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Optimizing financial decision-making for emerging adults: A compact Python-based personalized financial projection approach

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  • Zhu, Alex Yue Feng

Abstract

The financial decisions made by emerging adults significantly influence their long-term financial well-being. To make effective decisions, they need both financial literacy and a forward-looking mindset. However, existing literature lacks tailored financial interventions for this demographic. Addressing this gap, our study implemented and evaluated a Python-based Personalized Financial Projection (PFP) among emerging adults in Hong Kong. Our findings indicate that the Python-based PFP effectively promoted prudent financial behaviors by enhancing participants’ perceived financial literacy and future time perspectives. This represents a significant advancement, providing the first empirical evidence supporting the integration of computer programming into financial education initiatives. In an era of interactive Artificial Intelligence (AI) for financial guidance, the Python-based PFP stands out as a pivotal resource capable of leveraging interactive AI to enhance financial decision-making processes.

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  • Zhu, Alex Yue Feng, 2024. "Optimizing financial decision-making for emerging adults: A compact Python-based personalized financial projection approach," Technology in Society, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:teinso:v:77:y:2024:i:c:s0160791x24001477
    DOI: 10.1016/j.techsoc.2024.102599
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    References listed on IDEAS

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