMANCE PREDICTION - Research paper | PDF",1785818209,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-comparative-analysis-of-machine-learning-algorithms-for-ipo-underperformance-prediction-research-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-comparative-analysis-of-machine-learning-algorithms-for-ipo-underperformance-prediction-research-paper/123726/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are evaluated for predicting IPO underperformance?","Question",{"text":75,"@type":76},"The study evaluates AdaBoost, Random Forest, Logistic Regression, ANN, and SVM to predict IPO underperformance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What model performs best in the reported results?",{"text":80,"@type":76},"The Artificial Neural Network (ANN) model shows the best performance, achieving an accuracy of 68.11%.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the dataset prepared before training the models?",{"text":84,"@type":76},"The approach handles missing values (mean for numerical and mode for categorical), removes outliers using the IQR rule, and replaces outliers with the nearest non-outlier values.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]