IER-08-04-06-pp083--2121-Wojtowicz 2022, Vol. 8, No. 4 10.15678/IER.2022.0804.06 Questing benchmarks for the current ratio: An analysis of the Warsaw Stock Exchange firms Piotr Wójtowicz A B S T R A C T Objective: The first objective of the article is to assess the benchmarks for the current ratio commonly pro- vided in the accounting and financial analysis literature. The minor objective is to arrange a research method- ology permitting the accomplishment of the first objective. Research Design & Methods: The paper, apart from the literature review and its critique, presents the results of descriptive, quantitative research. The research sample consists of 5 148 firm-years. Data were retrieved from Worldscope Database via Refinitiv Eikon for domestic, going concern, non-financial companies listed on the Warsaw Stock Exchange from 2005 to 2021. For comparison, the U.S.-related data was retrieved from Internet resources as ready-made ratios. Methods used in the analysis include descriptive statistics, paramet- ric and nonparametric ANOVA, confidence intervals, and linear and parabolic trend analysis. Findings: The tests show that identifying a benchmark for the current ratio is problematic. Benchmarks vary between countries and industries; universal standards do not exist. The benchmark for the current ratio com- monly suggested, 1.2-2.0, may be used only as a rough evaluation of the desired value. The findings indicate that the acceptable range for CR is much broader, from 1.1 (the first quartile) to 2.3 (the third quartile) for the total sample. Moreover, the SIC division's quartiles vary from 0.7 to 1.2 (the first) and from 1.6 to 3.6 (the third). Statistical tests indicate that benchmarks do not vary annually. However, the distribution's median and the third quartile change slowly over time toward higher values. The Covid-19 pandemic resulted in a substan- tial increase. On the contrary, the first quartile of about 1 remains relatively stable over time, indicating the reasonable lower bound for CR. The mean of the distribution is useless as a benchmark because of its sensi- tivity to outliers. Various techniques must be used to assess the benchmark. Implications & Recommendations: Since there are material between-industry and between-country differ- ences in benchmarks, analysts and investors should be very sensitive to the standards suggested in the litera- ture and interpret them cautiously. Variability over time exists but is low unless a shock in the economy appears. Contribution & Value Added: The main contribution of the paper is empirical verification of the bench- marks for the current ratio provided in the literature. Tests show that suggested benchmarks are not uni- versal. Seeking a benchmark, an analyst must compare the firm's financial standing with other firms from the same country, industry, and period. Article type: research article Keywords: liquidity ratios; current ratio; financial analysis; liquidity benchmark; Warsaw Stock Ex- change JEL codes: M41, G15 Received: 29 September 2022 Revised: 3 November 2022 Accepted: 15 November 2022 Suggested citation: Wójtowicz, P. (2022). Questing benchmarks for the current ratio: An analysis of the Warsaw Stock Exchange firms. International Entrepreneurship Review, 8(4), 83-97. https://doi.org/10.15678/IER.2022.0804.06 INTRODUCTION The financial health of a firm can be assessed by comparison with other entities in the industry, change in the value of some vital markers, or assessment of the risk of bankruptcy. The industry-based com- parison requires a high-quality baseline. The following article is the quest for the benchmark for the current ratio. This ratio measures a company's ability to pay short-term obligations due within one International Entrepreneurship Review RI E 84 | Piotr Wójtowicz year. It is commonly believed that a current ratio that aligns with the industry average or is slightly higher is acceptable. On the other hand, a current ratio that is lower than the industry average may indicate a higher risk of bankruptcy. If a firm has a very high current ratio compared with its peer group, management may not be using its assets efficiently (Investopedia, 2022). I follow well-regarded In- vestopedia.com; however, the point of view presented here is widespread. Unfortunately, these gen- erally accepted rules are not necessarily valid. In other words, I am eager to challenge them. How can the financial statement reader be confident that the industry average is the proper bench- mark? The average of the distribution is susceptible to outliers; hence the benchmark may be biased towards the right tail of the distribution. What is the exact meaning of the slightly higher interval? Are differences between industries' current ratios significant? What about industries with high current as- sets turnover resulting in a low value of those assets and an extended maturity of liabilities resulting in an increased amount? In other words, is the current ratio below one always the signal of the risk of distress? In the next section, these issues are finally formulated in the form of hypotheses. I only concentrate on the issues around benchmarks for the current ratio. I exclude the quick ratio and cash ratio from the analysis. The main reason for this is the lack of an unequivocal answer to the question about the research methodology permitting identification of the benchmark. Hence, there are two intertwined objectives of the article. First, I investigate the benchmark for the current ratio, the commonly used liquidity ratio. Second, struggling with that, I