Tarihin başlangıcından itibaren sürekli evrim geçiren para, insanlık tarafından geliştirilen en önemli araçlardan biridir. Para, insanların gelecekteki ve anlık ihtiyaçlarını karşılamak için belirlenen bir değeri temsil eder. Para kavramı, dönemin koşullarına ve imkânlarına göre farklı şekillerde ortaya çıkar. Kripto paraların temelleri 1980'lerde atılmış olup, 2008 yılında Satoshi Nakamoto tarafından Bitcoin'in tanıtılmasıyla hayatımıza girmiştir. Geleneksel paralara alternatif olarak ortaya çıkan kripto paralar, teknolojik bir yenilik olup her geçen gün daha da popüler hale gelmektedir. Bitcoin, merkezi bir otorite tarafından yönetilmeyen ilk kripto paradır ve popülerliği ve başarısı diğer alternatif kripto paraların oluşmasına yol açmıştır. Julong Deng tarafından 1982 yılında geliştirilen “Gri Sistem Teorisi”, belirsiz sistemlerin davranışlarını tahmin etmek için kullanılan bir yöntem olup GM (1,1) modeli en sıklıkla kullanılan gri modeldir. Bu çalışma, Bitcoin'in fiyatlarını GM (1,1) modeli kullanarak tahmin etmeyi amaçlamaktadır. Araştırma sonucunda, modelin gelecek dönem tahminleri için uygun olduğu ve başarılı tahminler yaptığı belirlenmiştir
Abstract Bitcoin has emerged as a highly attractive and reliable investment asset for financial managers, businesses, and economic firms due to its unique features such as high security, decentralization, and potential for increased income. Consequently, Bitcoin price prediction has become a significant topic of interest among financial and economic analysts and researchers. Forecasting in such contexts often involves uncertain conditions and limited information. Grey systems theory, which specializes in analyzing problems with small samples and insufficient information, offers a promising approach. This study aims to predict the price of Bitcoin using an advanced model of grey systems theory: the fractional multivariable grey model (FGM(1, N )). The FGM(1, N ) model stands out by incorporating external factors into its predictions. Specifically, this research utilizes the FGM(1,3) model, considering the crude oil and gold prices to forecast Bitcoin price. The results demonstrate that the FGM(1,3) model provides more accurate predictions and better performance than the FGM(1,1) model, which does not include external factors like oil and gold prices. This study highlights the significant impact of crude oil and gold price trends on Bitcoin's market and underscores the effectiveness of the multivariable fractional grey model in financial forecasting.
Yunfei Yang, Jiamei Xiong, Lei Zhao, Xiaomei Wang · 6 authors
Cryptocurrency prices have the characteristic of high volatility, which has a specific resistance to cryptocurrency price prediction. Therefore, the appropriate cryptocurrency price predictive method can help reduce the investment risk of investors. In this study, we proposed a novel prediction method using a fractional grey model (FGM (1,1)) to predict the price of blockchain cryptocurrency. Specifically, this study established the FGM (1,1) through the closing price of three representative blockchain cryptocurrencies (Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC)). It adopted the PSO algorithm to optimize and obtain the optimal order of the model, thereby conducting prediction research on the price of blockchain cryptocurrency. To verify the predictive precision of the FGM (1,1), we mainly took MAPE, MAE, and RMSE as the judging criteria and compared the model’s predictive precision with the GM (1,1) through experiments. The research results indicate that within the data range studied, the predictive accuracy of the FGM (1,1) in the closing price of BTC, ETH, and LTC has reached a “highly accurate” level. Moreover, in contrast to the GM (1,1), the FGM (1,1) outperforms predictive capability in the experiments. This study provides a feasible new method for the price prediction of blockchain cryptocurrency. It has specific references and enlightenment for government departments, investors, and researchers in theory and practice.
Market traders trade gold, and Bitcoin is aim to maximize their return. This paper utilizes the grey prediction model to explore the optimal trading strategy and optimize fund allocation based on dynamic programming. In addition, by comparing with other traditional trading strategies, we discover that the grey prediction model can more accurately estimate future prices, enabling the trader to gain steadily growing returns at a low-risk level.
Abstract Bitcoin is currently the leading global provider of cryptocurrency. Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage. In recent years, the Bitcoin network has attracted investors, businesses, and corporations while facilitating services and product deals. Moreover, Bitcoin has made itself the dominant source of decentralized cryptocurrency. While considerable research has been done concerning Bitcoin network analysis, limited research has been conducted on predicting the Bitcoin price. The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory. The first order grey model (GM (1,1)) is used for this purpose. It uses a first-order differential equation to model the trend of time series. The results show that the GM (1,1) model predicts Bitcoin’s price accurately and that one can earn a maximum profit confidence level of approximately 98% by choosing the appropriate time frame and by managing investment assets.