Bitcoin Fiyat Tahmininde Makine Öğrenimi ve Nitel Değişkenler: Model Performansının Uygulamalı Değerlendirmesi

Öz

Bu çalışmada tahmin yöntemlerinin performans değerlendirmesi için Bitcoin fiyat tahmini problem kullanılmıştır. Bitcoin fiyat tahmini için geleneksel doğrusal regresyon yöntemi ile makine öğrenmesi uygulamalarından neural net fitting ve neural net time series yöntemleri karşılaştırılmıştır. Ayrıca Bitcoin fiyatının volatilitesinin yüksek olması ve sosyal, politik ve davranışsal olayların etkili olması sebebiyle kalitatif faktörlerin de tahmin performansı üzerindeki etkileri incelenmiştir. Bu bağlamda Bitcoin fiyat tahmininde kullanılan kantitatif değişkenlere ilave olarak kalitatif değişkenler olarak korku ve açgözlülük indeksi ve duyarlılık indeks değerleri de kullanılarak oluşturulan modellerin tahmini yapılmıştır. Elde edilen sonuçlara göre, geleneksel çoklu doğrusal regresyon yönteminin tahmin performansının oldukça zayıf olduğu, neural net fitting metodunun ise göreli olarak daha başarılı tahminler yapabildiği görülmüştür. Ayrıca, tek başına kantitatif değişkenlerin kullanıldığı modelin çalışmada kullanılan tüm tahmin yöntemlerinde zayıf performans sergilediği görülmüştür. Kalitatif değişkenlerin kullanıldığı tahmin modelinin ise tüm yöntemlerde en başarılı tahmin sonuçları ürettiği görülmüştür. Dolayısıyla Bitcoin fiyat tahmininde kalitatif değişkenlerin kullanılmasının tahmin performansını arttırdığı, bununla birlikte neural net fitting tahmin yönteminin düşük MSE değerleri ile en başarılı tahmin yönetimi olduğu belirlenmiştir.

Anahtar Kelimeler

• Tahmin
• Bitcoin fiyat tahmini
• Makine öğrenmesi
• Nitel değişkenler

Machine Learning and Qualitative Variables in Bitcoin Price Prediction: An Empirical Evaluation of Model Performance

Öz

This study employs the problem of Bitcoin price prediction to evaluate the performance of forecasting methods. Traditional linear regression is compared with machine learning techniques, specifically neural net fitting and neural net time series, to assess their predictive accuracy. Given Bitcoin’s high volatility and susceptibility to social, political, and behavioral influences, the study also examines the impact of qualitative factors on prediction performance. In addition to quantitative variables, qualitative variables—such as the Fear and Greed Index and sentiment analysis metrics—are incorporated into the models to enhance forecasting robustness. The results indicate that traditional multiple linear regression yields relatively weak predictive performance, whereas neural net fitting demonstrates superior accuracy. Furthermore, models relying solely on quantitative variables underperform across all tested methods. In contrast, the inclusion of qualitative variables significantly improves prediction outcomes in all approaches. The study concludes that integrating qualitative variables enhances Bitcoin price forecasting accuracy, with neural net fitting emerging as the most effective method due to its lower mean squared error (MSE) values.

Anahtar Kelimeler

• Forecasting
• Bitcoin price forecasting
• Machine learning
• Qualitative variables

Kaynakça

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