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UNIVERSITAS INDONESIA
FACIAL TRUSTWORTHINESS AND
IPO UNDERPRICING
TESIS
FAKULTAS EKONOMI DAN BISNIS
2024
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Tesis

Facial Trustworthiness and IPO Underpricing

Facial Trustworthiness dan IPO Underpricing

I Putu Sukma Hendrawan - ; Cynthia Afriani (Pembimbing/Promotor) - ; Maria Ulpah (Penguji) - ; Dwi Nastiti Danarsari (Penguji) - ;

This study is aimed at investigating the impact of facial-based perceived trustworthiness on stock valuation, particularly in the initial public offering (IP0). IPO settings provide opportunity to investigate whether information asymmetry resulted from company newness in the market would influence the incorporation of soft information in the form of executive facial trustworthiness in stock valuation. We use recent machine learning algorithm to detect facial landmarks and then calculate a composite facial trustworthiness measure using several facial features that has previously been observed in neuroscience and psychological study to be the most determining factor of perceived trustworthiness. We then regress the facial trustworthiness of IPO firms executives to IPO underpricing. We find that the facial trustworthiness of the company executive negatively impacts the extent of IPO underpricing. This result implies that investors incorporate the facial trustworthiness of company executives into stock valuation. With regard to the higher information asymmetry in IPO transactions, such a negative impact implies the role of facial trustworthiness in alleviating the impact of information asymmetry on IPO pricing. This study provides evidence on the impact of the perception of top management characteristics on firms' financial transactions in the Indonesian context. From the perspective of investors and other fund providers, this study shows evidence that heuristics still play an important role in financial decision making. This is also an indication of investor reliance on soft information. Our research method also provides a new opportunity for the use of machine-learning algorithms in processing non- conventional types of data in finance research, which is still relatively rare in emerging market like Indonesia. To the best of our knowledge, our study is the first to use personalized-measure of trust generated through machine-learning algorithms in IPO settings in Indonesia.


Ketersediaan

Call NumberLocationAvailable
T 024/24PSB lt.2 - Karya Akhir1
PenerbitDepok: Program Studi Pascasarjana Ilmu Manajemen, Fakultas Ekonomi dan Bisnis, Universitas Indonesia 2024
Edisi-
SubjekInformation Asymmetry
IPO underpricing
Facial trustworthiness
ISBN/ISSN-
KlasifikasiNONE
Deskripsi Fisikix, 84 p. ; 30 cm
Info Detail SpesifikTesis
Other Version/RelatedTidak tersedia versi lain
Lampiran BerkasTidak Ada Data

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