NFT Appraisal Using Machine Learning. Dawod, A. D. M., Munkhdalai, L., Park, K. H., Ryu, K. H., & Pham, V. H. In ACM International Conference Proceeding Series, pages 160–166, February, 2023. ACM. Series Title: APIT 2023![NFT Appraisal Using Machine Learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Non-Fungible Tokens (NFTs) are digital assets based on a blockchain and those are characterized as unique cryptographic tokens and non-interchangeable. To date, research into the NFT marketplace has been relatively limited. As it is an emerging platform with many unique elements, The NFT market has been impacted due to recent fluctuations in crypto-asset markets more broadly. This current bear market cycle has shed light on concerns around the value of NFTs, profit-based motivation, and environmental sustainability. However, periods of volatility and cyclicality are to be expected with any nascent technology as it develops a product-market fit. consequently, the appraisal of real-price for NFT collections is essential for individual financial security and investment making. In this study, we evaluate the machine learning algorithms to appraise their real-price based on NFT item's characteristics, market event information, and their rarity score data acquired by retrieved from the biggest marketplace OpenSea. Furthermore, the procedures were applied to meet the objectives of this study we built prediction models based on various machine-learning algorithms ranging from Random Forest, XGBoost, SVM, Lasso, ElasticNet, Ridge, Linear Polynomial Regression, TabNet, CatBoost, and LightGBM models. From the results, LightGBM regression model outperformed the other by RMSE around 0.905. The best R2 is only found in this model, which has a value of 0.917.
@inproceedings{Dawod_2023,
title = {{NFT} {Appraisal} {Using} {Machine} {Learning}},
isbn = {978-1-4503-9950-0},
url = {http://dx.doi.org/10.1145/3588155.3588181},
doi = {10.1145/3588155.3588181},
abstract = {Non-Fungible Tokens (NFTs) are digital assets based on a blockchain and those are characterized as unique cryptographic tokens and non-interchangeable. To date, research into the NFT marketplace has been relatively limited. As it is an emerging platform with many unique elements, The NFT market has been impacted due to recent fluctuations in crypto-asset markets more broadly. This current bear market cycle has shed light on concerns around the value of NFTs, profit-based motivation, and environmental sustainability. However, periods of volatility and cyclicality are to be expected with any nascent technology as it develops a product-market fit. consequently, the appraisal of real-price for NFT collections is essential for individual financial security and investment making. In this study, we evaluate the machine learning algorithms to appraise their real-price based on NFT item's characteristics, market event information, and their rarity score data acquired by retrieved from the biggest marketplace OpenSea. Furthermore, the procedures were applied to meet the objectives of this study we built prediction models based on various machine-learning algorithms ranging from Random Forest, XGBoost, SVM, Lasso, ElasticNet, Ridge, Linear Polynomial Regression, TabNet, CatBoost, and LightGBM models. From the results, LightGBM regression model outperformed the other by RMSE around 0.905. The best R2 is only found in this model, which has a value of 0.917.},
booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}},
publisher = {ACM},
author = {Dawod, Ahmed Dawod Mohammed and Munkhdalai, Lkhagvadorj and Park, Kwang Ho and Ryu, Keun Ho and Pham, Van Huy},
month = feb,
year = {2023},
note = {Series Title: APIT 2023},
keywords = {Blockchain, CatBoost, ElasticNet, Lasso, LightGBM, Linear and Polynomial Regression, Non-Fungible Tokens, Random Forest, Ridge, SVM, TabNet, XGBoost},
pages = {160--166},
}
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