On the survival of earnings fixated traders in an informational environment
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the long-run survival of earnings fixated traders. Design/methodology/approach This paper builds a theoretical model of a competitive securities market where both rational traders and earnings fixated traders receive an informational signal about the asset payoff before any trade occurs. Since earnings fixated traders underestimate the mean and variance of the risky asset payoff, earnings fixated traders is shown to make less expected profits than rational traders. Findings If traders’ types replicate according to the relative profitability of their trading strategies, then earnings fixated traders will disappear in the long run. The results of this paper provide analytical support to Tinic’s (1990) intuition about the eventual disappearance of earnings fixated traders. Research limitations/implications In the literature, the underestimation of risk is popularly viewed as the cause of irrational traders being better able to exploit the misvaluations (created by noise traders) than rational traders. Hence, it favors the survival of irrational traders over rational traders. However, this paper disapproves this intuition in the informational environment of the competitive securities market. Practical implications The market environment plays a crucial role in determining the long-run survival of irrational traders. Originality/value This paper is the first to present a theoretical result showing that in this informational environment of the competitive securities market, the underestimation of risk by irrational traders does not give them advantage over rational traders in exploiting the misvaluations (created by noise traders) as it does in Callen and Luo (2011) and Hirshleifer and Luo (2001).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".