Finansal yatırım piyasalarında fiyat tahminleme: Kripto para piyasasında yapay sinir ağları uygulaması
Price prediction in financial investment markets: Application of artificial neural networks in cryptocurrency market
Yazar: EREN ULUCAN
Danışman: PROF. DR. TAYFUN AKGÜL ; PROF. DR. AYBEN KOY
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Elektronik ve Haberleşme Mühendisliği Ana Bilim Dalı / Telekomünikasyon Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: Elektrik ve Elektronik Mühendisliği = Electrical and Electronics Engineering ; Maliye = Finance

Özet:
Günümüzde bireylerin daha yüksek kazanç ve refah amacıyla finansal yatırım 
piyasalarına yöneldiği bilinmektedir. Bu piyasalar, sermaye sahibi kişi ve kuruluşların 
çeşitli ürünleri alıp sattığı yerler olarak tanımlanmaktadır. Teknolojinin gelişmesiyle 
birlikte, yatırımcılar için bilgiye erişim kolaylaşmış ve piyasalara olan ilgi artmıştır. 
Ancak yoğun veri akışı, özellikle yeni yatırımcılar için kafa karıştırıcı olabilmektedir. 
Bu karışıklığın önüne geçebilmek için yatırım karar sürecinin iyi anlaşılması 
gerekmektedir. Yatırım karar süreci, sermaye temini, veri toplama, değerlendirme ve 
karar uygulama aşamalarından oluşmaktadır. Bu doğrultuda, yatırımcıların doğru 
karar verebilmeleri için temel ve teknik analiz yöntemlerini kullanmaları önem 
taşımaktadır. Temel analiz, bir varlığın içsel değerini belirlemeye çalışırken, teknik 
analiz geçmiş fiyat hareketlerini inceleyerek birtakım teknik indikatörler yardımıyla 
geleceği tahmin etmek amacıyla kullanılmaktadır. 
Son yıllarda, bu analiz yöntemlerinin yetersiz kaldığı anlaşıldıkça yapay zekâ ve 
özellikle yapay sinir ağları piyasa analizlerinde ve kapanış fiyatlarının 
tahminlenmesinde kullanılmaya başlanmıştır. İnsan beyninin çalışma biçiminden 
esinlenerek oluşturulmuş, karmaşık ilişkileri tespit etme yeteneğine sahip olan bu 
teknolojiler, büyük veri setlerini işleyerek daha tutarlı ve gerçeğe yakın tahminler 
yapma imkânı sunmaktadır. 
Bu çalışmada da finansal yatırım araçlarının yapay sinir ağları ile tahminlenmesi 
amaçlanmış, yatırım aracı olarak ise son dönemde popülerliği daha da artan kripto para 
piyasaları tercih edilmiştir. Çalışmada kripto para piyasalarında işlem hacmi en yüksek 
olan para birimleri BitCoin, Ethereum ve LiteCoin kullanılmış, çalışmanın veri seti ise 
her bir kripto para birimi için 15'er, 30'ar ve 60'ar dakikalık periyotlardaki kapanış 
fiyatları ile SMA, EMA, RSI, MACD ve BBand indikatör değerleri kullanılarak 
hazırlanmıştır. Kurulan ağ modeli eğitilip test edildikten sonra,  01 Ocak – 31 Aralık 
2022 yılı kapanış verileri girdi olarak modele eklenmiş ve modelin 2023 kapanış 
verilerini tahminlemesi beklenmiştir.   
Yapılan tahminleme sonucunda her bir veri setinin hata farkı ortalama yüzdesi 
(MAPE) değeri hesaplanmıştır.  BTC15 için 0.0088, BTC30 için 0.0021, BTC60 için 
0.0375, ETH15 için 0.000030205, ETH30 için 0.00022303, ETH60 için 0.00020702, 
LTC15 için 0.00070347, LTC30 için 0.0053 ve LTC60 için 0.00063393 şeklinde 
bulunan MAPE değerleri modelin ne kadar tutarlı tahminlemeler yaptığını ortaya 
koymuştur. Ayrıca tahminlenen ve gerçekleşen verilerin yer aldığı grafik de neredeyse 
birbiriyle tamamen örtüşmüştür. Bu sonuçlar yapay sinir ağı modellerinin kripto para 
piyasalarında tahminleme için kullanılabileceğini kanıtlamaktadır. Çalışmanın ayrıca; 
daha ileriki safhada, bu modellerin yardımıyla düzenli gelen veriyi işledikçe nihai 
kullanıcıya al-sat sinyali gönderebilecek bir robot uygulamanın geliştirilmesi için de 
bir kılavuz niteliği taşıyacağı da düşünülmektedir.

Abstract:
It is well known that individuals are turning to financial investment markets with the 
aim of achieving higher earnings and prosperity. These markets are defined as places 
where individuals and organizations buy and sell various products by using their 
capital. With the advancement of technology, access to information has become easier 
for investors and lead them to increase their interest to the financial markets. However, 
the intense data-flow can be confusing, especially for new investors. To overcome this 
confusion, it is essential to have a good understanding of the investment decision
making process. This process consists of stages such as capital procurement, data 
collection, evaluation, and decision implementation. In this context, it is important for 
investors to use fundamental and technical analysis methods to make informed 
decisions. Fundamental analysis aims to determine the intrinsic value of an asset, while 
technical analysis is used to predict the future by examining past price movements 
with the help of various technical indicators. 
