{"id":4569,"date":"2025-06-10T13:43:08","date_gmt":"2025-06-10T11:43:08","guid":{"rendered":"https:\/\/www.escience.ac.za\/?p=4569"},"modified":"2025-06-10T13:43:09","modified_gmt":"2025-06-10T11:43:09","slug":"temporal-dependency-modeling-in-financial-markets","status":"publish","type":"post","link":"https:\/\/www.escience.ac.za\/index.php\/2025\/06\/10\/temporal-dependency-modeling-in-financial-markets\/","title":{"rendered":"Temporal Dependency Modeling in Financial Markets"},"content":{"rendered":"

Researcher<\/strong>: Small Tshithavhana, University of the Witwatersrand, Johannesburg
Supervisor<\/strong>: Dr. Walter Mudzimbabwe, University of the Witwatersrand, Johannesburg<\/p>

Financial forecasting has become increasingly important in today\u2019s global market due to its ability to
assess risk and inform decision-making. However, accurately forecasting financial markets is challenging
due to their stochastic nature and complexity. To address this challenge, we suggest a state space
model, namely the Hidden Markov Model, which handles dynamic time series issues involving unseen
variables or parameters that represent the development of the underlying system\u2019s state.we test
our model on financial market information sourced from the Nasdaq online database and compare its
performance with standard forecasting machine learning models. The results under the MAPE matrix
indicate that the proposed model outperformed the Recurrent Neural Network (RNN) by 19.08% and
exhibited a superior performance of 19.09% relative to the ARIMA model. However, the proposed
model fell short in comparison to the GARCH model by a margin of 3.11%.<\/p>

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 <\/p>

<\/p>","protected":false},"excerpt":{"rendered":"

Researcher: Small Tshithavhana, University of the Witwatersrand, JohannesburgSupervisor: Dr. Walter Mudzimbabwe, University of the Witwatersrand, Johannesburg Financial forecasting has become increasingly important in today\u2019s global market due to its ability toassess risk and inform decision-making. However, accurately forecasting financial markets is challengingdue to their stochastic nature and<\/p>\n","protected":false},"author":3,"featured_media":4493,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[],"class_list":["post-4569","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-capstone-projects"],"_links":{"self":[{"href":"https:\/\/www.escience.ac.za\/index.php\/wp-json\/wp\/v2\/posts\/4569","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.escience.ac.za\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.escience.ac.za\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.escience.ac.za\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.escience.ac.za\/index.php\/wp-json\/wp\/v2\/comments?post=4569"}],"version-history":[{"count":1,"href":"https:\/\/www.escience.ac.za\/index.php\/wp-json\/wp\/v2\/posts\/4569\/revisions"}],"predecessor-version":[{"id":4571,"href":"https:\/\/www.escience.ac.za\/index.php\/wp-json\/wp\/v2\/posts\/4569\/revisions\/4571"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.escience.ac.za\/index.php\/wp-json\/wp\/v2\/media\/4493"}],"wp:attachment":[{"href":"https:\/\/www.escience.ac.za\/index.php\/wp-json\/wp\/v2\/media?parent=4569"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.escience.ac.za\/index.php\/wp-json\/wp\/v2\/categories?post=4569"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.escience.ac.za\/index.php\/wp-json\/wp\/v2\/tags?post=4569"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}