This article analyses the impact of ownership structure features and institutional settings on real activities manipulation. The analysis is based on a sample of listed companies in the underexplored Latin American market for the period of 2004–2016. Panel-data-based G.M.M. System Estimation is used in the empirical analysis. The results confirm that the monitoring role of the majority owner is crucial in mitigating managerial opportunistic behaviour. Here, opportunistic behaviour refers to engaging in real activities manipulation that reduces the informative content of financial statements. However, analysis of insider ownership revealed that managers had a negative impact on transparency. We observed that as insider ownership increases, managers engage more actively in real earnings management. We also find that the institutional ownership and the quality of the regulatory system proved to be effective mechanisms in reducing real activities manipulation.

This article investigates the role of virtual integration of financial markets on stock market return co-movements. In May of 2011, the Chilean, Colombian and Peruvian stock markets virtually integrated their stock exchanges and central securities depositories to form the Latin American Integrated Market (MILA). We utilize the dynamic conditional correlation model proposed by Engle (2002) to identify a statistically significant positive correlation between these markets. Moreover, we find strong evidence that the creation of the MILA increased the levels of dynamic correlation between stock returns. A higher correlation was also found during the dot-com bubble and the 2007 financial crises. Our results imply a decline in gains from international diversification by holding portfolios consisting of diverse stocks of these countries.

Agricultural product prices are subject to various uncertainties, including unpredictable weather conditions, pest infestations, and market fluctuations, which can significantly impact agricultural yields and productivity. Accurately assessing and understanding price is crucial for farmers, policymakers, and stakeholders in the agricultural sector to make informed decisions and implement appropriate risk management strategies. This study used the ordered weighted average (OWA) operator and its extensions as mathematical aggregation techniques incorporating ordered weights to capture and evaluate the factors influencing price variation. By generating different vectors related to different inputs to the traditional formulation, it is possible to aggregate information to calculate and provide a new view of the outcomes. The results of this research can help enhance risk management practices in agriculture and support decision-making processes to mitigate the adverse effects of price.