Hiding in Plain Sight : Sentiment Analysis and the Efficient Market Hypothesis
Bibliographic record
Abstract
The stock market is a notoriously complex and unpredictable system, and because of this has always been an alluring subject for academic research seeking to make the unpredictable more predictable. This major research project is no different as it aims to quantify the predictive value of financial sentiment, determine which sentiments are most meaningful, when they are most meaningful, and if meaningful sentiment varies depending on type of stock. To pursue these goals, the project finds its theoretical footing in Eugene Fama’s Efficient Market Hypothesis and Daniel Kahneman’s Prospect Theory. However, the methodological component of this project enters into emerging territory as it employs sentiment analysis and machine learning, which have only recently been made possible by advances in technology and communications practices. Specifically, through the use of the Loughran-McDonald dictionary for financial sentiment, corporate press releases were analyzed and tested using a Random Forest machine learning model. The results from this project show that financial senitiment found in press releases does provide a slight predictive edge, however the sentiments responsible for that edge vary based on type of stock, type of fluctuation being predicted, and timeframe.
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 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 | 0.040 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".