try to arrange a research methodol- ogy permitting answers to the vital question. The novelty of my article consists of the empirical verification of the benchmark for the current ratio provided in the literature. I empirically test whether the use of a country-based benchmark is the correct assessment procedure. Finally, I use various statistical methods to estimate benchmarks and identify patterns they follow. The use of a variety of techniques results in increased robustness of findings. The remainder of the article is organized as follows. The next section contains a literature review and develops the hypotheses. It is followed by research methodology, including the sample selection process and the description of the data. Then, results and discussion are presented. The last section concludes the study and explains the limitations and avenues for further research. LITERATURE REVIEW To run a financial analysis and compare the findings to other firms in order to assess a company's performance, an analyst requires a benchmark, i.e., a standard, or a baseline, that is used for com- parative purposes. Revenues, expenses, profits, and liquidity are the most frequent areas to be con- sidered. However, the answer to the simple question "How is a business doing?" is not easy because of the need for well-established benchmarks. Such analysis will (1) compare the performance of the company over time and (2) help understand how the company's financial performance stacks up against the industry. The former comparison is easy because the data is usually at the analyst's dis- posal. The latter requires high-quality benchmarks. Moreover, managers use liquidity ratios to en- sure business goals (Kochalski & Łuczak-Trąpczyńska, 2017). Surprisingly, internationally recognized accounting and finance literature concerning liquidity benchmarking is very scant. To my knowledge, there are no prior international research papers inves- tigating the benchmarks for the current ratio. Usually, the articles concentrate on the role or im- portance of liquidity in asset pricing (Keene & Peterson, 2007), liquidity monitoring (Ruozi & Ferrari, 2013), the liquidity-profitability tradeoff (Eljelly, 2004), and, recently, the uniqueness of liquidity man- agement during the covid-19 pandemic (Almeida, 2021). Liquidity is perceived a one of the factors ensuring sustainable growth (Pera, 2017). On the other hand, I received almost 1 million links to an- swer the question about the proper value for a Current Ratio asked on google.com. Even a glimpse of this content, not mentioning deep investigation, leads to the conclusion that the appropriate answer to this question arguably does not exist. However, some researchers attempted to do that. Analysts typically concentrate on five main groups of financial ratios: profitability, liquidity, sol- vency, efficiency/activity, and capital market-related ratios. Benchmarks for some of them are re- spectively easy to identify. For example, in the case of profitability ratios (Return on Equity and Re- Questing benchmarks for the current ratio. An analysis of the Warsaw Stock Exchange firms | 85 turn on Assets), the general logic behind the interpretation is simple: the higher, the better. In the same vein, generally, the lower the Assets Turnover Ratio in days, the better efficiency with which a company uses its assets to produce sales. The real challenge is the benchmark for ratios, which are supposed to vary between the lower and upper limits. Typical examples are the Current Ratio and Debt-to-Equity Ratio. The strengths and weaknesses of the Current Ratio are discussed in detail. However, the Current Ratio considered low does not necessarily indicate liquidity problems; a high ratio does not have to be financially sound (Fleming, 1986). In this article, I concentrate on benchmarks for liquidity ratios because they focus on a company's ability to meet short-term obligations. Liquidity ratios determine a debtor's ability to pay off current debt obligations without raising external capital. They determine a company's ability to cover short- term obligations and cash flows, while solvency ratios concern a longer-term ability to pay ongoing debts. Failure in the former area is hazardous because it may quickly result in the company's default or bankruptcy. In other words, a company may continue even for several years as a going concern making losses. It is hardly imaginable, however, that creditors would accept a lack of cash flows for so long period. Therefore, they will undoubtedly claim bankruptcy proceedings. Liquidity ratios include the Current Ratio (CR), Quick Ratio, and Cash Ratio. They are commonly known; however, all of them are defined in the next section of the article. One of the most distinguished Polish researchers and practitioners argues that the Current Ratio should range from 1.2 to 2.0 according to standards. They also suggest that the Quick Ratio should be close to or above 1 (Sierpińska & Jachna, 2004). But unfortunately, the authors do not provide any reference to literature or the mentioned standards. Therefore, the reader must trust them or start their investigation. Moreover, the international use of the suggested benchmark is doubtful. For in- stance, the ideal current ratio in India is suggested to be 2 (Chandra, 2008; Pandey, 2010). In Poland, a helpful hand is given by Maślanka (2019), providing reference to a bunch of articles and monographs on liquidity ratios, specifically on the suggested proper values of CR and QR, respec- tively. These findings, expanded by other references, are