THE IMPACT OF ETHEREUM ON ERC-20 TOKENS: A COMPARATIVE ANALYSIS WITH LSTM AND CNN MODELS

Year 2025, Volume: 18 Issue: 1, 476 - 492, 30.01.2025

Mehmet Çınar , Muhammet Apak

Abstract

Ethereum, developed by Vitalik Buterin in 2013, has significantly advanced blockchain technology through smart contracts and ERC-20 token standards. This study examines the impact of Ethereum on ERC-20 tokens using Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) models. For this purpose, LSTM and CNN models were trained using Ethereum data and then employed to predict ERC-20 token prices. According to the study's results, the LSTM model achieved high accuracy rates for LINK, MATIC, and UNI tokens but performed poorly in predicting RNDR token prices. The CNN model provided the highest accuracy for LINK tokens and yielded successful results in predicting RNDR token prices. However, the CNN model showed lower performance for MATIC and UNI tokens than the LSTM model. These findings indicate that both LSTM and CNN
models significantly impact the prediction of Ethereum's ERC-20 token price dynamics. The variability in model performances across tokens highlights the influence of market dynamics and liquidity levels. In light of these differences, the study emphasizes the importance of selecting the model based on the token's characteristics and market conditions.

Keywords

Ethereum, ERC-20, LSTM, CNN

Project Number

SYL-2024-1740

ETHEREUM'UN ERC-20 TOKENLARI ÜZERİNDEKİ ETKİSİ: LSTM VE CNN MODELLERİYLE KARŞILAŞTIRMALI BİR ANALİZ

Abstract

Vitalik Buterin tarafından 2013 yılında geliştirilen Ethereum, akıllı sözleşmeler ve ERC-20 token standartları ile blockchain teknolojisini önemli ölçüde ileri taşımıştır. Bu çalışmada Ethereum'un ERC-20 tokenları üzerindeki etkisi Long Short-Term Memory (LSTM) ve Convolutional Neural Networks (CNN) modelleri kullanılarak incelenmektedir. Bu amaçla Ethereum verileri kullanılarak LSTM ve CNN modelleri yardımıyla model eğitimleri gerçekleştirilmiştir. Daha sonra eğitilen modeller ERC-20 token fiyatlarını tahmin etmek amacıyla kullanılmıştır. Çalışmada uygulanan tüm analizler. Çalışma sonuçlarına göre, LSTM modeli; LINK, MATIC ve UNI tokenları için yüksek doğruluk oranlarına ulaşmış, ancak RNDR tokeni tahminlerinde daha düşük performans sergilemiştir. CNN modeli ise LINK tokeni için en yüksek doğruluğu sağlamış ve RNDR tokeni tahminlerinde de başarılı sonuçlar elde etmiştir. Bununla birlikte, CNN modeli MATIC ve UNI tokenlarında LSTM modeline göre daha düşük bir performans sergilemiştir. Bu bulgular, hem LSTM hem de CNN modellerinin Ethereum'un ERC-20 token fiyat dinamiklerini tahmin etmede belirgin bir etkiye sahip olduğunu ortaya koymaktadır. Model performanslarının token bazında değişkenlik göstermesi, piyasa dinamikleri ve likidite seviyelerinin etkisini işaret etmektedir. Çalışma, bu farklılıkların model seçiminde tokenin özelliklerine ve piyasa koşullarına göre yapılmasının önemini vurgulamaktadır.

Keywords

Ethereum, ERC-20, LSTM, CNN

Supporting Institution

Bursa Uludağ Üniversitesi Bilimsel Araştırma Projeleri Birimi

Project Number

SYL-2024-1740

Thanks

Desteklerinden ötürü Bursa Uludağ Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi’ne teşekkür eDERİZ.

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