The matching concept most significantly influences which financial statement

Although it was a broadly analysed topic until the 1970s, there has been little research effort aimed at matching in the last 20 years [].

According to Dichev and Tang [], one of the reasons related to this lack of research is that in earlier years the dominant paradigm of market efficiency implied that the market fully relays on accounting conventions and practices aimed to measure firms’ performance. In fact, it is only quite recently that there has been a renewed interest into fundamental analysis, that is a research stream related to the study of whether and how the knowledge on accounting yields superior insights into firm performance and security valuation (e.g. [, , , ]; and others).

Another reason for the relative lack of research about the matching process is the aforementioned evolution of accounting standards. Indeed, while early standards recognized the importance of matching on both conceptual and practical level, during the last two to three decades the FASB and the IASB have adopted a perspective where the determination of income is viewed more as resulting from revisions of asset and liability values rather than as the residual from revenues and matched expenses [].

In the spirit of fundamental analysis, it seems that the study of matching, and its determinants and consequences, can be viewed as a further step into enriching the knowledge about the determination and the properties of earnings. In particular, there are three studies that are close to the spirit of this kind of research. Such strand comprises Su [] and the related studies of Lane and Willet [] and Gibbins and Willet [].

The fil rougeof these studies is based on the idea according to which a proper matching of revenues and expenses has a smoothing effect on earnings that is beneficial because it allows for better estimation of long-run economic profitability. Therefore, they conclude that matching, as well as conservatism and other accounting practices, are not merely ad hoc or traditional rules which accountants arbitrarily apply, but have rational bases in the sense that they can allow a better decision-making process [].

Recently, through an historical retrospective on matching, which includes a review of more contemporary research and thought, Zimmerman and Bloom [] also confirm that matching, as an approach to income measurement, can be helpful in forecasting earning power. Consequently, they conclude that matching should be retained as a long-standing fundamental accounting principle in standard-setting and in practice.

Moving from the studies that support matching principle as a desirable practice that allows to obtain more useful and informative accounting numbers, and motivated by the aforementioned relative lack of recent research aimed at matching, some authors have tried to deepen the knowledge about this topic analysing trends, and potential determinants and consequences.

The reference study in this ‘new’ field is the analysis of Dichev and Tang [], who present a theory of matching and its effects on accounting variables. The principal insight of the theory is that poor matching acts as noise in the economic relation of advancing expenses to earn revenues. Empirically, they concentrate on time-series specifications using a sample composed by the 1000 largest US firms (for 34,785 observations) from 1967 to 2003, and measure matching as the coefficient (γ2) on current expenses in a regression of revenues on past, current, and future expenses.

Revi,t=γ0+γ1Expi,t−1+γ2Expi,t+γ3Expi,t+1+εi,tE4

Findings reveal a clear and economically substantial declining trend in the contemporaneous correlation between revenues and expenses, and an increase in the non-contemporaneous correlation between revenues and expenses. Therefore Dichev and Tang [] highlight a decline in matching, such that an increasing amount of expenses is being recognized before and after the period in which it affects revenues ().

Similar trends in the evolution of matching have been documented by other subsequent studies. Specifically, Donelson et al. [] selected a sample which consists of 32,645 US firm-year observations between 1967 and 2005, and that is generally consistent with the sample in Dichev and Tang []. Next, they estimate a cross-sectional regression which is identical to the regression model reported in Dichev and Tang []. As described in such study, Donelson et al. [] documented a decline in the contemporaneous association of revenue and expense, and an increase in the lag (lead) coefficient ().

Murdoch and Krause [] also analysed the US market but they began their investigation with 1987 data and, to allow for comparisons with earlier research, extend the analysis period through 2005, including all firms for which pertinent data are available rather than limiting the sample to large firms. In order to assess the degree of matching, Murdoch and Krause [] observe the correlation between revenues and two expenses measures from the 1987 to 1996 period and compare it to the correlation for the 1997–2005 period, adopting the same methodology of Dichev and Tang []. As a result, their findings also highlight a worsening in the degree of matching between revenues and expenses recognized in the same period.

Still focusing on US settings, Bushman et al. [] built a sample that consists of 228,847 firm-year observations from 1964 to 2012 and, still employing the same technique used in Dichev and Tang [], confirm the declining trend in matching between revenues and expenses as documented in previous studies.

