DETRENDED FLUCTUATION ANALYSIS OF THE US STOCK MARKET
Bibliographic record
Abstract
This paper extends the work in [Serletis & Shintani, 2003; Elder & Serletis, 2007; Koustas et al.; Hinich & Serletis, 2008] by re-examining the empirical evidence for random walk type behavior in the US stock market. In doing so, it uses daily data on the Dow Jones Industrial Average, over the period from January 3, 1928 to March 15, 2006, and a statistical-physical approach — "detrended fluctuations analysis" — providing a reliable framework for testing the information efficiency in financial markets as shown by Uritskaya [2005a, 2005b] and Uritskaya and Uritsky [2001]. The approach eliminates nonstationary market trends and focuses on the intrinsic correlation structure of stock market fluctuations at different time scales which is studied relative to random walks models. Our results indicate that the US stock market operates close to the state predicted by the efficient markets hypothesis. The observed transient deviations from this state are shown to have a statistical origin, consistent with a purely random geometric Brownian motion.
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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 | 0.003 |
| 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 | 0.001 |
| 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".