Summary financial statement measures and analysts' forecasts of earnings

Stöber, Thomas

doi:10.1016/0165-4101(92)90024-v

Cited by 79 publications

(47 citation statements)

References 10 publications

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“…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 (Holthausen and Larker 1992;Bernard et al 1997;Stober 1992;Setiono and Strong 1998;and Bird, Gerlach and Hall 2001) has been mixed. Holthausen and Larker (1992) suggest that a better strategy, yielding higher returns would be one that is based on an economic concept, such as the industry the company is traded in.…”

Section: Introductionmentioning

confidence: 97%

“…The mean profitability of their investment strategies produced excess return of 4.74% in the first year and 1.24% in the second year. Stober (1992) compared the model prediction ability to that of analysts' forecasts of earnings. Using the same time period as they found that the model accurately predicts the signs of one-year-ahead EPS 46% of the time, analysts' forecasts are correct about 54% of the time but a combined model correctly predicted the sign 78% of the time.…”

Section: Replication Of Ou and Penman (1989)mentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 97%

Section: Replication Of Ou and Penman (1989)mentioning

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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“…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.…”

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confidence: 99%

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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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“…Several studies have investigated the inefficiency of analysts in incorporating relevant information into their forecasts. Specifically, Stober (1992) showed that analysts do not include the necessary accounting information, and neglect to incorporate the pertinent accounting information into their forecasts. Additionally, Abarbanell and Bushee (1997) found that the analysts did not comprehend the implication of the information about future earnings and failed to fully reflect that information into their forecasts.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock Return Synchronicity and Analysts’ Forecast Properties

Cho

Park

2016

GADJAH MADA INT. J. BUS.

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Using stock return synchronicity as a measure of a firm's information environment, our research investigates how the firms' stock return synchronicity affects analysts' forecast properties for the accuracy and optimism of the analysts' annual earnings forecasts. Stock return synchronicity represents the degree to which market and industry information explains firm-level stock return variations. A higher stock return synchronicity indicates the higher quality of a firm's information environment, because a firm's stock price reflects more market-level and industry-level information relative to firm-specific information. Our study shows that stock return synchronicity positively affects the forecast properties. Our finding shows that when stock return synchronicity is high, analysts' annual earnings forecasts are more accurate and less optimistically biased. Abstrak

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Summary financial statement measures and analysts' forecasts of earnings

Cited by 79 publications

References 10 publications

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

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

The Prediction of Earnings Movements Using Accounting Data: Using XBRL

Stock Return Synchronicity and Analysts’ Forecast Properties

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