Further, using a sample composed by 189,608 US firm-year observations with valid data from the years 1970 through 2009, Srivastava [] replicates the model proposed by Dichev and Tang [] and obtain similar results in terms of declining matching between current revenues and expenses. Moreover, splitting the sample in two groups of firm he shows that for the new-firm segment, the average matching declines from 1.05 to just 0.59, while the average revenue-expense matching of the seasoned-firm segment declines by much less, from 1.05 to 0.94. As a result, he confirms a declining trend in matching current revenues and expenses, but also highlights that, relative to the seasoned-firm segment, the average matching for the new-firm segment’s is 37% lower.

In the same year, Kagaya [] examine changes in the relation between revenues and expenses over the last 16 years around the world. In particular, the final sample consists of 282,873 firm-year observations for the fiscal years 1991–2008, relative to 30,537 non-financial firms across nine countries (Canada, China, Germany, France, India, Japan, Korea, the UK, and the USA) which, in turn, are clustered in different cultural areas according to the definition of cultural area from Djankov et al. []. Referring to the matching measures proposed by Dichev and Tang [], Kagaya [] confirms that the correlation between revenue and expense has declined around the world (), and shows that such a trend is stronger among the English speaking countries ().

Figure 3.

Coefficients in regression of revenues on past, current, and future expenses. Source: Kagaya [36].

Figure 4.

International comparison of the correlation between revenues and current expenses. Source: Kagaya [36].

Along the lines of these studies, He and Shan [] measure matching by the contemporaneous correlation between revenues and expenses. Relying on a sample that includes 42 countries, they estimate the annual matching coefficient from 1991 to 2010, and find that the decline in matching is not unique to the United States, but a worldwide phenomenon during this period ().

Figure 5.

Matching between current revenues and expenses over time. Source: He and Shan [38].

The only dissenting voice in this strand of research belongs to Jin et al. [], who examine changes in the matching between contemporaneous revenues and expenses in Australian financial reporting. Specifically, relying on Dichev and Tang [] their results indicate that the revenue-expense relation has declined in Australia during 2001–2005, but improved in more recent years ().

Figure 6.

Correlation between current revenues and expenses in Australia. Source: Jin et al. [39].

Overall, looking at these studies focused on the identification of trends in the degree of matching, it seems clear that the major issue is related to a worsening of the relation between current revenues and expenses, which has been documented in different settings with the only exception of the Australian one, examined by Jin et al. []. However, the mere detection of these changes could be not fully revealing without a careful analysis of both possible determinants and consequences related to such declining trend in one of the milestones of accrual accounting, such as the process of matching revenues and expenses.

3.1. Determinants of changes in the degree matching

According to Dichev and Tang [], the possible determinants of the combined evidence that suggests a worsening of accounting matching over time can be identified in both the accounting system evolution and innate economic factors.

The reason underpinning this idea is due to the behaviour of accounting standard setters that, since the late 1970s, have taken a deliberate and far-reaching turn away from matching as the fundamental concept in the determination of earnings and towards a more balance sheet-based model of the determination of income. On the other hand, the authors are also aware that changes in the real economy, towards more fixed costs and R&D activities, can also imply a temporal decline in matching success, and that there is little that financial reporting can do about the nature of these changes per se. However, Dichev and Tang [] suggest that changes in the real economy have played a secondary role in the evolution of the properties of earnings. In addition, the authors state that if the point is ‘what can be done to counter the effect of these changes on the informativeness of earnings’, then the answer and the discretion lie again in the design of the financial reporting system and its relevant bodies.

Anyway, besides such theoretical aspects, the conclusions of Dichev and Tang [] are not merely conjectures, inasmuch they rely on the empirical evidence of their analysis. However, to date, Dichev and Tang [] remain the only ones who ascribe the declining in matching to the accounting system’s ground rules.

In fact, Donelson et al. [], using a simple decomposition framework, show that the decline in the relation between current revenues and expenses is attributable primarily to a single income statement line item, namely special items, and not to systematic issues across multiple line items in the income statement. Moreover, since the ‘weight’ of special items as a component of total expenses has increased with the incidence of special items over time, decreasing the relation between current revenues and total current expenses, empirical evidence suggests that changes in the frequency of economic events associated with special items have played a more important and sustained role relative to the role played by the adoption of individual accounting standards ().

Figure 7.

Correlation between current revenues and expenses in Australia. Source: Donelson et al. [4].

Results from Donelson et al. [] are then indirectly confirmed by Murdoch and Krause [], who conclude that recurring earnings (that does not include the effect of special items) are preferred to an earnings number that includes the impact of special items.

