Voluntary Disclosure Incentives and Earnings Informativeness

Roychowdhury, Sugata; Sletten, Ewa

doi:10.2139/ssrn.1220942

Cited by 30 publications

(32 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results remain qualitatively unchanged. Kothari et al 2009;Roychowdhury and Sletten 2011). Specifically, I provide incremental evidence that managers withhold bad news, while largely avoiding the usual methodological concerns about the timing of news arrival-since the characterization of news is relative to stock price, a stock price decline transforms bad news to good, inducing managers to reveal hidden information.…”

mentioning

confidence: 99%

The effect of stock price on discretionary disclosure

Sletten

2011

Rev Account Stud

Self Cite

View full text Add to dashboard Cite

I examine the impact of exogenous changes in stock prices on voluntary disclosure. Specifically, I investigate whether stock price declines prompt managers to voluntarily disclose firm-value-related information (management forecasts) that was withheld prior to the decline because it was unfavorable but became favorable at a lower stock price. Consistent with my predictions, I find that managers are more likely to release good-news forecasts following larger stock price declines but that there is no association between the likelihood of releasing good-news forecasts and the magnitude of stock price increases. Additional evidence indicates that the goodnews forecasts eventually conveyed by withholding firms after negative price shocks would likely have resulted in negative market reactions had they been released before the shocks. More generally, I provide evidence that managers withhold bad news and that exogenous stock price declines can induce its disclosure.

show abstract

mentioning

confidence: 99%

The effect of stock price on discretionary disclosure

Sletten

2011

Rev Account Stud

Self Cite

View full text Add to dashboard Cite

show abstract

“…The findings indicate that the composite model obtains significantly lower forecast errors relative to the benchmark models. Roychowdhury and Sletten (2012) find that earnings informativeness is higher in bad-news periods than in good-new periods. Alam and Brown (2006) were able to show that disaggregated earnings data were better able to predict next period's earnings in the banking industry.…”

Section: Introductionmentioning

confidence: 75%

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

Palas¹,

Baranes²

2017

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

View full text Add to dashboard Cite

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.

show abstract

“…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 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].…”

mentioning

confidence: 99%

The Prediction of Earnings Movements Using Accounting Data: Using XBRL

Baranes¹,

Palas²

2017

Int J Account Res

View full text Add to dashboard Cite

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...

show abstract

Voluntary Disclosure Incentives and Earnings Informativeness

Cited by 30 publications

References 49 publications

The effect of stock price on discretionary disclosure

The effect of stock price on discretionary disclosure

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

The Prediction of Earnings Movements Using Accounting Data: Using XBRL

Contact Info

Product

Resources

About