Forecasting Sustainability Reports with Financial Performance Indicators using Random Forest for Feature Selection and Gradient Boosting for Learning

Yıl 2024, , 279 - 302, 01.11.2024

Hakan Ayhan Dağıstanlı , Figen Özen , İlkay Saraçoğlu

https://doi.org/10.17134/khosbd.1492365

Öz

Dünyadaki aşırı nüfus artışı, iklim değişikliği, biyoçeşitliliğin kaybı ve kaynak kıtlığı gibi sorunlar, yıllar geçtikçe şirketlerde küresel farkındalığın artmasına neden olmuştur. Son dönemde şirketler mevcut ekonomi modelleri yerine sürdürülebilir modelleri tercih etmeye başladı. Bunun sonucunda sadece ekonomik rapor hazırlamak yerine sosyal ve çevresel raporları da içeren bir sürdürülebilirlik raporu hazırlamaya başladılar. Sürdürülebilir kalkınmanın hedeflerine ulaşmasında yeni bir yaklaşım olan döngüsel ekonomi modelinin son yıllardaki verileri sürdürülebilirlik raporları aracılığıyla da takip edilebiliyor. Araştırmalar, sürdürülebilirliğe önem veren şirketlerin yatırımcılar tarafından değerli görüldüğünü ve ülkelerin borsalarında sürdürülebilirlik endekslerinin oluşturulduğunu gösteriyor. Bu durum sürdürülebilirlik raporlamasının veya döngüsel ekonominin finansal performans üzerindeki etkisini inceleyen çalışmaların sayısını artırdı. Firmalar potansiyel yatırımcının dikkatini çekebilmek adına sürdürülebilirlik endekslerine dahil olmak istiyor. Bu çalışmada XUSRD'deki şirketlerin finansal performansına ilişkin zaman serisi verileri kullanılmıştır. Öte yandan, literatürdeki istatistiksel analizlerin aksine, şirketlerin XUSRD'de yer alıp almayacağını tahmin etmek için, özellik seçimi için rastgele orman ve öğrenme için gradyan artırma olmak üzere iki makine öğrenmesi yönteminin bir kombinasyonu kullanılıyor. Ayrıca veri kıtlığı sorununun aşılması amacıyla borsa endekslerinin tahmininde kanıtlanmış bir veri büyütme tekniği olan sütun bazında rastgele karıştırma yöntemi kullanılmıştır. Sonuçlar, rastgele orman ve gradyan artırma kombinasyonunun %94,74'lük bir test doğruluğuna ulaştığını ve k-en yakın komşu, rastgele orman, karar ağacı, destek vektörü, saf Bayes sınıflandırıcıları gibi en gelişmiş modellerden daha iyi performans gösterdiğini göstermektedir. Bu çalışmada karşılaştırma amacıyla kullanılmıştır.

Anahtar Kelimeler

Makine Öğrenmesi, BIST Sürdürülebilirlik Endeksi, Finansal Performans, Döngüsel Ekonomi, Gradyan Arttırma

Forecasting Sustainability Reports with Financial Performance Indicators using Random Forest for Feature Selection and Gradient Boosting for Learning

Öz

Problems such as excessive population growth, climate change, loss of biodiversity and resource scarcity in the world have led to an increase in global awareness in companies over the years. Lately companies have started to prefer sustainable models instead of existing economy models. As a result, they have started to prepare a sustainability report that includes social and environmental reports instead of just preparing an economic report. In recent years, the data of the circular economy model, which is a new approach for sustainable development to reach its goals, can also be followed through sustainability reports. Research shows that companies that attach importance to sustainability are seen as valuable by investors and sustainability indices are created in the stock markets of countries. This situation has increased the number of studies examining the impact of sustainability reporting or circular economy on financial performance. Firms want to be included in the sustainability indices in order to attract the attention of the potential investor. In this study, time series data of financial performance of companies in XUSRD are used. On the other hand, contrary to the statistical analyses in the literature, to predict whether companies will take part in XUSRD, a combination of two machine learning methods, namely random forest for feature selection and gradient boosting for learning, is used. In addition, to overcome the problem of data scarcity, the column-wise random shuffling method, which is a proven data augmentation technique in predicting stock market indices, has been used. The results show that the combination of random forest and gradient boosting reaches a test accuracy of 94.74% and outperforms state-of-the art models, namely, k-nearest neighbor, random forest, decision tree, support vector, naive Bayes classifiers that have been used in this study for comparison.

Anahtar Kelimeler

Machine Learning, BIST Sustainability index, Financial Performance, Circular Economy, Gradient Boosting

Kaynakça

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