Long Short Time Memory in the forecast of financial indices in the Brazilian market (Ibovespa)
Bibliographic record
Abstract
The present investigation seeks to evaluate different models of recurrent neural networks (LSTM, BLSTM and GRU) in comparison with the ARIMA model, whose purpose is to determine which of these models is capable of making a better forecast for the closing price of 5 steps forward in the stock index of the Sao Paulo Stock Index (IBOVESPA). The optimization of the parameters allows to reduce the cost function, for this reason, 8 configurations with more than 720 simulations were studied, discovering that the ADAMAX optimizer has worked better compared to the other optimizers, presenting a lower cost function (mean square error). In the simulations of the different configurations, the average and the standard deviation of different models have been considered. The GRU model with the ADAMAX optimizer was more efficient in more than 90% of the results obtained. The final configuration was the GRU model with a batch size equal to 5, with 250 epochs, a learning ratio equal to 0.001 and with 30 neurons. This configuration presented a lower mean square error and therefore better forecasts. The LSTM, BLSTM models presented a lower cost function compared to the GRU model. Likewise, the ARIMA model did not have an optimal result compared to the recurrent neural network models.
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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 | 0.041 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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; both teacher heads agree on what is shown here.
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".