Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9653
Title: A Study of Financial Distress in the Hotel Industry: Evidence from Listed Hotels on the Colombo Stock Exchange
Authors: Mithila, G.
Kengatharan, L.
Keywords: Financial distress prediction;Financial ratios;Forecasting model;Listed hotels;Sri Lanka tourism industry
Issue Date: 2023
Publisher: Sri Lanka Finance Association.
Abstract: The study aims to identify and analyze key financial indicators and ratios that can serve as reliable predictors of potential financial distress and to determine the best model for predicting the occurrence of financial distress among listed hotel companies in Sri Lanka. The research is carried out on a quantitative basis with a positive paradigm. Out of 36 listed hotels in Sri Lanka, 21 were selected based on the random sampling method, and the time spanning period is 2013 to 2022, with the observation of 201. Further, 18 variables are created under the profitability ratios, leverage ratios, liquidity ratios, market ratios, activity ratios, age, and company size and tested using a panel logistic regression model. Based on the Hausman test, the random effect model is considered for interpretation. The analysis revealed that Operating Profit Margin, Return on Assets, and Operating Cash Flow to Total assets exhibited a significant negative relationship with the probability of financial distress occurrence. In contrast, Debt to Equity exhibited a significant positive relationship with financial distress. The random model's Wald chi-square (χ^2) value is 105.97, with a probability of 0.000, highlighting their importance in predicting financial distress. The overall accuracy rate of the model is 88.6%. This model suggests that the probability of financial distress will increase when profit margins are low, assets are less effective in generating income, limited cash flow generation, and potential liquidity challenges. Hence, investors, bankers, and managers can use this model to predict the probability of financial distress when making financial decisions.
URI: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/9653
Appears in Collections:Financial Management

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