Future earnings in specific industry
The purpose of the study is attempted to examine the relationship between fundamental signals and future earnings in specific- industry. The sample is chosen from the listed companies in China's retail industry. The period of data is collected over 2005-2008.
The method adopted in this study is fundamental analysis. It employed a linear regression model with 12 fundamental signals, which are accounting- based, to analyze the sample. The analysis is based on 12 hypotheses, which derived from 12 fundamental signals. Among them, there are 5 signals assumed negatively related to future earnings, which are INV, account receivable, capital expenditure, GM and S&A expenses ; and yet R&D expenditure and audit qualification positively related. Besides, labor force and LIFO/FIFO is hypothesized significant associated with the dependent variable, and while provision for doubtful receivable is not significant related. There is only one signal is assumed not correlated to future earnings, which is order backlog.
The linear regression model is tested in the specific industry. It interprets the relationship between the 12 signals and future earnings. Only the assumption of gross margin is accepted by the test result of coefficients. Other assumptions are incomplete in accordance with the test result. The accordance and discrepancy between the examination and hypotheses are explained by the characteristics of China's retail industry.
Key word: fundamental analysis, fundamental signal, retail industry
In this chapter, it briefly introduced the characteristics and potential development of the retail industry; and the method of financial analysis used in the article. And yet, it interprets the main purpose of fundamental analysis on the specific- industry. Moreover, it contoured the outline of this article.
Since 90s of 20th century, with the accelerated process of economic globalization, an increasing number of commercial barriers have been broken. Many large- scale retail enterprises have expand their market to worldwide, and accelerated their expanding pace to worldwide investment. In Retail industry, majority of companies prefer to invest in good geographical and cultural market, especially which is in a good development status of economic. In addition, this industry is commonly using the way of direct investment to enter a new market. There is a high return on investing in this industry, and will also accompany the return with risk.
It is showed that, the profitability level of part large enterprises have already entered the world's top 500 enterprises, such as US's Wal-Mart and France's Carrefour. It is believed that it is an industry with substantial returns. As is known, direct investment in manufacturing industry has a high risk, but the risk accompanied with investing in retail industry is especially conspicuous. The reason is that the development of retail business is directly depend on the efficiency of products (such as whether in line with consumers' preferences) and good social relations(such as got stable supply). To enter a new market, retail companies need to input large amounts of funds which are difficult to recover. For the characteristic of high return and high risk, it aroused interests of many scholars to do research in this field.
The article is attempt to analysis the financial status of retail industry in China. Retail, is an industry which has huge potential in development; it is not only for diversification of merchandise, but also for it's closely concerned by people's livelihood. With the development of economy, the quality of life has been concerned as a considerable important element in the modern society of China. It further consolidated the important status of the retail industry in Chinese market. Therefore, in recent years, an increasing number of people have paid attention to this industry, suppose to get knowledge of the financial operation of China's retail inventory for investing or others purposes. To exhibit an overall state of the financial operation of retail companies in this market, it is of great significance to do financial statement analysis on them.
Financial statements analysis is able to provide much useful financial information, such as the forecast of the profitability and solvency of retail companies, to investors and other users. However, how to predict the future revenue of this industry? What accounting indicators will affect the change of future earnings in specific- industry? For the increasing demand on financial information, it is necessary to know the association between accounting-based fundamental signals and future earnings. In addition, to grasp the operating status of the industry, it is important to find the fundamental signals which are significant related to companies' future earnings in retail industry.
In the article, it adopts the method of fundamental analysis into China's retail market. The linear regression model used in this analysis is attempted to explain the relationship between accounting-based factors and future earnings. However, it elicits several financial fundamental signals which may have impact on the change of future earnings. By examining the data from financial statements and information in linear regression model, it aims to find the significant factors in this specific industry.
According to the fundamental signal of the linear regression model, it proposed 12 hypotheses. It is supposed that there are 6 signals negatively related to future earnings, which are inventory, account receivable, capital expenditure, gross margin, S&A expenses and audit qualification. Besides, effective tax, labor force and LIFO/FIFO are hypothesized significant correlated to future earnings, and while provision for doubtful receivables is not significant related. Additionally, R&D expenditure has a positive association with the dependent variable. These hypotheses are supported specifically by prior studies of scholars'.
Many opinions on the definition of financial statement analysis and fundamental analysis are given are given in chapter 2. In addition, it present the previous studies on the models used in fundamental analysis, followed which it introduced the reasons for several signals adopted in fundamental. At the end of this part, it listed a few limitations on fundamental analysis, such as quality of financial information.
In chapter 3, it displayed the background of Chinese retail industry. In addition, the characteristics of the market, and the operating revenue achieved by this industry in recent years are briefly stated in this chapter.
In chapter 4, it listed 12 hypotheses on the specific- industry fundamental analysis. Before these hypotheses are proposed, many views from prior studies are applied to support these hypotheses.
Chapter 5 is the main body of this article. At first, a sample is chosen from Chinese retail market. And then it is input into the linear regression model of fundamental analysis. By the examination of SPSS system, results such as descriptive statistics, model of summary and coefficients produced, which helps to explain the association between fundamental signals and future earnings. Finally, it summarized the difference between hypotheses and test result of coefficients.
This chapter exhibits the opinions from scholars. Firstly, it reviews the different conceptions of financial statement analysis and a specific analysis ----fundamental analysis. And secondly, it mainly introduced the models which adopted prior studies, which companied with previous evidence on the selecting of variables. Finally, it specified some limitations of this analysis.
Since entering the World Trade Organization, China's economy developed rapidly, meanwhile its capital market became more prosperous than before. Following these changes, more and more investors entered the market, investing in the stock market. Therefore, it becomes the most concerned issue for investors that making earnings maximum and risk minimum. But, how to ensure investors to face lowest risk and obtain maximum benefit? Because earning's forecast information reflects the potential earning level of firms in the forecast period, it can help investors to evaluate firms' future operating performance, as to make sound investment decisions. Therefore, the forecasting of future earnings of listed companies become a widespread concerned subject
However, the future earnings forecasting is an important issue in financial research; it is also extensively considered by vast investors, securities firms and creditors. It assumed that prior year's information can help to predict the future earnings. It is said in efficient market hypothesis that future earnings are unable to forecast, but numerous empirical research showed that market is not always efficient, and thus there exists short-term trends can be predict in the market. In the public information which can be used to forecast future earnings, the basic financial information from financial statement is the most direct and easy to obtain for investors, and they are important basis for investors to make decision. Moreover, investors can extract corporate earnings-related information from financial analysis, to predict the changes of companies' future earning, and thus make investment decision by the guidance of financial information.
Earnings and risks come together on investments. Normally, return on investment is in direct proportion to risk from investment, that is, the higher the return on investment, the greater the risk. With more and more investors entering the capital market, they have an urgent need to find an effective way to obtain greatest increase of revenue, and decrease risk. Accordingly, the research of future earnings forecast of listed companies has important theoretical significance and application value.
The basic financial information, which contained in the financial statements regularly published by listed companies, comprehensively reflects the past operating condition of companies. Therefore, investors highly concern about it, and to a certain extent, they use these information to predict future earnings. According to its predicting results, they can make decision for investment. However, this basic information contained in financial statements reflect many aspects of companies' performance. Then, how to decide what kind of financial information have a positive impact on future earnings, and how high the forecasting accuracy is, which are critical for investors and other users. Accordingly, to determine the level of financial information affect the ability of future earnings prediction become as a urgent issue to be verified, which is the purpose of this article.
