Earnings Less Risk-Free Interest Charge (ERIC) and Stock Returns—A Value-Based Management Perspective on ERIC’s Relative and Incremental Information Content
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the relative and incremental information content of KPMG’s recently developed metric for shareholder value creation: earnings less risk-free interest charge (ERIC). We assess if ERIC has a better ability to predict stock returns than earnings, cash flow from operations (CFO), earnings before extraordinary items (EBEI), residual income (RI), or economic value added (EVA). We evaluate data from 214 companies listed on the U.S. Standard & Poor’s 500 Index from 2003 to 2012 (2354 firm-year observations). Similar to previous studies, we confirm that CFO and EBEI have the strongest association with stock returns in the short term, while EVA trails behind all other metrics. In terms of new findings, ERIC is the best predictor of stock returns over a 5-year period, as well as during times of crises (from 2009 to 2010). In this period, ERIC also adds incremental information content beyond that of EBEI. However, the low-short-/mid-term predictive ability of shareholder value metrics (EVA, ERIC) raises concerns regarding their reliable use in future research on shareholder value creation. We consequently propose a research agenda that focuses less on the measurement and more on the management of shareholder value.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it