Predicting stock returns with financial ratios: A new methodology incorporating machine learning techniques to beat the market

This study proposes a methodology incorporating machine learning algorithms to predict stock returns and construct portfolios that beat the market. The performance evaluation is based on the statistical metrics as well as the return and Sharpe ratios of the portfolios. Additionally, a new performance evaluation metric, Safe-Side, is introduced to address the needs of conservative portfolio managers and investors. The results provide strong evidence that the machine learning algorithms can be used to predict the stock returns with approximately 86 classification accuracy. The proposed methodology also provides guidance for investors and portfolio managers for their portfolio selection problems.

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18 Mayıs 2024 16:47
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Eser Adı
(dc.title)
Predicting stock returns with financial ratios: A new methodology incorporating machine learning techniques to beat the market
Yazar
(dc.contributor.author)
Zeynep İltüzer
Yayın Yılı
(dc.date.issued)
2021
Tür
(dc.type)
Makale
Özet
(dc.description.abstract)
This study proposes a methodology incorporating machine learning algorithms to predict stock returns and construct portfolios that beat the market. The performance evaluation is based on the statistical metrics as well as the return and Sharpe ratios of the portfolios. Additionally, a new performance evaluation metric, Safe-Side, is introduced to address the needs of conservative portfolio managers and investors. The results provide strong evidence that the machine learning algorithms can be used to predict the stock returns with approximately 86 classification accuracy. The proposed methodology also provides guidance for investors and portfolio managers for their portfolio selection problems.
Açık Erişim Tarihi
(dc.date.available)
2021-11-27
Yayıncı
(dc.publisher)
Routledge Journals, Taylor & Francıs Ltd
Dil
(dc.language.iso)
En
Konu Başlıkları
(dc.subject)
Portfolio management
Konu Başlıkları
(dc.subject)
Financial ratios
Konu Başlıkları
(dc.subject)
Stock returns
Konu Başlıkları
(dc.subject)
Predictions
Konu Başlıkları
(dc.subject)
Machine learning techniques
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/20.500.14081/1321
ISSN
(dc.identifier.issn)
1608-1625
Dergi
(dc.relation.journal)
Asıa-Pacific Journal Of Accounting & Economics
Dergi Sayısı
(dc.identifier.issue)
3
Esere Katkı Sağlayan
(dc.contributor.other)
Iltuzer, Z
DOI
(dc.identifier.doi)
10.1080/16081625.2021.2007408
Orcid
(dc.identifier.orcid)
0000-0002-7960-539X
Bitiş Sayfası
(dc.identifier.endpage)
632
Başlangıç Sayfası
(dc.identifier.startpage)
619
Dergi Cilt
(dc.identifier.volume)
30
wosquality
(dc.identifier.wosquality)
Q4
wosauthorid
(dc.contributor.wosauthorid)
ABG-3815-2021
Department
(dc.contributor.department)
İşletme (İngilizce)
Wos No
(dc.identifier.wos)
WOS:000723071900001
Veritabanları
(dc.source.platform)
Wos
Veritabanları
(dc.source.platform)
Scopus
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