To help investors, creditors and other users to know better about the operation status and value of firms, analysts developed many methods to analysis firms' financial statements, meanwhile the results of these analysis also aid users to make decision validly. In addition, these methods can be used to test the effectiveness of listed companies' financial information, and some of them also have a strong ability to predict firm's payoff (Zheng, 2003). Especially, in these methods of financial statement analysis, linear analysis is commonly used, which adopted the multivariate financial model to realize its analysis. The multi- variables financial model is based on the standard model that derived from the data statistic method, using financial characteristics as its explanatory variables (Zheng, 2003). Moreover, the fundamental analysis is one of the important methods in linear analysis.
In this section, many scholars proposed their opinion on the conception of financial statement analysis. In addition, fundamental analysis as a method of financial statement analysis, it is also explained widely by scholars.
Definitions of financial statement analysis
It is stated by Ou and Penman (1989) that, the information from financial statements which are related to investment decisions are defined by the financial statement analysis. The analysis is aim to evaluate firm value from financial statements.To strengthen financial statement analysis, much empirical accounting research is made, try to find the accounting features which are related to firm value. In this work, it is supposed that market price is adequate to determine the value of firm, and thus act as a benchmark to assess the information in accounting measurements. Additionally, Accounting attributes adopted in the analysis are deduced to be value-relevant, for they are contemporaneously statistically correlated with stock prices.
It is said by Li (1991) that, financial statement analysis is also known as financial analysis. It refers that compare, analysis and research the relevant data in the financial statements, as to get knowledge of the financial status, operation problems of firms. However, it helps to forecast the trends of firm's future development, and provide scientific basis for decision-making.
It is interpreted by Ge and Liu (1999) that, financial statement analysis is a senior activity on information production. It refers to the information from the financial statements and related materials to make systematically analysis in depth. In addition, it suggested the trends between the relationship of related indicators and its change, in order to facilitate the evaluation and prediction of firm's financial activities and the relevant economic activities. With the result that, it provides users with more directly relevant information and more effective, help them make relevantly decisions on economy.
It is stated by Penman (2007) that, the essence of financial analysis is a process of judgement on searching the relevant financial information which related to decision- making, and then analysis and interpret them. Moreover, it is explained that, "financial statement analysis is the method by which users extract information to answer their questions about the firm"
It is stated by Bernstein and Wild (1999) that, "Financial statement analysis is a screening tool in selecting investment or merger candidates, and is a forecasting tool of future financial conditions and consequences". They proposed that, financial analysis is a distinguishing process to assess the current or past financial conditions and operating results of the business. Its main purpose is to make the best prediction of firm's future status and operating performance. Financial statement analysis lessen our sensitivity of hunches, guesses and intuition, but it decrease the uncertainty in decision- making.
It is indicated by Penman (2007) that, financial statements are usually considered as a place to find information about firms and actually information as such can be seen in the "analyzing information" step, which is analyzing the information in financial statement and outside of financial statement. It is the third step of his process in fundamental analysis, which will be introduced in the part of prior research of fundamental analysis.
Supposed all of the information investors need is easy to obtain, and it can be understand completely if it is examined and interpreted properly, and that the real challenge for the investor is combining the gap between finding information and applying to it the decision-making process (Thomsett, 1998). Nevertheless, well-facilitated fundamental analysis attempt to fill this gap.
Definition of fundamental analysis
It is defined by Thomsett (1998) that, fundamental analysis is a method of choice, since it provides dependable and consistent information. The tests by fundamental analysis, more than anything else, provide a means for comparison. Additionally, fundamental tests are a consistent application of standards to several companies. The fundamental approach use historical information (such as dividend rates, profits, or sales) to predict financial results(Thomsett, 1998). However, fundamental analysis is used to estimate the value of a company by investors, creditors and other users. It is a valuation approach, which employs basic accounting measures of financial fundamentals to forecast the future values of firm's financial attributes. It also be used to determine the risk and uncertainty associated with the future value.
It is identified by Penman (2007) that, predicting earnings to investment is at the heart of fundamental analysis. Generally, the standard regression model used in fundamental analysis is that:
R i is the explained variable, present future earnings. S ji is described as fundamental signals. This model is able to tell the correlation with the future payoff with firm's accounting characteristics.
According to Finweek (2006), the study of a company's basic and fundamentals with the motive of evaluating the worthiness of a listed company is known as fundamental analysis. This includes evaluation of the financial statements and ratios derived from the figures which responsible for giving more in depth knowledge of company's performance. Moreover based on the results of this analysis the investors will make their share trading decisions with that company.
As expressed by Spooner (1984), that the method of systematically modelling and evaluating the facts and figures of a company like economic and industry statistics, financial ratios etc. is called the fundamental analysis. The main aim of this analysis is to logical explanation and comprehensive knowledge and understanding of the observed phenomena and to make suitable decisions out of it.
Spooner (1984) also described the fundamental analysts as applied business scientists who observe any company with aim of modelling it and drawing conclusions for making progress. These analysts determine whether a firm is structurally fit to survive in a particular industrial growth and suitable for the dynamics of that economy. Therefore, these analysts contribute in a self-fulfilling way to the fitness of a company by buying the shares or debit issued by the structurally strong company.
The fundamental analysis helps us to ensure that a fall in share price is a good opportunity to buy or an indicator to sell because fundamental analysis points out the warning signs for a company which is on the verge of financial distress (Finweek, 2006). Moreover, it is defined by Salcodo (2006) that, fundamental analysis based on the theory that the price of a commodity at any given time is the equilibrium between .supply and demand.
It is stated by Lev and Thiagarajan(1993) that, Fundamental analysis are done to determine the value of corporate securities by examining carefully the key value-drivers, such as earnings, risk, growth, and competitive position. in this analysis, they classified a set of financial variables insisted by analysts to be useful in security valuation and examine these claims by assessing the incremental value-relevance of these variables over earnings. It is showed in their articles that, the relationship between earnings and fundamentals is considerably strengthened when it is conditioned on macroeconomic variables, thereby demonstrating the importance of a contextual capital market analysis.
It is demonstrated in Sneed's (1999) article that, "the importance of fundamental analysis is to identify factors other than prior years earnings that help to explain future earnings and to developed partitioning schemes that identify conditions under which measures are informative about future earnings." The fundamental analysis primarily aims to explain the relationship between financial statement information and future firm attributes and future earnings.
It is considered by Edirisinghe and Zhang (2007) that, fundamental analysis is the process of assessing a public firm's value for its investment by looking at its business at the basic or fundamental financial level. They defined the main goal of fundamental analysis is to reinforce the ability to forecast future security price movement and then use such predictions to design equity portfolios. Moreover, it is stated by Edirisinghe and Zhang(2007) that, Fundamental analysis takes on a more long-term perspective in determining which firms are most likely to perform well in the future, based on their fundamental business strengths.
It is sated by Thomsett (1998) that, "the fundamentals are-as their name implies-fundamental, basic, uncomplicated". The fundamentals information makes it possible for investors to know the financial consistency and reliability, profits, dividends, property and debts, and other dollars-and-cents information of companies. In addition, it is showed in his book that, by researching and analyzing fundamental financial information, investors could get access to forecast profits, supply and demand, industry strength, management ability, and other factors affecting a stock's market value and growth potential. The association between the fundamental signals and a company's intrinsic facts --- can be used to forecast financial performance and; to some degree, it is also able to predict stock price movements. More investors use fundamentals than other methods (Thomsett, 1998).
prior research of fundamental analysis
From the financial statements, the fundamental analysis identifies that company's value are displayed by its information. However, Ou and Penman emphasize that the methods about the firm's values extracted form financial statements are obscure. Prior papers do not show did not tell sufficient reasons for why financial statement ratios should be used, only gave the calculation of these ratios. Traditional financial statement analysis gives little guidance to support this task. Although Ratios are identified with such constructs as 'profitability', 'turnover', and 'liquidity', the relation among these operating characteristics to firm's value is not apparent. Ou and Penman's (1993) financial statement analysis try to put the conception of extracting values from financial statements into operation.
