A heuristic stock portfolio optimization approach based on data mining techniques
Bibliographic record
Abstract
Portfolio optimization is the process of making investment decisions on holding a set of financial assets to meet various criteria. A variety of investment assets around the world make this multi-faceted decision problem very complicated. Econometric and statistical models as well as machine learning and data mining techniques have been used by many researchers and analysts to propose heuristic solutions for portfolio optimization. However, a literature review shows that the existing models are still not practical as they do not always perform better than even the naive strategy of investing in all available assets in the market. The methodology proposed in this thesis is an alternative heuristic solution to help investors make stock investment decisions through a semi-automated process. The proposed solution is based on the fact that the investment decision cannot be fully automated because investors’ preferences that are the key factors in making investment decision, vary among different people. For this purpose, a semi-automated framework called SMPOpt (Stock Market Portfolio Optimizer) has been designed and implemented. In the proposed framework, the goal is to learn from the historical fundamental analysis of companies to discover the optimum portfolio by considering investors’ preferences. The Portfolio optimization problem is formulated and broken down into steps to be able to apply data mining techniques such as Clustering and Ranking, and Social Network Analysis. Some of these techniques are customized based on the temporal behaviour of financial datasets. For instance, the ranking algorithm based on Support Vector Machine (SVMRank) is modified and a new algorithm called Time-Series SVMRank is proposed. A comprehensive experimental study has been conducted using the real stock exchange market datasets from the past recent decades to evaluate the proposed portfolio optimization solution. The obtained results confirmed the strength of the proposed methodology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".