# External Monitoring, ESG, and Information Content of Discretionary Accruals

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Literature and Hypothesis Development

**Hypothesis**

**1**

**(H1).**

**Hypothesis**

**2**

**(H2).**

**Hypothesis**

**3**

**(H3).**

## 3. Data Description

#### 3.1. Data and Sample Selection Procedures

- Stocks that have market price below USD 5 or total assets that are less than USD 1 million.
- Data that have negative or infinite net sales/net income or book-to-market ratio.
- Observations where the value for either total accruals, current accruals, or debt scaled by average total assets are greater than 100%.
- Observations that do not have data to compute total accruals or the variables needed to estimate discretionary accruals.

#### 3.2. Defining Variables

#### 3.3. Control Variables for the Information Environment of a Stock

## 4. Multivariate Empirical Analysis

#### 4.1. Analysts’ Forecast Dispersion and Discretionary Accruals

#### 4.2. Effect of Institutional Investors

#### 4.3. Effect of ESG

#### 4.4. Robustness Check: Analyst Forecast Error

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Table 1.**Sample statistics of regression data over the period of 1991–2020. The sample consists of firms traded on the NYSE, AMEX, and NASDAQ covered by Compustat and I/B/E/S between 1991 and 2020. $ForecastDisperison$ is the standard deviation of analysts’ forecasts in a given year. $AbsoluteForecastError$ is the absolute difference between the mean of analysts’ forecasts and the two-digit SIC code industry mean of forecasts in a given year normalized by the industry mean. Abs_DA is the absolute value of Jones discretionary accruals measure. $Ln\_BM$ is a logarithm of book-to-market and $Ln\_Size$ is a logarithm of market capitalization. $NumAnalysts$ is the total number of analysts who forecast a firm’s LTG in a given year. $NSeg$ is the total number of unique segments that a firm has in a given year.

Panel A: Main Sample Period of 1991–2020 | ||||||
---|---|---|---|---|---|---|

Variables | Obs | Mean | Std. Dev. | |||

Forecast Dispersion | 23,609 | 7.549 | 13.647 | |||

Absolute Forecast Error | 23,609 | 0.678 | 1.351 | |||

Abs_DA | 23,609 | 0.051 | 0.087 | |||

Ln_BM | 23,609 | −0.988 | 0.790 | |||

Ln_size | 23,609 | 7.196 | 1.772 | |||

NumAnalyst | 23,609 | 6.958 | 5.835 | |||

Nseg | 23,609 | 1.396 | 0.898 | |||

Panel B: Institutional Ownership Sample Period of 1991–2020 | ||||||

Variables | Obs | Mean | Std. Dev. | |||

Forecast Dispersion | 23,383 | 7.501 | 13.558 | |||

Absolute Forecast Error | 23,383 | 0.674 | 1.339 | |||

Abs_DA | 23,383 | 0.051 | 0.088 | |||

IO | 23,383 | 0.646 | 0.276 | |||

HHI | 23,412 | 0.079 | 0.087 | |||

Blockholders | 19,549 | 0.573 | 0.640 | |||

Ln_BM | 23,383 | −0.897 | 0.735 | |||

Ln_size | 23,383 | 6.615 | 1.573 | |||

NumAnalyst | 23,383 | 6.156 | 5.252 | |||

Nseg | 23,383 | 1.169 | 0.536 | |||

Panel C: ESG sample period of 1991–2018 | ||||||

Variables | Obs | Mean | Std. Dev. | |||

Forecast Dispersion | 13,232 | 7.791 | 14.923 | |||

Absolute Forecast Error | 13,232 | 1.007 | 5.290 | |||

Abs_DA | 13,232 | 0.042 | 0.065 | |||

ESG score | 13,232 | 0.003 | 0.383 | |||

Ln_BM | 13,232 | −0.892 | 0.709 | |||

Ln_size | 13,232 | 7.215 | 1.608 | |||

NumAnalyst | 13,232 | 7.135 | 6.640 | |||

Nseg | 13,232 | 1.404 | 0.894 | |||

Panel D: Correlation Matrix | ||||||

Forecast Dispersion | Absolute Forecast Error | Abs_DA | IO | HHI | Blockholders | |

