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dc.contributor.authorMuganda, Brian Wesley
dc.date.accessioned2021-08-02T07:40:51Z
dc.date.available2021-08-02T07:40:51Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/11071/12066
dc.descriptionA Dissertation Submitted to the Faculty of Information in partial fulfillment of the requirements for the award of Master of Science in Computing and Information Systemsen_US
dc.description.abstractA portfolio is a collection of investments held by an investment company, hedge fund, financial institution or individual. This collection of investment features a combination of financial assets such as stocks, bonds or options. The designing of a portfolio (fund allocation to each asset and selection of the assets) is done according to the investor’s risk tolerance, investment time frame and investment objectives. Robo-advisors, which are automated algorithm-driven investment platforms that use quantitative algorithms to manage investors’ portfolios, are at times used to perform portfolio allocation for investors having defined their risk preferences and investment time frames. However a majority of these robo-advisors still rely on classical mean variance allocation techniques of modern portfolio theory. This research therefore, developed a robo-financial advisor prototype on a hybrid programming architecture by using artificial neural networks to predict portfolio returns and variances based on underlying multi-asset Uhlenbeck process (OU) and geometric Brownian motion (GBM) processes’ estimates. These results were subsequently used in the probability-based quadratic optimization algorithm to provide an optimal portfolio allocation strategy. This probability-based quadratic programming approach is novel and is based on return certainty probability and value-at-risk constraints acting as proxies for investor’s risk tolerance. The results showed that neural network algorithm performed averagely well forecasting being able to predict the correct level of 2 of 5 assets and to predict correctly the trends of the remaining 3 assets, it however yielded low standard deviations compared to the OU and GBM models. The quadratic optimization algorithm supported investment in shorter time horizons since portfolio risk was lowest. Diversified allocation was achieved in the shorter time horizons. Longer horizons allocations were biased towards asset with lower standard deviations. Lowest risk portfolio was the ones with a lower certainty probability of target return and vice versa. Also, it shows a hybrid programming paradigm is an effective approach to leverage on strengths, speed and functionality of different programming languages; an elixir for multifaceted dissociable programming problems.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectVAR –Constrained Optimizationen_US
dc.subjectCertainty Constrainten_US
dc.subjectPortfolio Selectionen_US
dc.titleA hybrid predictive prototype for portfolio selection using probability based quadratic programming and neural networksen_US
dc.typeThesisen_US


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