DETECTING FRACTAL/MULTIFRACTAL AND ASYMMETRIC PROPERTIES IN AN ARTIFICIAL QUOTE-DRIVEN FINANCIAL MARKET
Bibliographic record
Abstract
In this paper, we detected the fractal/multifractal and asymmetric properties in a simple financial market model which is an analog of the Ising model. We introduced the virtual market with heterogeneous agents characterized by agents with bounded rationality, by which we mean that agents only have local information, and a market maker who is responsible for market liquidity. To investigate the heterogeneity and psychological factors in real financial market, we designed the parameters of individual expectations of agents to this model. Applying fractal/multifractal and Zipf techniques, we conducted many simulations under different scenarios and then analyzed the generated time series of this virtual market. We acquired some nontrivial findings: first, the virtual price returns generated by our model display fractal and multifractal features; secondly, we found that the price have the asymmetric behaviors; finally, our findings have qualitative similarities with many empirical results, which imply that although our toy model is seemingly simple, it can generate complex dynamics and thus can be a useful tool to investigate complex market behaviors and phenomena.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".