summarized in Table 1. Table 1. Benchmarks for CR and QR suggested by various authors Ratio / Author(s) Suggested value of CR Suggested value of QR Czekaj & Dresler (2002) Wędzki (2002) 1.2-2.0 1.0-1.2 Bednarski (1997) Kowalak (2022) Pomykalska & Pomykalski (2007) Sierpińska & Jachna (2006) Waśniewski & Skoczylas (2002) 1.2-2.0 About 1.0 Gołaś & Witczyk (2010) 1.2-2.4 About 1.0 Olzacka & Pałczyńska-Gościniak (2004) 1.3-2.0 – Bednarski (2007) Michalski (2005) Nowak (2008) 1.5-2.0 Above 1.0 Nowak (2002) 1.5-2.0 1.2-1.5 Rogowski & Lipski (2014) 1.5-2.0 1.0-1.2 Ostaszewski (2013) 1.6-1.9 0.9-1.0 Bień (2018) About 2.0 – Świderska (2003) At least 2.0 – Kreczmańska-Gigol (2020) 1.0-2.0 0.7-1.5 Gołaszewski et al. (2001) 1.0-1.5 Above 1.0 Source: own study based on Maślanka (2019) and the references quoted in the table. Furthermore, some authors provide benchmarks for the Cash Ratio equal to 0.25 (Kowalak, 2022) or 0.2 (Dębski, 2005; Świderska, 2003). 86 | Piotr Wójtowicz Table 1 shows that generally accepted benchmarks for CR do not exist. The standard for CR ranges from 1.0 to 2.4, with the most frequent range from 1.2 to 2.0. Similarly, benchmarks for QR range from 0.7 to 1.5, with the mode above 1.0. Furthermore, the authors often quote one another, so the reader cannot be sure whether the benchmarks are based on sample data or represent the authors' beliefs and professional experience. Next, an interesting conclusion can be drawn from the comparison of Table 1 with the U.S.-based benchmark. In the latter case, a CR of 1.5 to 3 is often considered good (BusinessInsider, 2022) but with no generally accepted upper and lower limits. Likely there is no one perfect benchmark (Sierpińska & Jachna, 2004). Therefore, the "good" value for one firm is not neces- sarily good for the other, and industry benchmarks must be considered. Last but not least, when eval- uating a company's liquidity, the Current Ratio alone doesn't determine whether it's a good investment or not. It is, therefore, essential to consider other financial ratios in the analysis. There are many factors shaping the economic behavior of both individuals and organizations. The impact of formal (e.g., tax law, interest rates, financial market supervision) and informal (e.g., religion, culture) institutions may establish entirely different economic conditions and risk factors. Furthermore, the impact of culture is conditional on the level of regulation and monitoring in a country (Kanagaretnam et al., 2011). For that reason, the existence of international benchmarks, even for a specific industry, is hardly possible. To shed light on the expected between-country dif- ferences, I compare the distribution of the CR in Poland versus the U.S. The distribution of yearly values of financial ratios by industry is analyzed since 2002 by the Finan- cial Analysis Committee of the Scientific Council of the Accountants Association in Poland (Komisja ds. Analizy Finansowej Rady Naukowej Stowarzyszenia Księgowych w Polsce). The Committee publishes its findings through publicly available reports (Rachunkowość, 2022a). The sample selection process is straightforward. The company included must (Dudycz & Skoczylas, 2021): − prepare financial statements in line with Polish Accounting Act, − provide financial statements for two consecutive years for comparison, − have a fiscal year equal to 12 months, − have a nonnegative value of owner's equity. For that reason, the sample comprises a considerable number of companies. For example, the 2019 sample consisted of 157 022 companies (Dudycz & Skoczylas, 2021), ranging from small sole proprietor- ships to blue chips listed on the Warsaw Stock Exchange. Furthermore, the number of companies in- cluded increases year by year. In 2012, the sample consisted of only 37 624 companies (Dudycz & Skoczy- las, 2014). The usefulness of these reports for practitioners and researchers in Poland is certainly high. However, the diversity of the sample may result in some limitations of the results. The size of the business and its legal form result in different institutional conditions, so finally, firms are exposed to various risks influencing the ratios. The breakdown by industry follows the Classification of Business Activities in Po- land (Polska Klasyfikacja Działalności – PKD); hence an international comparison of these ratios is hardly possible. However, intra-industry trend analysis and between-industry comparison remain feasible. An investigation of the contents of huge tables published by the Committee shows that the average and median values of the ratios vary greatly. Table 2 shows selected examples. Means are much higher than medians; hence, the distributions are asymmetric, with substantial outliers. Fur- thermore, there are material differences between industries’ ratios. A comparison of the results presented in Table 1 and Table 2 suggests that the authors quoted in Table 1 built their suggestions on medians. Table 2 shows respectively high medians, suggesting that the most frequent range for CR 1.2-2.0 may not work in practice. The same issue refers to QR; however, medians for the 10 – Production of food and 47 – Retail (cars excluded) are very close to the benchmark. Presumably, the most surprising are medians for CHR, which are much higher than the suggested value equal to or just above 0.2. I conjecture that in the Polish context, the benchmarks for liquidity ratios are different than the ones suggested in the literature. Furthermore, benchmarks may vary over time because they reflect changing risk of business activity and