AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING

Mustafa Etcil; Burak Kolukisa; Burcu Güngör

TR EN

TEMEL ORANLAR KULLANILARAK HISSE SENEDI GETIRI TAHMINI VE PORTFÖY YENIDEN DENGELEME IÇIN AÇIKLANABILIR BIR MAKINE ÖĞRENIMI YAKLAŞIMI

Abstract

Bu çalışma, hisse senetlerinin piyasa göreli performansını tahmin etmek ve Borsa İstanbul’da (BIST) portföy yeniden dengelemesini yönlendirmek amacıyla açıklanabilir ensemble öğrenme çerçevesi geliştirmektedir. Analiz, 2009-Q1–2025-Q1 döneminde 20 firmanın çeyreklik verilerini kapsamakta ve on üç finansal oranla eğitilen Random Forest, XGBoost, LightGBM ve CatBoost algoritmalarını kullanmaktadır. Hiperparametreler zaman serisi çapraz doğrulamalı grid search ile seçilmekte, performans örneklem dışı dönemde değerlendirilmektedir. Portföyler tahmin edilen getirilere göre çeyreklik yeniden dengelenmekte ve eşit ağırlıklı portföy ile BIST 100 Endeksi’yle karşılaştırılmaktadır. Bulgular, model tabanlı portföylerin toplam getiri ve risk ayarlı performansta karşılaştırma stratejilerini geride bıraktığını; daha güçlü ve istatistiksel olarak desteklenen sonuçların geniş Top-k konfigürasyonlarında, özellikle CatBoost Top-7’de görüldüğünü göstermektedir. SHAP analizi, aktif kârlılığı, özkaynak kârlılığı ve temel değerleme oranlarını gelecekteki hisse performansının başlıca belirleyicileri olarak göstermektedir.

Keywords

Supporting Institution

Tubitak

Project Number

3230482

Ethical Statement

Bu araştırma, TÜBİTAK TEYDEB programı kapsamında 3230482 numaralı proje ile desteklenen "Sosyal Medya, Haber, Temel ve Teknik Analizleri Dikkate Alan Makine Öğrenmesi Temelli Hisse Senedi Yatırım Tavsiye Platformu" başlıklı proje tarafından desteklenmiştir.

AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING

Abstract

This research develops an explainable ensemble learning framework to forecast stocks’ market-relative performance and guide portfolio rebalancing in Borsa Istanbul (BIST). The analysis covers quarterly data from 20 firms between 2009-Q1 and 2025-Q1 and employs Random Forest, XGBoost, LightGBM, and CatBoost trained on thirteen financial ratios. Hyperparameters are selected using grid search with time-series cross-validation, and performance is evaluated out-of-sample. Portfolios are rebalanced quarterly using predicted returns and benchmarked against an equally weighted portfolio and the BIST 100 Index. Results show that model-based portfolios generally outperform benchmarks in total return and risk-adjusted performance, with stronger statistically supported results in broader Top-k configurations, particularly CatBoost Top-7. SHAP identifies profitability and valuation indicators, especially return on assets, return on equity, and key valuation ratios, as the main drivers of future stock performance. These findings support explainable ensemble learning for fundamental-based portfolio management in emerging markets.

Keywords

Supporting Institution

Tubitak

Project Number

3230482

Ethical Statement

This research was supported by the project titled "Sosyal Medya, Haber, Temel ve Teknik Analizleri Dikkate Alan Makine Öğrenmesi Temelli Hisse Senedi Yatırım Tavsiye Platformu" funded by the TUBITAK TEYDEB program under Project Number 3230482.

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Details

Primary Language

English

Subjects

Deep Learning, Data Engineering and Data Science

Journal Section

Research Article

Authors

Mustafa Etcil ^*
0000-0002-5573-3418
Türkiye

Burak Kolukisa
0000-0003-0423-4595
Türkiye

Burcu Güngör
0000-0002-2272-6270
Türkiye

Publication Date

September 3, 2026

Submission Date

October 22, 2025

Acceptance Date

June 24, 2026

Published in Issue

Year 2026 Volume: 29 Number: 3

IZ

https://izlik.org/JA75SH66HP

Cite

RIS / Bibtex

APA

Etcil, M., Kolukisa, B., & Güngör, B. (2026). AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 29(3), 1004-1019. https://izlik.org/JA75SH66HP

AMA

1.Etcil M, Kolukisa B, Güngör B. AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING. KSU J. Eng. Sci. 2026;29(3):1004-1019. https://izlik.org/JA75SH66HP

Chicago

Etcil, Mustafa, Burak Kolukisa, and Burcu Güngör. 2026. “AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 29 (3): 1004-19. https://izlik.org/JA75SH66HP.

EndNote

Etcil M, Kolukisa B, Güngör B (September 1, 2026) AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 29 3 1004–1019.

IEEE

[1]M. Etcil, B. Kolukisa, and B. Güngör, “AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING”, KSU J. Eng. Sci., vol. 29, no. 3, pp. 1004–1019, Sept. 2026, [Online]. Available: https://izlik.org/JA75SH66HP

ISNAD

Etcil, Mustafa - Kolukisa, Burak - Güngör, Burcu. “AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 29/3 (September 1, 2026): 1004-1019. https://izlik.org/JA75SH66HP.

JAMA

1.Etcil M, Kolukisa B, Güngör B. AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING. KSU J. Eng. Sci. 2026;29:1004–1019.

MLA

Etcil, Mustafa, et al. “AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING”. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, vol. 29, no. 3, Sept. 2026, pp. 1004-19, https://izlik.org/JA75SH66HP.

Vancouver

1.Mustafa Etcil, Burak Kolukisa, Burcu Güngör. AN EXPLAINABLE MACHINE LEARNING APPROACH USING FUNDAMENTAL RATIOS FOR STOCK RETURN PREDICTION AND PORTFOLIO REBALANCING. KSU J. Eng. Sci. [Internet]. 2026 Sep. 1;29(3):1004-19. Available from: https://izlik.org/JA75SH66HP