APPLICATION OF SUPPORT VECTOR REGRESSION AND HIDDEN MARKOV MODELFOR ELECTRONIC COMPANY STOCK PRICE PREDICTION
Physics is a natural science that studies scientific phenomena through measurement, experiment, and analysis. One discipline within physics is econophysics, which integrates the theoies and methods of physics into the analysis of economics and finance. The application of econophysics includes mod...
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id-itb.:782612023-09-18T14:44:39ZAPPLICATION OF SUPPORT VECTOR REGRESSION AND HIDDEN MARKOV MODELFOR ELECTRONIC COMPANY STOCK PRICE PREDICTION Adekeu Alif, Fernanda Indonesia Final Project Hidden Markov Model, Machine Learning, prediction, stocks, Support Vector Regression INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/78261 Physics is a natural science that studies scientific phenomena through measurement, experiment, and analysis. One discipline within physics is econophysics, which integrates the theoies and methods of physics into the analysis of economics and finance. The application of econophysics includes models of price movement using statistical physics and complex system. This research uses Support Vector Regression (SVR) and Hidden Markov Model (HMM) methods to analyzes stock prices. The dataset used is the stock prices of Apple, LG, Samsung, and Sony from January 1st 2015, to December 31st 2022. The data from January 1st 2015 until 31st December 2022 is used to train the model, and data from January 1st 2021 until 31st December 2022 is used as the test data. To make predictions, the variables used are opening price, closing price, adjusted price, highest price, lowest price, and volume. The HMM method produces a good prediction, while the SVR method produces more innacurate predictions. text |
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Physics is a natural science that studies scientific phenomena through measurement,
experiment, and analysis. One discipline within physics is econophysics, which
integrates the theoies and methods of physics into the analysis of economics and
finance. The application of econophysics includes models of price movement using
statistical physics and complex system. This research uses Support Vector
Regression (SVR) and Hidden Markov Model (HMM) methods to analyzes stock
prices. The dataset used is the stock prices of Apple, LG, Samsung, and Sony from
January 1st 2015, to December 31st 2022. The data from January 1st 2015 until 31st
December 2022 is used to train the model, and data from January 1st 2021 until 31st
December 2022 is used as the test data. To make predictions, the variables used are
opening price, closing price, adjusted price, highest price, lowest price, and volume.
The HMM method produces a good prediction, while the SVR method produces
more innacurate predictions. |
| format |
Final Project |
| author |
Adekeu Alif, Fernanda |
| spellingShingle |
Adekeu Alif, Fernanda APPLICATION OF SUPPORT VECTOR REGRESSION AND HIDDEN MARKOV MODELFOR ELECTRONIC COMPANY STOCK PRICE PREDICTION |
| author_facet |
Adekeu Alif, Fernanda |
| author_sort |
Adekeu Alif, Fernanda |
| title |
APPLICATION OF SUPPORT VECTOR REGRESSION AND HIDDEN MARKOV MODELFOR ELECTRONIC COMPANY STOCK PRICE PREDICTION |
| title_short |
APPLICATION OF SUPPORT VECTOR REGRESSION AND HIDDEN MARKOV MODELFOR ELECTRONIC COMPANY STOCK PRICE PREDICTION |
| title_full |
APPLICATION OF SUPPORT VECTOR REGRESSION AND HIDDEN MARKOV MODELFOR ELECTRONIC COMPANY STOCK PRICE PREDICTION |
| title_fullStr |
APPLICATION OF SUPPORT VECTOR REGRESSION AND HIDDEN MARKOV MODELFOR ELECTRONIC COMPANY STOCK PRICE PREDICTION |
| title_full_unstemmed |
APPLICATION OF SUPPORT VECTOR REGRESSION AND HIDDEN MARKOV MODELFOR ELECTRONIC COMPANY STOCK PRICE PREDICTION |
| title_sort |
application of support vector regression and hidden markov modelfor electronic company stock price prediction |
| url |
https://digilib.itb.ac.id/gdl/view/78261 |
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1822280979241238528 |