vary between industries. Finally, the average value of liquidity ratios is different from the median. Questing benchmarks for the current ratio. An analysis of the Warsaw Stock Exchange firms | 87 Table 2. Arithmetic means and medians for liquidity ratios in selected industries in 2019 Ratio / Industry Current Ratio CR Quick Ratio QR Cash Ratio CHR 01 – Agriculture production livestock and animal specialties Arithmetic Mean 5.50 3.57 1.55 Median 3.30 2.01 0.59 03 – Fishing Arithmetic Mean 5.18 4.25 2.41 Median 3.80 2.36 1.19 10 – Production of food Arithmetic Mean 2.19 1.63 0.60 Median 1.64 1.16 0.25 49 – Land transportation and pipelines Arithmetic Mean 1.92 1.77 0.51 Median 1.54 1.44 0.27 47 – Retail (cars excluded) Arithmetic Mean 2.28 1.30 0.66 Median 1.76 0.99 0.37 Source: (Dudycz & Skoczylas, 2021). The hypotheses address these issues as follows: H1: Mean value of CR differs from the median; H2: Mean value of CR differs from benchmarks most commonly provided in the literature; H3: Median of CR differs from benchmarks most commonly provided in the literature; H4: Mean values and medians of CR vary yearly; H5: Mean values and medians of CR differ between industries; H6: Mean values and medians of CR differ between Poland and the U.S. RESEARCH METHODOLOGY The size of the business and its legal form result in different institutional conditions. For instance, a sole proprietorship does not have a separate legal existence from its owner, so it does not pay income tax. Instead, the owner of the business pays personal income tax. However, unlike a sole proprietor- ship, a corporation is legally separate and distinct from the owner(s). A separate legal entity means a corporation is responsible for its acts and debts, including corporate income tax. Moreover, following Polish Accounting Act, an external audit of sole proprietorships' financial state- ments is usually not mandatory, while financial statements of joint-stock companies must be audited. The list of differences may be expanded. Hence, firms act in different institutional conditions and are exposed to various risks influencing the ratios. Wanting to limit the variety of factors influencing liquidity ratios, in my analysis, I concentrate on companies listed on the Warsaw Stock Exchange (WSE). I concentrate on the period 2005-2021. The reason for including 2005 as a first sample period is twofold. First, International Financial Reporting Standards have been mandatory in consolidated finan- cial reporting since the beginning of this year. Hence, despite rare substantial changes in the IFRS1, all financial statements in the sample are prepared according to the same methodological basis (71% of the sample) or at least under indirect influence of this basis (29% of the sample). Second, data availa- bility in the database was highly limited in the pre-2005 period. The reason for including the year 2021 as the last in the sample period is also twofold. An extended period is necessary to test hypothesis H4. Moreover, 2021 is the first fiscal year after the Covid-19 pandemic season. I conjecture that the pan- demic, a unique risk factor, heavily influenced liquidity ratios. 1 For example, IFRS 16 Leases superseded IAS 17 Leases. The influence of IFRS 16 on assets, liabilities, leverage on assets and leverage on equity is confirmed in the literature (Górowski et al., 2022). 88 | Piotr Wójtowicz I retrieve data from Worldscope Database via Refinitiv Eikon, including SIC codes. The sample consists of only domestic, going concern, non-financial firms. In line with many other studies, I ex- clude firms rendering financial services because they face different regulations and are monitored additionally by specialized national supervisory authorities. I exclude firms with incomplete data and firms with negative owner's equity. Standard Industrial Classification (SIC) codes are four-digit nu- merical codes assigned to business establishments to identify the primary business of the establish- ment (SIC, 2022)2. The so-called primary SIC code indicates a company's primary line of business. What determines a company's SIC code is the code definition that generates the highest revenue for that company at a specific location in the past year. The use of SIC codes is determined by data availability and the need for international comparison of the results. Regarding SIC classification, firms assigned to Division H: Finance, Insurance, and Real Estate are excluded from the analysis. Division J: Public Administration was not represented at all. My final sample consists of 5 148 firm-year observations. The population of firms listed on the WSE from 2005 to 2021 varies yearly due to newly listed firms, delisted firms, and rare bankrupt- cies. Therefore, the sample's number of observations per year varies. Table 3 shows the number of observations per year and the division of SIC classification. Definition of the Current Ratio (CR) and related fields in the Worldscope Database are the following: �� = ��� ��� (1) where: ��� - Total Current Assets, Worldscope field 2201; ��� - Total Current Liabilities, Worldscope field 3101. I do not use other liquidity ratios in my analysis; however, I include their definitions for clarity: 1. Quick Ratio QR �� = ��� − � − � ��� (2) where: � - Total Inventories, Worldscope field 2101; � - Prepaid Expenses, Worldscope field 2140. 