Alam, Muhammad Aizat bin Zainal and Yong, Chen Chen and Mansor, Norma (2022) Predicting savings adequacy using machine learning: A behavioural economics approach. Expert Systems with Applications, 203. ISSN 0957-4174, DOI https://doi.org/10.1016/j.eswa.2022.117502.
Full text not available from this repository.Abstract
This paper proposes a machine-learning-based method that can predict individuals' savings adequacy in the presence of mental accounting. The proposed predictive model perceives wealth and consumption, each of which is being divided into three non-fungible distinct classes. The predictive model has found that the mental accounting categories have predictive power on savings adequacy, whereby the emphasis is that the expenditure on luxury items is followed by the total current asset. Savings adequacy is best predicted by the decision tree model based on the Malaysian Ageing and Retirement (MARS) survey data. Surprisingly, it was found that future income and necessities had a lower predictive power on savings adequacy. The findings suggests that individuals, financial professionals, and policymakers should be cognizant that higher likelihood of achieving savings adequacy can be achieved by focusing on accumulation of current asset while lowering expenditure on luxury items.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Behavioural finance; Economics; Human decision-making; Psychology |
| Subjects: | H Social Sciences > HC Economic History and Conditions Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Business and Economics |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 18 Oct 2023 08:11 |
| Last Modified: | 18 Oct 2023 08:11 |
| URI: | http://eprints.um.edu.my/id/eprint/41990 |
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