Artificial Intelligence and Firm Performance: Does Machine Intelligence Shield Firms from Risks?
Bibliographic record
Abstract
We estimate and compare the impact of the coronavirus pandemic (COVID-19) on the performance of Artificial Intelligence (AI) and conventional listed firms using stock market indices. The single-group and multiple-group Interrupted Time-Series Analyses (ITSA) with panel data were used with four interventions: when the news of COVID-19 spread and the pandemic entered the first, second, third, and fourth months (24 February 2020, 23 March 2020, 20 April 2020, and 18 May 2020, respectively). The results show that the negative impact of COVID-19 on the AI stock market was less severe than on the conventional stock market in the first month of the pandemic. The performance of the AI stock market recovered quicker than the conventional stock market when the pandemic went into its third month. The results suggest that the AI stocks were more resilient than conventional stocks when the financial market was exposed to uncertainty caused by the COVID-19 pandemic. The deployment of AI in firms serves as a resilient, crucial driver for sustainable performance in challenging environments. Observing the performance of AI-adopted firms is an interesting direction for technical and fundamental analysts. Investors and portfolio managers should consider an AI market index to minimize risk or invest in stocks of AI-adopted listed firms to maximize excess returns.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".