Review of COVID-19 Pandemic’s Impact on Investment Decisions under Behavioral Finance
Bibliographic record
Abstract
Since the COVID-19 pandemic outbreak, many studies have explored the impact of the COVID-19 pandemic on financial markets and investors’ decisions. Most of the studies are conducted under the assumption of rationality and efficient market hypothesis, which imply that investors’ decisions are always aiming at the maximum profit. However, analyses of investors’ behaviors during the pandemic with a focus on irrationality are not common. Irrationality is the main theme of behavioral finance, which studies the psychological factors that bias investors’ decisions from rationality. This paper reviews common theories and biases studied in behavioral finance, including heuristics, mental accounting, disposition effects, overconfidence, and anchoring. In this paper, those concepts are linked to the increased volatility and strikes in financial markets during the pandemic. By analyzing the relationship between behavioral finance concepts, hypotheses are given regarding the impact of the pandemic on increase or decrease of the common irrational behaviors in the financial markets, especially in the stock market.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".