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Using a semisupervised topic model on 7 million New York Times articles spanning 160 years, we test whether topics of media discourse predict future stock market excess returns to test rational and behavioral hypotheses about market valuation of disaster risk. Media discourse data address the challenge of sample size even when disasters are rare. Our methodology avoids look-ahead bias and addresses semantic shifts. Our discourse topics positively predicts market excess returns, with War having an out-of-sample |$R^{2}$| of 1.35%. We call this effect the war return premium. The war return premium has increased in more recent time periods. [ABSTRACT FROM AUTHOR]

Issues of diversity and inclusion increasingly influence the purchasing process and brand loyalty of new generations of customers. Additionally, scholars and policymakers are calling for a more inclusive society and advocating for the integration of people with disabilities. However, there is a lack of research investigating these issues from a marketing perspective. Using a strategy of inquiry regarding in-depth multiple case studies from different players of the toy industry, this paper highlights how to set marketing strategies that develop and communicate new offerings by promoting both Marketing and Social Value and fostering the social inclusion of children with a disability. The proposed model contributes to the emerging literature of Inclusive Marketing and will help marketers implement a socially inclusive approach. [ABSTRACT FROM AUTHOR]

We study the interplay between cyber failures and information technology (IT) capability reputation (ITCAPR). Cyber failures often reveal vulnerable information systems (IS) processes and IT assets at their root that point to firm IT capability weakness. For external stakeholders, firm IT capability is unobservable and therefore a matter of perception or reputation. We theorize about cyber failures' adverse impacts on firm market value and firm ITCAPR based on how external stakeholders reconcile their ex ante perception of firm IT capability strength, or established ITCAPR, and their perception of an ex post revelation of IT capability weakness by cyber failures. Our analysis of 264 cyber failures finds empirical support for our theory-based predictions. We find that ex post revelation of IT capability weakness leads external stakeholders to adjust downward their ex ante perceptions of firm IT capability proportional to the severity of IT capability weakness. Two ex post consequences are commensurate loss of firm market value and decline in firm ITCAPR. Based on our main finding, that external stakeholders perceive IT capability weakness as an endogenous contributory factor to cyber failures that leads to erosion of firm ITCAPR, we contribute to theory in significant ways. First, we theoretically complement a predominant research focus on IT capability strength as a value-relevant intangible asset by confirming that IT capability weakness is a value-relevant intangible liability as well as adding "vulnerability" or "weakness" as a distinctive process aspect absent from prior literature linking IS processes and IT capabilities. Second, we theoretically add to an almost exclusive focus of research on cyber failures' short-term adverse effect on firm market value by documenting for the first time a time-lagged eroding effect on firm ITCAPR. Last, we theoretically show IT controls and particularly ones embedded in IS processes as an essential aspect of IT capability strength as well as cybersecurity protection and prevention. [ABSTRACT FROM AUTHOR]

We propose a loglinear present-value identity in which investment ("scale"), profitability ("yield"), and discount rates determine a firm's market-to-book ratio. Our identity reconciles existing influential market-to-book decompositions and facilitates novel insights from three empirical applications: (1) Both investment and profitability are important contributors to the value spread and stock return news variance. (2) Any cross-sectional return predictability has a mirror image in cash-flow fundamentals, providing asset pricing theories with additional moments to match. (3) The investment spread significantly improves the predictability of time-series variation in the value premium and justifies the poor performance of value in recent years. [ABSTRACT FROM AUTHOR]

The large divergence in the returns of top-performing star firms and the rest of the economy is substantially reduced when we account for the mismeasurement of intangible capital. Star firms produce and invest more per dollar in invested capital, have more valuable innovations as measured by the market value of patents, and are as exposed to competitive shocks as nonstars. Star firms have higher markups that are predicted early in their life cycle at a time when they are small. Overall, after we correct for the mismeasurement of intangibles, the evidence points to the superior ability of star firms. [ABSTRACT FROM AUTHOR]

