발간년도 : [2019]
| 논문정보 |
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| 논문명(한글) |
[Vol.14, No.6] A Study on Financial Time Series Data Volatility Prediction Method Using AI's LSTM Method |
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| 논문투고자 |
Han Jin Song, Heung Sik Choi, Sun-Woong Kim, Su-Hun Oh |
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| 논문내용 |
The purpose of this study is to predict the future volatility of financial instruments more effectively by utilizing Long Short Term Memory (LSTM) which is one of the artificial intelligence (AI) techniques. This study focused on the method of predicting the future forecasts based on the historical data through the LSTM technique. The future price of a financial instrument is inversely proportional to the instrument’s size of future volatility. Therefore, if future volatility can be predicted for the targeted financial instrument, the fluctuation in the instrument’s price in the future can be projected more effectively. This paper predicts the future price volatility by making the historical data of a financial instrument to learn by using AI technique. As a result, the volatility movement predicted by AI was significantly similar to the actual volatility value during the same period. Through this, the results of this study are expected to be used to forecast the price of financial products more effectively. In addition, it is expected that the results of this study, together with other existing methods for volatility prediction, will further contribute to the accuracy of projecting the future volatility, and ultimately contribute to the improvement in investment returns of financial investments products including funds. |
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