Implementasi Regresi-PLS Dalam Analisis Financial Distress

Abstract

There are several factors that affect Financial Distress, namely financial factors and non-financial factors. For financial factors, indicators that can be used include financial ratios such as profitability, liquidity, leverage, and activity that can be seen in financial reports. The purpose of this paper is to analyse the factors that influence Financial Distress in companies listed on the IDX in 2022 in the food and beverage sub-sector using partial least square (PLS) regression analysis. There are only 28 companies that fit the criteria for complete financial reports for the B&F subsector in 2022, which according to the central limit postulate will not meet the assumption of data normality if using multiple linear regression analysis so that the use of the PLS method can overcome assumption violations and the results of data analysis obtained a coefficient of determination of 0.885, which means that the contribution of the five predictor variables can explain the variance in the response variable, namely Financial Distress by 88.5 percent. The predictor variables that have the highest influence on the response variable are Return on Asset (ROA) and Debt to Asset (DTA) with VIP values of 1.345 and 1.226, respectively. By using PLS, violations in the fulfilment of classical linear regression assumptions can be overcome and the model can be used for the purpose of predicting financial distress. Keywords: Altman Z Score, PLS regression, financial distress.