Stock Price Prediction Using Artificial Intelligence and Neural Networks
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
Predicting the stock prices is a very complex task, and to predict an almost accurate stock price,we need a robust and accurate algorithm which can analyze and compute the longer-term share prices.Several researcher’s equally in the world and different industries have been very interested in the stockmarket. Stock processes are correlated within the nature of the market and that is why it is difficult topredict the share price. This project aims at processing and analyzing huge volumes of data (live data)and running comprehensive algorithms on the dataset. The purpose of the paper is to understand theshortcomings of the current prediction algorithms and to provide a method using neural networks andartificial intelligence through which we can predict the shared values with accuracy.By using the proposed method, anyone can monitor the preferred stock in real-time and can invest in thestock to make the most money by buying a large number of shares at the cheapest price and sellingthem at the highest price..
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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