The Stock Market/Unemployment Relationship in USA, China and Japan
Bibliographic record
Abstract
This study investigates the relationship between unemployment rate and stock prices in USA, China and Japan; the top three world economies. Recently, there have been some articles by financial analysts asserting that unemployment rate is a strong predictor of stock prices. They refer to certain short-term periods and posit a negative causal relation from unemployment rate to stock prices. They argue that declining (rising) unemployment would display an upturn (a downturn) in the economy, an increase (a decrease) in demand for goods and services, and would therefore lead to higher (lower) profits and stock prices. In this paper, using logical analysis, we argue that these views are misleading to potential investors. We hypothesize that there is no stable long-term causal relationship from unemployment rate to stock prices. Furthermore, using quarterly data in US, China and Japan over the 1970-2011 period, we provide empirical support for our hypothesis. The empirical analysis of this paper is based on cointegration and Granger Causality tests. Our findings have one important implication: it would be a mistake to rely on unemployment rate data to make investment decisions in the stock market.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".