Research on Stock Selection Method Based on LSTM Neural Network
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
In the field of investment, the selection of good target stocks is one of the keys to the ultimate success of the investment activity. Stock prediction is a study that every investor is trying to do, ordinary investors confirm stock selection for trading by means of technical analysis, and researchers analyze stock data by building mathematical models. Stock data are represented as classical financial time series, and the use of neural networks for stock data prediction is a hot research topic in recent years. In this paper, we analyze the stock investment risk and investment analysis methods based on the actual process of stock investment selection, and analyze the applicability of LSTM in stock investment selection from the perspective of stock selection ability under the large number of stock market investments. The experimental results show that the proposed method has improved the accuracy of stock prediction compared with the single LSTM prediction model, and can predict the stock trend accurately and effectively to a certain extent.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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