Wits Business School (ETDs)

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Author: search.filters.author.Bester, Wesley RyanSubject: search.filters.subject.Business model

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    The revenue model of an online South African stockbroking platform
    (University of the Witwatersrand, Johannesburg, 2024) Bester, Wesley Ryan; Godspower-Akpomiemie, Euphemia
    As technology advances at an unprecedented pace, many businesses and industries must adapt to the increasingly digital world. The online stockbroking industry is no exception and requires significant attention and change to keep up with the times. Business and revenue models in the stockbroking industry in South Africa have remained essentially unchanged over the past few decades. The variable-rate brokerage fee charged on transactions executed remains the primary source of income. This revenue model has rapidly become unsustainable with the decrease in these fees over the past few years. The study's main objective is to investigate revenue models that are more suitable for the digital trading environment. The study examines the background and appropriateness of alternative revenue models and platform models, along with the use of prospect theory to guide customer preferences. This quantitative case study utilises secondary data from a South African bank's online stockbroking division, analysing over 334,000 trades over 10 years. The entire dataset is analysed by looking at its descriptive, and inferential statistics, as well as time-series analysis. The study investigates the relationship between frequency, transaction amount, and their effect on brokerage over time, along with their association using the Chi-square model. Secondly, a model is built to predict a fixed monthly subscription fee for clients to replace the outdated variable-rate brokerage model. Clients will then choose a model of best value to answer the second research question. The study addresses two hypotheses, it also finds brokerage fees highly correlated to transaction values and inversely related to trade frequency. Based on the results, the model developed can effectively predict future fixed monthly subscription fees for online stockbroking platforms.