Forecast Dispersion | 1.000 | |||||

Absolute Forecast Error | 0.589 | 1.000 | ||||

Abs_DA | 0.049 | 0.011 | 1.000 | |||

IO | 0.060 | 0.037 | 0.004 | 1.000 | ||

HHI | 0.031 | 0.062 | −0.007 | −0.254 | 1.000 | |

Blockholders | 0.042 | 0.066 | 0.022 | −0.402 | 0.720 | 1.000 |

ESG score | −0.039 | −0.016 | 0.007 | −0.120 | 0.034 | 0.035 |

Ln_BM | −0.001 | 0.046 | −0.096 | −0.033 | 0.027 | 0.066 |

Ln_size | −0.048 | −0.051 | −0.107 | 0.065 | −0.269 | −0.363 |

NumAnalyst | 0.011 | −0.052 | −0.005 | 0.034 | −0.145 | −0.177 |

Nseg | −0.022 | 0.010 | −0.038 | 0.022 | −0.028 | −0.063 |

ESG Score | Ln_BM | Ln_size | Num Analyst | Nseg | ||

ESG Score | 1.000 | |||||

Ln_BM | −0.027 | 1.000 | ||||

Ln_size | −0.036 | −0.223 | 1.000 | |||

NumAnalyst | 0.028 | −0.179 | 0.522 | 1.000 | ||

Nseg | −0.051 | 0.060 | 0.172 | −0.019 | 1.000 |

**Table 2.**The relationship between Forecast Dispersion and Discretionary Accruals. This table estimates the relationship between discretionary accruals and forecast dispersion. The dependent variable is $ForecastDisperison$, the standard deviation of analysts’ forecasts in a given year. The main variable of interest is Abs_DA, the absolute value of Jones discretional accruals measure. As a robustness check, we also provide results with Abs_DA, the absolute value of modified Jones, and Abs_DA_matched, the absolute value of performance matched. The regressions control for $Ln\_BM$, $Ln\_Size$, $NumAnalysts,\mathrm{and}$ $NSeg$. Year and industry fixed effects are also included. The standard errors are clustered at the firm level and t-stats are reported in parenthesis. **, and *** indicate statistical significance at the 5%, and 1% levels, respectively.

Dependent Variable | Forecast Dispersion | ||
---|---|---|---|

(1) | (2) | (3) | |

DA = | Abs_DA | Abs_DA_modified | Abs_DA_matched |

DA | 5.119 *** | 6.898 *** | 5.300 *** |

(5.08) | (6.34) | (5.11) | |

Ln_BM | −0.780 *** | −0.514 ** | −0.779 *** |

(−3.51) | (−2.07) | (−3.51) | |

Ln_size | −1.109 *** | −1.078 *** | −1.110 *** |

(−11.68) | (−10.65) | (−11.70) | |

NumAnalyst | 0.062 ** | 0.061 ** | 0.062 ** |

(2.49) | (2.41) | (2.49) | |

Nseg | −0.608 *** | −0.526 *** | −0.607 *** |

(−4.39) | (−3.80) | (−4.38) | |

Observations | 23,609 | 21,487 | 23,609 |

R-squared | 0.106 | 0.107 | 0.106 |

**Table 3.**The Role of Governance in the Relationship between Dispersion and Accruals. This table presents the regression results on the role of governance in the relationship between discretionary accruals and forecast dispersion. The dependent variable is $ForecastDisperison$, and the main variable of interest is Abs_DA. IO is the institutional ownership, HHI is the measure for the ownership concentration, and Blockholders is the ownership by institutional blockholders. Positive ESG is a dummy equivalent to one if the ESG score is positive. We control for $Ln\_BM$, $Ln\_Size$, $NumAnalysts,\mathrm{and}$ $NSeg$. Year and industry fixed effects are also included. The coefficients on constants are omitted for simplicity. The standard errors are clustered at the firm level and t-stats are reported in parenthesis. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Institutional Ownership | ||||
---|---|---|---|---|

