A Study on Financial Time Series Data Volatility Prediction Method Using AI&#039;s LSTM Method (AI의 LSTM기법을 이용한 금융시계열 데이터 변동성 예측방법 연구)

Han Jin Song (송한진); CHOI,HEUNG SIK (최흥식); Kim Sun Woong (김선웅); Su-Hun Oh (오수훈)

doi:10.34163/jkits.2019.14.6.009

A Study on Financial Time Series Data Volatility Prediction Method Using AI's LSTM Method

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2019, 14(6), pp.665-673
DOI : 10.34163/jkits.2019.14.6.009
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Received : November 1, 2019
Accepted : December 7, 2019
Published : December 31, 2019

Han Jin Song ¹, CHOI,HEUNG SIK ¹, Kim Sun Woong ¹, Su-Hun Oh ¹

¹국민대학교

Accredited

ABSTRACT

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.

KEYWORDS

Volatility, LSTM, Forecast, Future price, Financial investment

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Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Study on Financial Time Series Data Volatility Prediction Method Using AI's LSTM Method

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* References for papers published after 2023 are currently being built.

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Study on Financial Time Series Data Volatility Prediction Method Using AI's LSTM Method

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.