Application of Multi-Armed Bandit Algorithm in Quantitative Finance
Bibliographic record
Abstract
The volatility and diversity of financial markets make it challenging for a single portfolio achieve better returns, therefore, adjustable portfolios based on the risk tolerance of clients are highly demanded. However, traditional portfolio strategies cannot meet this requirement. Regarding this issue, the paper combines Fuzzy C-means (FCM) with the Upper Confidence Bound (UCB) algorithm, Genetic Algorithm (GA) optimizing UCB parameters (GA-UCB) and UCB redefining the fitness of GA (UCB-GA) to construct an investment portfolio strategy that can be dynamically adjusted. The research methodology is as follows: the assets are grouped by FCM, using UCB to find the best cluster among the groups; UCB, UCB-GA, and GA-UCB are used to refine the weight distribution of the best cluster. The result shows that the cumulative return of the cluster recommended by the UCB is significantly higher than that recommended by FCM, the Sortino Ratio is improved by 1.18, and the Maximum Drawdown is reduced by 8%. In terms of the weights of the optimal cluster; the portfolio strategy from GA-UCB has the highest cumulative return of approximately 250% in algorithms. The Sortino Ratio of the GA-UCB is the largest at 3.23, which is 1.5 and 1.63 higher than the UCB and the UCB-GA, respectively. In addition, the Maximum Drawdown of the GA-UCB is 26%, which is 1% lower than UCB-GA and 3% lower than UCB. Combining FCM and GA- UCB can improve investment return and stability by adjusting the portfolio weight, which leads to better return risk ratios.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".