Although there have been many issues on market efficiency which is relevant to declaredly available accounting information, little research was done into the competing claim of fundamental analysis. Ou and Penman's article examined it. It contouredd a method of financial statement analysis that, acquiring a summary value measure from financial statements.
Ou and Penman's (1989) earnings forecasting studies using the fundamental analysis approach combined firms across industries when assessing their earnings forecasting model. Fitting one model across firms from different industries supposes that each variable has the same impact on the future earnings across industries. According to an extensive financial statement analysis, they have derived a summary measure from financial statements that forecasts future stock returns. It is derived from their study that the value measure based on observed association with one-year-ahead earnings and ignored earnings for years further in the future.
Ou and Penman conducted a research on the profitability abilities adopted at different trading strategies, which based on information from financial statements. They selected 68 financial variables as predicting variables, which is from the financial statements of companies listed at New York Stock Exchange. In addition, they set standards to simply measure current financial statements information, and defined as Pr, present the possibilities of one- year-ahead earnings growth. According to Pr portfolio, Ou and Penman establish a profitable trading strategy, which include long- term holding companies' stocks with possibly high growth of future earnings and short- term holding with possibly low growth of future earnings. Based on the data from fiscal year 1973 to1983, it is found that the return strategy in two-year' holding access to huge profits. This investment strategy of two- years' holding period got 14.53 percent to the hedge position to all stocks on the cumulative market adjusted rate of return . The one of three- year's holding period of Pr strategy got 20.83% on return. Therefore, they conclude that, financial statements include the information which can be used to predict the companies' future earnings' changes.
Lev and Thiagarajan (1993) made some studies on the relationship between fundamental financial information and the relevance of earnings. They selected the USA listed companies during the fiscal year 1974 to 1988 as research samples. In the selection of variables, they did not adopt the commonly used research terms, but followed the description of analyst, choosing 12 financial variables as independent variables, which are inventory, account receivable, capital expenditures, R&D, gross margin, selling and administrative expenses, provision for doubtful receivables, effective tax rate, order backlog, labor force, LIFO earnings and audit qualification.
Based on these two fundamental models, Lev and Thiagarajan started the fundamental analysis. They examined the relationship between future earnings and fundamental variables in there study. However, it is proved by Lev and Thiagarajan's (1993) finding that there are relevance between majority of basic financial information which are determined and incremental value of listed companies.
According to Selling and Stickney (1989) the environment faced by the different firms is different and also the market in which the products of these firms compete. They also said that business strategies and microeconomics lead to the difference in firm's profit margin and assets turnover. Moreover, these differences are caused due to different activities pursued by the firms in response to their competitive environments. Since an earnings forecasting model including financial statement information beyond prior years' earnings captures information about firms' operations, industry-specific models should reduce model error because firms in the same industry confront similar competitive environments.
Sellingand Stickney's  proposed that firms in different industries face different competitive environments and improvise adopt appropriate business strategies. As a result; the relations between earnings and firms' activities are expected to vary across industry. The industry-specific forecasting models should improve the explanatory power related to a model including firms from different industries because of the varied relations of models across the industry. The relations between the financial variables and future earnings can be easily interpreted by the industry-specific models.
The aim of Sneed's research is to decide whether the relationship between the independent variables and payoff are steady across industries in an earnings forecasting model, which is implicitly assumed when models are generated cross-sectionally.
In Sneed's (1991) study, it limited the sample to 3 industries in different environment to fit industry-specific models, which are crude petroleum and natural gas industry (SIC 1311), eating place industry (SIC 5812) and electronic computers industry (SIC 3571). This study try to prove whether an industry's environment influence the relationship between future payoff and the dependent variables. It adopted Ou's model as the earnings forecasting model.
During the analysis, Sneed made a hypothesis that whether those independent variables and payoffs are the same across industries. After doing the F-test, it is rejected. In addition, through the analysis, Sneed got some result. It is implied that the relationship between the independent variables and payoffs alter considerably across industries. Moreover, his results showed that some industries will be easier to forecast future earnings than other industry. In his analysis, it is illustrated that a main aim of fundamental analysis is to interpret the coefficients when models are developed across industry. Finally, this study got a result that each industry should be modelled separately in order to interpret the relation between firms' activities and earnings. Moreover, because of the huge difference between explanatory variables and earnings across industries, as a result misleading information can be provided by the combining firms across industries in the same model. It also indicates the significance of considering the competitive environment in an industry while interpreting accounting information.
It is stated by Penman (2007) that, "Fundamental analysis is the method of analyzing information, forecasting payoffs from that information, and arriving at valuation based on those forecasts." In addition, Penman set 5 stages of process for the fundamental analysis. The first step is that understanding the business firm adopted, such as the firm's products, marketing and production methods, market competition and regulatory constraints; and knowing the strategy which helps firm to appreciate. The second step is dealing with the data inside the financial statements, such as sales, cash flows, and earnings; and information outside the financial statement, such as consumer preference technological change. The third step is the phase of developing forecasts, which is the most important one in Penman's 5 stages. It is specifying and forecasting future earnings from operation. The final one is changing the forecast into a valuation. The final step is trading on the valuation for investment decision. It helps inside investor to make decision of cost strategy, and helps outside investor to decide trading value. In brief, fundamental analysis is a process that changing the knowledge of business into a strategy and trading valuation. During the process, the step 2 to 4 is piloted by the valuation model which used by analyst.
Based on Lev and Thiagarajan's (1993) cross- section multi-linear regression of, Abarbanll and Bushee (1997) established a future earnings forecasting model which adopted in listed companies. They identified Lev and Thiagarajan's 12 fundamental signals, and chose 9 financial variables as fundamental signal to the companies' future earnings change, which are inventory, accounts receivable, capital expenditures, gross margin, selling and administrative expenses, effective tax rate, earnings quality(LIFO, FIFO or others), audit qualification and labor force. They use the multi- linear regression; combine the firms' current financial data to the change of next year's earning. It is found that the accuracy of the forecasting is very high. The basic regression used in Abarbanll and Bushee's study is that,
It is a regression of future change in EPS on prior changes in EPS and fundamental signals. In this model, ?EPS is the change in earnings between yeart-1, and t. GHGEPS is the change in EPS between years t-1 and t.
However, it is proved in their research findings that, financial information have a strong ability to predict future earnings; investors can use financial fundamental information to forecast companies' future profitability, and then to calculate the stocks' future returns.
The selection of fundamental signals
In the fundamental analysis, the fundamental variables which used by scholars are frequently different. Additionally, in those regression models, they take advantage of different range of signals and apply them into different fields. For example, most of the models use Inventory as a leading signal in their models. In this part, some signals which are commonly been used is discussed.
Inventory and account receivable
It is showed by (Bernard and Noel, 1991) that, "inventory disclosures are incrementally useful in predicting sales and earnings". In addition, they take manufacture as an especial example to tell the relationship between inventory and future earnings or sales. They found that unexpected changes in raw materials and work-in-process inventory are positive leading indicators of future sales. In contrast, unexpected changes in manufacturers' finished goods inventory have little or no relation with future sales, and are negative leading indicators of future earnings, even after controlling for the impact of current sales on inventory levels.
Bernard and Noel (1991) focus on the inventory disclosures, not some other characters of the accounting system, there are some reasons. First, the production-inventory cycle is an important element in financial statement analysis. Second, although economists do not claim to understand inventory behavior well, there are established models of inventory behavior that can guide the analysis. Third, a firmer understanding of inventory behavior could enhance the ability to explain the results of several recent studies of the information content of accounting accruals, as well as some more general studies of the information reflected in accounting variables. It is stated by Wilson (1986) and Rayburn (1986) that, the incremental information in the current accruals component of earnings is in advance of cash (and /or working capital) from operations. And then, a significant portion of current accruals can be attributed to changes in inventory levels.
