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Journals / Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi / 2026 / Cilt: 7 Sayı: 2

Predicting Corporate Liquidity Using LASSO and Financial Ratios: Evidence From Borsa Istanbul

LASSO ve Finansal Oranlar Kullanılarak Şirketlerin Likiditesinin Tahmin Edilmesi: Borsa İstanbul’dan Bulgular (2015–2025)

Sayed Ziaullah Aminy(HİTİT ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, FİNANS VE BANKACILIK BÖLÜMÜ, FİNANS VE BANKACILIK PR.)0009-0000-3816-3022
Cihat Savsar(Hitit Üniversitesi)0000-0002-8152-1590
Pages
211–241
Publication Date
2026-10-22
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings offer practical implications for investors, financial analysts, and corporate managers seeking to evaluate firms’ short-term financial strength.

📌 3 Key Takeaways

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Objective & Hypothesis:Purpose: This study investigates the effectiveness of regularized machine-learning regression techniques in identifying the key financial determinants of corporate liquidity among non-financial firms listed on Borsa Istanbul (BIST).
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Key Findings & Data:The study further evaluates the predictive performance of alternative penalized regression models relative to conventional panel-data estimators.
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Conclusion & Impact:The findings offer practical implications for investors, financial analysts, and corporate managers seeking to evaluate firms’ short-term financial strength.
🔬Design:Modeling & Simulation
📍Scope:2015–2025 Verileri
⚙️Variables:Likidite · Finansal Kriterler · Lasso Daraltma (Contraction) Yapay Zekâ Yaklaşım

Abstract

Purpose: This study investigates the effectiveness of regularized machine-learning regression techniques in identifying the key financial determinants of corporate liquidity among non-financial firms listed on Borsa Istanbul (BIST). The study further evaluates the predictive performance of alternative penalized regression models relative to conventional panel-data estimators. Methodology: The analysis employs an unbalanced panel dataset obtained from the Finnet Financial Database covering the period 2015–2025. After data-cleaning procedures, the final sample consists of 1,834 firm-year observations from 236 non-financial firms. The Current Ratio measures corporate liquidity, while eleven financial ratios are used as explanatory variables. The empirical framework incorporates LASSO, Adaptive LASSO, Ridge Regression, Elastic Net, Square-Root LASSO, Post-LASSO OLS, Ordinary Least Squares (OLS), Fixed Effects, and Random Effects models. Model selection is conducted using 10-fold cross-validation and the Extended Bayesian Information Criterion (EBIC). Findings: The results consistently identify Return on Assets (ROA) as the most influential predictor of corporate liquidity across all estimation techniques. Leverage Ratio exhibits a robust negative relationship with liquidity, whereas Receivables Turnover Ratio and Operating Profitability exhibit positive relationships with liquidity performance. The Adaptive LASSO model provides the most parsimonious and economically meaningful specification, retaining only three significant predictors while maintaining strong predictive accuracy. Post-LASSO OLS and Elastic Net with Post-OLS achieve the highest explanatory power (R² ≈ 0.825). Originality/Value: The study contributes to the corporate finance and machine learning literature by demonstrating that regularized regression techniques outperform traditional econometric approaches for identifying the most relevant determinants of liquidity in an emerging-market environment. The findings offer practical implications for investors, financial analysts, and corporate managers seeking to evaluate firms’ short-term financial strength.

Özet

Amaç: Bu çalışma, Borsa İstanbul'da (BIST) işlem gören finans sektörü dışındaki (finansal olmayan) şirketlerde kurumsal likiditenin temel finansal belirleyicilerini belirlemede düzenlileştirilmiş makine öğrenmesi regresyon tekniklerinin etkinliğini incelemektedir. Ayrıca çalışma, alternatif cezalandırılmış regresyon modellerinin tahmin performansını geleneksel panel veri tahmincileriyle karşılaştırmaktadır. Yöntem: Analiz, 2015–2025 dönemini kapsayan ve Finnet Finansal Veri Tabanı'ndan elde edilen dengesiz panel veri seti kullanılarak gerçekleştirilmiştir. Veri temizleme sürecinin ardından nihai örneklem, finans sektörü dışındaki 236 şirkete ait 1.834 firma-yıl gözleminden oluşmaktadır. Kurumsal likidite Cari Oran (Current Ratio) ile ölçülmüş, bağımsız değişken olarak ise on bir finansal oran kullanılmıştır. Ampirik analiz kapsamında LASSO, Adaptive LASSO, Ridge Regresyonu, Elastic Net, Square-Root LASSO, Post-LASSO OLS, En Küçük Kareler (OLS), Sabit Etkiler (Fixed Effects) ve Tesadüfi Etkiler (Random Effects) modelleri uygulanmıştır. Model seçimi 10 katlı çapraz doğrulama (10-fold cross-validation) ve Genişletilmiş Bayes Bilgi Kriteri (Extended Bayesian Information Criterion-EBIC) kullanılarak gerçekleştirilmiştir. Bulgular: Bulgular, tüm tahmin yöntemlerinde Aktif Kârlılığı (Return on Assets-ROA) değişkeninin kurumsal likiditenin en güçlü belirleyicisi olduğunu göstermektedir. Kaldıraç Oranı (Leverage Ratio) likidite ile güçlü ve negatif bir ilişki sergilerken, Alacak Devir Hızı (Receivables Turnover Ratio) ve Faaliyet Kârlılığı (Operating Profitability) likidite üzerinde pozitif ilişkiye sahiptir. Adaptive LASSO modeli, yalnızca üç anlamlı değişkeni koruyarak güçlü tahmin doğruluğunu sürdürmesi nedeniyle en yalın ve ekonomik açıdan en anlamlı model olarak öne çıkmaktadır. Post-LASSO OLS ve Post-OLS’li Elastic Net modelleri ise en yüksek açıklayıcılık düzeyine (R² ≈ 0,825) ulaşmıştır. Özgünlük/Değer: Bu çalışma, gelişmekte olan bir piyasa ortamında kurumsal likiditenin en önemli belirleyicilerini belirlemede düzenlileştirilmiş regresyon tekniklerinin geleneksel ekonometrik yaklaşımlardan daha başarılı olduğunu ortaya koyarak kurumsal finans ve makine öğrenmesi literatürüne katkı sağlamaktadır. Elde edilen bulgular, şirketlerin kısa vadeli finansal gücünün değerlendirilmesinde yatırımcılar, finansal analistler ve şirket yöneticileri için önemli uygulamalı çıkarımlar sunmaktadır.

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Cite This Article

Aminy, S. Z., Savsar, C. (2026). Predicting Corporate Liquidity Using LASSO and Financial Ratios: Evidence From Borsa Istanbul. Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi, Cilt: 7 Sayı: 2, 211–241. https://ajindex.com/articles/1595779

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