Duygu Analizi ve Hisse Fiyat Bilgisinin Birleştirilmesiyle Hisse Senedi Piyasası Tahmini

Pay piyasasındaki bir enstrümanın fiyatını tahmin etmek, oldukça değerli ve aynı zamanda oldukça zor yapay öğrenme görevlerinden biridir. Araştırmacılar hisse fiyat tahmin modellerinin genellenebilirlik kabiliyetlerinin arttırılması için ileri teknikler kullanmaktadır. Ancak, hisse senedi piyasasının politik ve makroekonomik haberler ve yatırımcıların duygu durumu ile yakından ilişkili olduğu düşünüldüğünde, yalnızca hisse fiyat bilgisini kullanan modeller hisse senedi piyasasını etkileyen tüm faktörleri kapsama konusunda yetersiz kalmaktadır. Bu nedenle, bu çalışmada, hisse senedi tahmin başarısının arttırılması için piyasa ve ilgili hisse haberlerini duygu analizi uygulanmakta ve daha sonra duygu etiketleri ile hisse fiyatları ve en sık kullanılan teknik indikatörleri birleştirilmektedir. Elde edilen kümülatif veri kümesi bir kısa uzun-hafızalı bir özyinelemeli sinir ağının eğitilmesinde kullanılmış ve bu regresyon modelinin çıktısı bir sonraki günün kapanış fiyatının daha yukarıda olup olmayacağını tahmin etmek için kullanılmıştır. Sekiz yıllık hisse senedi verisi üzerinde yapılan deneyler, duygu analizi olmadan kurulan modelin f1 skoru 0,56 civarında iken, hisse fiyatı duygu etiketleri ile birlikte kullanıldığında bu değerin 0,65 civarına yükseldiğini göstermiştir. Elde edilen sonuçlar, özellikle piyasada yüksek volatilite olduğunda, duygu etiketini kullanan modelin gerçek hisse fiyatlarına daha yakın olduğunu göstermektedir.

Anahtar Kelimeler:

Özyinelemeli Sinir Ağları, Uzun Kısa-Dönem Hafıza, Metin Sınıflandırma, Duygu Analizi, Teknik Analiz

Stock Market Prediction by Combining Stock Price Information and Sentiment Analysis

Predicting the stock market instrument price is a valuable but challenging machine learning task. Researchers use advanced techniques to improve the generalization ability of stock prediction models. However, considering that the stock market highly depends on the political and macroeconomic developments as well as the mood of the related investors, the models that use only stock prices fail to cover all factors affecting the stock market. Therefore, to improve the prediction accuracy of stock market prediction, in this study, we first apply sentiment analysis to the news related with the market and related stock, and then combine the sentiment labels of the news with stock prices and commonly used technical indicators. The obtained cumulative dataset is used to train a long short-term memory recurrent neural network, and the output of this regression model is used in the prediction of the closing price movement to decide whether the closing price next day will be higher. The experiments performed on 8-year data showed that while the f1 score of the model built without sentiment analysis was around 0.56, it has increased to 0.65 when stock prices are combined with sentiment labels. The results show that the model with sentiment labels fits better to the actual prices especially when there is a high volatility in the stock price.

Keywords:

Recurrent Neural Network, Long Short-Term Memory, Text Classification, Sentiment Analysis, Technical Analysis,

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