Dependent Variable | Forecast Dispersion | |||

(1) | (2) | (3) | ||

Institutional Investor = | IO | HHI | Blockholders | |

Abs_DA | 1.636 | 7.399 *** | 7.484 *** | |

(0.81) | (5.10) | (4.66) | ||

Abs_DA$\times $Institutional Investor | 6.215 * | −0.230 *** | −2.597 * | |

(1.75) | (−2.72) | (−1.88) | ||

Institutional Investor | 0.027 | 0.047 *** | 0.144 | |

(0.04) | (2.81) | (0.86) | ||

Ln_BM | −0.817 *** | −0.779 *** | −0.798 *** | |

(−3.65) | (−3.54) | (−3.14) | ||

Ln_size | −1.130 *** | −1.024 *** | −1.088 *** | |

(−11.36) | (−9.73) | (−9.23) | ||

NumAnalyst | 0.067 *** | 0.064 ** | 0.058 ** | |

(2.67) | (2.54) | (2.07) | ||

Nseg | −0.589 *** | −0.601 *** | −0.568 *** | |

(−4.24) | (−4.33) | (−3.85) | ||

Observations | 23,383 | 23,412 | 19,549 | |

R-squared | 0.103 | 0.103 | 0.104 | |

Panel B: ESG Score | ||||

Dependent Variable | Forecast Dispersion | |||

(1) | (2) | (3) | (4) | |

ESG = | Positive ESG | Positive Governance | Positive Environment | Positive Social |

Abs_DA | 12.086 *** | 10.911 *** | 10.277 *** | 11.109 *** |

(2.82) | (3.43) | (3.99) | (3.54) | |

Abs_DA$\times $ESG | −2.710 ** | −1.121 * | −1.207 ** | −6.203 |

(−2.50) | (−1.90) | (−2.22) | (−1.29) | |

ESG | −0.745 * | −0.736 ** | −0.833 | −0.554 |

(−1.80) | (−2.04) | (−0.19) | (−1.34) | |

Ln_BM | −0.674 * | −0.490 | −0.677 * | −0.505 |

(−1.88) | (−1.31) | (−1.89) | (−1.44) | |

Ln_size | −1.226 *** | −1.286 *** | −1.178 *** | −0.971 *** |

(−8.12) | (−8.05) | (−7.70) | (−5.56) | |

NumAnalyst | 0.057 * | 0.094 *** | 0.061 ** | 0.122 *** |

(1.89) | (2.70) | (2.01) | (3.33) | |

Nseg | −0.427 *** | −0.314 * | −0.420 *** | −0.262 |

(−2.73) | (−1.75) | (−2.69) | (−1.29) | |

Observations | 13,232 | 10,484 | 13,216 | 8639 |

R-squared | 0.108 | 0.126 | 0.109 | 0.101 |

**Table 4.**The relationship between Forecast Error and Discretionary Accruals. This table presents the regression results on the relationship between discretionary accruals and forecast error from the two-digit SIC code industry mean of forecasts. The dependent variable is $AbsolouteForecastError$, and main variable of interest is Abs_DA. IO is the institutional ownership, HHI is the measure for the ownership concentration, and Blockholders is the ownership by institutional blockholders. Positive ESG is a dummy equivalent to one if the ESG score is positive. We control for $Ln\_BM$, $Ln\_Size$, $NumAnalysts,\mathrm{and}$ $NSeg$. Year and industry fixed effects are also included. The coefficients on constants are omitted for simplicity. The standard errors are clustered at a firm level and t-stats are reported in parenthesis. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Main Sample | ||||
---|---|---|---|---|

