Corporate in Financial Distress and Determinant Analysis of Successful Financial Turnaround - Ardy Primawan; Nanny Dewi Tanzil; Prima Yusi Sari (Jurnal Akuntansi, 2024)
Corporate financial distresses and turnarounds has always been relevant on business literatures because we have seen more than enough corporate bankruptcies over the past decades. Financial distress is a condition of declining financial performance, earlier phase prior to companies experiencing bankruptcy or liquidation. The response to this condition ranges from a denial of the problem, to reducing the scale and scope of operations, all the way to the top change of management and dissolution of corporation. With the complexities of issues and implications associated with financial distresses and the recoveries attempted by corporations, the ability to formulate appropriate strategic responses is becoming very much important for management, researchers and practitioners. This research is focusing on the determinant analysis of multiple organizational factors which are expense retrenchment, profitability, free assets, size, assets retrenchment and leverage on successful turnaround of manufacturing companies listed in Indonesia Stock Exchange (IDX) in research period 2015 to 2019. Data used in this research are secondary ones which obtained from Indonesian Capital Market Directory (ICMD). Financial data from 2015 to 2019 are used to determine financial distresses utilizing Altman’s Z-Score model, and data from 2016 to 2018 are processed as the independent variables. This research takes 125 sample of manufacturing companies which after screening come down to 36 companies in Non-Turnaround (NT) group and 8 companies in Turnaround (T) group. The analysis models used in this study include firm-specific variables from 2016 to 2018 to test the hypotheses. At the 5% level of significance, the logistic regression test showed that the research variables of profitability, free assets and leverage considerably affected the likelihood of a successful financial turnaround, and the prediction accuracy was 87%.