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Keywords = bull valuation

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24 pages, 376 KB  
Article
Causal Impact of Stock Price Crash Risk on Cost of Equity: Evidence from Chinese Markets
by Babatounde Ifred Paterne Zonon, Xianzhi Wang, Chuang Chen and Mouhamed Bayane Bouraima
Economies 2025, 13(6), 158; https://doi.org/10.3390/economies13060158 - 2 Jun 2025
Viewed by 3635
Abstract
This study investigates the causal impact of stock price crash risk on the cost of equity (COE) in China’s segmented A- and B-share markets with an emphasis on ownership structures and market regimes. Employing a bootstrap panel Granger causality framework, Markov-switching dynamic regression, [...] Read more.
This study investigates the causal impact of stock price crash risk on the cost of equity (COE) in China’s segmented A- and B-share markets with an emphasis on ownership structures and market regimes. Employing a bootstrap panel Granger causality framework, Markov-switching dynamic regression, and panel threshold regression models, the analysis reveals that heightened crash risk significantly increases COE, with the effects being more pronounced for A-shares because of domestic investors’ heightened risk sensitivity. This relationship further intensifies in bull markets, where investor optimism amplifies downside risk perceptions. Ownership segmentation plays a critical role, as foreign investors in B-shares exhibit weaker reliance on firm-level valuation metrics, favoring broader risk-diversification strategies. These findings offer actionable insights into corporate risk management, investor decision making, and policy formulation in segmented and emerging equity markets. Full article
25 pages, 659 KB  
Article
Market Phases and Price Discovery in NFTs: A Deep Learning Approach to Digital Asset Valuation
by Ho-Jun Kang and Sang-Gun Lee
J. Theor. Appl. Electron. Commer. Res. 2025, 20(2), 64; https://doi.org/10.3390/jtaer20020064 - 3 Apr 2025
Cited by 2 | Viewed by 4288
Abstract
This study introduces the Channel-wise Attention with Relative Distance (CARD) model for NFT market prediction, addressing the unique challenges of NFT valuation through a novel deep learning architecture. Analyzing 26,287 h of transaction data across major marketplaces, the model demonstrates superior predictive accuracy [...] Read more.
This study introduces the Channel-wise Attention with Relative Distance (CARD) model for NFT market prediction, addressing the unique challenges of NFT valuation through a novel deep learning architecture. Analyzing 26,287 h of transaction data across major marketplaces, the model demonstrates superior predictive accuracy compared to conventional approaches, achieving a 33.5% reduction in Mean Absolute Error versus LSTM models, a 29.7% improvement over Transformer architectures, and a 30.1% enhancement compared to LightGBM implementations. For long-term forecasting (720-h horizon), CARD maintains a 35.5% performance advantage over the next best model. Through SHAP-based regime analysis, we identify distinct feature importance patterns across market phases, revealing how liquidity metrics, top trader activity, and royalty dynamics drive valuations in bear, bull, and neutral markets respectively. The findings provide actionable insights for investors while advancing our theoretical understanding of NFT market microstructure and price discovery mechanisms. Full article
(This article belongs to the Special Issue Blockchain Business Applications and the Metaverse)
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14 pages, 2011 KB  
Article
BullVal$: An Integrated Decision-Support Tool for Predicting the Net Present Value of a Dairy Bull Based on Genetic Merit, Semen Production Potential, and Demographic Factors
by Allison Q. Gorr, Victor E. Cabrera, James Meronek and Kent A. Weigel
Animals 2023, 13(13), 2062; https://doi.org/10.3390/ani13132062 - 22 Jun 2023
Viewed by 1617
Abstract
Deciding when to replace dairy bulls presents a complex challenge for artificial insemination (AI) companies. These decisions encompass multiple factors, including a bull’s age, predicted semen production, and estimated genetic merit. This study’s purpose was to provide a practical, objective tool to assist [...] Read more.
Deciding when to replace dairy bulls presents a complex challenge for artificial insemination (AI) companies. These decisions encompass multiple factors, including a bull’s age, predicted semen production, and estimated genetic merit. This study’s purpose was to provide a practical, objective tool to assist in these decisions. We utilized a Markov Chain model to calculate the economic valuation of dairy bulls, incorporating key factors such as housing costs, collection and marketing expenses, and the bull’s probable tenure in the herd. Data from a leading AI company were used to establish baseline values. The model further compared a bull’s net present value to that of a potential young replacement, establishing a relative valuation (BullVal$). The range of BullVal$ observed spanned from −USD 316,748 to USD 497,710. Interestingly, the model recommended culling for 49% of the bulls based on negative BullVal$. It was found that a bull’s net present value was primarily influenced by market allocation and pricing, coupled with the interaction of semen production and genetic merit. This study offers a robust, data-driven model to guide bull replacement decisions in AI companies. Key determinants of a bull’s valuation included market dynamics, semen production rates, and genetic merit. Full article
(This article belongs to the Collection Advances in Cattle Breeding, Genetics and Genomics)
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16 pages, 1615 KB  
Article
Option Implied Stock Buy-Side and Sell-Side Market Depths
by Feng-Tse Tsai
Risks 2019, 7(4), 108; https://doi.org/10.3390/risks7040108 - 28 Oct 2019
Viewed by 4267
Abstract
This paper investigates option valuation when the underlying market suffers from illiquidity of price impact. Using option data, I infer trading activities and price impacts on the buy side and the sell side in the stock market from option prices across maturities. The [...] Read more.
This paper investigates option valuation when the underlying market suffers from illiquidity of price impact. Using option data, I infer trading activities and price impacts on the buy side and the sell side in the stock market from option prices across maturities. The finding displays that the stock market is active when the stock prices plummet, but becomes silent after the market crashes. In addition, the difference of option implied price impacts between the buy side and the sell side, which indicates asymmetric liquidity, increases with the time to maturity, especially on the day of the market crisis. Moreover, investors have different perspectives on the future liquidity after liquidity shocks when they are in a bull market or in a bear market according to the option implied price impact (or market depth) curves. I also calibrate three market indices simultaneously and reach the same conclusion that the three markets become erratic on the event date and calm down in the aftermath. Full article
(This article belongs to the Special Issue Measuring and Modelling Financial Risk and Derivatives)
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