Predicting Stock Market Trends Based on Moving Average Using LSTM Algorithm

Rizki Surya Permana; Veronica Windha Mahyastuty; Nova Eka  Budiyanta; Karel Octavianus Bachri; Maria Angela Kartawidjaja

doi:10.31154/cogito.v10i2.648.486-495

Authors

Rizki Surya Permana Master's Program in Electrical Engineering, Atma Jaya Catholic University of Indonesia
Veronica Windha Mahyastuty Master's Program in Electrical Engineering, Atma Jaya Catholic University of Indonesia
Nova Eka Budiyanta Master's Program in Electrical Engineering, Atma Jaya Catholic University of Indonesia
Karel Octavianus Bachri Master's Program in Electrical Engineering, Atma Jaya Catholic University of Indonesia
Maria Angela Kartawidjaja Master's Program in Electrical Engineering, Atma Jaya Catholic University of Indonesia

DOI:

https://doi.org/10.31154/cogito.v10i2.648.486-495

Keywords:

LSTM, Machine Learning, Moving average, Moving average crossover, RNN, Stock Market Trend Prediction

Abstract

Prediction of the stock market is highly needed to assist traders in making decisions. Many methods are used by traders to predict this such as technical analysis and moving averages. Moving averages predict stock trends based on the past data of the stock. The disadvantage of using a moving average analysis is the delay in crossover signals. As a solution, a deep learning technique known as LSTM is applied to the moving average strategy in this paper. In this research, the BBCA stock dataset spanning from 2010 to 2018 was utilized. The data was segmented into two parts: 2010-2017 for training data and 2018 for testing data. The training process employed Long Short-Term Memory (LSTM) networks, with the subsequent results being combined with moving average crossover techniques. Validation results indicate that BBCA shows a relatively minimal error. BBCA's average MAPE is 1.1%, and its RMSE is 65.402, classifying it within the "Highly Accurate Forecasting" category. Various combinations of moving average crossovers were tested during model training, with the combination of SMA05 and SMA50 for BBCA yielding the highest profit potential. Stocks that exhibit a downward trend are more likely to incur substantial losses. The model can predict the reversal of trends by predicting the trading signal given by the moving averages.

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