The Partnership Of EDGAR Online And XBRL - Should Compustat Care?

Tallapally, Pavani; Luehlfing, Michael S.; Motha, Madhu

doi:10.19030/rbis.v15i4.6011

Cited by 14 publications

(7 citation statements)

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“…The mean profitability of their investment strategies produced excess return of 4.74% in the first year and 1.24% in the second year [10][11][12][13][14][15][16] [20][21][22][23][24].…”

Section: Replication Of Ou and Penman (1989)mentioning

confidence: 99%

The Prediction of Earnings Movements Using Accounting Data: Using XBRL

Baranes¹,

Palas²

2017

Int J Account Res

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These studies highlight the necessity to develop a tool to better predict future earnings and help develop various investment strategies.Many research papers have concentrated on the importance of earnings announcements and forecasts in the determination of investment decisions. While earlier research has only been able to show relatively low informativeness of earnings later studies were able to show the incremental information content of specific components of the financial statements [4][5][6][7]. For example, Finger et al. show that earnings provide information for future earnings and cash flows, and predict sign changes in the earnings per share using forecasting models developed from various income statement and balance sheet components [8][9][10]14]. Shroff et al. assess the predictive ability of a "composite" model, which forecasts as a function of current earnings and current security prices, against three univariate benchmarks: a random walk model, a random walk with drift model, and a first order autoregressive/moving average (ARIMA) model [11]. The findings indicate that the composite model obtains significantly lower forecast errors relative to the benchmark models. Sletten et al. find that earnings informativeness is higher in bad-news periods than in good-new periods [12]. Alam et al. were able to show that disaggregated earnings data were better able to predict next period's earnings in the banking industry [13].Ou et al. were the first researchers to focus on the usefulness of accounting information to predict the direction of movement of earnings relative to trend adjusted current earnings [10,14]. The study is important because it evaluates whether accounting information can consequently be used as the basis for profitable investment strategy. Given investors' reliance on earnings this could be a valuable tool for a profitable investment strategy. The authors found that financial statement analysis can provide a measure that is an indicator of future earnings which in turn is used as a successful investment strategy. However, the evidence from subsequent studies has been mixed [15][16][17][18][19].One objective of this study is to repeat the original Ou et al. study over a more recent time period and provide a viable tool for investment decisions. However, the main objective is to examine the methodology using, not the original COMPUSAT database, but the XBRL database. XBRL (extensible Business Reporting Language) is a freely available and global standard for exchanging business information. XBRL allows the expression of semantic meaning commonly required in business reporting. One use of XBRL is to define and exchange financial information, such as a financial statement [10,14].The SEC has created the XBRL US GAAP Financial Reporting Taxonomy. This taxonomy is a collection of accounting data concepts and rules that enables companies to present their financial reports electronically. The SEC's deployment was launched in 2008 in phases, and all public US GAAP companies were required to file the...

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“…The mean profitability of their investment strategies produced excess return of 4.74% in the first year and 1.24% in the second year [10][11][12][13][14][15][16] [20][21][22][23][24].…”

Section: Replication Of Ou and Penman (1989)mentioning

confidence: 99%

The Prediction of Earnings Movements Using Accounting Data: Using XBRL

Baranes¹,

Palas²

2017

Int J Account Res

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“…Tallapally et al [11] compared the Cost of Goods Sold (COGS) of 26 firms retrieved from Compustat and EDGAR Online in 2009. They found that, among 26 firms, the COGS amounts of 23 companies were reported lower in Compustat than in EDGAR Online, whereas two companies showed the opposite values; however, they failed to identify the fundamental causes of the differences.…”

Section: Unexplainable Coding Errorsmentioning

confidence: 99%

“…Similarly, Kinney and Swanson [9] found differences in tax items between Compustat and firms' financial statements, but failed to identify the causes of the differences. Yang et al [11] also found unknown errors in several items (e.g., net sales, inventory, and gross plant) and argued that these errors were unexplained coding errors.…”

Section: Unexplainable Coding Errorsmentioning

confidence: 99%

“…A number of previous studies have shown the existence of errors in aggregators' data, despite aggregators being known for offering reliable and value-added financial data [8][9][10][11][12]. Most of these studies have focused on developed markets, particularly the U.S. market, in which data offered by aggregators is readily used, and in which a strict validation process is believed to be present.…”

Section: Introductionmentioning

confidence: 99%

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Are Commercial Financial Databases Reliable? New Evidence from Korea

Nam

Lee

2017

Sustainability

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The quality of financial information is crucial for the effective decision-making of practitioners and academics. A number of studies have shown the existence of errors in proprietary databases provided by financial data aggregators (e.g., Compustat and Value Line) in advanced markets like the U.S. However, no study has examined the quality of the financial data offered by aggregators in emerging markets. Research on such markets is needed as financial investment frequently occurs in emerging markets due to the globalization of capital. The purpose of this study is to fill this gap by investigating whether financial data provided by aggregators is the same as the data reported in firms' financial statements in emerging markets. Another purpose of this study is to examine the impact on academic research. Comparing the 18 most widely-used financial items found in the original filings of firms with the corresponding data provided by all three data aggregators currently available in South Korea (i.e., DataGuide, KisValue, and TS2000), we found a considerable number of differences; many of the differences are substantially greater than conventional materiality. We also found that the differences between data sources lead to different prediction results in bankruptcy prediction model.

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“…2010). In addition, the reliability of COMPUSTAT has also been questioned, prior studies have shown that COMPUSTAT data may differ from the original corporate financial data (San Miguel 1977;Kinney and Swanson 1993;Tallapally, Luehlfing, and Motha 2011) and data found in other accounting databases (Rosenberg and Houglet 1974;Yang, Vasarhelyi and Liu 2003).…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Earnings Movement Using Mandated XBRL Data – Industry Analysis

Palas¹,

Baranes²

2017

Proceedings of the 7th Economics &Amp; Finance Conference, Tel Aviv

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Abstract:The immediate availability and easy accessibility of the XBRL filings will allow researchers and investors, especially small investors, to implement investment strategies based on this information. The objective of this study is to examine whether previous studies, predicting the direction of movement of earnings, are still relevant when using the newly SEC required, XBRL database (standardized financial reporting system). The study analyzes NYSE companies' XBRL quarterly data, from 2011 to 2015, using a two-step Logit regression model. The model is then used to arrive at the probability of the directional movement of earnings between current quarter and subsequent quarter, adjusted for a drift. Additional models are created by dividing the sample into industry membership (based on SIC codes). The results classified the companies as ones that would realize an increase in earnings or a decrease in earnings. The final model indicated a significant ability to predict subsequent earnings changes. The predictions appear to be correct on average about 70.7% of the time (higher than those of previous studies based on COMPUSTAT).The industry based models, although do not increase the accuracy of the model (an average of 68%) do increase the portfolio size. In other words, the model, based on industry, is able to classify more companies with a higher probability. These results suggest that XBRL data can be used as a means for forecasting movements in earnings, and creating a profitable investment strategy.

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The Partnership Of EDGAR Online And XBRL - Should Compustat Care?

Cited by 14 publications

References 1 publication

The Prediction of Earnings Movements Using Accounting Data: Using XBRL

The Prediction of Earnings Movements Using Accounting Data: Using XBRL

Are Commercial Financial Databases Reliable? New Evidence from Korea

The Prediction of Earnings Movement Using Mandated XBRL Data – Industry Analysis

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