ABSTRACT
Companies in difficult financial situations may seek to survive through mergers and acquisitions. Managers must be able to use company resources efficiently to maintain and improve competitiveness and sustainable advantages. Managers' ability to make strategic decisions may determine whether a merger and acquisition is successful. This study aims to reveal the role of the acquirer's managerial ability in mergers and acquisitions based on short- and long-term performance as well as the type of M&A. Two metrics are used to assess short- and long-term performance: the market-to-book ratio (MTBR) as an indicator of operating performance and the buy-and-hold abnormal return (BHAR) as an indicator of stock return performance. The research sample consists of 153 M&A cases conducted by companies registered with the Business Competition Supervisory Commission in Indonesia between 2010 and 2017, and the performance till 2020. We used regression and difference analysis to analyze the data. We find that managerial ability has a positive impact on MTBR operating and BHAR stock performance. This result confirms that the higher ability of the acquirer's manager will ensure a successful M&A in the long run. Investors and potential investors might consider managerial ability in choosing investments in companies after an M&A. This study contributes to the M&A literature by examining the role of MA in the short- and long-term performance of acquiring firms following M&As in Indonesia.
ABSTRACT
COVID-19 (coronavirus disease 2019) is a new type of disease caused by a coronavirus, namely SARS-CoV-2, also known as the Corona virus. The Corona virus outbreak is causing concern all over the world, including in Indonesia. Many people became paranoid as a result of the virus's widespread spread, which was followed by reports of a number of deaths among victims. The spread of the Corona virus in Indonesia has had a negative impact on all aspects of life. Because of the density of settlements and the vastness of Indonesia's territory, this virus became out of control and spread quickly. The lack of socialization in dealing with this virus has an impact on the community's understanding of the importance of following health protocols. The goal of this research is to use data mining techniques to determine the pattern of symptoms caused by the Corona virus. The Association method is used in data mining. APRIORI is a popular association method that employs a high frequency pattem. This method is expected to be used to determine how likely it is that one case has the first symptom in addition to other symptoms. The parameters Support and Confidence support whether or not the association rules are required. After obtaining the results of the multiplication of Support and Confidence, the rule with the highest multiplication result will be discovered. The rule with the highest result is used as a rule to be applied in the next case, namely "If you have cough symptoms, they will be accompanied by fever symptoms with 90% support and 90% confidence".