[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291103-105":59,"doc-detail-291103-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","stock-price-prediction-using-machine-learning-evidence-from-pakistan-stock-exchange-proceedings","STOCK PRICE PREDICTION USING MACHINE LEARNING: EVIDENCE FROM PAKISTAN STOCK EXCHANGE - Proceedings","","This study explores the application of machine learning techniques to predict stock price movements in the market. Using a historical stock-price dataset based on the KSE-100 Index, a Random Forest classifier forecasts whether a stock will rise or fall in the future. The approach trains via a sliding window method and evaluates results with precision, recall, and F1-score. Back testing and hyper-parameter tuning are included. The model reaches 58% precision, improving over a prior 48% result, but overall accuracy remains 58%, indicating room for further improvement. It also proposes future research directions such as alternative data sources and sentiment analysis, with implications for investors and financial organizations.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/stock-price-prediction-using-machine-learning-evidence-from-pakistan-stock-exchange-proceedings/291103/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/stock-price-prediction-using-machine-learning-evidence-from-pakistan-stock-exchange-proceedings/291103.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data and model are used to predict stock price movement?","Question",{"text":112,"@type":113},"The study uses a historical dataset based on the KSE-100 Index and employs a Random Forest classifier to predict whether a stock will rise or fall in the future.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is the machine learning model trained and evaluated?",{"text":117,"@type":113},"Training is performed using a sliding window approach, and performance is assessed using precision, recall, and F1-score metrics.",{"name":119,"@type":110,"acceptedAnswer":120},"What results does the study report and what do they imply?",{"text":121,"@type":113},"The model achieves 58% precision, an improvement from 48%, but overall accuracy is also 58%, suggesting further improvements are needed and motivating future research such as alternative data sources and sentiment analysis.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},291103,1789643456,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","ICBiv e ISBN 978-967-2776-41-3  \nInternational Conference in Business Management & Innovation (ICBiv) 2023  \n“Leveraging Innovativeness Towards Sustainability”  \n18-19 SEPTEMBER 2023  \nProceeding Book  \nICBiv2023  \nEditor  \nTs Dr Umol Syamsyul Rakiman  \nProfesor Madya Dr Hazliza Haron  \nMadam Halimatussaadiah Iksan  \nMadam Nurul Fatihah Wan Ismail  \nDr Wan Maisara Wan Mohamad  \nDr Nawal Esa Yazid Esa  \nDepartment of Business and Management, UiTM Perak Branch  \nINTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT & INNOVATION (ICBiv) 2023  \nLeveraging Innovativeness towards Sustainability  \nProceeding Book  \n(Published November 2024)  \nEditor  \nTs Dr Umol Syamsyul Rakiman Profesor Madya Dr Hazliza Haron Madam Halimatussaadiah Iksan Madam Nurul Fatihah Wan Ismail Dr Wan Maisara Wan Mohamad Dr Nawal Esa Yazid Esa  \n© Unit Penerbitan UiTM Perak, 2024  \nAll rights reserved. No part of this publication may be reproduced, copied, stored in any retrieval system or transmitted in any form or by any means; electronic, mechanical, photocopying, recording or otherwise; without permission on writing from the director of Unit Penerbitan UiTM Perak, Universiti Teknologi MARA, Perak Branch, 32610 Seri Iskandar Perak, Malaysia.  \nPerpustakaan Negara Malaysia Cataloguing in Publication Data  \nNo e ISBN: 978-967-2776-41-3  \nCover Design: Ts Dr Umol Syamsyul Rakiman  \nTypesetting : Ts Dr Umol Syamsyul Rakiman  \nDr Wan Maisara Wan Mohamad  \n151  \nSTOCK PRICE PREDICTION USING MACHINE LEARNING: EVIDENCE FROM PAKISTAN STOCK EXCHANGE  \n[1] Hassan Raza  \n[1] Associate Professor Department of Management Sciences, SZABIST, Islamabad, Pakistan  \n[1] Email: [dr.hassan@szabist-isb.edu.pk](dr.hassan@szabist-isb.edu.pk)  \nABSTRACT  \nThis study explores the application of machine learning techniques to predict the movement of stock prices in the market. Using a dataset of historical stock prices (KSE-100 Index), a Random Forest classifier is employed to make predictions about whether a stock will rise or fall in the future. The model is trained using a sliding window approach and is evaluated using precision, recall, and F1-score metrics. The study also includes back testing and hyper-parameter tuning to improve the model's performance. The results show that the model achieves a precision score of 58%, an improvement from the previous score of 48%. However, the overall accuracy of the model is only 58%, indicating that further improvements are necessary. The study also suggests future directions for research, includingthe use of alternative data sources, sentiment analysis, and more sophisticated algorithms. The study's findings have implications for investors and financial organizations, demonstrating the potential of machine learning to make more educated investment decisions and enhance financial forecasting and analysis.  \nKeywords: Machine learning; Stock prices; Random Forest; Precision; Recall, Financial Forecasting.  \n1. INTRODUCTION  \nThe stock market is a dynamic and unpredictable environment that is influenced by a wide variety of factors, including economic data, company performance, political events, and investor emotions. It is crucial for investors to be able to make accurate forecasts regarding the swings of stock prices if they want to maximize their gains and minimize their losses. Traditional methods of predicting stock prices, such as fundamental and technical analysis, have been utilized for decades, but they have limitations in terms of accuracy and dependability. Newer ways of predicting stock prices have been developed.  \nIt has become increasingly popular to apply machine learning algorithms to financial markets as these algorithms have emerged as a potent tool for predicting stock prices. In the course of this investigation, we intend to build a machine learning algorithm with the purpose of determining whether the price of a stock will go up or down the next day. Our primary objective is to maximize the number of true positives, which ref","cbCaicBOGtwaWtLw","https://ap.wps.com/l/cbCaicBOGtwaWtLw","pdf",628902,15,"English","# Abstract\n# 1. Introduction\n# 2. Literature Review\n## 2.1 Review Stage","[{\"question\":\"What data and model are used to predict stock price movement?\",\"answer\":\"The study uses a historical dataset based on the KSE-100 Index and employs a Random Forest classifier to predict whether a stock will rise or fall in the future.\"},{\"question\":\"How is the machine learning model trained and evaluated?\",\"answer\":\"Training is performed using a sliding window approach, and performance is assessed using precision, recall, and F1-score metrics.\"},{\"question\":\"What results does the study report and what do they imply?\",\"answer\":\"The model achieves 58% precision, an improvement from 48%, but overall accuracy is also 58%, suggesting further improvements are needed and motivating future research such as alternative data sources and sentiment analysis.\"}]","STOCK PRICE PREDICTION USING MACHINE LEARNING: EVIDENCE FROM PAKISTAN STOCK EXCHANGE - Proceedings | PDF",38]