The study focuses on applying machine learning methodologies to football player data for predicting player market values in the dynamic football market. Player datasets are rich, encompassing performance metrics, physiological attributes, and contextual variables. Machine learning models, including both traditional and advanced methods, effectively extract insights from complex data to estimate player market values. Addressing challenges like overfitting and computational complexity involves applying regularization, feature engineering, and interpretability tools to manage high-dimensional data and improve predictive accuracy. In this study sensitivity of selected models (Support Vector Regression (SVR), Random Forest Regression (RFR), Extreme Gradient Boosting (XGB), and Categorical Boosting (CAT)) models to extracted data from FIFA 19 and Real-world Statistical Datasets evaluated by Shapley Additive Explanations (SHAP) and the 20 most relevant features selected in the ranking of SHAP for each regression model. Then, models optimized with two meta-heuristic algorithms demonstrated their performance in predicting the market values of players. Dempster-Shafer Theory (DST) was utilized to develop an ensemble of models to overcome overfitting problems, and Fourier amplitude sensitivity testing (FAST) gave insight for future data extractions. The analysis of market values for players revealed significant model performance variations. XGSC hybrid model demonstrated exceptional precision with a minimal error of 1.7 million dollars (10% of average measured value), followed by RSCX_SC with misestimations of 2 million dollars (13.3% of average measured value). Extracted results suggested that models, especially ensemble form, offer reliable accuracy for club managers and stakeholders, aiding in strategic player selection based on previous performance. This approach proves particularly beneficial for optimizing player salaries, especially when considering a prominent team with market values above average. [ABSTRACT FROM AUTHOR]

AbstractThis study explores the impact of Metaverse Gamified Marketing Activities (MGMA) on Positive Word-of-Mouth (PWOM) intention, purchase intention, and brand loyalty within the metaverse. Drawing from an online survey involving 516 consumers based in the UK, the empirical evidence demonstrates that MGMA has a favorable influence on brand satisfaction, hedonic value, utilitarian value, and engagement. Moreover, the results show that brand satisfaction, hedonic value, utilitarian value, and engagement all positively influence brand love. Furthermore, PWOM, purchase intention, and brand loyalty are positively influenced by brand love. From a theoretical perspective, this study extends the theoretical application of the Stimulus-Organism-Response (S-O-R) model to encompass interactive virtual brand experiences, thereby establishing a foundation for the development of S-O-R frameworks that incorporate the immersive characteristics of metaverse environments. Within the S-O-R model, our findings highlight how metaverse marketing influences brand PWOM, purchase intention, and loyalty, advancing its application in metaverse-gamified marketing. [ABSTRACT FROM AUTHOR]

Chenpi, or dried tangerine peel, is a traditional Chinese ingredient valued in medicine and edible for its digestive and respiratory benefits. The geographical origin of Chenpi is important, as it can impact its quality, active compounds and market value. This study develops a strategy to distinguish Chenpi samples on its origin. Thirty-nine samples from eight regions in Xinhui district (Guangdong, China) are analyzed by gas chromatography (GC) and mid-infrared (MIR) technique. Four machine learning methods are employed to establish discrimination models based on GC and MIR data, with two mid-level data fusion strategies to combine the data. The results show that data fusion significantly improves Chenpi origin discrimination. The K-nearest neighbors and artificial neural network models, using modified mid-level data fusion, provide the best performance, misclassified only one sample. Machine learning in combination with modified mid-level data fusion strategy provides effective classification of Chenpi samples from different geographical origins. [ABSTRACT FROM AUTHOR]

Potato common scab (CS) caused by Streptomyces scabiei is a severe disease that threatens tuber quality and its market value. To date, little is known about the mechanism regulating the resistance of potato to CS. In this study, we identified a presequence translocase-associated motor 16 gene from potato (designated StPAM16-1) that is involved in the response to the phytotoxin thaxtomin A (TA) secreted by S. scabiei. The StPAM16-1 protein was localized in the mitochondria, and the expression of the gene was upregulated in potato leaves treated with TA. The suppression of StPAM16-1 in potato led to enhanced resistance to TA and S. scabiei. Protein interaction analyses revealed that StPAM16-1 interacted with the subunit 5b of the COP9 signalosome complex (StCSN5). Similar to that of StPAM16-1, the expression levels of StCSN5 significantly increased in potato leaves treated with TA. These results indicated that StPAM16-1 acted as a negative regulator and was functionally associated with StCSN5 in the immune response of potato plants against CS. Our study sheds light on the molecular mechanism by which PAM16 participates in the plant immune response. Furthermore, both StPAM16-1 and StCSN5 could be potential target genes in the molecular breeding of potato cultivars with increased resistance to CS. [ABSTRACT FROM AUTHOR]

