Presenting an approach to predicting the sales of non-fungible token products based on neural networks

Since the introduction of so-called non-fungible tokens, a digital artwork can be worth several million dollars. These digital tokens are unique, representing ownership of certain unique digital objects. In this paper, we propose an approach to predict the sales of non-fungible token products based on neural networks, which addresses the equivalence and prediction of pricing between real and virtual effects and predict their sales. In the proposed method, an application of hedonic pricing modeling to the spatial properties of virtual assets is demonstrated, which is the first of its kind. Previous studies have only examined the volume-return or volume-volatility relationship of fungible tokens, such as Bitcoin and other “regular” cryptocurrencies. However, the results of this study contribute to a better understanding of the volume-return and volume-volatility relationships of non-fungible tokens, namely Theta, Tezos, and Enjincoin, which are built on alternative blockchains and are gaining popularity among the non-fungible token communities of the Ethereum protocol. In this study, neural networks were used for classification and good results were obtained in terms of accuracy or precision for predicting the sales of non-fungible token products based on this technique. Also, comparing the proposed method and other similar methods for predicting the price of non-fungible tokens in different evaluation criteria resulted in more optimal and appropriate results.