Through a Hazy Lens: Financial Distress to Firm Value Effect being Obscured
Description
The research hypothesis examines the relationship between financial distress, earnings management policies, the potential for financial reporting fraud, and their combined impact on a company's value. I posited that companies experiencing financial distress are more likely to employ earnings management strategies and potentially engage in financial reporting fraud to mask their true financial health, which, in turn, negatively affects their overall value in the market. The data was primarily sourced from Bloomberg and cross-referenced with the Indonesian Stock Exchange (IDX) classification to identify companies in the Cyclical and Non-Cyclical Customer Goods and Services sectors. Initially, there were 259 companies in these sectors, but 147 were excluded due to missing data for the period from 2016 to 2022. The remaining 112 companies were further categorized into those engaged in manufacturing activities and those that were not based on information from the Directories of Indonesian Manufacturing Industry and IDNFinancial. Key financial variables were collected, including Altman's Z-Score, Discretionary Accruals, Beneish's M-Score, and Tobin's Q ratio for the years 2017 to 2022. Altman's Z-Score and its Distress determinants were calculated differently for manufacturing and non-manufacturing companies. Discretionary Accruals were estimated using the Modified Jones Model based on Costa and Soares (2021). Outliers were identified and removed using statistical methods such as Leverage tests, Studentized Residual analysis, and Cook's Distance assessment. This process led to the elimination of 8 companies from the dataset. The findings from this study generally support previous research on the negative impact of financial distress on a company's value. Meanwhile, the hypothesis regarding the relationship between financial distress and earnings management policies aligns with prior research, albeit at a lower level of confidence than commonly used, suggesting that companies experiencing financial distress tend to implement income-decreasing earnings management policies. Another hypothesis addressing the link between financial distress and the potential for financial reporting fraud yields inconclusive results due to a lack of statistical significance. However, it provides a new perspective to complement previous research. Surprisingly, the study found that companies not indicating financial distress may be associated with a potential for financial reporting fraud. This is because financial reporting fraud has the potential to obscure the distress conditions experienced by the company. These findings shed light on the complexities of financial distress, earnings management, and the potential for fraud in financial reporting, suggesting that these relationships are nuanced and may vary in different contexts. Further research and analysis are needed to better understand the underlying mechanisms and implications for stakeholders and investors.
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I obtained financial data from Bloomberg for companies categorized as either Cyclical or Non-Cyclical Customer Goods and Services, cross-referencing with the Indonesian Stock Exchange (IDX) classification. Initially, there were 259 companies in these categories, but 147 were excluded due to missing data for the period from 2016 to 2022. Among the remaining 112 companies, I further distinguished those engaged in manufacturing activities using information from the Directories of Indonesian Manufacturing Industry (published by the Indonesian National Statistical Agency) and IDNFinancial. I collected key variables for my research, including Altman's Z-Score, Discretionary Accruals, Beneish's M-Score, and Tobin's Q ratio for the years 2017 to 2022. Notably, Altman's Z-Score and its Distress determinants were calculated differently for companies with manufacturing activities compared to those without. For estimating Discretionary Accruals, I employed the Modified Jones Model following the approach by Costa and Soares (2021). Subsequently, I performed data cleaning to eliminate outliers, employing various techniques such as Leverage tests, Studentized Residual analysis, and Cook's Distance assessment. This process led to the removal of 8 companies from the dataset. In the end, my research was based on a dataset comprising 624 observations from 104 companies.