Power-concentrated EMU trains have the advantages of being fast and comfortable, having a flexible formation and a short turn-back time, and so on. They can effectively release the transportation capacity of tense lines and hubs (the replacement of conventional trains with power-concentrated EMUs can reduce the time it takes to enter and exit locomotive yards by 40 min per train), optimize operating structures, improve the quality and efficiency of passenger products for conventional railways, and enhance the travel experience of passengers. Moreover, they have certain cost advantages and practical operational value for improving the market competitiveness of conventional railways. In this study, a two-stage, two-layer cycle method is adopted to solve the application plan of an EMU with the minimum total connection time. Through the decomposition of optimization objectives, the search space and the solution scale in each stage are reduced. In the first stage, the feasible number of routes and the number division plan of internal running lines are listed. In the second stage, an improved ant colony algorithm is designed to arrange and combine the internal running lines in each plan to improve the search quality and convergence speed, which changes the pheromone volatilization coefficient with iteration. The optimal number of routes, the number of internal routes, and the optimal sequence between routes are obtained. The study also puts forward a method of route division according to the passenger load factor, which can help railway bureaus adjust the capacity according to fluctuations in demand. A running diagram of six pairs of power-concentrated EMUs on an intercity railway is used as the background to solve the problem. The optimal connection plan with 14 groups of different route division plans was evaluated by using the entropy weight–TOPSIS method, and the optimal plan was obtained in the form of a route division method with two groups of routes with three pairs of trains in each group. Compared with the actual operation plan, the number of routes and the number of first-level repairs are reduced by 50%, respectively, which can effectively reduce the operation and maintenance costs of EMUs. Compared with the actual plan, the average operation mileage is increased by 100%, the average mileage loss is decreased by 54.6%, and the minimum distance traveled by EMUs is increased by 200%, which indicates that the mileage maintenance cycle of the actual operation plan is not fully used. The average number of tasks of EMUs is increased by 100%, indicating that the efficiency of EMUs in the actual operation plan needs to be improved. The traffic mileage balance is improved by 100%, indicating that the EMUs in different routes are more balanced. [ABSTRACT FROM AUTHOR]

This study investigates how energy retrofitting measures contribute to increasing the market value of multi-family residential buildings within the European real estate market. It examines how energy efficiency improvements, driven by EU decarbonization strategies, enhance the actual and perceived value of these properties. The research employs a dual-methodology approach, integrating the Cost Approach to estimate the financial impact of retrofitting with the Contingent Valuation Method (CVM) to evaluate consumer willingness-to-pay (WTP) for energy-efficient properties. Two real case studies are considered to evaluate the methodology and how the monetary value of buildings is affected by their energy efficiency. The results revealed that buildings subjected to deep energy retrofitting are more attractive to potential buyers, who are willing to pay a premium of 13.5% over properties in pre-retrofit conditions. This underscores the tangible market value increment attributed to energy efficiency enhancements. This study bridges the gap between the quantifiable costs of energy retrofitting and the market valuation, offering a dual perspective by integrating both actual cost analysis and perceived market value. Moreover, this work highlights the correlation between energy retrofit investments and increased market value in the European real estate sector. [ABSTRACT FROM AUTHOR]

The real growth of the stock market value of firms has increased from close to 0% on average per year between 1958 and 1980, to 5.2% between 1980 and today. This change coincides with the rise of market power and profits, starting in 1980. This paper proposes to decompose the value of firms based on profits (earnings) rather than dividends. Because firms on average pay out only half of profits in dividends, dividends poorly measure firm performance. I decompose the sources of the rise of the value of all publicly traded firms into the following categories: (1) the subjective and risk-free discount factor, (2) expected future profits, and (3) shareholder equity (retained earnings). I find that 20% of the rise is due to the discount factor and 80% is due to profits (45% current and future expected profits; 35% retained earnings). I build a general equilibrium model of the economy where firms have market power; I perform counterfactuals and evaluate the welfare implications. The objective is to study the impact of competition policy. If market power today dropped to the level of 1980, average stock market values would be 45% lower. Instead, if market power had never increased in 1980, the average stock values would be 80% lower. [ABSTRACT FROM AUTHOR]