Journal of Risk and Financial Management

This study examines the pricing dynamics of Non-Fungible Tokens (NFTs) in the secondary market using advanced machine-learning techniques. We construct a large dataset of Ethereum-based NFT transactions initially comprising over 500,000 raw blockchain observations spanning multiple NFT segments, including art, collectibles, gaming, metaverse, and utility assets, over the period from November 2018 to March 2023. Following data preprocessing, synchronization across data sources, and the construction of history-dependent features, the analysis focuses on a final analytical sample of approximately 70,000 transactions. To address the challenges of non-fungibility, thin trading, and high price dispersion, we develop an interpretable predictive framework that integrates domain-informed manual feature engineering, automated Deep Feature Synthesis, and dimensionality reduction via Principal Component Analysis. Three non-linear models—Random Forest, XGBoost, and a Multilayer Perceptron—are trained and evaluated using both random and time-aware validation strategies. The results indicate that XGBoost consistently achieves the highest predictive accuracy, both overall and across individual NFT segments, while historical transaction prices emerge as the dominant predictor of future prices. Segment-level analysis reveals substantial heterogeneity in predictability, with art and collectible NFTs exhibiting more stable pricing patterns than gaming and metaverse assets. Overall, the findings highlight strong path dependence and reputation-driven valuation in NFT markets and demonstrate that carefully designed machine-learning models can deliver high predictive performance without sacrificing economic interpretability.

This article provides a systematic review of the theoretical foundations underlying tax behavior in family firms. Drawing on 69 empirical studies indexed in Scopus and Web of Science, the review identifies three core limitations: the insufficient contextualization of socioemotional wealth (SEW) across cultural and institutional settings; the weak operationalization of SEW dimensions, often relying on indirect proxies; and the limited examination of the concrete actions firms take to minimize corporate income tax. We propose a research agenda anchored in a multi-layered framework that calls for contextual sensitivity, improved SEW measurement, and stronger empirical grounding in tax minimization mechanisms.

This study examines how business activity responds to local taxation, specifically property tax and local sales tax, in Nevada. Using county-level data for the period 1999–2014, we assess the impact of these taxes on various business activity indicators, including employment, annual payroll, the number of establishments, and the number of small establishments categorized by size. Unlike previous studies that primarily focus on state-level taxation, our research delves into the effects of local tax instruments. By analyzing different components of the property tax (e.g., school district, county, and special district rates) and evaluating the specific effects of local sales tax changes, we provide a nuanced understanding of the local tax–business activity relationship. To address potential policy endogeneity in the sales tax rate, we instrument the sales tax rate using the lagged share of registered Democrats and implement an IV (control-function) spatial Durbin framework, ensuring robust estimates of within-period associations and spatial spillovers. Our analysis is intentionally confined to the 1999–2014 institutional regime, when Nevada businesses were primarily exposed to property and sales taxes. The estimates should, therefore, be interpreted as evidence on how the local tax mix and its components correlate with business activity under this pre-2015 fiscal structure, rather than as a direct forecast for the post-2015 environment shaped by subsequent policy changes and macroeconomic shocks. Across specifications, the IV-identified total effect of the sales tax rate is consistently negative for establishment-related outcomes. Nonetheless, the results remain informative for current debates on the design of local revenue systems because the underlying tax–service bundle and cross-jurisdictional spillover mechanisms continue to be central to local public finance.