2. Cash Ratio CHR ��� = �� + �� ��� (3) where: �� + �� - cash and short-term investments, Worldscope field 2001. Methods used in the analysis include descriptive statistics, parametric and nonparametric ANOVA, confidence intervals, and linear and parabolic trend analysis. RESULTS AND DISCUSSION Table 4 shows descriptive statistics of the Current Ratio (CR) per year for the total sample. Figure 1 shows the histogram for CR for the sample. The distribution of CR is highly asymmetric3, with an arithmetic mean of 2.8 and a median of 1.5. In the sample, the skewness of 17.0 says that the distri- bution is heavily skewed to the right. Figure 1 shows that the distribution has a very long right tail. Kurtosis is a measure of the heaviness of the tails of a distribution4. In the sample, Kurtosis of 408.1 signals extremely tailed distribution. Even the lowest annual values of skewness (4.1) and kurtosis 2 The SIC system arrays the economy into 10 divisions, that are divided into 83 2-digit major industry groups, that are further subdivided into 416 3-digit industry groups, and finally disaggregated into 1,005 4-digit industries. For instance, Division D: 20-39 Manufacturing; Major Industry Group 25 Furniture and Fixtures; Industry Group 252 Office Furniture; Industry 2521 Wood Office Furniture. 3 A symmetric distribution has a skewness of 0. 4 A normal distribution has a kurtosis of 3. Questing benchmarks for the current ratio. An analysis of the Warsaw Stock Exchange firms | 89 (25.0) in 2006 indicate high asymmetry. The maximum value in the sample is 247.0. Mode, not tab- ulated in Table 4, is located in bar 1.2-1.4. Figure 1. Histogram for Current Ratio (CR) for the total sample Source: own elaboration based on current ratio calculation. There are many firm-years with a value of CR above 7.4. The threshold of 7.4 (Figure 1) is chosen because the 95th percentile equals 7.444444. Hence, for about 5% of the sample observations, the CR is above 7.4. There are many possible reasons for such a situation; however, disentangling the puzzle is out of my interest in this article. On the other hand, a considerable number of managers of listed companies run their operations with CR far from the benchmarks suggested in the litera- ture. An analyst may expect that managers are rational decision-makers. Hence, the apparently excessive current ratio may result from sound decisions. The interpretation of the interquartile range (Table 4) is especially interesting. The range for the sample says that 50% of firm-years have a value of CR ranging from 1.1 to 2.3, with a median of 1.5. Knowing that firms included in the sample are going concern units, we may interpret this range as empirical verification of the benchmarks presented in Table 1. Both the first (25%) and the third (75%) quartiles are reasonably close to the theoretical value of the benchmark. Moreover, the first quartile seems to be respectively stable over time, but the third quartile increases slowly in 2005-2019 and grows substantially in 2020 and 2021. Therefore, I fit trend lines to the first and the second quartile values to verify my temporary conclusions. Figure 2 shows the yearly median, first and third quartiles of CR, and regression results. The intercept for the first quartile linear model (Figure 2) says that its estimated value in 2004 was 1.19 and is decreasing yearly by 0.009 on average. On the other hand, the intercept for the third quar- tile linear model says its estimated value in 2004 was 2.06 and is increasing annually respectively quickly, by 0.029 on average. In other words, on average, the quartile range is becoming wider and wider year by year. Not surprisingly, the value of 1 is fairly stable over time because it is a reasonable minimum ratio of current assets to current liabilities. Both R2 coefficients are high, so linear models fit the data. I conjecture that the increase in CR is due to the increased risk of business activity, especially its abrupt jump in the Covid-19 pandemic season. The median is stable over time, with an average sample value of 1.5 (Table 4), because it is, per se, robust to new observations. The linear trend model does not fit yearly median data; R2 for this model equals 0.03 (not tabulated). In the case of CR, the 90 | Piotr Wójtowicz median heaves gently up and down around 1.5 value. There is an increase in the 2020-2021 (Covid-19 pandemic) and 2007-2010 (sup-prime crisis). Table 3. Number of observations per year and the division of SIC classification Year / Division A : A g ri c u lt u re , F o re st ry , a n d F is h in g B : M in in g C : C o n st ru c ti o n D : M a n u fa c tu ri n g E : T ra n sp o rt a ti o n , C o m m u - n ic a ti o n s, E le c tr ic , G a s , a n d S a n it a ry S e rv ic e s F : W h o le sa le T ra d e G : R e ta il T ra d e H : F in a n c e , In su ra n c e , a n d R e a l E st a te I: S e rv ic e s J: P u b li c A d m in is tr a ti o n Total 2005 2 2 18 64 10 16 8 – 25 – 145 2006 1 1 18 73 12 15 10 – 28 – 158 2007 3 3 22 83 14 17 10 – 34 – 186 2008 3 4 25 91 20 19 11 – 42 – 215 2009 3 5 27 98 23 21 10 – 50 – 237 2010 3 5 30 105 26 23 12 – 56 – 260 2011 3 5 34 111 30 24 12 – 66 – 285 2012 4 5 38 116 29 23 12 – 73 – 300 2013 4 5 38 116 29 25 17 – 72 – 306 2014 5 5 43 123 31 24 16 – 83 – 330 2015 5 5 45 130 33 24 19 – 96 – 357 2016 5 5 46 136 33 26 20 – 104 – 375 2017 3 5 44 130 33 26 21 – 110 – 372 2018 4 5 50 139 35 26 21 – 123 – 403 2019 4 5 48 146 33 25 24 – 133 – 418 2020 4 5 50 140 33 27 25 – 130 – 414 2021 2 4 47 128 28 22 24 – 132 – 387 Total sample 58 74 623 1929 452 383 272 – 1357 – 5148 Source: own calculations in Stata. The yearly mean CR is much higher than the median due to the sensitivity of the former to out- liers. The mean calculated after the rejection of observations greater than the 95th