An alternative explanation, to the declining in the relation between revenues and expenses, is offered by Srivastava []. In particular, he highlights that, in his sample, each new cohort of listed firms exhibits a lower degree of matching than its predecessors, mainly because of higher intangible intensity. Therefore, Srivastava [] concludes that the trend of decline in matching is due more to changes in the sample of firms than to changes in generally accepted accounting principles or in the quality of matching process of previously listed firms ().

YearCoefficient on past expensesCoefficient on current expensesCoefficient on future expenses1967−0.0101.029−0.0131968−0.0141.044−0.0151969−0.0041.030−0.01219700.0021.042−0.03319710.0261.003−0.01619720.0101.089−0.07719730.0630.9390.0201974−0.0531.106−0.03819750.0231.061−0.06619760.0280.9910.0051977−0.0011.0150.0071978−0.0071.053−0.0221979−0.0071.0270.0061980−0.0211.070−0.02819810.0630.965−0.0101982−0.0171.054−0.0241983−0.0161.087−0.05619840.0510.9720.00319850.0161.013−0.01319860.0390.9370.03819870.1450.7620.1111988−0.0131.0320.00719890.0661.003−0.05319900.1010.932−0.01819910.1760.8020.02819920.1170.8710.02919930.1680.6910.15219940.0330.9860.00619950.0290.9790.01819960.0201.0000.00619970.0930.8940.03819980.0320.9770.01619990.0810.952−0.00520000.0421.015−0.03720010.4640.533−0.01220020.0920.7150.20420030.1320.7970.091Mean 1967 to 19850.0071.031−0.020Mean 1986 to 20030.1010.8820.034Difference0.094−0.1490.055P-Value on Difference<0.001<0.0010.002

Table 1.

Regression of revenues on previous, current, and future expenses.

Revenues, is net revenues deflated by average assets for the current period.

Expenses, is the difference betweenRevenuesandEarningsfor the current period.

Expensest−1is the difference betweenRevenuesandEarningsfor the previous period.

Expensest+1is the difference betweenRevenuesandEarningsfor the next period.

The regression is run on a cross-sectional basis each year.

P-value on the differences is obtained forma two-tailed t-test.

Source: Dichev and Tang [].

PeriodExpt−1ExptExpt+11967–19850.0021.032−0.0301986–20050.0890.8950.025Difference0.087−0.1370.055P-Value on difference<0.001<0.001<0.001

Table 2.

Relation of revenues to lagged, current, and future expenses.

This table presents properties of earnings-related variables between two time periods, 1967–1985 and 1986–2005. Annual coefficients are obtained estimating the Dichev and Tang [] model each in both time periods.

Source: Donelson et al. [].

YearTotal firmsSeasoned firmsSeasoned firms (%)YearTotal firmsSeasoned firmsSeasoned firms (%)19702470230493.281990468494420.1519712786226381.231991486893519.2119722975221974.591992509892118.0719733121216969.501993531990517.0119743206210865.751994571387315.2819753213205163.831995616684713.7419763214197761.511996659381312.3319773105188660.741997657875711.5119783051180659.191998663570510.6319793247173153.311999650065110.0219803510165747.21200063476059.5319813656158743.41200163995869.1619824109153337.31200261835619.0719834273142833.42200360765468.9919844396134830.66200458525248.9519854526125727.77200557555108.8619864544118626.10200655974728.4319874661109823.56200754824558.3019884629102422.12200853444438.291989463697020.92200950914318.47

Table 3.

Number of seasoned firms.

All of the firms with a listing year before 1970 are classified as ‘seasoned firms’.

Source: Srivastava [].

A totally different position from Dichev and Tang [] is also assumed by He and Shan [], who analyse the impact of IFRS adoption on matching and do not find any significant result, excluding that changes in reporting system have a primary role in determining changes in the degree of matching between current revenues and expenses. In addition, they analyse several economic factors as potential determinants of matching, such as the proportion of firms reporting large special items, the national economic growth, the weight of the service industry in a country’s gross domestic product (GDP), and the intensity of R&D activities. Specifically, findings highlight that the degree of matching between contemporaneous revenues and expenses is weaker in countries where many firms report significant special items, GDP growth rates are low, more R&D activities are present, and the service sector accounts for a larger portion of the economy. Therefore, these results support the view that real economic factors are important determinants of matching. Finally, He and Shan [] also consider whether country-level governance quality affects matching between revenues and expenses, and show that the contemporaneous revenue-expense relation is weaker in countries with common law legal origins and stronger investor protections. However, in these countries, there is a stronger association between past expenses and current revenues, implying that expenses are more likely to be recognized before the associated revenues.

