| Naslov: | Detecting potential investors in crypto assets : insights from machine learning models and explainable AI |
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| Avtorji: | ID Jagrič, Timotej (Avtor)
ID Herman, Aljaž (Avtor)
ID Luetić, Davor (Avtor)
ID Mumel, Damijan (Avtor) |
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| Datoteke: |  https://www.mdpi.com/2078-2489/16/4/269
 Detecting_Potential_Investors_in_Crypto_Assets.pdf (1,58 MB)
MD5: 41FF47DBB258242C4E54A421DAB130B8
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Znanstveno delo |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | EPF - Ekonomsko-poslovna fakulteta
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| Opis: | This study explores the characteristics of individual investors in crypto asset markets using machine learning and explainable artificial intelligence (XAI) methods. The primary objective was to identify the most effective model for predicting the likelihood of an individual investing in crypto assets in the future based on demographic, behavioral, and financial factors. Data were collected through an online questionnaire distributed via social media and personal networks, yielding a limited but informative sample. Among the tested models, Efficient Linear SVM and Kernel Naïve Bayes emerged as the most optimal, balancing accuracy and interpretability. XAI techniques, including SHAP and Partial Dependence Plots, revealed that crypto understanding, perceived crypto risks, and perceived crypto benefits were the most influential factors. For individuals with a high likelihood of investing, these factors had a strong positive impact, while they negatively influenced those with a low likelihood. However, for those with a moderate investment likelihood, the effects were mixed, highlighting the transitional nature of this group. The study’s findings provide actionable insights for financial institutions to refine their strategies and improve investor engagement. Furthermore, it underscores the importance of interpretable machine learning in financial behavior analysis and highlights key factors shaping engagement in the evolving crypto market. |
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| Ključne besede: | crypto investors, identification, characteristics, machine learning, coarse tree model, artificial intelligence |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 29.01.2025 |
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| Datum sprejetja članka: | 25.03.2025 |
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| Datum objave: | 27.03.2025 |
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| Založnik: | MDPI |
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| Leto izida: | 2025 |
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| Št. strani: | str. 1-22 |
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| Številčenje: | Vol. 16, issue 4, spec. iss., [art. no.] 269 |
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| PID: | 20.500.12556/DKUM-92813  |
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| UDK: | 004.8 |
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| COBISS.SI-ID: | 235803139 |
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| DOI: | 10.3390/info16040269 |
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| ISSN pri članku: | 2078-2489 |
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| Datum objave v DKUM: | 09.07.2025 |
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| Število ogledov: | 146 |
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| Število prenosov: | 18 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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