Account receivable is also one of the leading indicators in fundamental analysis. It is mentioned in Bernard and Stober (1989)'s article that, financial analysts often indicated that receivables support important clues on a firm's past and future performance. In addition, for manufacturers, it appears to be most advantageous indicator to forecast future earnings and margins. Unexpected receivables balances are strong negative leading indicators of earnings and margins for all prediction horizons, in accordance with an "earnings quality" explanation.
Assumed that current and prior sales are already known, however, Bemard and Stober (1989) investigate the ability of both inventory and receivables balance to forecast future sales. Additionally, it is found by them that, the incremental usefulness of receivables in predicting future sales were sensitive to the deletion of outliers, many of which appeared to be due to acquisitions and divestitures. Their study extends the results of Bernard and Noel's (1991) to provide evidence on the incremental information content of receivables in predicting future sales, earnings, and profit margins. This is done by expanding the prediction equations of Bernard and Noel to include unexpected receivables along with the unexpected portions of inventory and its components. There are two aspects include by the potential usefulness of such evidence. The first one is obtaining such evidence represents one more step toward filling in the gaps necessary to understand and explain stock price reactions to unexpected accruals. The second one is more generally; this type of evidence is a logical building block in better understanding fundamental analysis. It is also suggested by they that, for retailers, receivables balances are not useful in predicting sales, inventories or margins.
It is indicated by Lev and Thiagrajan(1993) that the two signals Inventory and receivable are identified as leading indicators for future earnings.
Order backlog, which identified as a leading indicator of future sales and earnings by Lev and Thiagrajan(1993), is generally explained as the dollar amount of firm unfilled orders at year-end. Additionally, it is said by Lev and Thiagrajan(1993) that, Changes in order backlog relative to the level of operations are frequently used by analysts to signify future performance, particularly in the high technology and heavy industries (such as software, semiconductors, steel and aircraft manufacturers).
Financial analysts frequently comment favorably on announcements of corporate restructuring, especially labor force reductions. This provides yet another example of analysts' adoption of fundamental signals to assess the persistence of earnings, since in the year of a significant labor force reduction wage-related expenses (such as severance pay) commonly increase. Reported earnings, in such cases, do not mirror the future payoff from restructuring, and fundamentals, such as the signal of labor force, are used to provide a better estimation of future earnings (Lev and Thiagrajan, 1993)
Order backlog, as a leading business indicator, has been shown to be a strong indicator of firms' revenue in the future, because usually only orders that are secured by a down payment will be included in the calculation of order backlogs. Comparing to other leading indicators such as customer satisfaction, book-to-bill ratio, patent counts, product market share and web traffic, information on order backlog is more objective, available for a large number of firms that span most industries and it is comparable across firms.(Order backlog and momentum profit)
Although analysis the financial statement with fundamental analysis gives help to internal and external users, there are still some limitations with it, such as the quality of financial information.
Limitation of financial fundamental information
It is demonstrated by Thomsett (1998) that, accounting is the basis of fundamental analysis. The consistency of information is based on the quality of financial statements. Accounting has specific assumptions and basic measurement, while the financial statement is a product derived from accounting. It means that there is unavoidable for the implementation of financial statement been restricted by accounting standards and regulations
It is found by many scholars that, the publication of financial statements lagged. It makes the data which is used for financial analysis historical. Moreover, it lead to a state that, the results of the financial analysis can be only played for reference to predicting and decision-making. Timing is one limitations of financial information, and there is another important one being concerned which is the authenticity of published financial statements and information. It concerned that whether a company could present a true and objective financial statement to mirror its financial position, operating results and cash flows. In addition, whether a company is strictly following the accounting regulation and accounting standards. If the financial Information lack of authenticity, it will directly affect the validity of analytical results.
Furthermore, it is stated by analysts that, the consistency of accounting measurement methods will also affect the financial information. If a company use different methods in different fiscal period, it will provide data in different standards, which in some extent will affect the accuracy of the result from financial analysis. And also, different accounting method adopted in different companies, will cause errors when comparing data among them. For example, if one company use LIFO method to values its inventories, and while another company use the average cost method. It will be misleading the direct comparison of financial data such as inventory valuations and cost of goods sold.
Limitation of fundamental analysis
As proposed by Montgomery and Peck (1982) that the failure to control the differences between the explanatory variables and earnings can result in significant error in the forecasting model if the relation between the explanatory variables and earnings are not stable across industries. These differences also will make it difficult to interpret the relations between the explanatory variables and future earnings as the relations vary across industry.
Spooner (1984), suggested that as there are many complex factors involved in an environment therefore the fundamental analysts must recognize that a relatively modest debt to equity ratio or a wide gross margin or current ratio will not always best suit a company for a particular environment, hence the facts must be evaluated in context.
It is suggested by Ou and Penman (1983) that, the financial statements catch fundamentals which are not visible in prices. Thus, it highlights the limitations in the traditional approach in experimental analysis to calculate and make inferences about accounting numbers on the basis of synchronous associations with prices. Much of that research is derived from the work of Ball and Brown.
It is reported by Ball and Brown (1968) that, the annual report does not assess highly as a timely medium, for the majority of its content is captured by more timely media which perhaps include interim reports. Additionally, since the efficiency of the capital market is largely determined by the adequacy of its data sources, there has been a tendency for the market turn to other sources which can be acted upon more promptly than annual net income. They proposed that the relationship between the significant (and not only the sign) of the unexpected earnings change and the associated stock price adjustment could also be investigated. In addition, it is identified by they that there are some difficult econometric problems related with the relationship, including specifying the appropriate functional form, the expected statistical distributions of the underlying parameters, the expected behavior of the regression errors, and the extent and effects of measurement errors in both dependent and independent variables.
It is said by Thomsett (1998) that, fundamental analysis is able to a valuable tool if used as research for long-term prospects, but not for tracking day-to-day stock price movement, market reaction to news or rumor, and temporary popularity of one industry group over another. However, fundamental information is not difficult to find.
Timing is a problem of fundamental analysis. There is no timely fundamental information available, and financial statements are normally two or three months old by the time they are read. This situation should be thought of in light of an earlier observation: The moment-to-moment changes in the market, movements in the indexes, and news or gossip have no long-term effect on the investment decisions you make today. It is recognized by Thomsett (1998) that, the limitations of short-term fads, predictions, and odds-beating games; and accept the premise that the market rewards investors over time.
In addition, it is proposed by Salcodo (2006) that, fundamental analysis is useful to investors but not comprehensive. It is necessary for them to understand the fundamental drivers of market before trading, but should not make decisions with fundamentals alone.
Chapter Three Characteristics of China's retail industry
Retail is an industry which is the closest one to consumers, and it's an industry with a promoting future. Whatever the number of employees or the volume of sales, the retail industry occupies a very significant proportion in China's national economy. The retail industry learnt the operating method from from the manufacture industry, and practiced the chain management operation. In some extent, it realized the industrialization of retail. In addition, there will be much greater potential of retail industry than that of manufacture, once it industrialized. The reason is that, it is very difficult for manufacturing firms to realize multi-production management. In other words, these companies normally produce one line production at most. For example, firms who produce electrical applications seldom to produce clothing production, and stationery production companies are not going to produce dentifrice. However, retail industry can easily achieve multi-line operation, since the production which retail firms sales are exactly diversified lines of products.
According to the statistics from China National Commercial Information Center, 497 large-scale retail enterprises have exceeded 100 million RMB in their operating revenue, which are mainly department stores, supermarkets, household appliances specialty store, furniture, building materials specialty store). It is indicated that the economic returns of China's large-scale retail enterprise was growing rapidly in 2007(Beijing Business, 2008).
