PREDICTION ANALYSIS OF INFOBANK15 INDEX USING NADARAYA-WATSON ENVELOPE NON-REPAINTING WITH LONG SHORT-TERM MEMORY (LSTM) AND BIDIRECTIONAL LSTM (BI-LSTM)
Since 1990, the term Econophysics has been gaining popularity. Developed by physicists to address economic issues, econophysics often employed to predict stocks using machine learning approaches, one of which includes Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM). Statistical phy...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/78071 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Since 1990, the term Econophysics has been gaining popularity. Developed by physicists to
address economic issues, econophysics often employed to predict stocks using machine
learning approaches, one of which includes Long Short-Term Memory (LSTM) and
Bidirectional LSTM (Bi-LSTM). Statistical physics plays a role in calculating kernel
regression applied in Nadaraya-Watson Envelope Non-Repainting, which is useful for
establishing upper and lower bounds, serving as buy or sell indicators. This research aims to
create a predictive model for an index and compare the profits achieved with LSTM, Bi-LSTM
models, and investors. Starting with data collection and cleansing, moving on to data
preprocessing, and then creating and training models where the accuracy is assessed using
RMSE, MAE, and correlation coefficients. In this study, the Bi-LSTM method is considered a
better predictive model with an RMSE of 12.28, MAE of 9.24, and a correlation coefficient of
0.95, compared to LSTM which has an RMSE of 12.9, MAE of 9.66, and a correlation
coefficient of 0.94. The model was also simulated for profit taking, with the profit generated
by the LSTM model being 3.58% and the Bi-LSTM model being 5.75%. |
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