Research on the Nonlinear Impact of Investor Sentiment on Stock Returns Based on Deep Learning and Text Mining
Bibliographic record
Abstract
This paper focuses on the nonlinear correlation between investor sentiment and stock returns and conducts in-depth research with the aid of deep learning and text mining techniques. First of all, sort out the relevant theoretical cornerstones, covering behavioral finance, market efficiency theory and herd effect theory, to provide theoretical support for the research. Secondly, analyze and study the data context, including the characteristics of text data and stock market data, as well as the necessity of integrated correlation analysis. In terms of extracting investor sentiment indicators, compare the advantages and disadvantages of the sentiment dictionary method, the machine learning method and the deep learning method. Subsequently, an in-depth exploration was conducted on the manifestations, theoretical explanations and implications for investment decisions of the nonlinear impact of investor sentiment on stock returns. Empirical results show that investor sentiment has a significant nonlinear impact on stock returns, and the degree of influence varies among different emotional states. This research provides investors with more accurate sentiment analysis tools to help them predict the trend of the stock market more scientifically and optimize investment decisions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".