From joining the World Trading Organisation, China's retail industry has become to show remarkable development with two characteristics: First, China's consumer market is developing stably, and obviously presents an accelerated growth trend. It is said that China's total retail sales volume of social consumer goods reached an annual average growth rate of more than 10%, which provides a foundation and an important market conditions to growth of retail enterprises and China's retail sales development.
Second, the degree of opening up of China's retail industry constantly improved (by the end of 2004 has been fully open), the fast entering and expanding of foreign-funded retail enterprises, intensify the competition in China's retail industry. It has triggered an unprecedented expansion of domestic retail business boom.
In this market environment, China's domestic large-scale retail enterprises are ushering a rapid expansion. According to incompletely figures, in the year of 2006, the sales volume of Top 100 retail enterprises in China had reached 874.3 billion RMB. It is 3.7 times of the sales in 2001.
However, China's current retail market is still in developing stage. Compared with developed companies, its variety and quality of merchandise, management level and operation mode of enterprises still have a significant distance from them. Meanwhile, China is now in a period that transit from a planned economy to market economy, and its market mechanism has not been fully established yet. Moreover, the enterprises are also in the process of reform, adjustment, and adaptation; have not strong enough for competitiveness.
From the year of 2002, China abolished the region, equity and quantity restriction which is for foreign-funded commercial enterprises. It retail market is gradually open wide to the foreign investment. Furthermore, with the entry of large-scale translational retail group, China's domestic enterprises are faced with severe challenges. Accordingly, they are attempting to inspect the development trend of global retail under the economic globalization; compare and analyze the strengths and weaknesses of the domestic and international competition in retail industry; in order to find appropriate countermeasures to enhance China's international competitiveness of retail industry.
After getting access to the WTO in 2001, China has generally liberalized the retail market, and yet the joining of international retail enterprises accelerates the further development of China. Therefore, China's retail markets will directly facing the competition with international enterprises.
Chapter Four Hypothesis
There are many characteristics which are related to the forecasting of future earnings of retail companies. In addition, some of the characteristics affect the future returns a lot, while the others can't influence return in some extent. This chapter present the hypothesis of this financial statement analysis. These hypothesis is based on the fundamental signals, which proposed by Lev and Thiagaraian (1993).
Inventory is one of the key factors to affect the payoff of the retail business. And then in this signal, inventory management model is a crucial point to control the inventory level in the operating of retail chain business. It is said that the inventory of Retail and commercial enterprises frequently occupied as high as 80% in the proportion of liquid assets. Therefore, how well of the inventory management methods will directly affect company's operating performance, then affecting the corporate earnings.
Different from manufacturing industry, it does not add value on product, but to provide additional services on the product. Thus, the target that sell the products as soon as possible, have become the main task of retail enterprises. For present retail competition in the market, it is not only focused on providing customers with quality service, good value for the full range of goods, but the more crucial is to reduce costs meanwhile providing the most comprehensive business services. However, the cost of the chain retail enterprises including procurement cost, purchase cost, cost of carry, shortage cost, and quality costs, while the occurrence and amount size of these costs , are closely related to the inventory management model of chain retail enterprises. Choosing the right inventory management model is the key for chain retail enterprises to win the market
The control management of inventory is very important to retail enterprises. The reason is that, the inventory control is control of the time and bulk of inventory purchase, in order to realize the lowest inventory cost by proper amount of inventory, which is a protection to enterprises' operating activities. In addition, the stock commodity of companies will not only occupied funds, produce the cost of inventory maintaining, but also excess inventory will backlog companies' working capital to inventory, so that make unnecessary losses to business. However, the occupation of funds will affect firm's future earnings.
It is said by Bernard and Noel (1991) that, for retailers, inventory is negatively correlated with future earnings, even if it got a positive correlation with future sales, for this affect was short-lived.However, in some extent, if a company have high inventory, it will be commonly considered that there are some problem with the company's operating performance. As a result, it affects company's future sales. It is expected that inventory is negatively related to firm's future earnings.
H1: Inventory is negatively correlated with future earnings.
Accounts receivable has become one of the major fundamentals, which is concerned by the company's financial management. It has an important impact on the company's production and operation. It can help to increase the current sales revenue, and take advantage of market-occupation. Nevertheless, it will also delay company's time to receive payment, reduce the cash flow and increase the cost to manage the accounts receivable of company. In some extent, it zooming the operational risks of company.
This signal is often seen as a clue of impending earnings problems: but, it can also mirror the situations where growth in sales and profitability is elevated and/or supported by the expansion of credit.(Abarbanell and Bushee, 1997)
In O'glove's(1987) views, it is clearly regarded unexpected increases in receivables as "bad news." The difficulty comes when accounts receivable rise substantially over what they had been in the same reporting period during previous years. This can result from any of some factors. A spell of economic hard times for the country, industry, or region will often cause stretch outs in payments. A poor collection job might be another reason. Perhaps the retailer, his back against the wall and eager to make sales, has provided his customers liberal credit terms. It is proposed by O'glove (1987)that, whatever the cause is, major increases in accounts receivable is a danger sign.
It is said by Lev and Thiagarahan (1993) that, incremental account receivable may present problems in sales. Therefore, it reduces future earnings from the incremental receivables' provisions. In addition, the high accounting receivable may imply adjustment of earnings that write the unrealized revenues as sales. It adds value on the sales of current fiscal year, meanwhile, subtract return in future.
H2: accounting receivable is negatively related with future earnings.
It is believed that only low-cost operation can obtain high returns. Companies with High Capital spending only can provide investors with a minimum rate of return, while companies with low capital spending can provide an astonishing rate of return. The U.S. stock market, for example, almost in the half a century, those companies who spend few capital expenditures, realized the S & P 500 index level which is above 3.5% each year. It is also confirmed by Buffett that, in one word, only low-cost can get high returns. Negative correlation, said by Siegel said that, high capital expenditure meant the disappearance of profits and destruction of value.
It is said by Kim (2001) that, Despite of recent findings that capital expenditures are relevant to firm's value, papers have not shown direct evidence that a positive linear association exists between capital expenditure and future earnings. Moreover, it found that, it appears a strong positive (negative) linear association between capital expenditures and future earnings, when firms are without (with) at least one year of losses in the next five years (Kim, 2001). The signal is found to add value to (rather than deduct value to) future earnings for the majority of firms with future losses (i.e., those with no more than two years of losses).
However, it is found evidence that capital expenditures exhibit a positive linear association with future earnings (Kim, 2001). In addition, It has been shown, for instance, that increase in capital expenditures and its lagged terms are not positively related to next period' earnings. After the examination, he found that capital expenditures provide incremental information content to earnings.
The capital expenditures signal is significant mere in the high-growth years, suggesting that during economic expansion firms are expected to increase capital expenditures, and when this fails to materialize investors react negatively (Lev and Thiagarjan, 1993). It is expected that capital expenditure signal is negatively related with future earnings of retail industry.
H3: capital expenditure is negatively related with future earnings.
Research and development expenditure (R&D)
Relative decreases in capital expenditures and R&D intensities are often perceived negatively by analysts (Lev and Thiagarjan, 1993).The largely discretionary nature of these expenditures makes disproportionate decreases a priori suspect. The decrease of the capital expenditures may call managers' consideration on the adequacy of the cash flows to keep the previous investment level in the current and the future period. Similarly, a decline in R&D may wake up management's consideration on the adequacy of reported earnings and suggest attempts to promote earnings through decreasing the expenses which are spent on R&D. Generally, the drops in capital expenditures and R&D are equated by analysts with a short-term managerial direction, at the same time, the improvement in these items begin to achieve well for future earnings and cash flows.
