Analysis of Financial Statements to Measure Performance at PT. Kino Indonesia, Tbk 2015-2019
Keywords:Financial Performance, Liquidity Ratio, Solvency, Profitability
This study aims to analyze the financial performance of PT. Kino Indonesia Tbk from 2015-2019 by calculating financial ratios. The type of data used is quantitative data. The data collection methods used were documentation and literature study which were analyzed using financial ratios, namely liquidity ratios, solvency ratios, and profitability ratios. The results show that the liquidity ratio, namely the current ratio, is not yet effective because the company has not been able to pay current debts using assets, while the quick ratio is declared effective because the company is able to pay short-term debt with current assets without taking inventory into account. The solvency ratio, namely the debt to asset ratio, is declared ineffective because the company has not been able to pay all of its debts using assets and the debt to equity ratio is effective because the company is able to pay all its debts using all equity. Profitability ratios, namely return on assets and return on equity, fluctuate every year because the company has not been able to obtain maximum profit.
M. Werner, M. Wiese, and A. Maas, “Embedding process mining into financial statement audits,” Int. J. Account. Inf. Syst., vol. 41, p. 100514, 2021, doi: 10.1016/j.accinf.2021.100514.
K. Maka, S. Pazhanirajan, and S. Mallapur, “Selection of most significant variables to detect fraud in financial statements,” Mater. Today Proc., no. xxxx, 2020, doi: 10.1016/j.matpr.2020.09.613.
S. Dhole, L. Liu, G. J. Lobo, and S. Mishra, “Economic policy uncertainty and financial statement comparability,” J. Account. Public Policy, vol. 40, no. 1, p. 106800, 2021, doi: 10.1016/j.jaccpubpol.2020.106800.
A. M. Ali, A. Saputro, M. F. Nurani, and M. N. Hayatie, “Financial Performance Analysis using Economic Value Added (EVA) Method at PT. Darma Henwa Tbk. Period 2017-2019,” Int. J. Res. Vocat. Stud., vol. 1, no. 1, pp. 51–55, 2022, doi: 10.53893/ijrvocas.v1i1.79.
T. Wijayati, M. Zein, M. Ghalih, and M. Fajar, “An Empirical Case Study of DEMATEL Method Focus on Calculating the Students Organization Improvement in POLITALA,” Int. J. Res. Vocat. Stud., vol. 1, no. 1, pp. 43–50, 2021, doi: 10.53893/ijrvocas.v1i1.39.
J. Wyrobek, “Application of machine learning models and artificial intelligence to analyze annual financial statements to identify companies with unfair corporate culture,” Procedia Comput. Sci., vol. 176, pp. 3037–3046, 2020, doi: 10.1016/j.procs.2020.09.335.
M. Tumpach, Z. Juhászová, Z. Kubaščíková, and P. Krišková, “Datasets of impact of the first-time adoption of IFRS 16 in the financial statements of Slovak compulsory IFRS adopters,” Data Br., vol. 36, 2021, doi: 10.1016/j.dib.2021.106996.
M. Khofi, N. Amelia, and Karolina, “Financial Management Process of Pesantren Nurul Muhibbin Tanah Laut,” Int. J. Res. Vocat. Stud., vol. 1, no. 3, pp. 26–31, 2021, doi: 10.53893/ijrvocas.v1i3.38.
C. H. Cheng, Y. F. Kao, and H. P. Lin, “A financial statement fraud model based on synthesized attribute selection and a dataset with missing values and imbalanced classes,” Appl. Soft Comput., vol. 108, p. 107487, 2021, doi: 10.1016/j.asoc.2021.107487.
P. Leonov, A. Kozhina, E. Leonova, M. Epifanov, and A. Sviridenko, “Visual analysis in identifying a typical indicators of financial statements as an element of artificial intelligence technology in audit,” Procedia Comput. Sci., vol. 169, no. 2019, pp. 710–714, 2020, doi: 10.1016/j.procs.2020.02.174.
How to Cite
Copyright (c) 2022 Yuli Fitriyani, Mufrida Zein, Radna Nurmalina, Mega Putri Diyani
This work is licensed under a Creative Commons Attribution 4.0 International License.