Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach

Can Verberi; Muhittin Kaplan

Makale detayları

Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach

Yazarlar:

Can Verberi | Muhittin Kaplan

Yayıncı:

Istanbul University

Ana Başlıklar:

Finans ve Yatırım (Diğer)

Dergi:

İstanbul İktisat Dergisi Vol.74 Issue 1, pp.281-314

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001326564

pISSN:

2602-4152

Anahtar kelimeler:

Özet:

 This study aims to investigate the effects of personality traits, in addition to basic financial literacy, private pension literacy and behavioural factors on Private Pension System PPS participation using machine learning algorithms. The PPS participation model was trained using both random forest and LightGBM algorithms, and the contributions of model inputs in the prediction of pension participation were interpreted using the Tree SHAP algorithms with swarmplots. The data employed in the empirical analysis is survey data collected from the Şırnak province of Türkiye with a sample size of 449. The findings of the study shows that: i PPS participation is more likely for females and middle-aged people; ii High basic financial literacy has a negative impact on PPS participation; iii Extraversion is the key personality trait affecting PPS participation; iv Advanced pension literacy has more impact on participation than simple pension literacy: v Present-fatalistic tendency is key behavioural factor and it negatively affects PPS; vi Present-hedonistic, conscientiousness, future-time orientation, and locus of control tendencies increase PPS participation. Furthermore, the distribution of colours in LightGBM has a greater degree of uniformity in both directions compared with the random forest algorithm. Finally, to increase PPS participation, the results of the study suggest the implementation of the following policy measures: Tailored pension literacy programmes can help to increase pension participation rates. Incentives should be created to prevent narrow-minded behaviour and establish a sense of protection and control around PPS, targeting middle-aged individuals and women. JEL Classification : C60 , G41 , J32.

 This study aims to investigate the effects of personality traits, in addition to basic financial literacy, private pension literacy and behavioural factors on Private Pension System PPS participation using machine learning algorithms. The PPS participation model was trained using both random forest and LightGBM algorithms, and the contributions of model inputs in the prediction of pension participation were interpreted using the Tree SHAP algorithms with swarmplots. The data employed in the empirical analysis is survey data collected from the Şırnak province of Türkiye with a sample size of 449. The findings of the study shows that: i PPS participation is more likely for females and middle-aged people; ii High basic financial literacy has a negative impact on PPS participation; iii Extraversion is the key personality trait affecting PPS participation; iv Advanced pension literacy has more impact on participation than simple pension literacy: v Present-fatalistic tendency is key behavioural factor and it negatively affects PPS; vi Present-hedonistic, conscientiousness, future-time orientation, and locus of control tendencies increase PPS participation. Furthermore, the distribution of colours in LightGBM has a greater degree of uniformity in both directions compared with the random forest algorithm. Finally, to increase PPS participation, the results of the study suggest the implementation of the following policy measures: Tailored pension literacy programmes can help to increase pension participation rates. Incentives should be created to prevent narrow-minded behaviour and establish a sense of protection and control around PPS, targeting middle-aged individuals and women. JEL Classification : C60 , G41 , J32.

 This study aims to investigate the effects of personality traits, in addition to basic financial literacy, private pension literacy and behavioural factors on Private Pension System PPS participation using machine learning algorithms. The PPS participation model was trained using both random forest and LightGBM algorithms, and the contributions of model inputs in the prediction of pension participation were interpreted using the Tree SHAP algorithms with swarmplots. The data employed in the empirical analysis is survey data collected from the Şırnak province of Türkiye with a sample size of 449. The findings of the study shows that: i PPS participation is more likely for females and middle-aged people; ii High basic financial literacy has a negative impact on PPS participation; iii Extraversion is the key personality trait affecting PPS participation; iv Advanced pension literacy has more impact on participation than simple pension literacy: v Present-fatalistic tendency is key behavioural factor and it negatively affects PPS; vi Present-hedonistic, conscientiousness, future-time orientation, and locus of control tendencies increase PPS participation. Furthermore, the distribution of colours in LightGBM has a greater degree of uniformity in both directions compared with the random forest algorithm. Finally, to increase PPS participation, the results of the study suggest the implementation of the following policy measures: Tailored pension literacy programmes can help to increase pension participation rates. Incentives should be created to prevent narrow-minded behaviour and establish a sense of protection and control around PPS, targeting middle-aged individuals and women. JEL Classification : C60 , G41 , J32.

 This study aims to investigate the effects of personality traits, in addition to basic financial literacy, private pension literacy and behavioural factors on Private Pension System PPS participation using machine learning algorithms. The PPS participation model was trained using both random forest and LightGBM algorithms, and the contributions of model inputs in the prediction of pension participation were interpreted using the Tree SHAP algorithms with swarmplots. The data employed in the empirical analysis is survey data collected from the Şırnak province of Türkiye with a sample size of 449. The findings of the study shows that: i PPS participation is more likely for females and middle-aged people; ii High basic financial literacy has a negative impact on PPS participation; iii Extraversion is the key personality trait affecting PPS participation; iv Advanced pension literacy has more impact on participation than simple pension literacy: v Present-fatalistic tendency is key behavioural factor and it negatively affects PPS; vi Present-hedonistic, conscientiousness, future-time orientation, and locus of control tendencies increase PPS participation. Furthermore, the distribution of colours in LightGBM has a greater degree of uniformity in both directions compared with the random forest algorithm. Finally, to increase PPS participation, the results of the study suggest the implementation of the following policy measures: Tailored pension literacy programmes can help to increase pension participation rates. Incentives should be created to prevent narrow-minded behaviour and establish a sense of protection and control around PPS, targeting middle-aged individuals and women. JEL Classification : C60 , G41 , J32.

Makale detayları

Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach

Yazarlar:

Can Verberi | Muhittin Kaplan

Yayıncı:

Istanbul University

Ana Başlıklar:

Finans ve Yatırım (Diğer)

Dergi:

İstanbul İktisat Dergisi Vol.74 Issue 1, pp.281-314

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001326564

pISSN:

2602-4152

Private pension system

Behavioural factors

Personality traits

Machine learning algorithms

Tree SHAP

Sayı

Makale detayları

Exploring the Impact of Behavioural Factors and Personality Traits on Private Pension System Participation: A Machine Learning Approach

Yazarlar: Can Verberi | Muhittin Kaplan

Yayıncı: Istanbul University

Ana Başlıklar: Finans ve Yatırım (Diğer)

Dergi: İstanbul İktisat Dergisi Vol.74 Issue 1, pp.281-314

Belge Tipi: Makale

Yayın Tarihi: 4/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001326564

pISSN: 2602-4152

Private pension system

Behavioural factors

Personality traits

Machine learning algorithms

Tree SHAP

Sayı

Yazarlar:

Can Verberi | Muhittin Kaplan

Yayıncı:

Istanbul University

Ana Başlıklar:

Finans ve Yatırım (Diğer)

Dergi:

İstanbul İktisat Dergisi Vol.74 Issue 1, pp.281-314

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001326564

pISSN:

2602-4152