Analysis of the Financial Information Contained in the Texts of Current Reports: A Deep Learning Approach
Bibliographic record
Abstract
An important role in the fundamental analysis is played by the acquisition and analysis of various types of information about the company. Text documents are an increasingly important source of this information. Their accurate and quick analysis is an increasingly important challenge for financial analysts. Research in the area of financial text analysis is based on sentiment analysis. The deep neural networks and the stocks’ cumulative abnormal return are used in this article to analyze the sentiment of financial texts. The proposed approach, unlike those used so far, does not require manual labeling of data or the creation of dictionaries and is free from the subjective assessment of the researcher. Taking into account the broad context of words and their meaning in financial texts, it also eliminates the problem of ambiguity of words in various contexts. The sentiment of financial texts presented in this paper is directly related to the market reaction to the information contained in these texts. For texts belonging to one of the two classes (positive or negative) with the highest probability, the deep learning model gives predictions with a precision of 62% for the positive class and 55% for the negative class. The event study results show that the sentiment calculated under the proposed method can be successfully used to determine the probable direction of the market reaction to the information contained in current reports with a 1 percent significance level. The results can be used in market efficiency research, investment strategy development or support of investment analysts using fundamental analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".