percentile equals 1.8 and is much lower than the mean for the entire sample, equal to 2.8. On the other hand, it is significantly higher than the median of 1.5. To sum up, hypothesis H1 should not be rejected. When looking for a benchmark, an analyst must decide whether to use the mean or median. Furthermore, the benchmarks suggested in the literature (Table 1) likely involve medians. To strengthen the tests of hypotheses H2 and H3, I calculate confidence intervals for mean and median with a confidence level of 0.95; 0.99; and 0.999. The last confidence level is beyond the traditional testing approach. Still, I want to construct a confidence interval with a 0.999 confidence level to be confident that 99.9 out of 100 times, virtually always, the estimate falls between the upper and lower bounds. Table 5 shows the results. Table 5 shows that the higher the confidence level, the wider the confidence level. However, re- jection of the 95th percentile results in very narrow intervals close to mean and median, respectively. In other words, these intervals may be useless for an analyst because they do not convey any new information. On the other hand, confidence intervals for the total sample lead to the following conclu- sions. First, intervals for mean and median differ significantly, so hypothesis H1 definitely should not be rejected. Second, literature benchmarks indeed rely on medians. Third, the most common bench- mark for CR, 1.2-2.0, is very close to the interval for the confidence level of 99.9% ranging from 1.07 to 1.93. In summary, hypothesis H2 should not be rejected. On the contrary, H3 may be rejected for the total sample. Essentially, the sample median sticks to the benchmark. Questing benchmarks for the current ratio. An analysis of the Warsaw Stock Exchange firms | 91 Table 4. Descriptive statistics of Current Ratio (CR) per year Year / Parameter No. of Obs Mean Std. Dev. Min 25% Median 75% Max Skewness Kurtosis 2005 145 1.9 1.9 0.2 1.2 1.4 2.0 20.0 6.4 55.2 2006 158 2.0 1.9 0.4 1.2 1.5 2.2 16.0 4.1 25.0 2007 186 2.2 2.2 0.3 1.2 1.6 2.2 21.3 4.8 34.2 2008 215 2.3 3.1 0.2 1.1 1.5 2.3 29.2 5.7 41.6 2009 237 2.5 6.2 0.1 1.1 1.6 2.2 91.7 12.8 183.3 2010 260 2.6 6.4 0.0 1.2 1.6 2.3 74.3 9.9 107.4 2011 285 2.5 5.9 0.0 1.1 1.5 2.3 94.4 13.7 211.3 2012 300 2.4 3.7 0.1 1.1 1.5 2.2 42.9 6.7 61.4 2013 306 2.5 5.7 0.0 1.1 1.5 2.2 92.5 13.1 202.5 2014 330 3.4 16.4 0.0 1.1 1.5 2.4 247.0 13.0 178.2 2015 357 2.8 4.6 0.0 1.1 1.5 2.2 40.0 4.6 27.1 2016 375 2.8 5.7 0.0 1.1 1.5 2.4 82.8 8.7 107.5 2017 372 2.8 5.5 0.0 1.1 1.5 2.4 66.8 7.4 71.0 2018 403 2.7 5.7 0.1 1.0 1.4 2.4 80.2 10.1 125.3 2019 418 2.7 5.6 0.0 1.0 1.4 2.4 66.2 7.7 74.4 2020 414 3.4 9.9 0.0 1.0 1.5 2.6 143.0 10.1 125.2 2021 387 3.6 11.7 0.0 1.1 1.6 2.7 193.6 12.5 189.2 Total sample 5148 2.8 7.5 0.0 1.1 1.5 2.3 247.0 17.0 408.1 Source: own calculations in Stata. Figure 2. Median, first (25%), and third (75%) quartile of CR per year Source: own elaboration based on current ratio calculation. Table 5. Confidence intervals (CI) for mean and median for CR Parameter / Conf. interval Total sample – 5148 observations 95th percentile rejected – 4890 observations Mean Median Mean Median CI level of 0.95 0.99 0.999 0.95 0.99 0.999 0.95 0.99 0.999 0.95 0.99 0.999 CI Left bound 2.56 2.49 2.42 1.25 1.17 1.07 1.78 1.76 1.75 1.41 1.40 1.38 CI Right bound 2.97 3.03 3.11 1.76 1.84 1.93 1.84 1.86 1.87 1.50 1.51 1.53 Source: own calculations in Stata. I build the above conclusions on a fair calculation; however, I must raise one more issue. The me- dian confidence interval for 2020-2021, the confidence level of 0.999, ranges from 0.0 to 3.1. In that period, both boundaries differ substantially from the benchmarks. The difference is likely the result of 92 | Piotr Wójtowicz the confidence interval calculation formula, i.e., the increase of standard error due to the decrease in the number of observations. However, it may also stem from the substantial change in the CR in the pandemic period. In my opinion, the last conclusion weakens the power of literature benchmarks. I run ANOVA to verify whether there were any statistically significant differences between the yearly means and medians of the CR ratio. The p-value is 0.5127, and the null hypothesis (all means are equal) should not be rejected. The exclusion of outliers greater than the 95th percentile does not change the result, p=0.6077. Unfortunately, CR is not normally distributed, and the p-value in Bartlett's test for equal variances is p<0.0001, so the ANOVA assumptions are broken. To verify my results, I run the nonparametric Kruskal-Wallis ANOVA. The hypotheses for the test are: H0: Population medians are equal. H1: Population medians are not equal. In that case, the p-value equals 0.7825. Statistic tests show that CR medians do not vary yearly, so hypothesis H4 should be rejected. The above results of the ANOVA tests must be interpreted with caution because there is a consid- erable difference between statistical significance and substantive significance. Table 4 and Figure 2 show that CR varies over time. Moreover, the third quartile slowly increases yearly. Table 6 shows descriptive statistics of the CR per division of SIC. Means and medians differ sig- nificantly. For each division, the distribution is skewed to the right (Skewness 1.8 or more), and tails are heavy (kurtosis 6.2 or more). Means are much higher than medians due to their sensitivity to outliers; hence hypothesis H1 is supported. Two divisions, D (Manufacturing) and I (Services), contribute almost 64% of the observations. On the contrary, divisions A (Agriculture, Forestry, and Fishing) and B (Mining) are narrowly represented. The over-representation of divisions D and I biases the descriptive statistics for the sample towards parameters for these divisions. In other words, an investigation by division is necessary. Table 6. Descriptive statistics of Current Ratio (CR) per SIC division Division / Parameter A B C D E F G I No. of Obs 58 74 623 1 929 452 383 272 1 357 Mean 2.3 1.4 2.3 2.4 2.0 1.9 1.6 4.3 Std. Dev. 1.6 0.9 3.3 6.7 4.5 1.7 2.6 11.4 Min 0.6 0.3 0.0 0.0 0.0 0.3 0.0 0.0 25% 1.1 1.0 1.2 1.2 1.0 1.1 0.7 1.1 Median 2.0 1.2 1.6 1.5 1.3 1.3 1.1 1.7 75% 2.6 1.7 2.2 2.2 2.0 1.9 1.6 3.6 Max 8.2 5.7 40.0 193.6 82.8 12.4 27.0 247.0 Skewness 1.8 2.3 7.1 18.4 14.4 3.5 7.2 12.0 Kurtosis 6.2 10.4 63.8 424.3 240.4 17.7 64.5 204.8 Source: own calculations in Stata. I rerun ANOVA to verify whether there were any statistically significant differences between the means of the CR by SIC division. For p<0.0001, the null hypothesis is rejected. To verify my results, I rerun the nonparametric Kruskal-Wallis ANOVA; the p=0.0001. Statistic tests show that CR means and medians vary between divisions (industries), so hypothesis H5 should not be rejected. This result leads to a crucial conclusion: there is no benchmark for the economy. A useful benchmark may be built for industry only, whereas an economy-based benchmark may be useless. The last conclusion is the premise for an in-depth analysis of the CR for a division. Table 7 shows the structure of division D (industry groups) and descriptive statistics for CR. Means and medians per major industry group differ, but there are two exceptions, industry groups 26 and 32 - the pos- itive and close to zero skewness signals symmetry of the distribution. Kurtosis less than three is a signal of light right tail. Hence, CR varies substantially between groups, p=0.0001 for ANOVA and Kruskal-Wallis ANOVA. The results support hypotheses H1 and H5. Questing benchmarks for the current ratio. An analysis of the Warsaw Stock Exchange firms | 93 The first and third quartiles differ between 2-digit major industry groups (Table 7). Moreover, they are close to the typical benchmark for CR, 1.2-2.0, only for four major groups5 (24; 34; 35; 37), out of nineteen. On the other hand, the highest interquartile range has boundaries of 1.5 to 4.3 for group 386. In general, sample-based means and medians differ from the literature benchmark, and hypotheses H2 and H3 should not be rejected. Tests for hypothesis H4 (yearly variability) require enough annual data, while the number of ob- servations per group decreases for detailed analyses. Accordingly, I verify H4 only for the major groups 20; 28; and 34; contributing over 200 observations each in the sample period. For these groups in parametric and nonparametric ANOVA, the p-value is 0.5 or higher. Undoubtedly, H4 should not be rejected. However, even statistically not significant differences may be material for an analyst. Table 7. Descriptive statistics of Current Ratio (CR) per SIC 2-digit major industry group in division D: Manufacturing Industry group / Parameter No. of Obs Mean Std. Dev. Min 25% Median 75% Max Skewness Kurtosis 20 201 1.6 1.2 0.3 1.0 1.3 1.7 7.7 2.7 11.8 22 65 2.4 1.2 0.7 1.5 2.1 2.6 6.2 1.5 4.8 23 60 3.1 4.0 0.5 1.2 1.5 2.2 17.8 2.4 7.5 24 62 1.7 0.5 1.1 1.3 1.5 1.9 3.5 1.6 5.3 25 39 1.4 1.0 0.0 0.7 1.4 1.8 3.4 0.5 2.6 26 64 1.2 0.4 0.4 0.9 1.2 1.4 2.1 0.0 2.8 27 75 6.0 24.9 0.2 1.0 1.5 2.4 193.6 6.6 47.1 28 278 3.5 10.7 0.1 1.0 1.4 2.2 94.4 7.4 59.0 29 62 2.0 0.9 0.8 1.4 1.7 2.3 5.0 1.8 5.4 30 58 1.5 0.5 0.9 1.2 1.4 1.6 3.7 2.5 11.5 31 23 2.7 1.2 1.1 1.8 2.3 3.3 5.7 1.0 2.9 32 32 1.5 0.6 0.4 1.1 1.4 1.8 2.8 0.2 2.7 33 100 1.5 0.5 0.3 1.2 1.5 1.8 3.4 0.4 3.9 34 254 2.0 1.5 0.2 1.2 1.6 2.1 12.3 2.4 3.6 35 200 1.8 1.4 0.0 1.1 1.5 2.1 13.0 3.7 27.3 36 146 2.5 2.5 0.8 1.3 1.6 3.1 28.1 7.2 71.4 37 73 1.7 0.7 0.7 1.3 1.5 2.0 4.2 1.5 5.1 38 127 4.0 5.0 0.8 1.5 2.3 4.3 42.9 4.6 31.9 39 24 3.2 3.4 0.0 1.5 2.0 3.2 13.1 1.8 5.3 Source: own calculations in Stata. Table 8. Mean and Median of Current Ratio, Poland v.s. the U.S. Year / Parameter 2016 2017 2018 2019 2020 2021 Mean, Poland 2.82 2.81 2.67 2.72 3.45 3.64 Mean, the U.S. 26.89 21.79 66.06 30.14 95.14 54.29 Median, Poland 1.49 1.49 1.41 1.42 1.51 1.59 Median, the U.S. 1.53 1.56 1.58 1.69 1.94 2.03 Source: for Poland, own calculations in Stata; for the U.S., (ReadyRatios, 2022). To test hypothesis H6, I retrieve ready-made ratios7 from Internet resources (ReadyRatios, 2022). They provide two measures of position for industry financial ratios for the U.S. listed companies: mean and median. Table 8 shows these measures for Poland and the U.S. I compare the period 2016- 2021 because of the limited availability of the U.S. data. 5 24 Lumber And Wood Products, Except Furniture; 34 Fabricated Metal Products, Except Machinery and Transportation Equipment; 35 Industrial and Commercial Machinery and Computer Equipment; 37 Transportation Equipment. 6 38 Measuring, Analyzing, and Controlling Instruments; Photographic, Medical and Optical Goods; Watches and Clocks. 7 The current ratio is calculated in line with the following formula: Current Assets / Current Liabilities. The amounts are retrieved form the balance sheet (statement of financial position) prepared in line with IFRS or US GAAP, respectively (ReadyRatios, 2022). 