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Record W1520196410

The Effects of Legal Environment on Voluntary Earnings Forecasts in the U.S. versus Canada

2005· article· en· W1520196410 on OpenAlexaboutno aff
Ronald A. Stunda

Bibliographic record

VenueJournal of Legal Ethical and Regulatory Issues · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEarningsVoluntary disclosureIncentiveBusinessTurnoverProductivityAccountingEconomicsActuarial scienceMicroeconomicsManagement
DOInot available

Abstract

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ABSTRACT Past research documents managers' reluctance to issue voluntary earnings forecasts in part due to legal considerations. Since Canadian laws create a less litigious environment than those of the U.S., this study finds that when the two environments are compared, Canadian managers issue voluntary earnings forecasts more frequently across the board. In addition, the Canadian forecasts tend to be more precise than those of their American counterparts. INTRODUCTION Prior research in the study of voluntary earnings disclosures finds that managers release information that is unbiased relative to subsequently revealed earnings and that tends to contain more bad news than good news [Baginski et al.(1994), and Frankel (1995)]. Such releases are also found to contain information content [Patell (1976), Waymire (1984), and Pownell and Waymire (1989)]. Although forecast release is costly, credible disclosure will occur if sufficient incentives exist. These incentives include bringing investor/manager expectations in line [Ajinkya and Gift (1984)], removing the need for expensive sources of additional information [Diamond (1985)], reducing the cost of capital to the firm [Diamond and Verrechia (1987)], and reducing potential lawsuits [Lees (1981)]. More recently, studies show that managers are more likely to issue voluntary forecasts in a less litigious environment [Frost (2001)], [Johnson et al, (2002], while another [Baginski et al. (2002)] indicates that there are legal environment differences between the U.S. and Canada in issuing earnings forecasts when smaller size firms are evaluated. My research extends the aforementioned studies by evaluating U. S. and Canadian firms of all sizes and over a more extended period. The research question becomes: Do Canadian firms issue voluntary earnings forecasts with greater regularity than U.S. firms and which forecasts exhibit greater accuracy? Clarkson and Simunic (1994) note that unlike the U.S., courts in Canada generally require unsuccessful plaintiffs to pay the costs of a successful defendant. Also, because plaintiffs have no absolute right to a jury trial in Canada, judges hear technical cases and are less likely to award large settlements. In addition, Canadian provinces do not permit trial lawyers to work on a contingency basis. Also, it is much more difficult to bring a class action suit in Canada. All of these differences in the legal systems create a natural environment in which voluntary earnings releases may be perceived differently. HYPOTHESIS DEVELOPMENT Three hypotheses are tested. First, King et al (1990) finds that forward-looking information disclosure in the U.S. increases the firm's exposure to legal liability. It is, in part, for this reason that many U.S. firms have exhibited a reluctance to issue voluntary forecasts on a consistent and ongoing basis. The first hypothesis, stated in the alternative form is: H1: Canadian firms, faced with a less-litigious legal environment, engage in more voluntary earnings forecasts relative to U.S. firms. The second hypothesis, also stated in the alternative form, relates to previous studies that indicate U.S. firms are less likely to issue voluntary forecasts during good news periods for fear of litigation: H2: Canadian firms, faced with a less-litigious legal environment, engage in voluntary forecast releases that are less-related to earnings than U.S. firms. The third hypothesis, stated in the alternative form, centers around the notion that as voluntary forecast are made with greater frequency, they also tend to exhibit greater accuracy over the long term: H3: Canadian firms engage in more precise forecasting of earnings information. RESEARCH DESIGN The sample consists of all quarterly and annual estimates made during the period 1983-2003 meeting the following criteria: 1) The voluntary earnings forecast was recorded by the Dow Jones News Retrieval Service (DJNRS). …

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.208
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2005
Admission routes1
Has abstractyes

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