[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127062-en":3,"doc-seo-127062-105":30,"detail-sidebar-cat-0-en-105":95},{"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},127062,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting Stock Prices in the Pakistan Market Using Machine Learning and Technical Indicators","The study applies advanced machine learning models to predict stock price movements in Pakistan’s stock market using 27 technical indicators. Four techniques are evaluated for binary classification: ANN, SVM, LSTM, and Random Forest. ANN and SVM achieve the highest accuracy at 85%, followed by Random Forest at 84% and LSTM at 78%. Key predictors such as %R, Momentum, and Disparity 5 consistently contribute across models, supporting data-driven decision-making. The study also recommends exploring hybrid approaches with real-time data, sentiment signals, and external factors to further improve forecasting.","Article  \nPredicting stock prices in the Pakistan market using machine learning and technical indicators  \nHassan Raza1* and Zafar Akhtar2  \nCitation: Raza, H., & Akhtar, Z.  \n(2024). Predicting stock prices in the Pakistan market using machine learning and technical indicators. Modern Finance, 2(2), 46-63.  \nAccepting Editor: Adam Zaremba  \nReceived: 24 July 2024  \nAccepted: 29 August 2024  \nPublished: 30 August 2024  \nCopyright: © 2024 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([http://creativecommons.org/licenses](http://creativecommons.org/licenses)[ ](http://creativecommons.org/licenses)[/by/4.0/](/by/4.0/)) .  \n1 SZABIST University, Islamabad Campus; [hassanrazaa@live.com](hassanrazaa@live.com)  \n2 SZABIST University, Islamabad Campus; [akhtar.zafar@gmail.com](akhtar.zafar@gmail.com)  \n* Correspondence: SZABIST University, Islamabad Campus, 44000, Pakistan; [hassanrazaa@live.com](hassanrazaa@live.com)  \n[Abstract:](Abstract: This study uses advanced machine learning models to predict stock prices in the Pakistani)[ This study uses advanced machine learning models to predict stock prices in the Pakistani](Abstract: This study uses advanced machine learning models to predict stock prices in the Pakistani)[ ](Abstract: This study uses advanced machine learning models to predict stock prices in the Pakistani)[stock market using 27 technical indicators. It evaluates the predictive capabilities of four](stock market using 27 technical indicators. It evaluates the predictive capabilities of four)[ ](stock market using 27 technical indicators. It evaluates the predictive capabilities of four)[techniques](techniques), SVM, LSTM, and Random Forest for binary classification of stock price movements. ANN and SVM show the highest accuracy at 85%, followed by Random Forest at 84% and LSTMat 78%. Key indicators such as %R, Momentum, and Disparity 5 are critical across all models. The research provides valuable insights for investors and analysts to improve decision-making. It underscores the importance of technical indicators and establishes a data-driven approach to navigating the complexities of the Pakistani stock market. The study further emphasizes the importance of technical indicators and suggests exploring hybrid models that incorporate real-time data, sentiment analysis, and external factors for better stock price prediction.  \nKeywords: Technical Indicators; Stock Price Prediction; Machine Learning Algorithm; ANN; SVM; LSTM and Random Forest  \n1. Introduction  \nThe inherent unpredictability of stock markets has long been a focal point of intrigue and challenge within the dynamic fields of finance and econometrics. The pursuit of understanding and forecasting stock market price movements is driven by both the potential for financial gain and the intellectual complexities involved in decoding market behavior. While much research has historically focused on forecasting stock price indices, the task of directional prediction—particularly in volatile markets—has gained significant prominence in recent years.  \nAccurate and effective market tactics rely heavily on detailed and precise forecasting, especially within highly unpredictable financial environments. Financial time series, characterized by the volatile and often chaotic nature of stock markets, exhibit unpredictability and non-linearity, resulting from a complex interplay of various interconnected elements. These include the balance between supply and demand, interest rate fluctuations, key economic indicators, and political transformations. Such factors contribute to the market’s volatility, leading to abrupt fluctuations and, at times, severe downturns.  \nIn this sophisticated context, prediction extends beyond mere modeling expertise; it requires robust data cleansing, preparation, and the application of innovative forecasting approaches. Traditional methods such ","cbCaid3b3RvozbPo","https://ap.wps.com/l/cbCaid3b3RvozbPo","pdf",1200664,1,18,"English","en",105,"# Introduction\n## Stock market unpredictability and directional prediction\n## Traditional time-series methods vs machine learning approaches\n# Methodological Approach\n## Feature set: 27 technical indicators\n## Models evaluated: ANN, SVM, LSTM, Random Forest\n# Results and Key Indicators\n## Accuracy comparison across models\n## Consistent indicator importance (%R, Momentum, Disparity 5)\n# Discussion and Future Directions\n## Data-driven forecasting and decision support\n## Hybrid models with real-time and external signals","[{\"question\":\"Which machine learning models are used to predict stock price movements in the study?\",\"answer\":\"The study evaluates ANN, SVM, LSTM, and Random Forest for binary classification of stock price movements.\"},{\"question\":\"How many technical indicators does the study use as features?\",\"answer\":\"The research uses 27 distinct technical indicators to build the predictive feature set.\"},{\"question\":\"What accuracy results does the study report for each model?\",\"answer\":\"ANN and SVM reach 85% accuracy, Random Forest achieves 84%, and LSTM reports 78% accuracy.\"},{\"question\":\"Which technical indicators are identified as critical across the models?\",\"answer\":\"%R, Momentum, and Disparity 5 are highlighted as critical indicators contributing across all models.\"}]","Predicting Stock Prices in the Pakistan Market Using Machine Learning and Technical Indicators | 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