Machine Learning in Finance: A Case Study on Forecasting Google's Stock Price
Bibliographic record
Abstract
Stock price prediction remains an attractive and essential area in financial markets, with researchers constantly working hard to improve existing models or develop new ones to achieve more accurate predictions. Foreseeing the future direction of stock prices allowing people to plan and formulate effective investment strategies. However, predicting stock prices remains a difficult challenge due to many uncontrollable factors. Traditional forecasting methods rely primarily on economic data analysis and formulation. However, these traditional methods often provide limited information and forecast accuracy due to market uncertainty. Many business organizations and individual investors started to utilize the programmed approaches to improve the accuracy of stock price predictions as machine learning and deep learning capabilities continue to advance. Through an actual case study, this essay examines the innovative use of machine learning methods for researching in the field of predicting stock prices. In the case study, an LSTM model is built to find the transforming trend of the stock price, while Google’s stock price is collected to use as the dataset for training the model. The article finally conducts a comparative study on stock price prediction based on LSTM is conducted to clarify its working progress and accuracy of the outcome.
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.
How this classification was reachedexpand
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".