A Comparative Study of LSTM and DNN for Stock Market Forecasting
Bibliographic record
Abstract
Prediction of stock markets is a challenging problem because of the number of potential variables as well as unpredictable noise that may contribute to the resultant prices. However, the ability to analyze stock market trends could be invaluable to investors and researchers, and thus has been of continued interest. Numerous statistical and machine learning techniques have been explored for stock analysis and prediction. We present a comparative study of two very promising artificial neural network models namely a Long Short-Term Memory (LSTM) recurrent neural network (RNN) and a deep neural network (DNN) in forecasting the daily and weekly movements of the Indian BSE Sensex index. With both networks, measures were taken to reduce overfitting. Daily predictions of the Tech Mahindra (NSE: TECHM) stock price were made to test the generalizability of the models. Both networks performed well at making daily predictions, and both generalized well to make daily predictions of the Tech Mahindra data. The LSTM RNN outperformed the DNN in terms of weekly predictions and thus, holds more promise for making longer term predictions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".