Enhancing Equity Trading through Ensemble Learning with Reddit Sentiment Analysis and Explainable Artificial Intelligence
Bibliographic record
Abstract
The challenge of accurately predicting stock market movements largely stems from its nature as a game with incomplete information. With the emergence of social media, platforms like Reddit became key arenas for expressing financial senti-ments, offering new insights into market dynamics influenced by retail investors. In this study, we trained an XGBoost model using daily financial data for Tesla, including technical indicators and sentiment analysis derived from Reddit posts about the company. This approach enhanced the model's ability to predict market directions, achieving a statistically significant improvement in accuracy with a p-value of 0.000237. Furthermore, our model is equipped with Explainable AI to allow users to understand the basis of its predictions, increasing transparency and trust. Implementing a straightforward trading strategy based on these predictions yielded a 174.36% gain in a backtest from December 2022 to December 2023, outperforming the baseline stock by 42.40 points and the S&P 500 index by 147.97 points. This paper highlights the potential of leveraging social media sentiments to gain deeper market insights.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".