China Finance Review International最新文献

Purpose

The non-fungible token (NFT) market had undergone dramatic growth and a sudden decline during 2021–2022. The market experienced a surge in prices in late 2021 and early 2022, with NFTs being sold at inflated prices. Despite this, by April 2022, the market underwent a correction, and the prices of NFTs returned to more reasonable levels. This can be a result of imitating the actions or judgments of a larger group, which is not systematically proven yet. Therefore, this study systematically investigates the applicability of herding behavior in the NFT market.

Design/methodology/approach

This research employs cross-sectional absolute deviation (CSAD) of returns and ordinary least squares (OLS) to test herding behavior with moving time windows of 10, 20 and 30 days based on the sales data collected from public interface of OpenSea between July 1, 2021 and June 30, 2022. Additionally, NFT-related keyword usage analysis is done for the detected herding periods.

Findings

As per the results of the data analyzed, herding behavior was evidenced using 10-, 20- and 30-day time windows from July 1, 2021 to June 30, 2022because of media movement. The findings revealed that this behavior was present and aligned with the overall behavior of the market.

Originality/value

This study introduces CSAD to examine herding behavior patterns within the NFT market. Complementing this method, keyword count-based analysis is employed to identify the underlying causes of herding behavior. Through this comprehensive approach, this study not only uncovers the roots of herding behavior but also offers an assessment of the time windows during which it occurs, considering the plausible socioeconomic contexts that influence these trends.

Purpose

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Design/methodology/approach

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Findings

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

Originality/value

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

Purpose

This study empirically examined consumer adoption attitudes and behaviors toward nonfungible tokens (NFTs). Findings indicate that consumer attitudes toward NFTs are influenced by perceived usefulness, reliability and profit expectancy and that strong attitudes are associated with purchase intentions. Additionally, the relationship between attitudes and purchase intentions was moderated by technology optimism.

Design/methodology/approach

The authors utilized a partial least squares (PLS) model to examine the hypotheses in this empirical analysis. Obtaining a sample of actual NFT holders or experienced users is challenging. A total of 105 individuals participated in the study as valid responders by answering the screening question in the questionnaire. The authors opted for the PLS model as a research approach due to the limited size of the consumer population in the NFT market.

Findings

This study discovered that the adoption of NFTs was affected by technical aspects such as usefulness and reliability and the potential for future asset growth. Furthermore, the degree of attitude-to-intention conversion varied based on optimism, an inherent characteristic.

Research limitations/implications

This study offers valuable insights for NFT owners, content providers and trading firms. For the NFT market to expand, it must meet consumers' expectations for the desired content features and asset investment attributes. Additionally, customer targeting strategies should attract and appeal to technology enthusiasts with an optimistic outlook on technology.

Originality/value

The authors conducted an initial empirical analysis of actual NFT consumers, an area of research studied sparingly despite its significance.

Purpose

It is documented that companies and government agencies in the USA invest more in the fourth fiscal quarter without having higher investment opportunities. While previous studies focus on the agency conflicts and information asymmetry within organizations, this study is motivated by Scharfstein and Stein's (2000) two-tiered agency model and aims to examine how firms' external business environment affects the “fourth quarter effect.”

Design/methodology/approach

The authors implement this study in a sample of 41 countries and observe similar seasonality in firm investment as documented in the US market.

Findings

More importantly, using country characteristics, this study finds that firms from countries with better investor rights and protection, and more developed financial markets show less severe over-investment in the fourth fiscal quarter.

Originality/value

This paper contributes to the literature of law and finance, and the internal capital market, by investigating the quarterly investment patterns of firms from 41 countries. The authors find that similar to the results in earlier studies on the US market, firms in the global market increase their capital expenditure in the fourth fiscal quarter, indicating that the internal agency conflicts between the headquarters and divisional managers are widespread across the world. The authors also find that firms that operate in countries with higher investor rights and protection, and more developed financial markets, tend to show less severe “fourth quarter effect”.

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.