DETECTING FRACTAL/MULTIFRACTAL AND ASYMMETRIC PROPERTIES IN AN ARTIFICIAL QUOTE-DRIVEN FINANCIAL MARKET
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
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it