Technological diversification and resilience to systematic risk: Evidence from listed firms in China
Bibliographic record
Abstract
This paper investigates whether a firm with diversified technological bases is more resilient to systematic downside risk. By investigating Chinese listed manufacturing firms for 2015-2023, we find that diversified technological bases significantly reduce the left-tail correlation between its stock return and the market return, suggesting that the more diversified technology bases, the less likely systematic downside risk would negatively influence its stock price. Compared to state-owned enterprises (SOEs) or firms in traditional industries, the technological diversification effect is more pronounced among non-SOEs or firms in high-tech industries. Further analysis suggests that by improving production efficiency and enhancing market power, a firm with a diversified technological base transmits a capacity signal to investors and thus enhances its resilience to systematic risk. By exploiting the 2018 U.S. tariff increase as a negative exogenous shock, we find that exporting firms in targeted industries, relative to those in nontargeted industries, had greater resilience to systematic risk after the shock when their technological bases were more diversified. Overall, this paper confirms the signaling role of technological diversification in the financial market and advances the understanding of firms' ability to resist systematic downside risk.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".