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On March 11, 2021, NFT artwork “Everydays: The first 5000 Days” by digital artist Beeple was sold for $69 million in the auction at Christie’s. This work is the collage of all the works done and published by Beeple in the past 5000 days, and its final auction price made it become the most popular NFT with the highest value (Kapoor et al., 2022). After that, NFT market started to be known to the general public, and people began to discuss “What is NFT, and why it has such a high value?” NFT, also known as Non-fungible token, is the ID or certification of a digital asset in metaverse. Unlike the currencies people use to trade, NFT is not interchangeable, and every token is unique. Its uniqueness shows its scarcity as well as explaining why it has potential to appreciate. Also, NFT is traded via cryptocurrencies such as Ethereum. Through blockchain technology, its ownership will be certified to make sure that the process is decentralized and transparent; everyone will know who owns which NFTs at what time and the price of each resell (Wang et al., 2021). Until the second quarter of 2022, the total volume of NFTs has reached $12.22 billion (Akamo, 2022). However, since NFT market is nascent, there are only limited amount of researches. Therefore, having a comprehensive and deep market analysis is critical and helpful to fully unveil the mask of NFT market. Certain challenges include NFT’s relative nature of unpredictability and irregular volatilities.
However, with the help of technologies and big data, many current challenges can be effectively solved. Big data represents velocity (the high speed of data collecting and processing), volume (the large amount of data), and variety (different types of data such as image, text, and gif etc) (Laborde, 2020). Two challenges described above: the high volatility and limited research of NFT market, can be solved, or at least mitigated, by big data. Through big data, researchers are able to build a comprehensive database that is powerful enough to be used to as the basic of the following analytical tasks, such as training models and performing sentimental analysis via machine learning and deep learning and making predictions based on the results. The predictive analysis aims to solve the challenge of the relatively unpredictable nature. By finding out patterns of the price flow, trading frequency, as well as social media hypes, researchers can gain a more exhaustive understanding of the market structure as well as the insights behind the irregular changes.
This paper aims to analyze both the current and future NFT market structure and trend as well as the motivations, rationales, methods, and challenges in current research that have been done. The first focus will be the overall analysis of NFT market, including characteristics of top NFTs on OpenSea, noticeable market volatilities, and future market trends. The second part will be the summary of current research trends, investigation of why these trends prevail, and potential challenges involved. Finally, the paper will discuss the data collection and integration methods, bid data analytics, as well as decision-making and predictive models and analysis by machine learning and deep learning to further investigate the NFT market and solve significant challenges.