Product Innovations, Advertising, and Stock Returns
Bibliographic record
Abstract
Under increased scrutiny from top management and shareholders, marketing managers feel the need to measure and communicate the impact of their actions on shareholder returns. In particular, how do customer value creation (through product innovation) and customer value communication (through marketing investments) affect stock returns? This article examines, conceptually and empirically, how product innovations and marketing investments for such product innovations lift stock returns by improving the outlook on future cash flows. The authors address these questions with a large-scale econometric analysis of product innovation and associated marketing mix in the automobile industry. They find that adding such marketing actions to the established finance benchmark model greatly improves the explained variance in stock returns. In particular, investors react favorably to companies that launch pioneering innovations, that have higher perceived quality, that are backed by substantial advertising support, and that are in large and growing categories. Finally, the authors quantify and compare the stock return benefits of several managerial control variables. The results highlight the stock market benefits of pioneering innovations. Compared with minor updates, pioneering innovations have an impact on stock returns that is seven times greater, and their advertising support is nine times more effective as well. Perceived quality of the new car introduction improves the firm's stock returns, but customer liking does not have a statistically significant effect. Promotional incentives have a negative effect on stock returns, indicating that price promotions may be interpreted as a signal of demand weakness. Managers can combine these return estimates with internal data on project costs to help decide the appropriate mix of product innovation and marketing investment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".