[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128694-en":3,"doc-seo-128694-105":29,"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"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},128694,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Decoding Internet Signals for Stock Market Movement - A Machine Learning Study on Pharma Sector During Covid-19","The COVID-19 pandemic enables an examination of how Internet signals affect financial markets during a period of rapid news diffusion that can overwhelm investors and obscure original information. The study integrates Internet signals with traditional datasets to forecast stock market movements, focusing on the pharmaceutical sector and the pandemic’s peak. Signals are collected from news portals and search engines and combined with intra-day technical indicators. Results show that Internet signals significantly improve predictive performance, supporting machine-learning-driven business decision-making.","Proceedings of the 2024 Pre-ICIS SIGDSA Symposium  \nSpecial Interest Group on Decision Support and Analytics (SIGDSA)  \n12-2024  \nDecoding Internet Signals for Stock Market Movement: A Machine Learning Study on Pharma Sector During Covid-19  \nGaurav Dixit  \nSatyam Kamalakar Morankar  \nFollow this and additional works at: [https://aisel.aisnet.org/sigdsa2024](https://aisel.aisnet.org/sigdsa2024)  \nThis material is brought to you by the Special Interest Group on Decision Support and Analytics (SIGDSA) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in Proceedings of the 2024 Pre-ICIS SIGDSA Symposium by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please contact [elibrary@aisnet.org](elibrary@aisnet.org).  \nInternet Chatter and Stock Market Movement  \nDecoding Internet Signals for Stock Market Movement: A Machine Learning Study on Pharma Sector During Covid-19  \nResearch-in-Progress  \nGaurav Dixit  \nIndian Institute of Technology Roorkee [gauravdixit.fdm@gmail.com](gauravdixit.fdm@gmail.com) ; [gaurav.dixit@ms.iitr.ac.in](gaurav.dixit@ms.iitr.ac.in)  \nSatyam Kamalakar Morankar  \nMIT World Peace University, Pune, India [morankarsatyam@gmail.com](morankarsatyam@gmail.com)  \nAbstract  \nThe COVID-19 pandemic offers a unique opportunity to examine the role of Internet signals in financial markets, specifically during the peak period when rapid dissemination of news about emerging threats can overwhelm investors, making it crucial for them to find original signals from the Internet for their decisionmaking. We study the effectiveness of integrating Internet signals with traditional datasets to predict stock market movements. We selected the pharmaceutical industry and the pandemic’s peak period to obtain pronounced and relevant signals from news portals and search engines, along with technical indicators for intra-day trading. We employed state-of-the-art models for tabular data, such as Random Forest, Gradient Boosting, and XGBoost, and plan to experiment with deep learning models next. Our findings indicate that incorporating Internet signals contributes to significantly better predictions of stock market movements. This study offers significant practical implications and contributes to machine learning-based research for business decision-making.  \nKeywords  \nInternet signals, stock market, machine learning, deep learning, COVID-19  \nIntroduction  \nStock markets are one of the most volatile and unpredictable financial markets, driven by various factors such as economic, political, and social events. The emergence of big data and machine learning algorithms in recent years has opened up new opportunities for more accurately predicting stock prices and their movements (Gan et al., 2020) . During COVID-19, many users started to increasingly follow online news from varied Internet sources and increased searches for their information of interest using Google or other search engines (Valle-Cruz et al., 2022) . Online interactions and engagements were critical to keep people updated about the COVID-19 pandemic and the general situation, leading to increased Internet chatter. These spikes in Internet chatter might also influence stock markets due to the potential shifts in investors’decision-making mechanisms. Therefore, the COVID-19 pandemic presents an opportunity to study the research question,“How can Internet signals be decoded to better predict stock market movement?”Investors might prefer to keep a tap on Internet chatter related to financial markets since it can help them make more informed judgments in the stock markets (Gan et al., 2020) . By extracting relevant and rich features from Internet data, investors can do rapid analysis and decision-making, allowing them to respond promptly to volatile market conditions. Periodic detection of positive or negative public signals about financial markets can be critical in the stock market investors’decision-making. Combining","cbCaimkUiywSNXQv","https://ap.wps.com/l/cbCaimkUiywSNXQv","pdf",262998,1,"English","en",105,"# Internet Chatter and Stock Market Movement\n## Research-in-Progress\n## Abstract\n## Keywords\n## Introduction","[{\"question\":\"本研究的核心问题是什么？\",\"answer\":\"研究关注“如何解码互联网信号以更好预测股价/市场波动”。通过从互联网数据中提取更丰富的特征来提升预测与决策速度。\"},{\"question\":\"研究使用了哪些数据来源来建模？\",\"answer\":\"在COVID-19峰值期间，使用三类关键信息：药企股票市场数据、来自新闻门户和搜索引擎的互联网信号，以及股票的日内技术指标。\"},{\"question\":\"模型与实验方法包括哪些关键技术？\",\"answer\":\"研究采用面向表格数据的先进模型，如Random Forest、Gradient Boosting与XGBoost，并计划进一步尝试深度学习模型以提升互联网信号解码与预测效果。\"}]","Decoding Internet Signals for Stock Market Movement - A Machine Learning Study on Pharma Sector During Covid-19 | PDF",1786002705,20,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":27},"decoding-internet-signals-for-stock-market-movement-a-machine-learning-study-on-pharma-sector-during-covid-19","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"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":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/decoding-internet-signals-for-stock-market-movement-a-machine-learning-study-on-pharma-sector-during-covid-19/128694/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"本研究的核心问题是什么？","Question",{"text":75,"@type":76},"研究关注“如何解码互联网信号以更好预测股价/市场波动”。通过从互联网数据中提取更丰富的特征来提升预测与决策速度。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"研究使用了哪些数据来源来建模？",{"text":80,"@type":76},"在COVID-19峰值期间，使用三类关键信息：药企股票市场数据、来自新闻门户和搜索引擎的互联网信号，以及股票的日内技术指标。",{"name":82,"@type":73,"acceptedAnswer":83},"模型与实验方法包括哪些关键技术？",{"text":84,"@type":76},"研究采用面向表格数据的先进模型，如Random Forest、Gradient Boosting与XGBoost，并计划进一步尝试深度学习模型以提升互联网信号解码与预测效果。","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":28,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":28,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]