Due to the complexity and dynamic nature of investment markets, influenced by 
macro-environmental factors, the belief that fundamental and technical analysis 
methods have become inadequate in evaluating the dynamic structure of these markets 
has been increasingly prevalent over time. As a result, time series analysis techniques 
and multiple regression models have also been developed alongside these two 
analyses. However, it has been observed that these methods, particularly linear models, 
are more successful in making predictions but fall short in capturing sudden changes 
in investment markets that do not exhibit linear characteristics due to the constantly 
changing economic environment. The interaction of factors such as political events, 
investor expectations, and economic conditions complicates forecasting in investment 
markets. 
To overcome this difficulty, it is emphasized that computational artificial intelligence 
methods, which incorporate regression techniques using technical indicators and 
candlestick charts, should be employed to analyze this variability and non-linear 
structure, enabling stronger predictions. In recent years, to enhance the effectiveness 
of these analysis methods, artificial intelligence, particularly artificial neural networks, 
has started to be used. These technologies offer the potential for more accurate 
predictions by rapidly processing large data sets and have become critical tools for 
investors in their decision-making processes, allowing them to monitor market 
movements and take strategic actions. 
Neural networks are modeled based on the parallel processing methods of the human 
brain. The biological brain consists of billions of interconnected processing elements 
known as neurons, which transmit information and strengthen as the brain learns. In 
this context, neural networks utilize interconnected processing elements that enable 
them to learn from errors, learn from examples, recognize patterns in noisy data, and 
work with missing information. Artificial neural networks aim to overcome the 
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limitations of traditional computers by evaluating the processing capabilities of the 
human brain. 
An Artificial Neural Network (ANN), in its simplest form, is defined as a model 
composed of several highly interconnected computational units called neurons or 
nodes. Each neuron performs a simple operation on an input to generate an output that 
will be transmitted to the next neuron. This parallel processing provides significant 
advantages in data analysis. 
Artificial neural networks are widely used in various branches of engineering and 
science and are considered a useful tool in econometric analyses due to their ability to 
approximate complex and non-linear equations. They have emerged as a powerful tool 
and statistical modeling technique in modern quantitative finance, serving as an 
attractive alternative for both researchers and practitioners. They can detect underlying 
functional relationships within a data set and perform tasks such as pattern recognition, 
classification, evaluation, modeling, prediction, and control. Additionally, with 
artificial neural networks, investors have the opportunity to develop customized 
models that align with their strategies and objectives, making it possible to create more 
personalized and effective investment strategies. Since artificial neural networks 
operate independently of human psychology, they help minimize emotional decision
making processes, thus facilitating more objective and rational investment decisions. 
They are also effectively used in portfolio optimization and risk management, enabling 
investors to create more balanced and profitable portfolios by analyzing the risk and 
return profiles of different assets. 
Despite the advantages mentioned above, there are various issues that may arise in the 
applications of artificial neural networks. Foremost among these is data quality; 
incomplete, erroneous, or misleading data can negatively impact the model's 
performance. Investment analysis is typically conducted using time series data, and 
the nature of time series data can sometimes complicate the accurate application of 
artificial neural network models. Similarly, sudden fluctuations in markets and 
unexpected events such as economic crises or political instability can invalidate the 
predictions made by neural networks. These challenges indicate that careful planning 
and implementation are required for artificial neural networks to be effectively used in 
investment analysis. To overcome the challenges faced in investment analysis with 
artificial neural networks, methods such as improving data quality, optimizing the 
model, managing computational power, using time series methods, accounting for 
market anomalies, and enhancing the model's transparency are employed. These 
methods are effective in overcoming the challenges of using artificial neural networks 
in investment analysis, ensuring flexibility and adaptation during applications, thereby 
improving the quality of analysis results significantly. 
This study aims to predict financial investment instruments using artificial neural 
networks, with cryptocurrency markets, which have gained increasing popularity 
recently, being chosen as the investment instrument. The cryptocurrency market is 
defined as a technology developed around the 2010s that creates virtual trading among 
investors. Cryptocurrencies are considered digital assets developed using an end-to
end encryption method called cryptography, which enhances transaction security and 
ensures the circulation of excess money within the system during exchange processes. 
Although they have gained popularity today, with nearly a thousand different 
cryptocurrencies available, they are not backed by central banks, which means they 
lack any legal framework or government guarantee. Therefore, they are considered 
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difficult to analyze and track financially. Due to these characteristics, this study 
focuses on predicting cryptocurrencies, specifically examining the cryptocurrencies 
with the highest trading volumes in the market: Bitcoin, Ethereum, and Litecoin. 
The study utilized the Bitcoin, Ethereum, and Litecoin cryptocurrencies which have 
the highest trading volumes in the cyrptocurrency market. The dataset was prepared 
using the closing prices for each cryptocurrency at 15, 30, and 60-minute intervals, 
along with the SMA, EMA, RSI, MACD, and BBand indicator values. After training 
and testing the established network model, the closing data for the year 2022 (from 
January 1 to December 31) was added as input to the model, which was expected to 
predict the closing data for 2023. 
As a result of the predictions made, the mean absolute percentage error (MAPE) values 
for each dataset were calculated: 0.0088 for BTC15, 0.0021 for BTC30, 0.0375 for 
BTC60, 0.000030205 for ETH15, 0.00022303 for ETH30, 0.00020702 for ETH60, 
0.00070347 for LTC15, 0.0053 for LTC30, and 0.00063393 for LTC60. These MAPE 
values illustrate how consistent the model's predictions are. Moreover, the graph 
displaying the predicted and actual data nearly completely overlaps. These results 
demonstrate that artificial neural network models can be utilized for predictions in 
cryptocurrency markets. Additionally, it is believed that this study will serve as a guide 
for the development of a robot application capable of sending buy-sell signals to end 
users as it processes incoming data with the help of these models in a future works.