A Generative Adversarial Network-Based Investor Sentiment Indicator: Superior Predictability for the Stock Market
Bibliographic record
Abstract
Investor sentiment has a profound impact on financial market volatility; however, it is difficult to accurately capture the complex nonlinear relationships among sentiment proxies with the existing methods. In this study, we propose a novel investor sentiment indicator, SGAN, which uses generative adversarial networks (GANs) to extract the nonlinear latent structure from eight sentiment proxies from February 2003 to September 2023 in the Chinese A-share market. Unlike traditional linear dimensionality reduction methods, GANs are able to capture complex market dynamics through adversarial training, effectively reducing noise and improving prediction accuracy. The empirical analyses show that SGAN significantly outperforms existing methods in both in-sample and out-of-sample prediction capabilities. The GAN-based investment strategy achieves impressive annualized returns and provides a powerful tool for portfolio construction and risk management. Robustness tests across economic cycles, industries, and U.S. markets further validate the stability of SGAN. These findings highlight the unique advantages of GANs as sentiment-driven financial forecasting tools, providing market participants with new ways to more accurately capture sentiment-shifting trends and develop effective investment strategies.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".