Uzun-Kısa Süreli Bellek Ağı Kullanarak Hisse Senedi Fiyatı Tahmini

Abstract

Finans dünyasında hisse senedi ticareti en önemli faaliyetlerden biridir. Hisse senedi verileri finansal bir zaman serisi olarak ele alınmaktadır. Zaman serisi olarak hisse senedi tahmini, bir finansal borsada işlem gören hisse senedinin gelecekteki değerini belirlemeye çalışma eylemidir. Finansal varlıkların fiyatını tahmin etmek, doğru tahminlerle yatırımcıların alacağı kararlarda risk faktörünü azaltabileceğinden önemlidir. Ancak hisse senedi piyasası çok çeşitli faktörlere bağlı olarak değişkenlik gösterdiğinden tahminlemesi zor bir sektör olarak bilinmektedir. Makine öğrenme yöntemleri özellikle de derin öğrenme algoritmaları birçok alanda olduğu gibi finans alanında da tahminlemede sıkça kullanılmaktadır. Bu çalışmada, derin öğrenme yöntemlerinden olan Uzun-Kısa Süreli Bellek ağları kullanarak hisse senedi tahmini yapılmıştır. Borsa İstanbul, Teknoloji Endeksi kapsamındaki dört hisse belirlenerek 2012-2022 yılları arasında 2578 günlük bir veri seti oluşturulmuş ve kurulan model ile eğitim ve test işlemi gerçekleştirilmiştir. Test işlemi sonucunda tutarlı ve gerçeğe yakın tahminler elde edilmiştir.

Keywords

• Derin Öğrenme
• Uzun-Kısa Süreli Bellek Ağı
• Hisse Senedi Fiyatı Tahmini

Stock Price Prediction Using Long-Short-Term Memory Network

Abstract

One of the most important transactions of the financial system is stock trading. Stock price data is handle as a financial time series. Stock price predictions using time series analysis are the activity of determining the future value of stocks listed on the stock market. Predicting the price of the stock correctly reduces the risk factor in the decisions to be taken by the investors. Therefore, it is an important issue for the investor. However, because there are many variables that affect the stock price, it is a very complex process to predict. Machine learning methods, especially deep learning algorithms, are frequently used in prediction in the field of finance, as in many other fields. In this study, stock price prediction was made using Long-Short-Term Memory networks, which is one of the deep learning methods. Four stocks within the scope of Borsa İstanbul Technology Index were determined and a 2578-day data set was created between 2012 and 2022, and training and testing was carried out with the established model. As a result of the test process, consistent and realistic predictions were obtained.

Keywords

• Deep Learning
• Long-Short-Term Memory Network
• Stock Price Prediction

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