Mergers and acquisitions: does performance depend on managerial ability?

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.

Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model.

Demand forecasting is a scientific and methodical assessment of future demand for a critical product.The effective Demand Forecast Model (DFM) enables pharmaceutical companies to be successful in the global market. The purpose of this research paper is to validate various shallow and deep neural network methods for demand forecasting, with the aim of recommending sales and marketing strategies based on the trend/seasonal effects of eight different groups of pharmaceutical products with different characteristics. The root mean squared error (RMSE) is used as the predictive accuracy of DFMs. This study also found that the mean RMSE value of the shallow neural network-based DFMs was 6.27 for all drug categories, which was lower than deep neural network models. According to the findings, DFMs based on shallow neural networks can effectively estimate future demand for pharmaceutical products.