Dependent Variable | Absolute Forecast Error | |||

(1) | (2) | (3) | ||

DA = | Abs_DA | Abs_DA_modified | Abs_DA_matched | |

DA | 0.231 *** | 0.355 *** | 0.255 *** | |

(3.34) | (4.96) | (3.62) | ||

Ln_BM | −0.079 *** | −0.054 *** | −0.079 *** | |

(−4.83) | (−2.94) | (−4.81) | ||

Ln_size | −0.088 *** | −0.084 *** | −0.088 *** | |

(−11.32) | (−10.18) | (−11.31) | ||

NumAnalyst | −0.012 *** | −0.012 *** | −0.012 *** | |

(−6.16) | (−5.85) | (−6.17) | ||

Nseg | −0.048 *** | −0.043 *** | −0.048 *** | |

(−4.03) | (−3.56) | (−4.02) | ||

Observations | 30,441 | 27,522 | 30,441 | |

R-squared | 0.156 | 0.157 | 0.157 | |

Panel B: Institutional Ownership | ||||

Dependent Variable | Absolute Forecast Error | |||

(1) | (2) | (3) | ||

Institutional Investors = | IO | HHI | Blockholders | |

Abs_DA | 0.029 | 0.470 *** | 0.478 *** | |

(0.21) | (4.66) | (4.53) | ||

Abs_DA$\times $Institutional Investors | 0.187 | −0.022 *** | −0.275 *** | |

(0.30) | (−3.61) | (−4.21) | ||

Institutional Investors | 0.017 | 0.003 ** | 0.017 | |

(0.33) | (2.51) | (1.36) | ||

Ln_BM | −0.080 *** | −0.078 *** | −0.073 *** | |

(−4.79) | (−4.72) | (−3.78) | ||

Ln_size | −0.090 *** | −0.081 *** | −0.089 *** | |

(−10.86) | (−8.93) | (−9.06) | ||

NumAnalyst | −0.012 *** | −0.012 *** | −0.012 *** | |

(−6.04) | (−6.10) | (−5.73) | ||

Nseg | −0.046 *** | −0.047 *** | −0.048 *** | |

(−3.88) | (−3.96) | (−3.76) | ||

Observations | 30,089 | 30,140 | 24,905 | |

R-squared | 0.157 | 0.157 | 0.161 | |

Panel C: ESG Score | ||||

Dependent Variable | Absolute Forecast Error | |||

(1) | (2) | (3) | (4) | |

ESG = | Positive ESG | Positive Governance | Positive Environment | Positive Social |

Abs_DA | 1.995 | 0.476 | 1.030 | 1.218 |

(1.39) | (0.44) | (1.59) | (1.53) | |

Abs_DA$\times $ESG | −1.858 ** | −0.672 | −1.210 | −1.289 |

(−2.14) | (−0.57) | (−1.39) | (−1.09) | |

ESG | 0.022 | −0.441 ** | 0.244 * | −0.028 |

(0.15) | (−2.54) | (1.92) | (−0.20) | |

Ln_BM | −0.140 * | −0.124 | −0.136 * | −0.239 *** |

(−1.76) | (−1.31) | (−1.72) | (−3.05) | |

Ln_size | −0.178 *** | −0.213 *** | −0.184 *** | −0.133 *** |

(−4.03) | (−3.89) | (−3.94) | (−2.60) | |

NumAnalyst | −0.023 *** | −0.020 * | −0.024 *** | −0.015 |

(−2.78) | (−1.84) | (−2.92) | (−1.48) | |

Nseg | −0.039 | −0.024 | −0.039 | 0.001 |

(−0.89) | (−0.45) | (−0.92) | (0.02) | |

Observations | 15,746 | 12,433 | 15,726 | 10,310 |

R-squared | 0.051 | 0.056 | 0.052 | 0.064 |

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**MDPI and ACS Style**

Hong, K.; Kim, J.; Kwack, S.Y.
External Monitoring, ESG, and Information Content of Discretionary Accruals. *Sustainability* **2022**, *14*, 7599.
https://doi.org/10.3390/su14137599

**AMA Style**

Hong K, Kim J, Kwack SY.
External Monitoring, ESG, and Information Content of Discretionary Accruals. *Sustainability*. 2022; 14(13):7599.
https://doi.org/10.3390/su14137599

**Chicago/Turabian Style**

Hong, Kihoon, Jinhee Kim, and So Yean Kwack.
2022. "External Monitoring, ESG, and Information Content of Discretionary Accruals" *Sustainability* 14, no. 13: 7599.
https://doi.org/10.3390/su14137599