Performance evaluation metric for statistical learning trading strategies
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
<p>We analyze how the sentiment of financial news can be used to predict stock returns and build profitable trading strategies. Combining the textual analysis of financial news headlines and statistical methods, we build multi-class classification models to predict the stock return. The main contribution of this paper is twofold. Firstly, we develop a performance evaluation metric to compare multi-class classification methods, taking into account the precision and accuracy of the models and methods. By maximizing the metric, we find optimal combinations of models and methods and select the best approach for prediction and decision-making. Secondly, this metric enables us to construct profitable option trading strategies, which can also be used as an assessment tool to analyze models' prediction power. We apply our methodology to historical data from Apple stock and financial news headlines from Reuters from January 1, 2012 to May 31, 2019. During validation (May 31, 2018, to May 31, 2019), our models consistently outperformed the market, with two-class one-stage models yielding returns between 30% and 45%, compared to the S &amp; P500 index's 1.73% return over the same period.</p>
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 | 0.027 | 0.009 |
| 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 | 0.004 |
| 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