[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121145-en":3,"doc-seo-121145-105":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},121145,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","PREDICTING CRISES ON THE AFRICAN FRONTIER STOCK MARKETS WITH INVESTOR SENTIMENT INDICATORS - A MACHINE LEARNING APPROACH","This study examines the predictive capability of machine learning algorithms for identifying crises in African stock markets. Seven machine-learning models are tested using historical stock prices from eight markets, three investor sentiment indicators, and exchange rates of local currencies versus the US dollar, covering May 1, 2007 to April 1, 2023. XGBoost delivers the strongest crisis-prediction performance, with stock prices and exchange rates as key features. Investor sentiment signals include S&P 500 volatility expectations via VIX and daily News Sentiment Index.","Journal of International Technology and Information Management  \nManuscript 1593  \nPREDICTING CRISES ON THE AFRICAN FRONTIER STOCK MARKETS WITH INVESTOR SENTIMENT INDICATORS: A MACHINE LEARNING APPROACH  \nDavid Korsah Lord Mensah  \nFollow this and additional works at: [https://scholarworks.lib.csusb.edu/jitim](https://scholarworks.lib.csusb.edu/jitim)  \n Part of the Business Intelligence Commons, Communication Technology and New Media Commons, Computer and Systems Architecture Commons, Corporate Finance Commons, Data Storage Systems Commons, Digital Communications and Networking Commons, E-Commerce Commons, Information Literacy Commons, Management Information Systems Commons, Management Sciences and Quantitative Methods Commons, Operational Research Commons, Science and Technology Studies Commons, Social Media Commons, and the Technology and Innovation Commons  \nPREDICTING CRISES ON THE AFRICAN FRONTIER STOCK MARKETS WITH INVESTOR SENTIMENT  \nINDICATORS:  \nA MACHINE LEARNING APPROACH  \nDavid Korsah  \nUniversity of Ghana  \nLord Mensah  \nUniversity of Ghana  \nABSTRACT  \nThis study examined the predictive ability of machine learning algorithms in identifying crises within African stock markets. The study employed seven distinct machine-learning models, analyzing historical stock prices from eight stock markets, three major sentiment indicators, and the exchange rates of local currencies against the US dollar, with each data spanning from May 1, 2007, to April 1, 2023. Extreme Gradient Boosting (XGBoost) emerged as the most effective algorithm for predicting crises. Historical stock prices and exchange rates were identified as the most critical features for prediction. On the sentiment side, investors’ perceptions of potential volatility on the S&P 500, as captured by the CBOE Volatility Index (VIX), and the daily News Sentiment Index were recognized as significant predictors. The study advances the understanding of market sentiment’s role in stock market dynamics and highlights the importance of employing advanced computational techniques for risk management and market stability.  \nKeywords: Crisis Prediction, African Stock Markets, Machine Learning  \n©International Information Management Association, Inc. 2021 116 ISSN: 1941-6679-On-line Copy  \nBACKGROUND OF THE STUDY  \nThe African financial sector has made significant strides, particularly following a series of reforms to bolster the harmonization of financial markets and eliminate restrictions associated with foreign investments, capital controls, and so on (Moyo et al., 2014) . These initiatives, coupled with the advent and subsequent advancement in Information Communication Technology (ICT) , have rendered the sector competitive and more efficient (Appiah et al., 2022) . Soumaré et al. (2021) postulate that the stock market is a major player in the continent’s financial sector. The stock market absorbs savings and provides liquidity for both short-and longterm investments, providing long-term capital for key sectors ofthe economy, such as businesses and government (Bernanke & Kuttner, 2005), thereby contributing immensely to sustainable economic development (Strine, 2016) . Indeed, the stock exchange index has increasingly become a key barometer for assessing economic health of economies (He et al., 2020; Jebabli, Kouaissah & Arouri, 2022) .  \nUnfortunately, over the years, stock markets across the globe have suffered the devastation of numerous crisis episodes in the financial sector, which in many cases has resulted in the complete depletion of investors’ gains. Notable among them are the Great Depression of the 1930s, the Black Monday crash of 1987, the Dot-Com bubble burst of 2000, the Global Financial Crisis (GFC) of 2008, and the COVID- 19 pandemic. The impacts of these phenomena have become more pronounced in recent times due to increased interdependence and connectedness among financial markets (Mensi et al., 2018; Choi, 2021) .  \nEmerging stock markets, particularly","cbCaim8BDGFTxbSN","https://ap.wps.com/l/cbCaim8BDGFTxbSN","pdf",905448,1,29,"English","en",105,"# Abstract\n# Background of the Study\n## Financial sector development in Africa\n## Global crisis episodes and market interdependence\n## Vulnerability of emerging African markets\n## Behavioral finance and investor irrationality","[{\"question\":\"Which machine learning algorithm performs best for predicting crises in African frontier stock markets?\",\"answer\":\"Extreme Gradient Boosting (XGBoost) emerges as the most effective algorithm for predicting crises.\"},{\"question\":\"What data and indicators are used to build the prediction models?\",\"answer\":\"The models use historical stock prices from eight stock markets, exchange rates versus the US dollar, and three investor sentiment indicators: VIX and a daily News Sentiment Index, along with volatility perception related to the S\\u0026P 500.\"},{\"question\":\"Which features are identified as most important for crisis prediction?\",\"answer\":\"Historical stock prices and exchange rates are identified as the most critical predictive features, while sentiment measures contribute significant information as well.\"}]","PREDICTING CRISES ON THE AFRICAN FRONTIER STOCK MARKETS WITH INVESTOR SENTIMENT INDICATORS - A MACHINE LEARNING APPROACH | PDF",1785734070,73,{"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},"predicting-crises-on-the-african-frontier-stock-markets-with-investor-sentiment-indicators-a-machine-learning-approach","",{"@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/predicting-crises-on-the-african-frontier-stock-markets-with-investor-sentiment-indicators-a-machine-learning-approach/121145/",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","2026-08-03",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 algorithm performs best for predicting crises in African frontier stock markets?","Question",{"text":75,"@type":76},"Extreme Gradient Boosting (XGBoost) emerges as the most effective algorithm for predicting crises.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and indicators are used to build the prediction models?",{"text":80,"@type":76},"The models use historical stock prices from eight stock markets, exchange rates versus the US dollar, and three investor sentiment indicators: VIX and a daily News Sentiment Index, along with volatility perception related to the S&P 500.",{"name":82,"@type":73,"acceptedAnswer":83},"Which features are identified as most important for crisis prediction?",{"text":84,"@type":76},"Historical stock prices and exchange rates are identified as the most critical predictive features, while sentiment measures contribute significant information as well.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]