Efficient Market Hypothesis and Fundamental Analysis: An Empirical Test in the European Securities Market
Bibliographic record
Abstract
This paper is to make a contribution to the empirical analysis of the efficient market hypothesis, specifically to appraise the potential of fundamental analysis as a predictor of abnormal returns following dividend announcements in European financial markets. The authors use a sample of 1, 708 manufacturing and service businesses. The findings obtained are evidence that fundamental analysis does help predict abnormal returns, that the prediction model is barely influenced by the overall economic cycle and that the efficiency level of the European financial market is not of the semi-strong type. The misalignment observed between the market prices and fundamental values of securities may either be traced to the inability of investors to correctly interpret and use the information that is made available or, and even more probably, to the fact that economic agents adopt principles and procedures other than those recommended by traditional theorists. The findings have led to the emergence of behavioural finance, a discipline which emphasises the irrational conduct of many investors in financial markets, as well as their tendency to underrate the risks associated with given investments on the wrong assumption that events held to be associated with disclosed information will ultimately be averted.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".