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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.

This study seeks to evaluate the stability of financial ratios across industry and over time. The sample comprises companies listed on the Stock Exchange of Singapore from 1980 to 1991 over six industry groupings. A set of 29 most commonly used ratios was selected for the study. Descriptive statistics, factor analysis and analysis of variance were performed. From the factor analysis results, eight representative ratios were identified. Analysis of variance and multiple comparisons were subsequently performed for each representative ratio to test if it is significantly different across industry and over time. The results indicate that financial ratio averages of the various industries are significantly different. This implies that the appropriate benchmark for evaluating company performance and position should be industry‐specific instead of economy based. Also, five of the representative ratios are significantly different over time and not all the industrial averages move consistently over time (i.e., interaction effects of industry and time exist). Thus, industry averages are not necessarily appropriate benchmarks for setting and evaluating performance through time.

The application of factor analysis to the area of financial ratio analysis was pioneered by Pinches, Mingo, and Caruthers (1973) in a study of U.S. industrial firms. During the last two decades numerous studies have applied the technique as a means of eliminating redundancy among financial ratios and/or reducing the number of ratios selected as a basis for further investigation to a limited but crucial subset. It is observed that all studies reported were on the manufacturing and retailing sectors. The international commercial airline sector was chosen as the subject of the present research in an attempt to study the factor groupings in a sector whose financial characteristics differ from manufacturing or retailing. Results show that factor categorization reflects the sector's financial characteristics. The study also draws conclusions on some observed differences between the empirical and theoretical ratio classification observed in the literature. The study lends support to the conclusion that factor analysis provides a useful means by which to develop and test the theoretical structure and grouping of financial ratios.

Sees the objective of teaching financial management to be to help managers and potential managers to make sensible investment and financing decisions. Acknowledges that financial theory teaches that investment and financing decisions should be based on cash flow and risk. Provides information on payback period; return on capital employed, earnings per share effect, working capital, profit planning, standard costing, financial statement planning and ratio analysis. Seeks to combine the practical rules of thumb of the traditionalists with the ideas of the financial theorists to form a balanced approach to practical financial management for MBA students, financial managers and undergraduates.

This paper seeks to model and evaluate the comparative operating efficiency of banks using a non‐parametric methodology known as the data envelopment analysis (DEA). The paper adopts a modified version of DEA in which no inputs are specified. The only variables considered are the financial ratios. The results obtained suggest that the majority of banks investigated are fairly inefficient over the period 1991‐94. In addition to calculating efficiency scores for all banks in the sample, the study results revealed the composite reference set and their shadow prices, major determinants of banks’ relative performance, and the target financial ratios.

This paper aims to examine the significance and performance analysis of the Malaysian Real Estate Investment Trust (M-REIT) from 2014 to 2018.

Performance analysis is done through operating ratio (current ratio), leverage ratio (debt ratio) and efficiency ratio (return on asset and return on equity).

M-REIT has been ranked 27th globally and 7th in Asia Pacific REIT market, implying the significance of the market. The trend of market capitalisation of M-REIT had flourished from 2014 to 2017 but declined in 2018. The total assets of M-REIT have been seen thriving over the years with both Islamic REIT market capitalisation and total assets showing improvements throughout the year. From the viewpoint of efficiency ratios of ROA and ROE, Islamic REIT is deemed more favourable to investors than conventional REITs, implying the high receptive of Islamic REITs.

In terms of efficiency of operation, it is evident that several sectors of REITs may be at risk of liquidity due to the decline in current ratio from 2014 to 2018, as current ratio of less than 1 is considered a red flag.

Performance analysis on the performance of each sector as the outcome of the research could ease investors’ decision-making as whether it can be considered as one of the viable investments available in the market.

Ratio analysis is an excellent way of looking into a firm’s financial status. However, in performing the ratio analysis, an often underemphasized task is the selection of ratios for use. In a multi‐criteria decision‐making framework, poor quality of criteria selection, i.e. financial ratios, will consequently undermine the quality of evaluation. Aims at establishing a systematic approach for finding critical financial ratios to assist in financial analysis for the construction industry. As each sector of the industry is intrinsically unique, the set of critical ratios for different sectors will certainly vary. However, the approach for finding useful financial ratios will not distinguish itself with respect to the concerned sector rather than the purpose of analysis. The proposed approach incorporates the concept of multi‐criteria decision making and the entropy method. The approach is demonstrated in a case study in which major property development firms in Taiwan are evaluated.

This paper examines the profitability and performance measurement of U.S. regional banks during the period 1994–2011, using the GMM estimator technique. Our study extends prior research by including several factors not previously considered using U.S. data.

We use bank-specific, industry-specific, and macroeconomic determinants of profitability contemporaneous with our performance indicators. We follow the accounting fundamental analysis path in explaining the bank performance.

Among the performance measures, the efficiency ratio and provisions for credit losses are negatively and equity scaled by assets is positively related to profitability. However, these relationships either reverse (efficiency ratio and provisions for credit losses) or become insignificant (equity scaled by assets) when the target becomes change in profitability. The level of nonperforming assets is negatively related to profitability across all measures of profitability used. Macroeconomic variables are largely unrelated to profitability during the year they are measured. However, they have a significant relationship with earnings change measures, suggesting they have a lagged effect on profitability. The slope of the yield curve is especially strong in this regard.

We use our determinants to model changes in bank profitability one year ahead, in addition to including several factors not previously considered, using the predictive focus of the fundamental analysis research.