Juan-Angel Jimenez-Martin

The AI technology diffusion over the past decade remains less understood. In this paper, we address this question by studying the information content of the “AI technology premium”. We construct novel, unified measures of firm-level AI exposures through product and labor channels and analyze the US stock market from 2012 to 2024. We document a significant and highly time-varying AI technology risk premium. The product premium is positive and driven primarily by large firms, with positive shocks observed only after 2018. In contrast, the labor premium is weaker and is primarily driven by small firms. These patterns result from slow realization processes, varying investor attention, and the complex pricing of uncertainties related to AI adoption. Consequently, our results demonstrate that AI technology adoption represents a multifaceted process characterized by evolving investor perceptions and gradual realization of potential benefits, providing crucial insights for understanding market dynamics in the AI era.

The paper discusses how to better construct ESG risk factors. It constructs and compares the environmental and social factor using different methods, ESG categories and sets of data. In terms of different methods, the paper first constructs E/S risk factors under the two most commonly used risk factors construction methods: the Fama/French (FF) and the Fama/MacBeth (FM) method. Then, it tests these factors in asset pricing models. In addition, the paper compares four kinds of ESG categories to construct ESG risk factors. Finally, it discusses if a change of sets of data helps identify ESG risk factors. The paper finds that the FF method performs better than the FM method in capturing ESG risk premiums. The ESG category and sets of data are more important than methods. A change of ESG category will significantly improve asset pricing results. When using a more focused data set, in our case the energy and utility sector, the ESG risk factor can better explain asset returns. Therefore, in order to have a better estimation of ESG risk factors, policy-makers, academics and investors should be well aware of which aspect of ESG they want to measure (carefully choose the ESG category) and which group of stocks they want to model (sets of data).

Over the past few decades, numerous studies have been conducted on the trade-off between guns and butter, namely defence spending versus social sector expenditure. It is striking that none has examined the situation in Spain. Previous research on other countries has not provided strong and unambiguous evidence of either positive or negative effects for military expenditure on social spending. We test whether government expenditure on defence contributed positively or negatively to education spending in twentieth-century Spain, attempting to identify policy-making processes within a historical framework. The results show both direct and indirect effects and bring to light the negative consequences of military spending on education expenditure, in particular between the second and fifth year after the military expenditure increases.

We compare Value at Risk (VaR) and Expected Shortfall (ES) following a Stochastic Dominance (SD) approach frequently used to order distributions in terms of welfare and in portfolio selection. Basel Committee on Banking Supervision (BCBS) recommends bank risk managers to shift the current quantitative risk metrics system, based on Value-at-Risk (VaR), to Expected Shortfall (ES). “Welfare costs” of such a reform in terms of capital requirements and penalties are a central concern for risk managers and regulators. Policy makers’ concerns can be addressed with many different value functions. A uniform ranking analysis based on stochastic dominance is provided here as an effective tool for comparing distributions of daily capital requirement charges produced under different regulations. On the basis of empirical results, it is concluded that ES should be preferred by risk averse policy makers who favour larger but less volatile capital requirements, and reduces the sensitivity of capital charges to changes in the probability of default.