PREDICTING THE ISSUANCE OF MODIFIED OPINION ON GOING CONCERN ISSUE BASED ON THE COMPANY FINANCIAL RATIOS DURING THE COVID- 19 CRISIS PERIOD

• Main Author: Hilmy Arsadipura, Fathi
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/63722
• Institution: Institut Teknologi Bandung

Under the reign of COVID-19 crisis which has brought with it various types of risk and a potentially fraudulent market. Exploring this uncharted territory full of uncertainty, knowing the company's current and future actual condition becomes incredibly important for capital markets. Since the audited financial statements contain fundamental information from the internal management, in particular financial ratios and the audit opinions, it will provide a signal to help assess the company's financial performance and offer information regarding whether or not the business is in the process of failing or succeeding as a going concern (business continuity). Recognizing the company's going concern status from the beginning will enable measures to be made to prevent occurrences that might lead to conditions of financial distress. The author is driven to achieve this and to see if the opinion issued is truly representative of the actual condition of the company, as the financial statement variables can be used to construct future financial predictions for the business various financial aspects. Trying to build an architecture of multiple desires formed on signalling theory and the conservatism paradigm, the purpose of this research is to see whether the financial ratios of companies that receive a modified opinion in relation to going concern uncertainty are not significantly better in statistical value than companies that do not receive a modified opinion in relation to going concern uncertainty during the COVID-19 pandemic crisis. By conducting a follow-up analysis of financial ratios, this research is intended to formulate an equation model of the prediction that a modified opinion will be issued, using financial ratios as the proxy variable. The financial ratios explored in this research are that of the liquidity ratio (as measured by the current ratio (CR) and cash ratio), the profitability ratio (as measured by the return on equity (ROE), return on assets (ROA) and net profit margin (NPM)), the leverage ratio (as measured by debt-to-equity ratio (DER) and debt to total assets (DTA)), the activity ratio (as measured by total asset turnover (ATO)) and the market ratio (as measured by price to earnings (PER)). There are a total of nine proxy variables. The population in this research are all manufacturing companies listed on the Indonesia Stock Exchange for the years 2018 to 2020. Through a purposive sampling method, the samples taken totalled to 352 manufacturing companies. Which contains nine financial ratios from nine companies that received a modified opinion that are compared to 343 companies that did not receive a modified opinion from 2018 to 2020. The period 2018 to 2020 was chosen to reflect the impact of the COVID-19 crisis and the economy that surrounds the study. The parametric t-test was performed to compare the financial ratios of the two sample groups. Logistic regression analysis was deployed as a statistical instrument to predict the issuance of a modified opinion using the SPSS version 25.0. This research has documented various conclusions, including the findings that three of the nine financial ratio proxy variables tested show significant differences and it is indeed considered to be in a better position. These ratios are that of liquidity ratio which are proxied by current ratio (CR) with significance level of 0.017 < 0.05 and cash ratio with significance level of 0.032 < 0.05 and the other is profitability ratio which is proxied by return on assets (ROA) with significance level of 0.037 < 0.05. This research also succeeded in producing a predictive model for the issuance of a modified opinion, namely: Prob = -3,152 + 0,099*Current Ratio – 0,327*Cash Ratio – 0,014*ROA. The internal validation of the research model demonstrated that the prediction model was accurate for 97.7%.