How Well Does Fundamental Analysis Explain the Returns of the Thirty Stocks in the Dow Jones Industrial Average?
Bibliographic record
Abstract
Due to the volatility recently experienced in the United States Stock market, this study aims to explain the relationship between fundamental analysis and stock returns over a quarterly time frame to take advantage of the swings in the market. This study includes both micro (firm-specific) fundamental indicators and macro variables that help explain the economy's health as a whole. This study takes the thirty stocks currently in the Dow Jones Industrial Average (DJIA) as of February 2024 and analyzes their returns and underlying financials. It uses accounting and financial ratios along with measures of the overall economy to try and capture opportunities to generate financial returns in the Stock Market. This paper offers evidence based on company panel data analysis on a quarterly basis from quarter one of 2014 to quarter four of 2023. The results produced from the model indicate an increase in recession risk and federal funds rate generate negative stock returns. In contrast, an increase in the Price-earnings ratio and analyst recommendations generate positive stock returns. The above variables are significant at the 5% level with an R squared of 0.126.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".