| Title: | Detecting potential investors in crypto assets : insights from machine learning models and explainable AI |
|---|
| Authors: | ID Jagrič, Timotej (Author)
ID Herman, Aljaž (Author)
ID Luetić, Davor (Author)
ID Mumel, Damijan (Author) |
|---|
| Files: |  https://www.mdpi.com/2078-2489/16/4/269
 Detecting_Potential_Investors_in_Crypto_Assets.pdf (1,58 MB)
MD5: 41FF47DBB258242C4E54A421DAB130B8
|
|---|
| Language: | English |
|---|
| Work type: | Scientific work |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | EPF - Faculty of Business and Economics
|
|---|
| Abstract: | 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. |
|---|
| Keywords: | crypto investors, identification, characteristics, machine learning, coarse tree model, artificial intelligence |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Submitted for review: | 29.01.2025 |
|---|
| Article acceptance date: | 25.03.2025 |
|---|
| Publication date: | 27.03.2025 |
|---|
| Publisher: | MDPI |
|---|
| Year of publishing: | 2025 |
|---|
| Number of pages: | str. 1-22 |
|---|
| Numbering: | Vol. 16, issue 4, spec. iss., [art. no.] 269 |
|---|
| PID: | 20.500.12556/DKUM-92813  |
|---|
| UDC: | 004.8 |
|---|
| ISSN on article: | 2078-2489 |
|---|
| COBISS.SI-ID: | 235803139 |
|---|
| DOI: | 10.3390/info16040269 |
|---|
| Publication date in DKUM: | 09.07.2025 |
|---|
| Views: | 148 |
|---|
| Downloads: | 18 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
