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- W3045168638 abstract "This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution." @default.
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- W3045168638 date "2020-01-01" @default.
- W3045168638 modified "2023-09-27" @default.
- W3045168638 title "Modelling and Forecasting Portfolio Inflows" @default.
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- W3045168638 doi "https://doi.org/10.4018/978-1-7998-3645-2.ch014" @default.
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