Unlikely, the theoretical relation between capital expenditures (or R&D expenditures) and sales is tenuous, which is different from the preceding inventories and accounts receivable cases. For example, the research done in 1988 points out that comparing with current sales, the liquidity position and cash flow are stronger determinants of investment (capital expenditures and R&D) decisions. Because of the lack of a clear theoretical guidance, an industry benchmark is selected by researchers for the capital expenditures and R&D innovations (signals) and defined them as the annual percentage change in total two-digit industry capital expenditures (or R&D) minus the annual percentage change in the corresponding firm's items. As far as other signals are concerned about, the measures are considered to produce a negative correlation with stock returns that is because positive values of the two signals (i.e., industry growth larger than the firm's) deliver bad news. therefore, there is a negative relation between those measures and stock returns.
It is examined in Ho, Liu, and Schaefer's (2007) study that, whether reported values for firms' research and development (R&D) have impact on analysts' annual payoff prediction revisions following quarterly earnings announcements. However, they found that, R&D expenses are positively related with analysts' forecast revision activity. In addition, a positive and significant association was found between the level of R&D expenses and the magnitude of analysts' forecast revisions following quarterly announcements. These results signified a greater amount of analyst scrutiny when reported earnings are accompanied by high levels of R&D expenditure.It is expected that there is a positive relationship between this signal and future earnings in retail industry.
H4: R&D expenditure is positively related to future earnings.
Gross margin (GM)
It is illustrated by Lev and Thiagarajan(1993) that, a disproportionate (to sales) decrease in the gross margin balance (sales minus cost of sales) is viewed negatively by analysts. Gross margin is generally a less noisy indicator than earnings of the relation between the firm's input and output prices. This relation is driven by underlying factors, such as intensity of competition and the relation between fixed and variable expenses (operating leverage). Variations in these fundamental factors (indicated by disproportionate changes in gross margin) obviously affect the long-term performance of the firm and are therefore informative with respect to earnings persistence and firm values.
Abarbanell and bushee(1997) said that, the GM signal is negatively related to future earnings in outlay the high P./E partition. Combined with a similar result for the bad prior earnings news staples, the evidence suggests that the GM signal is likely to be most informative about future earnings for firms with temporarily low earnings.
H5: Gross Margin is negatively related to future earnings.
Sales and administrative expenses
Selling, general, and administrative expenses were the largest component of total costs, so if it the increase of sales and administrative expenses will lead a decrease of firm's profits. Therefore, it is expected that this signal is negatively related with firm's future earnings.
It is stated by Lev and Thiagarajan(1993) that, most administrative expenses are approximately fixed, accordingly, a disproportionate (to sales) increase is considered a negative signal suggesting, among other things, a loss of managerial cost control or an unusual sales effort.
H6: S&A expenses is negatively correlated with future earnings
Provision for doubtful receivables
Firms with insufficient provisions for doubtful receivables are supposed to suffer future earnings decreases from provision increases. The sources from prior study commonly referred to the adverse implications of inadequate bad debt provisions (in recent years particularly for loan losses of financial companies) for the persistence and growth of earnings (Lev and Thiagarjan, 1993).
It is interpreted by McNichols and Wilson's (1988) examination that, some firms are exercising discretion over the level of the provision is able to cause a small percentage changes in reported revenue. Indeed, their evidence suggests that discretion in the provision for bad debts averages from 1-4% of income (for firms with extreme earnings). Although this may seem immaterial, exercising discretion over the provision for bad debts, together with other discretionary accruals, can help managers achieve target revenue when their annual earnings growth targets are 10-15%. (McNichols and Wilson, 1988) It expected that the signal is not significantly related to future earnings.
H7: Provision for doubtful receivables is not significant related to future earnings
A considerable change in the firm's effective tax rate which is not originated by a statutory tax change is frequently considered transitory by analysts. In consequence, an unusual decrease in the effective tax rate is generally considered a negative signal about earnings persistence (Lev and Thiagarjan, 1993).
It is showed in Kutcher, Guenther and Jones' study (2009), that the persistence of the tax change component of earnings, interpreted as earnings resulting from a change in a firm's effective tax rate (ETR), is a complex combination of both the persistence of pre-tax earnings and the persistence of the ETR. Thus even when ETRs are highly persistent, the tax change component will tend to be less persistent than other earnings components.
H8: ETR is significant related to future earnings
Order backlog is the aggregate of the sales price of orders received from customers minus the revenue recognized. It represents the unfulfilled portion of contractual orders and is an important leading indicator of future sales and earnings. The level of order backlog is associated with product cycles. Firms with long product cycles tend to have higher order backlogs, while those firms with short product cycles are likely to have lower order backlogs.
It is identified by Lev and Thiagarajan (1993), to indicating real changes in the demand for the firm's products; a relative (to sales) decrease in order backlog may suggest that yet unrealized sales were recorded in the current period, an "earnings management" procedure prevalent among high-tech companies
They provide evidence suggesting that the stock market overweighs the contribution of order backlog in predicting future earnings. They find that firms with higher order backlog earn lower future returns.It seems that order backlog is more functional to industries such as manufacturing. It is suggested that this signal is insignificantly related to future earnings.
H9: Order backlog is not correlated to future earnings
Financial analysts generally comment favorably on announcements of corporate restructuring, particularly labor force reductions. This provides yet another example of analysts' adoption of fundamental signals to estimate the persistence of earnings, since in the year of a significant labor force reduction wage-related expenses (e.g., severance pay) generally increase. Reported earnings, in such cases, do not reflect the future benefits from restructuring, and fundamentals, such as the labor force signal, are used to provide a better assessment of future earnings.
H10: Labor force is significantly related to future earnings
When input prices are increasing, LIFO earnings are regarded as more sustainable or closer to "economic earnings" than FIFO earnings, since LIFO cost-of-sales is a closer proxy to current (replacement) cost than FIFO cost-of-sales. Therefore, The adoption of the LIFO inventory method is, considered a positive signal,
There are two characters of LIFO's purpose. The first one is to avoid paying taxes on inventory profits, and while LIFO's function is to offset losses. The second one is to stabilize earnings. In unfavorable operating years, "LIFO Charges" generally further reduce reported results. In contrast, in favorable operating periods, "LIFO Credits" gradually add to reported earnings. The resulting large swings in profits create interesting stock market situations, as security prices in the past have tended to move in concert with the earnings fluctuations. (Spencer, 1967)
However, Spencer, 1967 found that, the fluctuation in earnings under LIFO is much greater. If the Company had valued its Inventories on a FIFO basis, the swing from trough to peak earnings would be much lower than that on LIFO. In broad terms, LIFO inventory accounting affects earnings of individual companies as well as the industry as a whole.
H11: LIFO earning is significantly to future earnings.
It is examined by Bartov, Gul, and Tsui (2000) that, high discretionary accruals indicate earnings manipulations. Thus, if discretionary accruals indicate earnings manipulations, they should be associated with the likelihood of auditors' issuing qualified audit reports. In addition, it is described by them that, the role of the auditor is regarded as a monitoring mechanism to reduce agency costs, which include managers' incentives to manage earnings. It also reduces positive bias in pre-audit net earnings and net assets. A qualified, disclaimed, or adverse audit opinion obviously sends a negative message to investors.
H12: Audit qualification is positively related to future earnings
However, these 12 hypotheses will be examined by the model. The result for accordance and discrepancy between hypotheses and test will be explained by the section of summary in the following chapter.
Chapter Five Fundamental analysis
This part is mainly doing the specific analysis. It starts with the introduced the model which is used in this analysis. And then guide in the samples which will be analyzed. After the examination, it educed some results of the model, which is analysis in the article.
Model used in fundamental analysis
Studying the links between fundamental signals and future earnings changes allows us to test directly the validity of the economic intimation that underlies the original construction of the signals. An alternative, and less direct, approach, followed by Lev and Thiagarajan (1993), is based on an examination of the relations between the fundamental signals and contemporaneous returns.