Even more diametrically opposite to Dichev and Tang [], there is the study of Jin et al. []. In fact, as viewed in the previous paragraph, they detect an increasing trend of matching between contemporaneous revenues and expenses for the Australian context, but only after the mandatory adoption of IFRS. Therefore, they suggest that changes in accounting rules have positively affected the matching process effectiveness.

Overall, a wide range of determinants has been proposed in order to justify the detected trend of matching and there seems to be no prevailing ideas among them.

3.2. Consequences of changes in the degree of matching

In addition to the determinants of changes in matching effectiveness, another fundamental issue is the analysis of the consequences of the modified degree of correlation between revenues and expenses.

The essence of the milestone of this research stream [], is that mismatched expenses act as noise in the economic relation of advancing expenses to earn revenues, and therefore poor matching decreases the contemporaneous correlation between revenues and expenses. However, Dichev and Tang [] also documented an increased volatility of earnings, a declining persistence of earnings, and an increased negative autocorrelation in earnings changes ().

PeriodEarnings volatilityRevenues volatilityExpenses volatilityCorrelation rev. – exp.Mean 1967 to 19850.0140.1010.0940.973Mean 1986 to 20030.0210.0930.0880.914Difference0.007−0.008−0.005−0.059P-Value on difference<0.0010.0570.140<0.001PeriodEarnings persistenceAutocorrelation in earnings changesMean 1967 to 19850.8550.019Mean 1986 to 20030.7050.234Difference−0.150−0.215P-Value on Difference<0.001<0.001

Table 4.

Volatility and persistence of earnings, and autocorrelation in earnings changes.

Source: Dichev and Tang [].

Therefore, looking at the combined evidence of their study, Dichev and Tang [] suggest that accounting matching has become worse over time and that this trend had a pronounced effect on the properties of resulting earnings. Therefore, since earnings are the most widely used accounting number, these results also suggest that a consideration of degree of matching effectiveness can bring useful insights to financial reporting users.

The same view can be detected in Murdoch and Krause [], who employ a cash flow prediction criterion to investigate whether the decrease in matching has compromised earnings’ usefulness in forecasting future cash flows. In particular, their results indicate that earnings from earlier periods, in which matching was better, can be used to make more accurate predictions of operating cash flows, relative to earnings from later periods with poorer matching. Therefore, Murdoch and Krause [] conclude that the documented decline of matching damages the ability of earnings to aid in the prediction of future cash flows, thus being at odds with the primary purpose of financial statements.

A different position is assumed by Bushman et al. [], who examines the timing role of accrual accounting and show that the timing role of accruals has dramatically declined over the past 50 years and has largely disappeared in more recent years. However, in exploring several potential reasons for such observed attenuation, they find that the decline in matching between revenues and expenses is less drastic than the decline in the timing role of accrual accounting. Furthermore, they highlight that the effect of the mismatch on the attenuation of the timing role of accruals is subsumed by the effect of the changes in cash flow volatility. This means that Bushman et al. [] do not believe that a worsening in the degree of matching affects one of the basic functions of accrual accounting.

Srivastava [], on his own, analysed some determinants of the deterioration of the quality of earnings, considering matching as one of the of earnings quality components. However, although he confirms that there has been a decline in matching between revenues and expenses, he fails in neglecting the possibility that matching, as a ground rule of accrual accounting, could act as a moderator between the determinant of the documented erosion of earnings quality and the earnings quality measures and attributes. Consequently, the analysis is not able to prove if the downward trend of matching could have had some consequences on the quality of accounting numbers.

Going on, Kagaya [] investigates the relation between earnings smoothness and matching, and analyses the relation between current accruals, and current and next cash flows from operations. Evidence shows that the degree of matching is positive related to the stability of earnings. Therefore, Kagaya [] states that matching contributes to the presentation of permanent incomes, controlling for the volatility of earnings. Moreover, his results suggest that the accrual process, supported by matching and accruals, improves earnings smoothing and the signalling ability of future cash flows.

Overall, among these studies, that analyse the effects following the declining in matching revenues and expenses, the prevailing idea is that a higher degree of matching is a desirable quality to obtain more informative and useful earnings.

Which financial statements use the matching principle?

What is matching concept in financial accounting?

What is the matching concept related to?

What is the main implications of the matching concept?