Decision-Making Techniques using LSTM on Antam Mining Shares before and during the COVID-19 Pandemic in Indonesia

Authors

  • Ahmad Kamal Badri University of Bakrie
  • Jerry Heikal University of Bakrie
  • Yochebed Anggraini Terah University of Raharja
  • Deden Roni Nurjaman University of Raharja

DOI:

https://doi.org/10.33050/atm.v6i2.1776

Keywords:

Stocks, COVID-19, Historical Stock Prices, LSTM, Decision Making Technique

Abstract

Stocks, apart from having volatile and chaotic characteristics, also have various kinds of noise, non-linear and non-stationary movements, making them difficult to predict accurately. Therefore, the risk of investing in stocks depends on the skills of investors or traders in making judgments and decisions. This study aims to use Long Short-Term Memory (LSTM) as a decision-making technique with historical stock prices as the sole predictor, then implement it in conditions before and during the COVID-19 pandemic. The study results concluded that Long Short-Term Memory (LSTM) could be used as a decision-making technique in conditions before and during the COVID-19 pandemic with historical price inputs as the sole predictor. Based on the research that has been done, the following conclusions can be drawn: The LSTM model can predict stock prices well using historical stock prices as the sole predictor. The LSTM model can be used as a trading decision-making technique for day traders. The risk of stock prediction using the LSTM method in 2019 before the COVID pandemic was proven to be lower than in 2020 during the COVID pandemic. For further research, researchers can conduct more in-depth research on the risk criteria for making trading decisions as an essential reference that can be used to select the LSTM model.

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Published

2021-12-22

How to Cite

Badri, A. K. ., Heikal, J. ., Terah, Y. A., & Nurjaman, D. R. . (2021). Decision-Making Techniques using LSTM on Antam Mining Shares before and during the COVID-19 Pandemic in Indonesia. APTISI Transactions on Management (ATM), 6(2), 167–180. https://doi.org/10.33050/atm.v6i2.1776