Sample for fundamental analysis
The sample used in this analysis is consisted of 48 retail companies from 171 listed companies in Osiris system. They are chosen from listed companies in retail industry, which listing on Shanghai and Shenzhen Exchange market. Data is collected from 2005 to 2008, 4 - years' period. Some companies which included in these 169 available companies is obviously not belong to retail industry, such as banks and tech companies, were excluded from the sample; and some other companies whose operating business is manufacturing, is also excluded. In addition, there are few companies have some data unavailable, unable to get fundamental information, so they are abandoned. The chosen sample is including the retail companies which mainly operate on supermarket, department store and others. After sifting through those listed companies and getting the sample of 48, it has been considered that the size of the selected sample is too small. But when attempted to get more companies, it is found that only 49 retail companies are available at the search summary of listed company, world region - China, and NAICS 2007 - retail trade (44&45), among which there are even some companies new listed. Therefore, the original restricted sample of 48 is adopted.
Results of the analysis
This part is the specific study of fundamental analysis. It applied the data from the sample mentioned above into the linear regression model of fundamental analysis. Firstly, the financial information which used for calculation of fundamental signals is collected from the financial statements of the samples. They are including pre tax earnings, sales, account receivables, no. of employees, gross margin, inventory, accounting methods and others. Secondly, according to the calculation method of section 1, this financial information is used to obtain the numeric of 14 signals, which include 1 dependent indicator, 13 independent indicators. Thirdly, importing the numeric of indicators into SPSS system, and then come out the result of linear regression analysis.
The descriptive statistics for all variables of the chosen sample are shown in table 1. It presents the most basic information of the 14 indicators, which are mean value, standard deviation, and number of samples; and these 14 indicators are adopted in LT's model, which have been introduced in previous sections. These data is calculated by the base of financial information, which is from the 48 companies' sample in retail industry.
It is showed in the table that, the signal ?PTE have the greatest value on average, which is 1.68 in positive. And yet, the signal of order backlog gets a smallest mean, which is a negative number of 0.619. There is a gap valued 2.399 between ?PTE and order backlog. In addition, the dependent variable future earnings take a positive mean about 0.227, which is much higher than inventory. Additionally, inventory and account receivables which are frequently used as important signals in fundamental separately got a positive 0.0533 and a negative 0.0104 for their mean value. And then, R&D expenditure got the highest value of standard deviation, while LIFO/FIFO got the smallest one.
Table 2 displayed the result of the estimates of the full sample adopted in fundamental analysis' model. It is depicted that, the R square of tested model is a positive number of 0.197, which is quite small. It is said that if the closer the value of R square is to 1; the stronger the interpretation ability of model's independent variables is, to explain the dependent variable. The absolute value of the R square of this model is 0.197, which is much lower than 1. However, this restricted sample is not especially representative, because it is only chosen from listed companies, which are only concentrated in those large-scale chain companies. Additionally, it may only display half scene of retail industry, and also its size is very small. It is indicated that the model can only explain only 19.7 percent of these predictors. To some extent, this model is not ideal in interpreting in this sample.
Table 3 presents estimates of specific industry regression and a significant test. From the table of coefficients, it can be seen that, the absolute number of inventory's t value is 1.824, which is high than the level of 1.5; it means that the yearly coefficient of inventory is statistically significant related to future earnings. In addition, it got positive 42.9% on Beta, which is suggested to have a positive 42.9% impact on the dependent variable of tested model. Moreover, there are 2 indicators whose absolute t value is between 1 and 1.5. It is indicated that to some extent they are associate with future earnings, but not significant. Those indicators are capital expenditure and gross margin. They are separately positive 20.1 percent and negative 56.6 percent correlated with future earnings. It is shown in table 3 that, majority of the signals got a absolute number of t value which is lower than 1 level, which are account receivable,R&D expenditure, S&A expenses, provision for doubtful receivables, effective tax, order backlog, labor force, LIFO/FIFO earnings, audit qualification and ?PTE. It is demonstrated that, these variables are not associated with future earnings.
Despite that the model adopted in this sample can not precisely interpret those fundamental signals, to a certain extent; it can depict some problems on these signals. An extensive reason to explain this result of yearly coefficients in table 3 is that, the operating management of China retail market is not mature. In many companies' process of operation and administrative, their technology are relatively lagged. In other words, they lack of advanced technical method to supervise the companies' performance. Therefore, it lead to a result that, retail companies is more focusing on the basic signal ---- inventory which is most easy to be in touch with and pay less attention to project such as research and development expenditures. And also, it is possible that the operations of many companies do not strictly follow the regulation on their accounting treatments; it draws the data from the published financial statements on inadequate sensitive to respond to the examination of fundamental model.
In this section, it compared the test result with 12 hypotheses. In addition, it analyzed the difference matching states between the test and hypothesis. And then it summarized the possible factors which influence the result.
The coefficient of inventory got a t value at 1.824(beta equal to 0.429). It presets that the test result of inventory is not consistent with H1, which assumed that the signal is negatively related to explained variable. It is proved that there is a significant association with inventory and future earnings, and yet the signal has a positive 42.9% relation with the dependent variable. In addition, this result of test is different from the result of Lev and Thiagarajan's (1993) across-years significant test; it indicated that inventory is negative related to future earnings, which is in accord with H1. The discrepancy between the test result and prior study can be explained by the characteristic of retail industry and Chinese market. Inventory, in fact, is especially important to Retail industry. It is not like manufacturing industry that has its own line products; it retails various brands of products in different categories from different suppliers. To maintain going concern of trading, retail companies need to purchase numerous series of merchandise ahead. To present good operating performance, retail companies must have sufficient inventories. In particular, the management of Chinese retail industry have not achieved informationization. Its level of logistics is relatively backward, which accompanied with the low efficient of distribution. Moreover, because of the high cost of stock holding, a high level of inventory tends to embody a financial strength of a company.
It is presented in the test result that, account receivable got a t value at negative 0.794, whose absolute value is lower than 1; it is suggested that it is basically not related to future payoff in this specific- industry analysis. As a result, H2 is rejected, which is assumed that account receivable is negative related with the dependent variable. LT's study is agreed with H2, but incompatible to the test result of this study. It can be explained by some facts. In general, it is traded by cash or bank cards (including credit card and debit card) in retail market. It educes a relationship between banks and trading companies that, which is the cash flows. Possible accounting receivables may be caused by the delay of process that bank transfer the trading funds from buyers to seller. In addition, to promote the sales of stores or department, some companies will propose advantage program, such as selling merchandise on the instalment payment. It is likely a source of retail companies' account receivable. Retail industry, as it were, not like manufacture industry, in which the account receivables occupied large proportions of assets. On the contrary, retail companies largely engage the funds of manufacture companies (supplier).
The result of the test illustrated that, Capital expenditure has a positive association with future earnings (absolute value of t is 1.021, more than 1), which is in accordance with Abarbanell and Bushee's(1997) examination. However, the test result of coefficients presents that, an increase on capital expenditure is positive related to the increasing of future revenue. H3 is opposite the result of test, rejected. This result can be explained by enterprises' motives to increase revenue. It is found that, many companies try to add value to its capital expenditure (such as regularly decorating their stores or departments), as to increase the distribution of sales. In addition, several retail companies boost their sales by adding services on products (such as provide a good environment for consumers).
However, the result of the examination does not conform to H4. The t value of R&D expenditure is -0.207, whose absolute value is much lower than 1. It means that the signal is not a correlated signal to future earnings in this specific-industry analysis. It is also found in LT's study, which is interpreted as a failure to capture the R&D innovation. In addition, it is related to the characteristics of China's retail market. Many retail companies do not realize the importance of advanced technologies in trading, and also lack of innovations in inventory management. For many reasons, such as affected by the traditional culture or lack of qualified managers, it is a bit difficult for part of companies in China input large amount into R&D expenditure. Therefore, it is found in the research that this information is not available in many companies' financial statements.
