ANALYSIS OF NASDAQ COMPOSITE INDEX PRICE PREDICTION USING WAVELET NEURAL NETWORK METHOD
Econophysics is a discipline that applies models and concepts derived from physics to economic and financial phenomena. One of things that studied in econophysics is stock price prediction. Stocks are securities that signify the ownership of a fraction of a company and are used as a form of inves...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/70320 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Econophysics is a discipline that applies models and concepts derived from physics
to economic and financial phenomena. One of things that studied in econophysics
is stock price prediction. Stocks are securities that signify the ownership of a
fraction of a company and are used as a form of investment. Stock prices continue
to change as they adjust to the market. Investors can gain profit by selling stocks at
higher prices than its purchase price. This makes stock price analysis and prediction
important to gain profit and avoid losses when investing in stocks. A method that
can be used to predict stock price is wavelet neural network. Wavelet neural
network is a machine learning method that combines wavelet transformation and
artificial neural network. This thesis aims to analyze performance of the wavelet
neural network method in predicting the close price of four stocks that are part of
the Nasdaq Composite stock index. Wavelet neural network used is a combination
of Discrete Wavelet Transform (DWT) and Long-Short Term Memory (LSTM). As
a comparison of wavelet neural network, LSTM method is used. Close price of the
four stocks that are used are from April 2020 until March 2022. This data divided
to train data and test data. The result shows that wavelet neural network outperform
LSTM. |
|---|