Generative Adversarial Neural Networks for Realistic Stock Market Simulations
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
Stock market simulations are widely used to create synthetic environments for testing trading strategies before deploying them to real-time markets. However, the weak realism often found in these simulations presents a significant challenge. Improving the quality of stock market simulations could be facilitated by the availability of rich and granular real Limit Order Books (LOB) data. Unfortunately, access to LOB data is typically very limited. To address this issue, a framework based on Generative Adversarial Networks (GAN) is proposed to generate synthetic realistic LOB data. This generated data can then be utilized for simulating downstream decision-making tasks, such as testing trading strategies, conducting stress tests, and performing prediction tasks. To effectively tackle challenges related to the temporal and local dependencies inherent in LOB structures and to generate highly realistic data, the framework relies on a specific data representation and preprocessing scheme, transformers, and conditional Wasserstein GAN with gradient penalty. The framework is trained using the FI-2010 benchmark dataset and an ablation study is conducted to demonstrate the importance of each component of the proposed framework. Moreover, qualitative and quantitative metrics are proposed to assess the quality of the generated data. Experimental results indicate that the framework outperforms existing benchmarks in simulating realistic market conditions, thus demonstrating its effectiveness in generating synthetic LOB data for diverse downstream tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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