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Related Concept Videos

Quality Assurance01:19

Quality Assurance

3.9K
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Outliers and Influential Points01:08

Outliers and Influential Points

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Unusual Results01:16

Unusual Results

4.2K
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
4.2K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.9K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Earnings Quality Measures and Excess Returns.

Pietro Perotti, Alfred Wagenhofer

    Journal of Business Finance & Accounting
    |August 25, 2015
    PubMed
    Summary

    This study evaluates earnings quality measures, finding accruals-based metrics most effectively reduce stock mispricing. Higher earnings quality, particularly via accruals, leads to more accurate investor decisions.

    Area of Science:

    • Accounting Research
    • Financial Reporting Quality
    • Investor Decision-Making

    Background:

    • Financial reporting aims to enhance investor decision usefulness.
    • Assessing the effectiveness of various earnings quality measures is crucial.
    • Existing measures need validation against a key objective: reducing investor mispricing.

    Purpose of the Study:

    • To evaluate how common earnings quality measures serve the goal of improving investor decision usefulness.
    • To introduce and utilize a novel stock-price-based metric for assessing earnings quality measures.
    • To empirically test if higher earnings quality correlates with reduced stock mispricing.

    Main Methods:

    • A stock-price-based measure of mispricing was developed, using mean absolute excess returns of portfolios.
    Keywords:
    abnormal returnsaccruals qualityearnings qualityexcess returnssmoothingvalue relevance

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  • Examined multiple earnings quality indicators: persistence, predictability, smoothness, abnormal accruals, accruals quality, earnings response coefficient, and value relevance.
  • Analyzed a large sample of US non-financial firms from 1988-2007.

    • Main Results:

    • Most earnings quality measures, except smoothness, showed a negative association with absolute excess returns.
    • Smoothness was found to be a generally favorable attribute of earnings.
    • Accruals-based measures demonstrated the largest spread in absolute excess returns, indicating superior performance.

    Conclusions:

    • Accruals measures are robust indicators of earnings quality, effectively reducing stock mispricing.
    • The findings support the prevalent use of accruals measures in academic literature.
    • The study validates the importance of earnings quality in enhancing investor decision-making accuracy.