Gezegen hareketlerinin ve açılarının finansal piyasalar üzerindeki etkisi: Derin öğrenme tabanlı bir yaklaşım

Öz

Bu çalışma, gezegen hareketleri, açıları, retro hareketleri, ay fazları ve tutulmaların finansal piyasalar üzerindeki etkisini bilimsel temellere dayandırarak analiz eden öncü bir araştırmadır. Finansal piyasaların yalnızca ekonomik göstergelerden değil, gezegensel dinamiklerden de etkilenebileceği hipotezini derin öğrenme modelleriyle test etmektedir. İlk kez, gezegen konumları finansal piyasa tahmin modellerine sistematik olarak entegre edilerek geniş ölçekli bir veri seti oluşturulmuştur. NASA JPL Horizons veritabanından alınan gezegensel parametreler, finansal fiyat hareketleriyle (açılış, kapanış, en yüksek, en düşük ve düzeltilmiş kapanış fiyatları) birleştirilmiş, Mutual Information (MI) ve Recursive Feature Elimination (RFE) ile en güçlü öznitelikler belirlenmiş, PCA ile veri boyutu optimize edilmiştir. LSTM, GRU, ANN ve RNN gibi ileri düzey derin öğrenme modelleri finansal piyasa hareketlerini tahmin etmek için eğitilmiş, Granger Nedensellik Testi ile gezegenlerin piyasalar üzerindeki istatistiksel anlamlılığı değerlendirilmiş ve en anlamlı öznitelikler modele girdi olarak sağlanmıştır. Sonuçlar, gezegen hareketlerinin piyasa tahmin gücünü artırabileceğini göstermektedir. LSTM ve GRU modelleri NYSE için %98,7+, BITCOIN ve RUSSELL için %96,8+ doğruluk oranına ulaşmış, gezegensel parametrelerin modele entegrasyonu tahmin doğruluğunu %10 oranında artırmıştır. Granger testi, belirli gezegensel hizalanmalar ile finansal piyasa dönüşleri arasındaki istatistiksel ilişkileri doğrulamıştir. Bu araştırma, finansal piyasaların gezegensel hareketlere duyarlılığını bilimsel verilerle ortaya koyarak yatırım ve risk yönetimi stratejilerine alternatif bir bakış açısı sunmaktadır.

Anahtar Kelimeler

• Derin öğrenme
• granger nedenselliği
• zaman serisi analizi
• finansal piyasalar
• finansal tahminleme

The impact of planetary movements and aspects on financial markets: A deep learning-based approach

Öz

This study is a pioneering research that analyzes the impact of planetary movements, aspects, retrogrades, lunar phases, and eclipses on financial markets based on scientific principles. It tests the hypothesis that financial markets can be influenced not only by economic indicators but also by planetary dynamics using deep learning models. For the first time, planetary positions have been systematically integrated into financial market prediction models to create a large-scale dataset. Planetary parameters obtained from the NASA JPL Horizons database were combined with financial price movements (opening, closing, highest, lowest, and adjusted closing prices), the strongest features were identified using Mutual Information (MI) and Recursive Feature Elimination (RFE), and data dimensionality was optimized using PCA. Advanced deep learning models such as LSTM, GRU, ANN, and RNN were trained to predict financial market movements, the statistical significance of planets on markets was evaluated using the Granger Causality Test, and the most significant features were provided as input to the model. The results demonstrate that planetary movements can enhance market prediction power. LSTM and GRU models achieved accuracy rates of 98.7%+ for the NYSE and 96.8%+ for Bitcoin and Russell, with the integration of planetary parameters increasing prediction accuracy by 10%. Granger testing confirmed statistical relationships between specific planetary alignments and financial market reversals. This research offers an alternative perspective on investment and risk management strategies by demonstrating the sensitivity of financial markets to planetary movements using scientific data.

Anahtar Kelimeler

• Deep learning
• granger causality
• time series analysis
• financial markets
• financial forecasting

Kaynakça

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