Entropy as a Measure of Risk or a Source of Information to Mitigate Risk: A Comparison Across Various Financial Assets
Bibliographic record
Abstract
Since the application of entropy in financial economics has been growing extensively as a measure of volatility, in portfolio selection and to detect anomalies in markets. It’s really complicated to establish that increase in entropy is a source of the useful information for the financial markets that tantamount to mitigate risk, or it is in fact an indicator of disorder reflecting the growing risk scenario in the financial market. To explore the more effective application of entropy in the field of financial economics, this study evaluates entropy in both contexts, as a source of information to mitigate risk and as an indicator of disorder reflecting volatility. Twelve years daily data of 29 financial assets have been used to measure the intrinsic entropy in addition to other eight volatility estimators and three GARCH models-based volatilities. Various assessment techniques are used to test the role of entropy in both contexts including, Run Test, Mean, Variance and Coefficients of Variation, Mean Squared Errors, Proportional Bias and Efficiency Estimator, in addition to spearman rank-order correlation. Results emphasis that entropy is more suitable as a volatility measure rather a source of information in the financial market.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".