94 | Piotr Wójtowicz Table 8 leads to some critical conclusions. First, the U.S. means are more than ten times higher than their Polish counterparts. Without a detailed distribution analysis, I cannot tell the reason for such a situation. As a rule of thumb, high mean values are useless for an analyst or investor. Further- more, the high variability of means makes trend analysis impossible. Second, medians also differ, but the difference is moderate. In the respectively short period 2016-2021, medians increase, and they do substantially during the pandemic season, especially in the U.S. Figure 3 shows the yearly median for both countries and the quadratic trend functions. The general shape of parabolas is the same, so yearly medians in both countries increase faster than proportionally year by year. The R2 coefficients are very high, so the quadratic trend fits the data well. Not surprisingly, the observed abrupt jump in CR during the Covid-19 pandemic season remains visible. It is especially high in the U.S.; hence the medians of CR differ between the two analyzed countries. Covid was a global risk factor, impacting the world economy. However, a particular economy's reaction depends on many variables. Undoubtedly a global factor affecting all economies results in a similar answer pattern but of a different magnitude. These findings corroborate my hypothesis H6, and their practical consequence for an analyst is the following. Even using industry benchmarks, they should be sensitive to the country and period chosen for comparison. Figure 3. Medians of CR per year, Poland vs. the U.S. Source: own elaboration based on own CR calculation and (ReadyRatios, 2022). CONCLUSIONS The main objective of the article was to test the values of the benchmark for the current ratio, the commonly used liquidity ratio. The minor objective was to arrange a research methodology permit- ting answers about the benchmark. The tests show that identifying a benchmark for the current ratio is problematic. Concerning this ratio, there is no simple answer to the question – How is a business doing? Benchmarks vary between countries and industries. The benchmark commonly suggested in Poland, 1.2-2.0 (Table 1), may be used only as a very rough evaluation of the desired value. The findings indicate that the acceptable range for CR is much broader, from 1.1 (the first quartile) to 2.3 (the third quartile) for the total sample. Moreover, the SIC division's quartiles vary from 0.7 to 1.2 (the first) and from 1.6 to 3.6 (the third). Statistical tests indicate that benchmarks do not vary annually; however, a lack of statistically sig- nificant difference does not imply a lack of substantive difference. The latter may be material for an analyst, even in the absence of the former. The total sample median and the third quartile vary slowly over time towards higher values. The Covid-19 pandemic resulted in a substantial increase. On the contrary, the first quartile of about 1 Questing benchmarks for the current ratio. An analysis of the Warsaw Stock Exchange firms | 95 remains relatively stable over time, indicating the reasonable lower bound for CR. In other words, nei- ther the U.S.-based rough benchmark (1.5 to 3) nor its Polish counterpart reflects the current state of the art. Therefore, an analyst must very carefully choose the benchmark for comparison. The arithmetic mean of the distribution is very sensitive to outliers; hence, it is useless as an estimator of the benchmark. In the case of point estimation, an analyst may rely only on the quar- tiles, especially the median. Interval estimation seems to be a helpful technique; however, for me- dian only. Outlier bias confidence interval for the mean. Trend analysis permits the identification of the pattern and magnitude of the change in the benchmark. Arguably, linear trend fits empirical data in the long run. 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(2002). Teoria i praktyka analizy finansowej w przedsiębiorstwie. Fundacja Roz- woju Rachunkowości w Polsce. Wędzki, D. (2002). Strategie płynności finansowej przedsiębiorstwa. Przepływy pieniężne a wartość dla właścicieli. Oficyna Ekonomiczna Grupa Wolters Kluwer. Questing benchmarks for the current ratio. An analysis of the Warsaw Stock Exchange firms | 97 Author Piotr Wójtowicz Prof. Piotr Wójtowicz, PhD, Associate Professor at the Cracow University of Economics (Poland). His research in- terests include financial reporting, earnings management, and the impact of cultural factors on earnings quality. Correspondence to: Prof. Piotr Wójtowicz, Cracow University of Economics, Department of Accounting, 31-510 Cracow, 27 Rakowicka Street, Poland, e-mail: piotr.wojtowicz@uek.krakow.pl ORCID http://orcid.org/0000-0001-5054-9964 Acknowledgements and Financial Disclosure The publication was financed from the subsidy granted to the Cracow University of Economics – Project nr 065/ZZR/2022/POT. The author would like to thank the anonymous referees for their valuable comments, which increased the value of this article. Conflict of Interest The author declare that the research was conducted in the absence of any commercial or financial relation- ships that could be construed as a potential conflict of interest. Copyright and License This article is published under the terms of the Creative Commons Attribution – NoDerivs (CC BY-ND 4.0) License http://creativecommons.org/licenses/by-nd/4.0/ Published by Cracow University of Economics – Krakow, Poland The journal is co-financed in the years 2022-2024 by the Ministry of Education and Science of the Republic of Poland in the framework of the ministerial programme “Development of Scientific Journals” (RCN) on the basis of contract no. RCN/SP/0251/2021/1 concluded on 13 October 2022 and being in force until 13 October 2024.