Indonesian Banking Stock Price Prediction with LSTM and Random Walk Method

Investing in stock market is the challenging for every new investor, as the stock market always move in dynamic way. When using technical or fundamental analysis approach, investor can reduce the loss probability and increase the profit probability. When one tries to analyze the stock market data, any techniques can be used. For example, the LSTM as the part of Neural Network and Machine Learning, which need past data to train the model and try to give the best prediction result based on the model generated by the data. The other example of techniques used in this paper is the Random Walk which come from Integrated Nested Laplace Approximation (INLA) library of R language which approximate the Bayesian Inference. Both methods are used to get the best prediction result. To get some comparison, the data can be split to several period and from several choices, the best result can be generated. As a result, the LSTM always predict the best result (comparison using the RMSEP / Root Mean Square Error for Prediction value) and the more data fed to the model will produce lower rate of RMSE, which is good for prediction result.