Application of machine learning models in forecasting stock market volatility: case study of the Nairobi Securities Exchange

dc.contributor.author	Wanjau, G. G.
dc.date.accessioned	2026-05-29T16:40:43Z
dc.date.issued	2024
dc.description	Full - text thesis
dc.description.abstract	The investment universe in the stock market is divided into two major categories namely; traditional and Alternative products. Traditional investments involves trading of products such as stocks, bonds, mutual funds and cash. The remaining investments are categorized as alternative products which include Derivatives, Real Estate Investment Schemes, Hedge Funds, Commodities, Structured products, Managed Funds, Private Equity/ Venture Capital, among others. Alternative investments are basically an alternative to the traditional stock market products and they offer potential higher returns, exhibit lower volatility and can be used for capital preservation.
dc.identifier.citation	Wanjau, G. G. (2024). Application of machine learning models in forecasting stock market volatility: Case study of the Nairobi Securities Exchange [Strathmore University]. https://hdl.handle.net/11071/16577
dc.identifier.uri	https://hdl.handle.net/11071/16577
dc.language.iso	en
dc.publisher	Strathmore University
dc.title	Application of machine learning models in forecasting stock market volatility: case study of the Nairobi Securities Exchange
dc.type	Thesis

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