Investor sentiment and the NFT hype index: to buy or not to buy?

IF 9 1区 经济学 Q1 BUSINESS, FINANCE China Finance Review International Pub Date : 2023-12-05 DOI:10.1108/cfri-06-2023-0175
Valeriia Baklanova, Aleksei Kurkin, Tamara Teplova
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Abstract

Purpose

The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.

Design/methodology/approach

The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.

Findings

The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.

Practical implications

The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.

Originality/value

The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

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投资者情绪与NFT炒作指数:买还是不买?
本研究的主要目的是对构建的机器学习模型提供精确的解释,并产生明确的摘要,以评估投资者情绪对不可替代代币(NFT)资产整体销售的影响。为了实现这一目标,构建了NFT炒作指数,并采用了XAI的几种方法来解释黑盒模型,并评估所使用特征影响的大小和方向。设计/方法/方法研究论文涉及构建一个称为NFT炒作指数的情绪指数,其目的是衡量NFT行业内市场参与者的影响力。该指数是通过分析62名知名人士和意见领袖在社交媒体平台推特上发布的书面内容而得出的。作者从推特账户中收集了帖子,然后在自然语言处理模型VADER的帮助下根据调性进行分类。然后应用机器学习方法和XAI方法(特征重要性、排列重要性和SHAP)对得到的结果进行解释。使用梯度增强回归模型和可解释的人工智能技术对构建的指数进行了严格的分析,证实了其显著的解释力。值得注意的是,与众所周知的情绪指数相比,NFT炒作指数表现出更高程度的预测准确性。NFT炒作指数由Twitter文本数据构建,作为NFT市场投资决策的创新、基于情绪的指标。它为投资者提供了对市场情绪的独特见解,可以与传统的金融分析技术一起使用,以增强快速发展的NFT生态系统中的风险管理、投资组合优化和整体投资结果。因此,该指数在促进信息灵通、数据驱动的投资决策和确保数字资产市场的竞争优势方面发挥着至关重要的作用。原创性/价值作者基于市场影响者发布的短信开发了一种新的投资者对NFT资产的兴趣指数(NFT炒作指数),并将其与传统情绪指数进行了解释能力的比较。通过可解释人工智能的应用,表明情绪指数可以作为NFT销售的重要预测指标,并且NFT炒作指数在所有考虑的情绪指数中效果最好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.40
自引率
1.20%
发文量
112
期刊介绍: China Finance Review International publishes original and high-quality theoretical and empirical articles focusing on financial and economic issues arising from China's reform, opening-up, economic development, and system transformation. The journal serves as a platform for exchange between Chinese finance scholars and international financial economists, covering a wide range of topics including monetary policy, banking, international trade and finance, corporate finance, asset pricing, market microstructure, corporate governance, incentive studies, fiscal policy, public management, and state-owned enterprise reform.
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