arXiv subjects

Samiha Tariq

Publications and source records attributed to Samiha Tariq.

4 recordsLinked to original sources

arXiv2026

Hype Has Worth: Attention, Sentiment, and NFT Valuation in Major Ethereum Collections

Do online narratives leave a measurable imprint on prices in markets for digital or cultural goods? This paper evaluates how community attention and sentiment relate to valuation in major Ethereum NFT collections after accounting for time effects, market-wide conditions, and persistent visual heterogeneity. Transaction data for large generative collections are merged with Reddit-based discourse measures available for 25 collections, covering 87{,}696 secondary-market sales from January 2021 through March 2025. Visual differences are absorbed by a transparent, within-collection standardized index built from explicit image traits and aggregated via PCA. Discourse is summarized at the collection-by-bin level using discussion intensity and lexicon-based tone measures, with smoothing to reduce noise when text volume is sparse. A mixed-effects specification with a Mundlak within--between decomposition separates persistent cross-collection differences from within-collection fluctuations. Valuations align most strongly with sustained collection-level attention and sentiment environments; within collections, short-horizon negativity is consistently associated with higher prices, and attention is most informative when measured as cumulative engagement over multiple prior windows.

econ.GN↗

arXiv2025

Pixels to Prices: Visual Traits, Market Cycles, and the Economics of NFT Valuation

Pixels and market cycles both move NFT prices. Non-fungible tokens (NFTs) are unique digital assets, often used to represent ownership of digital art, collectibles, and other media, secured on blockchain networks like Ethereum. The rise of NFTs has led to the creation of a multi-billion-dollar market for digital art and collectibles, making it a key area of interest for researchers, artists, and investors. Using 94,039 transactions from 26 major generative Ethereum collections, this study extracts 196 machine-quantified image descriptors - color, composition, palette structure, geometry, texture, and deep-learning embeddings - and applies a three-stage filter to identify stable predictors for hedonic regression. A static mixed-effects model shows that market sentiment and transparent, interpretable image traits have significant and independent pricing power: higher focal saturation, tighter compositional concentration, and greater curvature are rewarded, while clutter, heavy line work, and dispersed palettes are discounted; deep embeddings add limited incremental value once explicit traits are included. To assess state dependence, a Bayesian dynamic mixed-effects panel with cycle effects is estimated, allowing Composition Focus - Saturation - the ratio of saturation in the central region to the whole image, capturing vividness and concentration at the focal area - to vary across market regimes. Collection-level heterogeneity (brand premia) is absorbed by random effects. The time-varying coefficients exhibit clear regime sensitivity, with stronger premia in expansionary phases and weaker or negative loadings in downturns, while the grand-mean effect is small on average. Overall, NFT prices reflect both observable digital product characteristics and market regimes, and the framework offers a cycle-aware tool for asset pricing, platform strategy, and market design in digital art markets.

econ.GN↗

arXiv2025

Borrowing on Belief? Consumer Confidence and U.S. Credit -- A VECM Study

This study explores the interdependent relationship between consumer credit and consumer confidence in the United States using monthly data from January 1978 to August 2024. Utilizing a Vector Error Correction Model (VECM), the analysis focuses on the interplay between household borrowing behaviour and consumer sentiment while controlling for macroeconomic factors such as interest rates, inflation, unemployment, and money supply. The results reveal a stable long-run equilibrium: heightened consumer confidence is associated with increased credit utilization, reflecting greater financial optimism among households. In the short run, shifts in consumer confidence exert relatively modest immediate influence on credit usage, whereas consumer credit adjusts slowly, displaying significant inertia. Impulse-response analysis confirms that shocks to consumer confidence generate sustained positive effects on borrowing, while unexpected increases in credit initially depress sentiment but only fleetingly. These findings underscore the critical role of the relationship between consumer confidence and credit-market dynamics and highlight its policy relevance for fostering balanced and stable household finances.

econ.GN↗

arXiv2025

Cognitive Biases at Play? Insights from a Bayesian Game Framework

This paper examines the impact of cognitive biases on financial decision-making through a static Bayesian game framework. While traditional economic theory assumes fully rational investors, real-world choices are often shaped by loss aversion, overconfidence, and herd behavior. Integrating psychological insights with economic game theory, the model studies strategic interactions among investors who allocate wealth between risky and risk-free assets. Solving for the Bayesian Nash Equilibrium reveals that each bias distorts optimal portfolios and alters aggregate market dynamics. The results echo Herbert Simon's notion of bounded rationality, showing how biases can generate market inefficiencies, price bubbles, and crashes. The findings highlight the importance of incorporating psychological factors into economic models to guide policies that foster market stability and more informed financial decision-making.

econ.TH↗