Stock Market Prediction using LSTM and Markov Chain Models: A Case Study of Royal Bank of Canada Stock
Date
2023-08-09
Authors
Kumar, Amer
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Abstract
Stock price prediction is one of the most important aspects of financial investment.
This research aims to provide insights into the dynamics of stock prices, enabling more
informed decision-making in financial investments by combining these two modeling
approaches. Using a four-layer long short-term memory (LSTM) architecture and the
Root Mean Square Error (RMSE) as the loss function, we aim to capture temporal
dependencies and patterns to predict closing prices. Furthermore, we employ a threestate Markov chain to estimate the transition matrix, and metrics like steady-state
distribution and mean hitting times have been used to calculate the matrix. The preliminary results indicate that this approach shows promising results for stock market
prediction as LSTM has predictive power that caters more to long-term temporal
trends while Markov Chain provides probabilistic values for staying and transitioning
to states. The findings of the study highlight the effectiveness of combining LSTM
and Markov Chain in capturing the intricate dynamics of the stock market data and
predicting stock market prices.
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Keywords
LSTM, Markov Chain, Stock Market, RMSE, RBC