Analysis of Non-Fungible Token Pricing Factors with Machine Learning

Ho, Kin-Hon; Hou, Yun; Chan, Tse-Tin; Pan, Haoyuan

doi:10.1109/smc53654.2022.9945566

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…The research by Kin-Hon Ho, Haoyuan Pan, and Tse-tin Chan employs machine learning to analyze factors influencing Non-Fungible Token (NFT) pricing, offering valuable insights into the determinants shaping the value of NFTs in various markets. The study contributes to understanding the complex dynamics and predicting pricing trends within the NFT ecosystem [15] .…”

Section: Literature Surveymentioning

confidence: 99%

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Cryptocurrency Price Prediction Using Machine Learning

Kawli,

Chaudhari,

Ingale

et al. 2024

AJCT

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The application of machine learning algorithms in predicting cryptocurrency prices has gained significant attention in recent years. Researchers have explored various approaches such as recurrent neural networks, deep learning neural networks, Bayesian regression, k-nearest neighbor, support vector machine, and other algorithms to forecast the prices of cryptocurrencies like Bitcoin, Ethereum, Dogecoin and Litecoin. This paper will draw on established literature on price prediction using machine learning, including studies on NFT sales predictability, NFT sale price fluctuations prediction, gold price prediction, and silver price forecasting. The research paper has focused on utilizing high-dimensional features, time-series analysis, as well as the comparison of different statistical models and machine learning algorithms. Additionally, the prediction models have incorporated factors such as market liquidity, exchange market dynamics.While the literature acknowledges the potential of machine learning in cryptocurrency price prediction, gold, silver and NFT's there is a recognized gap in the application of these techniques across a broader range of cryptocurrencies. The proposed methodology will integrate various machine learning models and statistical methods to predict the prices of cryptocurrencies, gold, silver, and NFTs, taking into account factors such as market trends, trade networks and visual features. Furthermore, the studies emphasize the importance of feature engineering, sample dimension engineering, and the use of various machine learning techniques to enhance the accuracy and stability of cryptocurrency price predictions. As the cryptocurrency market continues to expand, there is a need for further research to develop robust machine learning models that can effectively forecast the prices of diverse cryptocurrencies, contributing to the advancement of this field.

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Section: Literature Surveymentioning

confidence: 99%

“…Cryptocurrency, gold, silver and Non-Fungible Token (NFT) are distinct assets which having unique features [15] . Gold is often seen as a hedge against economic uncertainty, while silver serves both investment and industrial purposes.…”

Section: Introductionmentioning

confidence: 99%

Cryptocurrency Price Prediction Using Machine Learning

Kawli,

Chaudhari,

Ingale

et al. 2024

AJCT

View full text Add to dashboard Cite

show abstract

“…For example, if an ERC-20 token is pegged to the U.S. dollar, its value is fixed at one U.S. dollar per token. However, the value of ERC-721 tokens relies on subjective evaluation or price history, making their value assessment difficult [15], [19], [20]. ERC-721 tokens are primarily used for digital collections (e.g., CryptoKitties) or representation of rights (e.g., Uniswap v3 [18]), due to the difficulty in valuation.…”

Section: Introductionmentioning

confidence: 99%