(Ed. Note: This article has been updated.)For any financial professional, it is important to know how to effectively analyze the financial statements of a firm. This requires an understanding of three key areas:
Although often challenging, financial professionals must make reasonable assumptions about the future of the firm (and its industry) and determine how these assumptions will impact both the cash flows and the funding. This often takes the form of pro-forma financial statements, based on techniques such as the percent of sales approach.
Financial Statement Analysis
It is a sober assessment of the profession; throughout the remainder of the book, Fridson and Alvarez repeatedly demonstrate that financial statements often conceal as much information as they reveal. And to complicate matters, the authors show that much of what transpires in the industry is perfectly legal, even when it seems that it should not be.
Obviously, items that recur annually, or even biannually, are not extraordinary, nonrecurring, or unusual. Analysts need to be aware of these situations and be prepared to distinguish between truly one-time events and those that are merely designated as such by the statement issuer.
Taking a harder line may not produce fuller disclosure for investors but merely mean sacrificing the auditing contract to another firm with a more accommodating policy. Given the observed gap between theory and practice in financial reporting, users of financial statements must provide themselves with an additional layer of protection through tough scrutiny of the numbers.
My only reservation about recommending this book is that sophisticated security analysts may find it too elementary. Although I am a CFA charterholder, my background is not in accounting or financial statement analysis, and so I found the book to be a veritable treasure trove of interesting insights. A seasoned analyst, however, will undoubtedly be more familiar with the issues the authors discuss and thus may not find the book quite as valuable. But for anyone with a passing interest in the subject, or for new analysts still learning the trade, Financial Statement Analysis is an amazing resource.
The Chinese Telecoms Industry has been rapidly growing over the years since 2001. An analysis of financial performance of the three giants in this industry is very important. However, it is difficult to know how many ratios can be used best with little information loss. The paper aims to discuss this issue.
A total of 18 financial ratios were calculated based on the financial statements for three companies, namely, China Mobile, China Unicom and China Telecom for a period of 17 years. A principal component analysis was run to come up with variables with significance value above 0.5 from each component.
At the end, the authors conclude how financial performance can be analysed using 12 ratios instead of the costly analysis of too many ratios that may be complex to interpret. The results also showed that ratios are all related as they come from the same statements, hence, the authors can use a few to represent the rest with limited loss of information.
This study will help different stakeholders who are interested in the financial performance of each company by giving them a shorter way to analyse performance. It will also assist those who do financial reporting on picking the ratios which matter in reflecting the performance of their companies. The use of PCA gives unbiased ratios that are most significant in assessing performance.
Mbona, R.M. and Yusheng, K. (2019), "Financial statement analysis: Principal component analysis (PCA) approach case study on China telecoms industry", Asian Journal of Accounting Research, Vol. 4 No. 2, pp. 233-245. -05-2019-0037
The Chinese Telecom industry was heavily controlled by the government through the ownership and formation of policies on investment, areas of operations and tariffs charged. In late 2001, China successfully joined the World Trade Organization and this meant that it had to adjust some of its policies including regulations on players in its telecoms industry. Even though they opened the doors for foreigners, this industry remained monopolised by the three state-owned companies who have been competing for the highest market share, best financial performance and top innovation into new technologies including 5G network. Over the past years, they have cemented their dominance by taking over the other small players in the industry that were state owned as well. This study, thus, seeks to assess the financial performance of this industry since the doors for open competition were opened in this sector.
Even though ratios were seen as less significant due to the introduction of more sophisticated statistical analysis tools, authors still believe that they are still a useful tool in measuring performance. For example, a study which was done by Altman (1968) proved how ratios are still useful in prediction of bankruptcy using the case of manufacturing firms. Other studies on proving the importance and usefulness of ratios by Lewellen (2004) and Floros et al. (2009) found that investment ratios are useful in predicting market values of shares.
A lot of other case studies have been done on financial performance analysis using ratios (Eversull and Rotan, 1997; Collier et al., 2010; Hossan and Habib, 2010; Grubman, 2010; Bhargava, 2017). For example Al-Jafari and Al Samman (2015) investigated the determinants of profitability for industrial firms in Oman. By utilising ordinary least squares (OLS) model on seven ratios, they drew up conclusions on the relationship between profitability ratios and other calculated non-profitability ratios. They found that there is a positive significant relationship between profitability, firm size, growth, fixed assets and working capital. Additionally, they also conclude that management efficiency on these large firms gives them better profit returns.
While Burja (2011) only focussed on the micro or internal environment in his regression analysis of financial performance, Allen et al. (2011) carried out an investigation on both internal and external environment to see how it impacts the profitability of the firm. This was a distinguished study as it included both internal and external factors in the regression analysis.
According to Buse et al. (2010) economic rate of return (ERR) is an important ratio in financial statement analysis because they considered it as an indicator of the economic performance of a company. In their study, they took ERR as a comprehensive ratio that looks at the organisation return and contribution with consideration of both internal and external factors affecting the business.
While the literature reviewed covers a number of case studies on financial statements using ratios, some gaps exist. First, we have found that none has so far focussed on the Chinese telecoms especially the period after the Chinese Government opened its doors to the world to invest in their industries. Second, few research studies have used PCA to find out which ratios give best performance analysis with least loss of data from amongst the pool of all ratios. From the review of the past studies, we realized that different ratios were used and some are correlated because they are all from the same statements. A similar study was done by Taylor (1986) which focussed on the Australian firms. The study did not point to a specific industry; hence, with differences in industries the model might not be a one-size-fits-all especially given also the differences in the operational environment between China and Australia. Third, using PCA allows the use of at least 18 ratios reducing subjectivity effects on which ratios should be used for further analysis which include regression analysis on performance. Finally, as shown in our correlation matric we can see that all ratios are related which means that there is no independency to carry out the regression. This relationship comes from the fact that these ratios use data from the same statement. By applying PCA we create new independent variables that allow for effective further analysis with even lesser variables.
In previous studies on relationship analysis for financial statements and financial ratios, two models have mainly been used. The first one is the panel OLS regression model which has been applied by a number of accounting articles on financial performance and in most literature less than ten variables are used (Al-Jafari and Al Samman, 2015; Jakob, 2017; Burja, 2011). The second model has been the multiple regression which was adopted by other researchers (Bhunia et al., 2011; Kofi-Akrofi, 2013; Buse et al., 2010).
The data used are from 2001 to 2017. This is the period that has financial statements available on the websites of the companies. Statistical Package for Social Sciences 20 was then used for the analysis of the financial statement with Microsoft Excel 13 used in the calculation of the final coefficients for each principal component. Standardized data were used instead of the original data as the ratios have different units of measurement. If the raw data are used, then a PCA will tend to give more emphasis to those variables that have higher variances than those variables that have lower variances meaning results will depend on the unit of measurement for each variable and since these data do not have that we used the standardized values.
In this section, we first examined the components that have been extracted and the new independent variables according to our first objectives. Then second, we looked at the ratio mix that can be used for analysis of the telecoms industry performance.
Table VI shows us the mix of ratios that PCA extract to give significant analysis on the performance of the telecoms industry. The 12 ratios represent the mix that has important ratios from the classes that we have in general. The performance of the telecoms industry is measured mainly with four classes of ratios. 2ff7e9595c
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