Gross margin, is described as being a negative 56.5% relation with future earnings in the section of coefficients, which got an absolute t value at 1.223. It is proved to be in accordance with H5. Additionally, the negative relationship between future earnings and gross margin is also examined by LT's study. It is mentioned in previous section that, there are some potential reasons existing in retail companies, such as the intense competition and relation between fixed and variable expense. It has been introduced that, especially, retail is an industry with intensive competition; frequently win the market at pricing of merchandise. To some extent, an incremental value on gross margin will create a positive influence on the increase of future earnings.
In general, selling and administrative expenses should be a great expenditure in retail companies. However, H6 is rejected by the test, for it has a t value at negative 0.872, whose absolute value is lower than 1 level; the signal is not related to future earnings. The result is also testified by Abarbanell and Bushee, but LT's test shows a negative relation. There are some potential factors to explain the result of test. As mentioned that, the idée of operating management which used by retail companies in China is not still not open-minded. In the long run, managers still attempt to increase their earnings by reducing costs. Additionally, it is possible that, some companies try to reduce its S&A expenses to add value to their earnings. That is, the reliable of the amount of this signal may affect the result of the test.
Provision for doubtful receivables got a t value at positive 0.326, which is much lower than 1. The signal is tested to be unrelated to future earnings, which does not conform to hypotheses 7, so H7 is rejected. LT's test presents a significant relationship between provision for doubtful receivables and future earnings, which is differ from the result of the study. It is supposed that the inflation of the specific sample is not as high as the one in LT's. The signal is link with account receivables. As explain above, receivables are not significant account in retail companies. Therefore, provision for doubtful receivables is not correlated with future earnings in this industry.
Effective tax is not related to future earnings in this model, since its absolute t value is lower than 1. It reversed the H8 and the result of LT's, Abarbanell and Bushee's examination. As specified by newly published "PRC enterprise income law" in 2008, the corporate income tax rate is 25 %( previous one is 33%). It is one quarter of the earnings. Many companies try to avoid paying the full tax by dealing payoff with accounting methods or other ways. However, it is definitely existing adjustment of earning before tax, which in a way reduced the reliability of data on effective tax. Meanwhile, the test result does agree with H9. Order backlog is normally significant in manufacture industry, but not retail.
Obviously, labor force is found not related to future earnings in this test, for it got a positive o.345 at t value, which is lower than 1 level. It is totally different for the H10 and the test result of prior study (LT's research, for example).However, labor force is a very important signal in modern economy. In addition, many large enterprises in China have been aware the importance of labor force. However, majority of companies are unable to significantly improve their staff welfare, for the large number of staffs which will produce a large cost of companies. Additionally, most of the staff worked in China's retail market is not well-skilled, and there are few high-skilled, so the payment of labor force is not as great as high-tech companies. Therefore, this signal does not appear sensitive to the model in the sample.
According to the coefficients in table 3, LIFO/FIFO is not related with the dependant variable, for it got a t value at minus 0.145. LT's test showed that the signal is related to future earnings; and while Abarbandell and Bushee got a significant relationship in their study, which is in accordance with H11. But according to the test result of the study, it is inconsistent with H11 and other two tests. It is explained that, because the methods of accounting measurement used across- companies are different in fiscal period. It reduced the accuracy of data. As inventory is significant related to future earnings, LIFO/FIFO should be correlated with the dependent variable. But it is not, the reason is able to interpret this situation is the irregularity of inventory measurement.
According to the result of examination, audit qualification is not relevant to future earnings, for its t value is positive 0.816, lower than 1. Thus, hypothesis 12 is refused. It is not consistent with LT's research, whose result of examination on audit qualification is statistically significant in a few years. The difference between the specific test and LT's examination is possible due to the low power, since there are only 2 companies of the sample of 48 companies qualified in audit report in the specific, which is really small in proportion. It reduced the reliability of published financial information. Besides, it is considered that whether those companies strictly abiding by accounting regulations to prepare their statements. As mentioned above, companies will adjust some account to avoid paying full tax or increase (decrease) its income, which greatly affect the auditing result. In addition, investors is frequently not paying too much attention to audit report, to some extent, it can't affect the future earnings. Moreover, high audit fee for Big 4 results in many companies turn to local public accounting companies. It reduces the audit quality of companies.
Chapter Six Conclusion
The article adopted the fundamental analysis, which is one of the methods in financial analysis, in the study of China's retail industry. Before inputting the sample into this analysis, the 12 fundamental signals from the model were quoted in the hypotheses, in which the basic functions of these factors were explained. In addition, the model applied in this specific- industry study is a linear regression model from Lev and Thiagarajan's (1993) study. However, in this specific study, it tested a sample of 48 companies, which is chosen from 169 listed companies in China's retail industry. Besides, covered year of the sample is from 2005 to 2008, four year's period. It is concerned that the sample is restricted, and to some extent, it affects the result of the examination of coefficients.
According to the result of test, it got an R square at 0.197, which is much lower than 1 level. It indicated that, the chosen model is not really precise in interpreting the relationship between its independent variables and dependent variable. It is suggested that, this model can only explain 19.7 percent of its variables. In chapter 5, it mainly explained the possible reasons for the discrepancy between the test results of coefficients and the 12 hypotheses. It is displayed in table 3, that only 3 indicators are related to future earnings. Among them, inventory is significant correlated to the explained variable (absolute t value is 1.824, much higher than 1.5 level), and while capital expenditure and gross margin is separately 20.1% positive (its absolute t value is 1.021) and 56.5% negative related to future earnings (its absolute t value is1.223) . In this study, these 3 fundamental signals are related correlative to the future earnings of China's retail industry. The other signals are explained by the model that, not related to the future earnings of the specific- industry.
To some extent, the results of the examination of coefficients interpret the partial operating status of China retail industry. For instance, lack of advanced technologies and technical managers in the management of inventory. It embodied in the signal of R&D expenditure, which is found not related to the future earnings of retail industry. However, the positive relationship between capital expenditure and future earnings outlined a new scene for the development of China's retail industry. It is indicated that, retail companies have stated to pay attention to newly marketing strategy, which is advantageous for the promotion of the industry. It suggests that, to improve future earnings, the retail companies should pay more attention to the technical methods of management, such as employ advanced software and technical talent.
It is found that, the R square estimated by the model is very small. It is indicated that the model in specific fundamental analysis is not proper to describe those fundamental signals in retail industry. It may be explained by some reasons. Firstly, the size of the selected sample is very small, although the companies in the sample are large- scale, which are operating in national chain. However, it can't present the overall situation of the retail industry in China, for there is still large numbers of local retail stores and unlisted companies in this industry. Additionally, there are some new listed companies, with part of unavailable data. To avoid error of calculation in fundamentals, they are excluded. It is also found in Lev and Thiagarajan's (1993) estimation of the 1974-88 year- by- year cross sectional regressions. They considered that, the sample they used is restricted and not representative, because the sample they chosen is only include companies with R&D expenditures. It is indicated that, the width of selected sample is very important to fundamental analysis. It is one of the limitations in the specific fundamental analysis done in section 5.
On the other hand, the years covered by the sample are not long enough. It only used the data from fiscal year 2005-2008. In addition, it is not comprehensive enough to depict an overall status of these companies in the study. Moreover, there are only 2 of 48 companies provide audit qualified financial statements, which can not show adequate reliability of these published financial information. Besides, there are many methods of accounting measurement are used in these companies' preparation of financial statements, such as standard cost, average cost and FIFO. To some extent, the difference between measurements will affect the result of estimates, and also the comparability of accounting-based signals. Furthermore, some data of companies is unavailable, which reduced the size of sample. It largely affects the accuracy of the specific analysis.
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