Soft Computing as A Tool to Optimize an Investment Portfolio
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper describes the creation and application of an investment portfolio. Main aim of the paper is to perform statistical analysis of selected financial instruments and to find a connection between the input data. Authors use application Adaptrade from the Adaptrade software company which is based on genetic algorithms basis and is able to process this difficult task in real time. The case analysis is performed for three world currencies—U. S. dollar, Canadian dollar and Swiss franc. Statistical analysis was performed specifically on the currency couple USD: CAD and USD:CHF. The input data consists of time series, which records the progress of prices of the financial instruments with a period of 15 minutes continuously from Monday 00:00 to Friday 23:00 for the period 2.1.2009 – 14.3.2011. JEL classification: C61, G11. Keywords: Optimization, soft computing, Adaptrade, genetic algorithms, investment portfolio.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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 it