[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126324-en":3,"doc-seo-126324-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126324,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Comparative Analysis of Machine Learning Algorithms for Predicting LQ45 Stock Index Prices - Research Report","LQ45 index performance is a key gauge of Indonesia’s capital market strength, yet forecasting its direction in volatile conditions remains difficult. This study evaluates nine machine learning algorithms—Random Forest, Decision Tree, AdaBoost, SVC, XGBoost, Naive Bayes, KNN, Logistic Regression, and ANN—using a 10-year historical dataset and technical indicators in both continuous and binary forms. Three preprocessing frameworks are compared: raw trading data, unsmoothed indicators, and smoothed indicators. Model assessment uses accuracy, precision, recall, F1-score, and ROC AUC, showing that smoothed continuous inputs improve Random Forest and XGBoost results, while Naive Bayes performs best for binary classification, supporting robust data-driven prediction tools for developing markets.","Comparative Analysis of Machine Learning Algorithms for Predicting LQ45 Stock Index Prices  \nAmin Hidayat1)*, Ade Putra Prima Suhendri2)  \n1)2) Informatics Engineering, Faculty of Computer Science, Pamulang University, Tangerang Selatan, Indonesia 1)[dosen02615@unpam.ac.id](dosen02615@unpam.ac.id)  \n2)[dosen02555@unpam.ac.id](dosen02555@unpam.ac.id)  \nArticle history:  \nReceived 18 July 2025;  \nRevised 21 July 2025;  \nAccepted 24 July 2025;  \nAvailable online 10 August 2025  \nKeywords:  \nAlgorithm Comparison Financial Forecasting LQ45 Index Machine Learning Stock Price Prediction  \nAbstract  \nAn essential metric for assessing the success of the country's capital markets is the LQ45 index, which is made up of 45 stocks with the biggest market capitalization and liquidity on thb e Indonesia Stock Exchange. Stock price prediction, particularly in volatile markets, remains complex challenge that benefits from advanced analytical approaches. While machine learning (ML) techniques have demonstrated significant promise in financial forecasting, comprehensive comparative evaluations across multiple algorithms and preprocessing strategies remain limited. In order to evaluate the predictive performance of nine machine learning algorithms Random Forest, Decision Tree, AdaBoost, Support Vector Classifier (SVC), XGBoost, Naive Bayes, KNearest Neighbors (KNN), Logistic Regression, and Artificial Neural Networks (ANN) in predicting the direction of movements of the LQ45 index, this study presents a structured comparative framework. The models are trained using a 10-year historical dataset, incorporating both continuous and binary representations of technical indicators. Three data preprocessing approaches are explored: raw trading data, unsmoothed indicators, and smoothed indicators. Accuracy, precision, recall, F1-score, and ROC AUC are all important factors in model evaluation. The findings show that when applied to continuous data with smoothed technical indications, Random Forest and XGBoost produce the best prediction results. For binary classification tasks, Naive Bayes emerges asthe most effective model. These results demonstrate how important data representation and preprocessing in particular, smoothing are to enhancing the accuracy and robustness of models. Research aids in the creation of trustworthy, data-driven stock prediction tools that are suited for developing markets. Financial analysts, portfolio managers, and algorithmic traders looking to improve investment strategies through well-informed model selection and  \npreprocessing design can benefit from the findings.  \nI. INTRODUCTION  \nInvesting is the process of putting money into assets or financial instruments in the hope of earning returns in the future [1] . One prominent benchmark in the Indonesian capital market is the LQ45 index, which comprises 45 companies with the highest levels of market capitalization and liquidity. These constituents play a crucial role in influencing national economic dynamics [2], making the LQ45 index a key reference point and initial screening tool for domestic and international investors.  \nThe volatility of stock prices, driven by a multitude of endogenous and exogenous variables, renders price forecasting a highly complex undertaking that necessitates the application of advanced analytical methodologies. In this context, machine learning (ML) techniques recognized for their ability to model nonlinear relationships, detect latent patterns within high-dimensional datasets, and dynamically adapt to evolving market conditions—present significant opportunities to enhance the accuracy of investment-oriented predictions [1] . However, the predictive performance of ML algorithms is not uniform; it is largely contingent upon the structural properties of the dataset and the prevailing dynamics of the financial market environment [3] . Selecting the most appropriate algorithm is therefore a critical component in developing reliable models for forec","cbCaigc19OsScLeo","https://ap.wps.com/l/cbCaigc19OsScLeo","pdf",441209,9,1,10,"English","en",105,"# Introduction\n## Problem of stock price volatility and forecasting\n## Motivation for algorithm comparison and preprocessing\n# Methodology\n## Algorithms evaluated\n## Dataset and input representations\n## Preprocessing strategies\n# Evaluation\n## Metrics used for model performance\n# Results and Discussion\n## Best-performing models under each data representation\n# Conclusion\n## Key findings and implications","[{\"question\":\"Which machine learning algorithms are compared for LQ45 index movement prediction?\",\"answer\":\"The study compares Random Forest, Decision Tree, AdaBoost, Support Vector Classifier (SVC), XGBoost, Naive Bayes, K-Nearest Neighbors (KNN), Logistic Regression, and Artificial Neural Networks (ANN).\"},{\"question\":\"What preprocessing approaches are tested in the research?\",\"answer\":\"Three frameworks are used: raw trading data, unsmoothed technical indicators, and smoothed technical indicators.\"},{\"question\":\"How do the results differ between continuous and binary representations?\",\"answer\":\"With continuous inputs using smoothed technical indicators, Random Forest and XGBoost achieve the best prediction performance. For binary classification, Naive Bayes is the most effective model.\"}]","Comparative Analysis of Machine Learning Algorithms for Predicting LQ45 Stock Index Prices - Research Report | PDF",1785904456,25,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"comparative-analysis-of-machine-learning-algorithms-for-predicting-lq45-stock-index-prices-research-report","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/comparative-analysis-of-machine-learning-algorithms-for-predicting-lq45-stock-index-prices-research-report/126324/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Which machine learning algorithms are compared for LQ45 index movement prediction?","Question",{"text":77,"@type":78},"The study compares Random Forest, Decision Tree, AdaBoost, Support Vector Classifier (SVC), XGBoost, Naive Bayes, K-Nearest Neighbors (KNN), Logistic Regression, and Artificial Neural Networks (ANN).","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What preprocessing approaches are tested in the research?",{"text":82,"@type":78},"Three frameworks are used: raw trading data, unsmoothed technical indicators, and smoothed technical indicators.",{"name":84,"@type":75,"acceptedAnswer":85},"How do the results differ between continuous and binary representations?",{"text":86,"@type":78},"With continuous inputs using smoothed technical indicators, Random Forest and XGBoost achieve the best prediction performance. 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