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Analysis the Correlation of SET100 Index |
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| รหัสดีโอไอ | |
| Creator | Natchamol Srichumroenrattana |
| Title | Analysis the Correlation of SET100 Index |
| Contributor | Kairat Jaroenrat |
| Publisher | Faculty of Management Science Nakhon Pathom Rajabhat University. |
| Publication Year | 2566 |
| Journal Title | Journal of Management Science Nakhon Pathom Rajabhat University |
| Journal Vol. | 10 |
| Journal No. | 2 |
| Page no. | 44-55 |
| Keyword | SET Index, SET100, Numerical Analysis |
| URL Website | https://so03.tci-thaijo.org/index.php/JMSNPRU/issue/view/17616 |
| Website title | https://so03.tci-thaijo.org/index.php/JMSNPRU/index |
| ISSN | 2392-5817 |
| Abstract | The research โAnalysis the Correlation of SET100 Indexโ aims to study the relationship of SET100 stocks in the financial business, resource and service groups with time, and to be able to forecast SET100 price trends in the financial business, resource and service groups. As well as finding a decision-making process for trading SET100 stocks in the financial, resource and service groups. This research project proposes an application of numerical analysis algorithm to analyze the correlation of such stocks and get a guideline for selecting stocks that are of interest to investment. As well as guidelines for making decisions on the trading of such shares Numerical analysis where data were analyzed numerically by using the value of SET100 data with reference from the Stock Exchange of Thailand.The data used are: opening price, closing price, average price, daily close. Data is filtered by selecting the desired date data, then importing the Exponential Moving Average (EMA), Relative Strength Index (RSI), Stochastic, Moving Average (MA) formula and analyzing the data to show a clear picture of the stock's fluctuation. However, numerical analysis and the use of statistical analysis procedures can help visualize the correlation and trends of stocks in the financial, resource and service groups which are useful information to make more confident investment decisions. In addition, the data has been tested by using the Keras model to create a model to analyze and predict the trend of SET100 stock prices in each group with an accuracy of more than 95%. This indicates that this model can predict the trend of stock prices well and can be adapted to help make stock trading decisions in the future. |
