Stock Price Prediction Using Machine Learning Techniques
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
Large variations in stock price and unstable fluctuations will not only bring huge losses but also might influence the decisions made by investors. Machine learning in financial areas has been widely used in the past few years, and they are especially superior in forecasting tasks. This article will talk about how machine learning techniques could be used to predict stock prices and some possible ways of improvements. The main method used in this article is the LSTM structure, and the building blocks are constructed into a full Recurrent Neural Network to predict the price for several famous technology companies from NASDAQ. According to the results in this article, a forecasting algorithm based on the LSTM model can minor the error down to a few dollars. It is overall not accurate enough and cannot be used to draw a financial conclusion about investment, but still possible to explain some personal confusion and give solutions to individual difficulties. In addition, this study can be further upgraded in terms of structure and logic. This article hopes to provide a broader research perspective for the application of machine learning in the financial field.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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