Stock Market Prediction using LSTM and Markov Chain Models: A Case Study of Royal Bank of Canada Stock
Bibliographic record
Abstract
Stock price prediction is one of the most important aspects of financial investment. \nThis research aims to provide insights into the dynamics of stock prices, enabling more \ninformed decision-making in financial investments by combining these two modeling \napproaches. Using a four-layer long short-term memory (LSTM) architecture and the \nRoot Mean Square Error (RMSE) as the loss function, we aim to capture temporal \ndependencies and patterns to predict closing prices. Furthermore, we employ a threestate Markov chain to estimate the transition matrix, and metrics like steady-state \ndistribution and mean hitting times have been used to calculate the matrix. The preliminary results indicate that this approach shows promising results for stock market \nprediction as LSTM has predictive power that caters more to long-term temporal \ntrends while Markov Chain provides probabilistic values for staying and transitioning \nto states. The findings of the study highlight the effectiveness of combining LSTM \nand Markov Chain in capturing the intricate dynamics of the stock market data and \npredicting stock market prices.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".