Testing The Adaptive Efficiency Of U.S. Stock Markets: A Genetic Programming Approach
Bibliographic record
Abstract
<p class="MsoNormal" style="text-align: justify; margin: 0in 0.5in 0pt; mso-pagination: none; mso-layout-grid-align: none;"><em style="mso-bidi-font-style: normal;"><span style="color: black; font-size: 10pt;"><span style="font-family: Times New Roman;">Genetic programming is employed to develop trading rules, which are applied to test the efficient market hypothesis. Most previous tests of the efficient market hypothesis were limited to trading rules that returned simple buy-sell signals. The broader approach taken here, developed under a framework consistent with the standard portfolio model, allows use of trading rules that are defined as the proportion of an investor&rsquo;s total wealth invested into the risky asset (rather than being a simple buy-sell signal). The methodology <span style="mso-bidi-font-weight: bold;">uses average utility of terminal wealth as the fitness function, as a means of adjusting returns for risk. With data on </span>daily stock prices from 1985 to 2005, the algorithm finds trading rules for 24 individual stocks. These rules then are applied to out-of-sample data to test adaptive efficiency of these markets. Applying more stringent thresholds to choose the trading rules to be applied out-of-sample (an extension of previous research) improves out-of-sample fitness; however, the rules still do not outperform the simple buy-and-hold strategy. <span style="mso-bidi-font-weight: bold;">These findings therefore imply that the 24 stock markets studied were adaptively efficient during the period under study.</span></span></span></em></p>
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".